determination of sra habitat indicators by remote sensing · very high for future inclusion in the...

C S I R O L A N D a nd WAT E R

Determination of SRA Habitat Indicators

by Remote Sensing

By Environmental Remote Sensing Group

CSIRO Land and Water, Canberra

Technical Report 28/03, April 2003

Technical Scoping Document

Determination of SRA Habitat Indicators

by Remote Sensing

By Environmental Remote Sensing Group

CSIRO Land and Water, Canberra

Technical Report 28/03, April 2003

Technical Scoping Document

SRA – MDBC Remote Sensing Report - April 2003

Copyright 2003 CSIRO and MDBC i

Contributors

This report was supported by the Murray-Darling Basin Commission and CSIRO - Division of Land and Water. Whilst authorship of the overall document resides with the ‘Environmental Remote Sensing Group’ at CSIRO Land and Water, we would like to acknowledge important contributions and valuable advice by Dr. Joe Walker (CSIRO Land and Water), Leo Lymburner (CRC for Catchment Hydrology), Dr. Stuart Phinn (University of Queensland), Susan Day (University of Canberra) and Dr. Jane Roberts (independent consultant).

© 2003 CSIRO. To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO Land and Water and MDBC.

Important Disclaimer: CSIRO Land and Water advises that the information contained in this publication comprises general statements based on scientific research and technical expertise. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO Land and Water (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

ISSN 1446-6163


Copyright 2003 CSIRO and MDBC ii

Contents 1 EXTENDED SUMMARY.....................................................................1

2 CONTEXT AND SCOPE OF WORK ..................................................92.1 Background .......................................................................................................... 92.2 Reporting Scales.................................................................................................. 92.3 Desk Study Objectives....................................................................................... 102.4 Project tasks....................................................................................................... 10

3 BASICS OF REMOTE SENSING.....................................................113.1 Theory ................................................................................................................. 113.2 Sensors............................................................................................................... 123.3 Platforms............................................................................................................. 133.4 Data Sources ...................................................................................................... 133.5 Abbreviations for Table Interpretation.............................................................. 183.6 Use of Remote Sensing to Support of Natural Resource

Management ....................................................................................................... 193.7 Requirement Definition Process ....................................................................... 193.8 Mapping vs. Monitoring .................................................................................... 203.9 Spectral Range: VIS-NIR vs. VIS-SWIR ............................................................ 213.10 Digital Numbers to Reflectance Units............................................................... 223.11 Field Validation and Measurements.................................................................. 22

4 MAPPING OF SPECIFIC SRA INDICATORS VIA REMOTE SENSING .........................................................................................24

5 METHODOLOGIES, OPERATIONAL REQUIREMENTS AND SAMPLE APPLICATIONS ...............................................................475.1 Infrastructure requirements............................................................................... 475.2 Considerations on Remote Sensing Project Costs.......................................... 495.3 Sample Pilot Project Costs................................................................................ 525.4 Additional Applications ..................................................................................... 585.5 Image Examples of River Systems using Different

Sensor Types...................................................................................................... 605.6 Floodplain Modelling Examples ........................................................................ 75

6 PROPOSED SRA PILOT STUDY ....................................................79

7 REFERENCES.................................................................................80

APPENDIX 1: DATA SUPPLIERS........................................................87

APPENDIX 2: BASIC REMOTE SENSING TECHNOLOGIES..............88

APPENDIX 3: ASTER DETAILS...........................................................93

APPENDIX 4: REMOTELY SENSED DIGITAL ELEVATION MODEL DATA SOURCES...............................................................95


Copyright 2003 CSIRO and MDBC iii

APPENDIX 5: SPECTRAL REFLECTANCE OF VEGETATION...........97

APPENDIX 6: REMOTE SENSING OF WATER QUALITY ................100


Copyright 2003 CSIRO and MDBC 1

1 Extended Summary

This desktop review has three objectives. First, identify the capacity of remote sensing technologies to report on the measurement variables currently proposed for the Physical Habitat Theme in the Sustainable Rivers Audit, and other Themes in the Audit as appropriate. Second, provide a cost estimate of using remote sensing for various sets of Physical Habitat Metrics, and for various sites. Third, outline the infrastructure and costs associated with establishing and maintaining this infrastructure.

Remote Sensing as a Measurement and Monitoring Tool Remote sensing refers to a commonly used ‘earth observing’ technique of identification

and measurement of an object’s characteristics, using its reflected or emitted electromagnetic radiation (e.g. light and microwaves) as the carrier of information between the object and the detection system (e.g. satellite or aircraft mounted ‘camera’ systems). The images produced by such sensors are usually processed with specialised mapping software to create digital representations of the distribution and density of different objects (e.g. trees, macrophytes) distributed along river systems. This information can then be easily incorporated into standard geographic information systems (GIS).

The rapidly growing number of available technologies for remote assessment offers increasingly better uses of such information in support of riparian zone management. Together with traditional ground assessment, remotely sensed data provides a powerful dataset for historical analysis of the changing condition of riparian zones in Australia. Remotely sensed images of the country are available since the late 70’s, when some of the first earth-observing satellites were launched.

A variety of such sensors are available in Australia for mapping riparian zones at different levels of spatial detail (i.e. ground resolution) and spectral detail (‘colour’ discrimination). A relatively new technique used for environmental assessments called ‘hyperspectral’ imaging (or imaging spectroscopy) offers up to 288 spectrally different information layers (bands); these in turn present a greater opportunity to better discriminate among vegetation types, or to allow for development of quantitative water quality measures.

The information derived from such mapping systems per se is most often only a surrogate for the actual metric of river ‘health’ or indicator type required. It therefore requires an additional computational step for conversion of the surrogate measurement to the actual information required, such as tree cover, fragmentation, density and width of the riparian corridor.

The advantage of such data is that they cover 100% of the ground surface and are very precise in a geographical sense. This reduces the costs associated with high density ground sampling campaigns, and also minimises errors caused by interpolation & extrapolation of point-based ground measurements. Unlike human based, somewhat more subjective interpretation methodologies, the geophysical nature of remotely sensed data allows for objective auditing and accurate multi-year change mapping. However, as with many such technologies, there are also limitations: Remote sensing cannot replace many of the specific



measurements required to measure indicators nominated by ecologists and water resource managers.

Optical sensor types currently used in remote sensing differ in their spectral resolution, spatial resolution, and temporal resolution; and in the raw data costs, and how those costs are calculated. There are seven main optical sensor types:

•Air Photography (A-Ph);

•Airborne multi-spectral (A-Ms);

•Airborne Hyper-spectral (A-Hs);

• Airborne Lasers (A-L);

•Satellite Multi-spectral Medium Resolution (S-MsM);

• Satellite Multi-spectral Fine Resolution (S-MsF); and

• Satellite Hyper-spectral (S-Hs).

Table 1 summarises the principal differences between these, together with examples of different sensors.

Microwave (imaging radar – synthetic aperture radar, SAR) sensors have a number of advantages, especially for all-weather, day and night mapping purposes. However, at their current state of development and with still low levels of processing/interpretation expertise in Australia, these are not yet considered mature for SRA purposes. In addition these tools tend to still be somewhat costly at the high resolution (sub-10 m) required for SRA purposes, and most of these high-resolution systems are not based in Australia. While the potential is very high for future inclusion in the SRA remote sensing suite, microwave systems were not included in this particular review at this time.

Remote Sensing and the SRA Physical Habitat VariablesThere are 26 components in the Physical Habitat Theme, each with 1 to 8 measurement

variables, a total of 88 measurement variables. These 88 variables, together with a further 6 from the Water Processes Theme, were evaluated for usefulness and ready applicability to the Sustainable Rivers Audit using three criteria: technical feasibility of current remote sensing technologies; availability of suitable expertise and infrastructure; known or expected routine application to large areas. This resulted in four evaluation categories: Operational, Feasible, Likely / Possible; Unlikely / Impossible. Each of the 94 measurement variables was ranked, the sensor and data types given, the level of spatial resolution and spectral resolution achievable were identified, and the task given, and the results tabulated by Theme component.

For the Physical Habitat Theme (Table 1, below) nine components were rated Operational with >80% confidence of remote sensing and GIS application, and another eight were rated Feasible, with >70% confidence. Ten were rated Likely, ie requiring development before being applicable to the Sustainable Rivers Audit, whilst two components (Embeddedness and Standing Litter) were rated as unlikely. Two of the Water Processes theme were rated Operational, and the third was ranked Feasible.



Table 1: Summarised Assessment of Utility of Remote Sensing for Broad Physical Habitat and Water Processes Themes Characterisation

Broad SRA Component

Confidenceof Remote

Sensing & GIS

Solution*

Recommended Data type Complementary

Data type

Vegetation vigour 95% S-MsM A-Ms

Floodplain 95% S-MsM A-Ls

Riparian Vegetation Width 95% A-Ms S-MsEF

Riparian Vegetation Cover 95% A-Ms S-MsF

Riparian Habitat Fragmentation 90% S-MsEF A-Ls

Water Processes - primary indicators

assessment 85% A-Hs S-MsF

Water Processes - ancillary indicators

assessment 85% A-Hs S-MsF

Riparian Canopy Complexity (trees & shrubs) 85% A-Hs S-MsEF

Pool assessment 83% A-Ms A-Lds

Meso Habitat diversity 70% A-Hs Selected ground information

Riparian demography 70% A-Ms S-MsF

Riparian Vegetation Density 70% A-Ms S-MsF

Vegetation Connectivity 70% A-Ms S-MsF

Emergent aquatic macrophyte area and relative abundance

70% A-Ms S-MsEF

Vegetation Overhang 70% A-Ms A-Ls

Riparian regeneration 70% S-MsF A-Hs

Waterbody type assessment 70% S-MsF A-Hs

River Bank 60% A-Ls S-MsF



Lateral and longitudinal inclusions to migration

barriers 56% S-MsF A-Ms

Channel Form assessment 55% A-Ls A-Lds

Proportions of; clay, silt, sand, gravel, cobble,

boulders, bedrock, and detritus

50% A-Hs Selected ground information

Riparian Vegetation Species 50% A-Hs S-MsEF

Potential input of large woody debris 50% S-MsF A-Ls

Sediment regime (and grazing) assessment 47% S-MsM S-MsF

River Reach depth assessment 40% A-Lds

Cover of algae/periphyton/biofilm 40% A-Hs -

Snag assessment 34% A-Ms A-Ls

Embeddedness 10% A-Hs A-Lds

Standing Litter 10% A-Hs A-Ls

*Note: These values have been derived by relative weightings of the various Measurement variables listed in more detail in Tables 3 to 32, which have been summarised here and then expressed as a percentage of the total number of variables mappable with remote sensing tools within that broad SRA Component. This ranking is based on the collective experience of the authors and tends to be somewhat conservative.

Ranking Guide and Colour Coding

Operational = >80%

Feasible = >70%

Likely = >30%

Unlikely = <30%

Operational - For variables in this category, sensors are commonly available in Australia., image analysis methodologies are well established and have been more or less standardised, and map products can now be produced more or less routinely over broad areas in Australia relevant to the scales of the SRA reporting process. The relevant data expertise and infrastructure are in place in Australia to use the



sensors and methodologies, or adapt these to specific SRA applications if used for other related projects.

Feasible – Initial large scale trials are being developed, usually with advanced sensor systems. Present knowledge, past and present case studies suggest that relevant information can be derived from available data, but large-scale operational demonstrations have not been performed.

Likely/possible - This group includes variables where present data are inadequate, but future studies are anticipated. It includes variables where there is knowledge of relationships between the indicator and remotely sensed data, but further research is required to identify suitable processing for SRA reporting.

Unlikely/ impossible - For these indicators, the assessment is that remote sensing is unlikely to deliver operational results, either because of lack of ability to measure the variable of interest, or because the scale and logistics suggest that monitoring for SRA reporting purposes would be impracticable.

Detailed measurement variables which have potential for characterisation via some form of remote sensing, with or without standard GIS analysis are:

1. Floodplain assessment (proportions, and spatial distribution of major habitat types)

2. Floodplain size and flooding scenario mapping [proposed new metric]

3. River bank assessment of bank slope, shape, erosion extent, proportion of slumping & lateral scour

4. Riparian vegetation assessment of dominant riparian tree & shrub species, the vegetation association

5. Riparian vegetation cover (percentage cover of riparian trees, shrubs & floodplain)

6. Riparian & Floodplain vegetation density

7. Riparian vegetation evenness

8. Riparian vegetation width assessment

9. Riparian habitat fragmentation assessment

10. Riparian canopy complexity (percentage cover of trees and shrubs) & demography

11. Vegetation overhang (distance of canopy from the channel)

12. Vegetation vigour assessment (leaf area dynamics)



13. Emergent aquatic macrophyte assessment (species type & relative abundance)

14. Emergent aquatic macrophyte assessment (cover of macrophytes and percentage area)

15. Channel form assessment

16. River reach depth in clear water

17. Pool assessment

18. Meso habitat diversity assessment

19. Snag number, type and distribution (large and non-submerged snags only)

20. Assessment of river bed material and the embeddedness (clear water only)

21. Assessment of the proportion of coverage by algae or fine silt & type of biofilm (clear water)

22. Riparian regeneration assessment

23. Potential input of woody debris assessment (clear water and large woody debris)

24. Connectivity assessment (as applied to habitat quality of riparian vegetation)

25. Lateral and longitudinal connectivity (distances, heights & number of barriers & extents of alienation)

26. Waterbody type assessment

27. Water processes primary indicator assessment (identification & quantification of pelagic chlorophyll-a)

28. Water processes ancillary indicator assessment (quantification of dissolved organic matter, turbidity, secchi depth and temperature)

Measurement variables that are more difficult or impossible to identify from remote sensing, include:

29. River bank erosion type

30. Stock access to riparian areas & assessment of sediment load & channel erosion

31. Riparian herb vegetation assessment (percentage cover)

32. Riparian vegetation assessment of percentage of native species present

33. Riparian canopy complexity (percentage cover of understorey shrubs, herbs & ground vegetation)



34. Standing litter assessment

35. Snag number, type and distribution (submerged)

36. Proportion of native emergent aquatic macrophyte species

37. Stem density of emergent aquatic macrophytes

38. River current assessment

39. Snag diameter & water column position

40. Thickness of algal biofilms

41. Return period of bankful discharge

42. Stock density assessments

Recommended Sensor Suite Due to the different type and scale of the SRA indicators to be mapped, no single sensor

technology was deemed able to quantify all the ‘operational’, ‘feasible’ and ‘likely’ indicators listed above. Thus at this stage, from a purely technical point of view, we recommend the use of a suite of sensors, which in our opinion would be able to quantify the largest number of indicators. In line with the need to provide quantitative assessments of a number of the listed indicators, we suggest the synergistic use of a high spectral resolution airborne imaging spectrometer (e.g. Hymap or CASI), complemented with a regional-scale, moderately high resolution space borne sensor such as ‘Spot-5’ or ‘Quickbird’. The value of high resolution digital elevation models (DEMs) and additional information on riparian structure, both of which are measurable now from airborne laser systems and future high resolution radars, can not be understated. Therefore the acquisition of a high resolution DEMs for the SRA monitoring sites is also recommended.

In case of budgetary constraints, we consider that Landsat 7 imagery, combined with high resolution Spot 5 or ‘DMSI (Airborne Digital Multi Spectral Imager)’ data may be lower-cost options. However this combination of sensors may be less effective in identifying those SRA indicators which require higher spectral resolution for differentiation of different vegetation types or for quantitative mapping. Spot data can also be collected in stereo mode and thus capable of producing a basic, relatively coarse (10m) DEM.

In addition, we suggest the development of a comprehensive pilot study to help gain better cost-benefit metrics for evaluation of the merit of these different remote sensing technologies. We estimate that such a study would cost in the vicinity of $150,000, including image data costs, field campaigns and specialised analysis. This initial cost however, should not be considered as representative for the lower-cost, routine uses of remote sensing for SRA purposes, where economies of scale would apply. Such a pilot study also offers the opportunity to develop new indicators which could be unique to this form of synoptic and spatially dense measurement.



The language and technical detail in this report is written for a relatively wide audience and fields of expertise outside remote sensing. It will be of most use, however, to those at the MDBC or associated catchment management agencies responsible for the implementation of geographic information systems, and those with some basic remote sensing knowledge. It does cover basic principles and concepts of remote sensing with reference to even more detailed information in the appendices. Specific reference is made on the use of remote sensing as it relates to the Sustainable Rivers Audit (SRA) objectives, including the context and scope of this work, followed by a detailed assessment of each of the components in terms of ability of these to be assessed via remote sensing.



2 Context and scope of work

2.1 Background

The Sustainable Rivers Audit (SRA) is establishing a protocol for the assessment of physical habitat in rivers of the Murray-Darling Basin (MDB). The specific objectives of this assessment are to:

• develop a common reporting framework for the assessment of physical habitat and reporting of habitat condition at the river-valley scale, and potentially at the Valley Process Zone Scale;

• build on the knowledge and experience of existing monitoring programs;

• develop a series of indicators that will enable the cost-effective assessment of the existing condition of riverine habitat in the Basin, and monitor change over time.

2.2 Reporting Scales

Natural resource management at the Basin scale requires information on resource condition to be measured and reported at an appropriate scale.

The Sustainable Rivers Audit has adopted a geomorphic approach, stratifying valleys into similar zones at two scales:

• Functional Process Zones (FPZs) and

• Valley Process Zones (VPZs).

Functional Process Zones are lengths of a river that have similar discharge and sediment regimes. Their gradient, stream power, valley dimensions and boundary material define them. The characteristics of FPZs and detailed descriptions of the geomorphic characteristics for each of the FPZs can be found in the framework report. This Audit, however, is designed to report health at the river-valley scale; where the Valley Process Zone scale reports river health for the upper, mid-slopes and the lowland parts of the river separately. The study design developed for the Audit does not report river condition at a site.

The assessment of physical habitat is a complex task made more difficult by limited understanding of many organisms use of habitat and the extreme variability of habitat within lowland river ecosystems. Therefore the habitat assessment protocol needs to recommend data collection in a manner that anticipates future knowledge requirements. This can be achieved by collecting quantitative spatially explicit data on a wide variety of parameters.



Remote sensing has been identified by the MDBC and partner agencies as having potential for providing a cost effective means of data collection, however further information was required on the capacities and costs of the various remote sensing options. As remote sensing capabilities are developing fast, current and near future systems also needed further assessment.

2.3 Desk Study Objectives

The objectives of this review were to:

1. review the habitat metrics proposed for the Physical Habitat Theme for the SRA and identify the capacity of remote sensing techniques (eg LIDAR, aerial photography, satellite imagery) to deliver information at an appropriate resolution

2. briefly review the indicators for other themes in the SRA to determine if they could also be assessed by remote sensing

3. indicate what ground truthing will be necessary both at inception and into the future, and provide a preliminary cost of this and list skills and other requirements

4. provide estimates of the cost of using remote sensing for assessing SRA indicators

5. outline the infrastructure requirements and costs associated with establishing and maintaining such infrastructure for storing, analysing and manipulating remotely sensed data

2.4 Project tasks

This project will be a desk-top review to identify the options and capacity for remote sensing to deliver information on the physical habitat metrics. The project tasks are to:

1. review options and costs of remote sensing for suitable indicators and/ or surrogates, including the sensitivity of the cost to adding or deleting indicators

2. identify constraints on where measures can be obtained (eg vegetation overhang, stem density in forest areas, different VPZ’s, upland vs. lowland rivers)

3. identify opportunities where additional useful data can be extracted (including similar metrics and metrics for other themes)

4. identify the resolution available, the confidence and any data interpretation issues for appropriate metrics, and what level of ground-truthing is required for each metric

5. identify any seasonal or timing constraints and recommend appropriate timing (i.e. preferred seasons & preferred frequency)

6. outline data acquisition, management, interpretation and infrastructure needs.



3 Basics of Remote Sensing

3.1 Theory

Remote sensing detects reflected or emitted radiation in a number of specific, well-defined wavelengths of the electromagnetic radiation spectrum (EMS), as illustrated in Figure 1.

Spatial resolution is described by the pixel or cell size in equivalent ground units (Richards, 1994) and refers to the minimum dimensions of the sensor's sampling element on the ground i.e. the area from which reflected or emitted EMS is measured. Interaction with landscape features determines the smallest feature visible on an image. The swath width determines the extent of the scene.

Spectral resolution is defined by the number and width of the sensor’s bands. Low-spectral resolution broad-band sensors such as digital multi-spectral imagers, air photography and satellite systems such as Landsat TM and Spot, tend to cover mostly the visible and near infrared spectrum with light detectors in three to seven ‘colour’ regions. On the other extreme, high spectral resolution sensors such as hyperspectral imagers have detectors in nearly the same spectral range, but with hundreds of very narrow (0.01 µm) bands, allowing for better detection of sharp and subtle spectral differences between materials.

Figure 1: The electromagnetic spectrum. (Source: Harrison and Jupp, 1989)

Analogue prints from airborne photographic film constitute the first ever remotely sensed images. Today most commercial remote sensing instruments produce images in digital format, which are visualized and manipulated using computers.



3.2 Sensors

A number of commonly used satellite sensor systems (e.g. Landsat MSS & TM, Spot HRV) measure reflected light in the visible (400 – 700 nm), near infra-red (700 – 1300 nm), and in the case of Landsat, also in the shortwave infra-red (1300 – 3000 nm) part of the EMS. While most sensors use the sun’s reflected radiation as the light source for their measurements (Figure 1), some also detect radiation emitted from the ground in the thermal infrared portion of the electromagnetic spectrum.

Figure 2: Diagram of the Landsat satellite and its surface coverage characteristics

(Source: Harrison and Jupp, 1989)

All these sensors are generically considered ‘passive’. In contrast, ‘active sensors’, generate their own energy which is transmitted towards the ground. In these sensor systems, the interaction with objects or surfaces on earth and the return signal characteristics are then measured and interpreted. Active systems which operate in the microwave part of the spectrum are called ‘Radio Detection and Ranging’ - RADAR sensors; active systems that operate in optical frequencies, using mostly concentrated laser pulses, are called ‘Laser Detection and Ranging – LIDAR’ systems.

By virtue of the EMS frequencies involved, most optical – thermal passive systems provide information primarily on the chemical nature of the surfaces they measure, while radar and laser systems provide information predominantly on the three-dimensional nature and structure of the objects they encounter.



Determining which remotely sensed data type is most appropriate for a specific application, means also considering the type of sensor, whether active or passive, as well as which platform: airborne or satellite.

3.3 Platforms

Airborne systems allow for higher spatial resolution in image collection and provide better flexibility of image acquisition after special events or natural disasters. Airborne systems often contain more advanced sensors, provide image data at higher spatial resolution and are often capable of data collection when a uniform, high cloud cover is present. On the other hand, satellite systems are more stable platforms, provide generally lower spatial resolution, but can cover larger areas more rapidly and at a lower cost per unit area. Satellite systems also have fixed overpass times, capable of routine and multi-temporal data collection over larger areas. The more recent satellite systems approach the high spectral and spatial resolution of airborne systems and can be pointed sideways to a target of interest. Satellite data of the earth’s surface has been collected since the mid-1970’s, so there is a good satellite image archive for investigation of change from early ERTS, AVHRR, Landsat MSS and Landsat TM sensors.

3.4 Data Sources

Table 2 below summarises primarily optical image and surface data sources that are deemed most applicable for SRA purposes.

Microwave (imaging radar) sources have a number of advantages, specially for all-weather, day and night mapping purposes, specially in tropical areas. However, at their current state of development these have are not considered suitable for SRA purposes at this point in time. This is primarily because they can only provide relatively few SRA indicators at high spatial resolution; also they tend to be costly for high resolution (sub-10 m) mapping requirements. There is also a lack of high resolution airborne systems residing and operating routinely in Australia. Thus, although developments of this particular technology are advancing rapidly, microwave is not included in this review.

The costs listed below, are given in Australian dollars and are valid at the time of writing this report. In the interest of brevity this and some of the following tables contain abbreviations that represent various data source and types. A detailed list of the suppliers and contact details of suppliers in Australia is found in Appendices 1 and 2.

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3.5 Abbreviations for Table Interpretation

Data Type Code

Airborne

Photography A-Ph

Video A-Vd

Laser Depth Sounder A-Lds

Scanner Multi-spectral A-Ms

Scanner Hyper-spectral A-Hs

Laser A-Ls

Satellite

Multi spectral (Fine resolution) S-MsF

Multi spectral (Medium resolution) S-MsM

Hyper spectral S-Hs

Spatial (pixel) resolution

Extremely fine <5m EF

Fine 5-20m F

Medium 20-250m M

Coarse 250 - >1000m C

Spectral resolution

Low panchromatic or analogue images L

Medium multi spectral, 3-30 spectral discrete bands Ms

High hyper spectral, contiguous spectral bands Hs

(typically between 30 and 300 bands)

VIS Stands for Visible spectrum (350 – 700 nm)

NIR Near Infra-Red spectrum (700 – 1000 nm)

SWIR Short-wave Infra-Red spectrum (1000 – 2500 nm)

MIR Middle Infra-Red Spectrum (2500 nm ~ 5000 nm)

TIR Thermal Infra-Red Spectrum (typically 8000 – 13000 nm)



‘User Dependent’, indicates the sensor would be deployed opportunistically, when the user requires.

‘Weather Dependent’ indicates the sensor deployment is subject to favourable weather conditions. Most airborne sensors can be operated effectively under high, dense cloud.

3.6 Use of Remote Sensing to Support of Natural Resource Management

Although remote sensing technologies have been available since the 1970’s and used in routine broad-scale mapping with the launch of the Landsat-series satellites, it is only in the last five years that technical developments in high resolution airborne and space borne remote sensing have produced the spatial and spectral resolutions which are especially useful for mapping specific ecosystems such as narrow vegetation clusters associated with riparian zones.

Parallel advances in sensor calibration and radiative transfer modelling (atmosphere and in-water) have also lead to improved accuracy for their use in quantitative remote sensing, as opposed to basic qualitative, statistical mapping of different object types. Imaging spectroscopy now enables the accurate measurement of an increasing range of standard environmental variables.

The growing archive of historical image data from broad-resolution satellites is also used increasingly to underpin multi-temporal studies of changes in landscape condition. When coupled with other spatial data sets, invaluable background-, or contextual information is often derived. For example Digital Elevation Models (DEM’s) from traditional survey or from airborne laser or photogrammetry can be combined with climate, soils data and ‘Habitat Type’ classes (based on remotely sensed data) to model the patterns of saline inputs or flooding.

In addition, as many of the drivers or causal factors of some locally measured variables often extend beyond the riparian zone, remote sensing at a broad scale can help identify catchment-scale causes and characterise much of the surrounding ecosystems and catchment areas around riparian areas.

3.7 Requirement Definition Process

In general, a number of technical details need considering during initial dialog between river and catchment managers, ecologists and remote sensing experts, before deciding on specific sensors and methodologies for natural resource mapping. These are;

Q What is the natural scale of the process or indicator that needs to be mapped?

Q What is the relationship between the spatial and spectral resolution?

Q Is there a sensor(s) best suited?

Q Can the indicator be measured via; passive optical sensors, or active sensors such as laser or radar, or, a combination of both?

Q If optical data are to be used, is the process or indicator resolvable using visible and near infra-red sensor systems (VIS-NIR) or, do you need a system that will map



reflectances up into the short wave infrared (SWIR), or thermal infra-red (TIR) range?

Q Can the indicator be mapped (spatially and through time), using raw digital numbers (DN) i.e. at sensor radiance values, or do you need atmospherically corrected data providing, in surface reflectance units.

Q If any, what type of field data is required?

Q Is the entire process cost-effective?

This report aims to answer some of these questions in the context of the SRA indicator program.

3.8 Mapping vs. Monitoring

Wallace and Campbell (1988) make the point that while mapping and monitoring are complementary activities, there are substantial differences between mapping and monitoring applications using remote sensing.

In terms of costs, one-off mapping projects can often be more expensive than multi-temporal monitoring with remote sensing, as there are few repetitive costs and often detailed information is required.

Mapping, groups elements in the landscape based on predefined decision rules and the patterns of spatial and digital (or spectral) associations in the data. At its simplest, a mapping protocol allows work in un-calibrated brightness units or raw digital numbers (DN). This is the initial numeric domain of most sources of remotely sensed data. Converting these data into geophysical units (surface or sub-surface reflectances) that have true physical basis is essential, in order to detect the absolute presence/absence of a material or plant, or to monitor change through time using a range of image data sources.

To implement a robust monitoring program, decisions are needed on how the image data sets (captured at different dates and possibly with different sensors) will be bought into a common numeric domain. Wallace and Campbell (1988) state,

“the calculation of surrogate indicators based on statistical classification is greatly simplified when using calibrated images from the base products, since cover type

signatures can be transferred spatially and temporally”

and this advantage is further supported by the increased effectiveness of field spectra, since these quantitative relationships are directly comparable to the imagery and are also transferable to similar environments. Moreover direct and indirect biological, geological, chemical, and physical models become feasible when using physically well-defined remotely sensed information. A similar approach was undertaken by Phinn et al (1999) for identification of remote sensing for wetlands.

So, scale issues that need to be considered are:

Temporal scale requirements are based on the changes and the speed of change in indicators that is anticipated or deemed necessary to determine. Seasonal changes need to be distinguishable from long-term trends.

Spatial scale required is also of importance (and important driver of costs). The higher the spatial resolution (i.e. the smaller the pixels) the higher the data acquisition and processing



costs become. Spatial scale is linked to accuracy too; many airborne sensor systems provide very high positional accuracy (1-2 pixel RMS) due to the use of inertial navigation systems and the ability to incorporate DEM information during the image geocorrection process. These levels of accuracy are essential when seeking to detect pixel-by-pixel changes.

In terms of the level of spatial detail or resolution required for mapping of the different indicators, there are a number of choices (see Sensor Table 2). In most cases the decision has to be made during the data specification process, based on individual indicator types and what level of detail is required to generate the final map products. This process has already been largely done for the nominated SRA indicators, in the next chapter of this report.

It is quite common for users to request the higher possible resolution imagery for an application, because they like to be able to see the individual objects in the imagery, such as tree crowns or individual snags. For this reason air photography, or new sub-metre satellite data from new satellites like QuickBird or Ikonos are such popular products. However technically speaking, this may not really be necessary for creation of maps, such as percent cover, for instance. In many cases the decision on the spatial resolution also has to be a pragmatic trade-off between spatial detail, spectral resolution and cost. A very useful and more rigorous geostatistical technique for selecting the relevant resolution for mapping is described in Phinn (1998).

For monitoring purposes, the mapping frequency, e.g. once every 3 months or once every three years, will play a more important role in terms of costs.

3.9 Spectral Range: VIS-NIR vs. VIS-SWIR

Spectral scale mainly relates to how many species, materials and substrate types (e.g. mud, silt, sand) need to be mapped. The higher the spectral resolution, the greater the opportunities for improved discrimination among species. High spectral resolution gives capacity to determine overgrowth of algal biofilms on substrates. The high spectral detail may also help in identification of algae groups which may have different pigment composition (such as differences between cyanobacteria vs. green algae)

Remote sensing systems are best characterised by their spectral (‘colour’) sensitivity (Figure 1). There are a large number of visible-infra red (VIS-NIR) (350 – 1000 nm range) sensor systems currently in use (Table 2). They range from the orbiting Landsat, Spot, Ikonos, Quickbird and ASTER platforms to airborne video and sensors such as the CASI.

In turn, Visible – shortwave (VIS-SWIR) (350 – 2500 nm range) satellite systems include the orbiting 36-band ‘Modis’ sensors on NASA’s ‘Terra’ and ‘Aqua’ platforms, and the 220-band experimental Hyperion sensor on the EO-1 satellite. In Australia the airborne hyperspectral sector in this spectral range is singularly covered by Hyvista’s HYMAP system (Table 2).

VIS-SWIR data sets contain a wealth of information - relating not only to plant vigour - but are essential, if the study calls for dry soils and regolith (loose rock fragments, soil, alluvium which lie on the bedrock) mapping. These data sets are larger due to the greater number of bands and the costs - often higher, and tend to have slightly lower spatial resolution than VIS-NIR sensors.

Operationally VIS-NIR systems produce simpler and smaller data sets that are stable and well-calibrated. They are also well suited to water-based applications (optical water quality or benthic plant cover), as light in the NIR and SWIR regions are absorbed by water and is of no use in this application.



In the context of truly operational methodologies, multi-spectral VIS-NIR systems are more established, having a research and application history dating back to the early 1980’s, and are widely accepted. However, hyperspectral VIS-SWIR technologies are rapidly expanding into environmental applications and as cost effective mapping tools for natural resource management. Due to their very high calibration stability and sensitivity, these sensors are frequently used as test beds for new algorithms and application development for other sensors and satellites.

3.10 Digital Numbers to Reflectance Units

If an application or SRA indicator metric calls for image data to be in physical reflectance units, a more complex processing methodology is required. The rewards however are substantial, in that the power of measurement spectrometry becomes available, as does multi-temporal analysis or change detection. This means a true comparison can be undertaken between data sets of different dates and resolutions, and quantitative measurements can be derived from the image products. Not correcting the data to reflectance units means that observed changes between data sets may be a function of atmospheric or illumination and look angle conditions, and not a true representation of what is occurring on the ground.

There are a growing number of well-documented methods and tools to make these corrections. They range in complexity from linear based corrections to sophisticated radiative transfer models that define correction coefficients for each wavelength, in turn based on the modelled atmospheric turbidity and sensor illumination geometries. To implement this form of correction, image data from a calibrated sensor is required, often with certain ancillary data sets (meteorological and surface reflectances).

3.11 Field Validation and Measurements

For remote sensing results to be valid and reliable, they must initially have been supported by appropriate field campaigns, ancillary data (including simulation models), and significant local expert knowledge. This is most important in the developing stages of an application. Once operational, a remote sensing based methodology needs minimal field support, although selected validation field trips are recommended to check the accuracy of the mapping processes.

A well-defined remote sensing project containing the correct balance of these elements will in turn deliver a spatially accurate, dense, and information rich data layer that has a high degree of reliability and is very cost effective.

Working in physical reflectance units brings much technical rigour, but there is an increased requirement for field work – especially in the establishment phases of the project. Field spectral measurements of the component land covers and soil types are needed to provide the basis of a spectral library. If an airborne data set with Very Fine (<= 5m) spatial resolution is being captured, it is common to deploy reflective ground targets in the area being imaged (usually one bright and one dark) that can be used to constrain the atmospheric correction. When using image data with fine to medium pixel resolution (5m to 250m), natural surfaces can be used as surrogates for the reflective targets; these should be suitably large and spectrally homogeneous, such as bare fields, large areas of paving (carparks), roofs etc.



Local expert knowledge about plant phenology is of great help during the field-work planning process, since, most vegetation has seasonal reflective variations and often these characteristics can be used to enhance the separability of different plant types that appear ostensibly similar through other seasons of the year. Plant vigour and health will also affect its spectral properties. Over time it is desirable that the spectral library or data-base be developed to include these variations. Once most of these variants are recorded, the ongoing need for field spectra diminishes.



4 Mapping of Specific SRA Indicators via Remote Sensing

This section examines remote sensing options for replication or spatial extension of SRA measurement variables that are already being estimated in the ground-based SRA Pilot Study. At present the ground assessment of these SRA metrics is performed in a spatially distributed sampling framework of 4 rivers, with 22 sites per river valley of which 16 are in the lowland slopes.

Thirty SRA Components including twenty-six from the Physical Habitat Theme and their Measurement Variables across three MDBC themes are assessed (in table form) against the remote sensing options currently available.

In addition remote sensing, is discussed in terms of provision of additional metrics, as well as on the whole-of-River, if not the whole-of-Basin scales, where drivers of condition are extensive and spatially distributed , such as land cover and land use change.

The Tables 3 - 32 contain a colour coded Evaluation column.

‘Evaluation’ in this context refers to the current strictly technical ability (or confidence), in measurement of the variable using remote sensing technologies (data and processing techniques); it does not comment on its cost-effectiveness relative to current ground assessment. The form and definition of the evaluation rankings are similar to those used by Wallace and Campbell (1998).

The Evaluation values and their colour coding are:

Operational - For variables in this category, sensors are commonly available in Australia., image analysis methodologies are well established and have been more or less standardised, and map products can now be produced more or less routinely over broad areas in Australia relevant to the scales of the SRA reporting process. The relevant data expertise and infrastructure are in place in Australia to use the sensors and methodologies, or adapt these to specific SRA applications if used for other related projects.

Feasible - Initial large scale trials are being developed, usually with advanced sensor systems. Present knowledge, past and present case studies suggest that relevant information can be derived from available data, but large-scale operational demonstrations have not been performed.

Likely/possible - This group includes variables where present data are inadequate, but future studies are anticipated. It includes variables where there is knowledge of relationships between the indicator and remotely sensed data, but further research is required to identify suitable processing for SRA reporting.



Unlikely/ impossible - For these indicators, the assessment is that remote sensing is unlikely to deliver operational results, either because of lack of ability to measure the variable of interest, or because the scale and logistics suggest that monitoring for SRA reporting purposes would be impracticable.

In most cases, variables can be measured by a number of remote sensing systems, each operating at different spatial and temporal scales; hence, information and trade-offs need to be taken into account. The more frequently a satellite passes over, the wider its swath width or its spatial coverage. This commonly results in larger pixels and resulting coarser maps. Conversely the finer the pixel or cell size, the narrower the extent covered in a ‘scene’, but the higher its information content (and cost) per unit area.

Table 3: Floodplain Assessment Component

Measurement variables

Evaluation Data type(s) Spatial Resolution

Spectral Resolution

Task

Proportions of major

habitat types O

A-Ph, A-Ms, A-Hs, S-MsF, S- MsM, S- Hs

F to M L to Hs Classification and change detection

[New Metrics] Floodplain

size and flooding scenariomapping

O A-L F to M - DEM analysis

and hydrologic flood modeling

Comments: Habitat mapping is an established remote sensing application. High resolution DEM analysis for flood prediction is becoming a mature technique (see image examples below). Seasonally the habitat indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: In the absence of available information on habitats, vegetation transects are needed to develop ‘training sets’ for different habitat type identification and to undertake some field radiometry for sensor calibration and development of a ‘spectral library’ of vegetation types.

Confidence: High

Constraints: Relative scales of the available image data versus habitat elements.

Table 4: River Bank Component

Measurementvariables

Evaluation Data type(s)Spatial

ResolutionSpectral

Resolution Task

Bank slope F/O

A-Ls EF to M - Analyse DEM



Bank shape F/O

A-Ls EF to M - Analyse DEM

Erosion type U

- - - R&D

Erosion extent (proportion of

bank) FA-Ls EF-F - Change

detection

Slumping (proportion of

bank) F A-Ls EF-F - Change

detection

Lateral scour (proportion of

bank) F A-Ls, A-Ms EF-F - Change

detection

Comments: There is significant scope in developing these indicators further using a combination of LIDAR and high resolution imagery. As airborne laser systems become more wide spread and cost effective, their application for a number of SRA indicators increasingly moves into the ‘operational’ category. Except for climatic limitations on the airborne mission, these indicators can be characterised any time of year.

Field Work: Morphology survey with total station or GPS

Confidence: Moderate

Constraints: A necessary DEM based on 2.5m LIDAR sampling intervals is still an interpolated - not a complete surface. Increasing levels of vegetation density will further degrade the LIDAR sampling interval for generation of the DEM.

Table 5: Riparian Vegetation Component



Spectral Resolution

Task

Dominant tree and shrub

speciesO

A-Ls

A-Hs

S-MsF

EF to M Ms to Hs Classification

Vegetationassociation O

A-Ph, A-Ms, A-Ls,

S-MsF, S- MsM, S-Hs


% native species L A-Hs, S-MsF EF Ms to Hs Classification



Riparianevenness U - - - -

Comments: In complex communities, species discrimination requires very high-resolution hyperspectral data. When there are only a few tree-types of species to map, and differences are spectrally distinguishable, then this becomes an operational task. Broad vegetation associations are more easily mapped with multi-spectral imagery and EF resolutions (once the groupings are verified by field work), and have less need for high spatial resolution. Native species are often difficult to separate spectrally – unless there are seasonal variations (flowering cycles etc.) that can be exploited. Seasonally the habitat indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Expert ground knowledge and vegetation transects. Field radiometry and species identification

Confidence: Medium

Constraints: Requires vegetation types to be spectrally and structurally distinct from each other.

Table 6: Riparian Vegetation Cover Component

Measurement variables Evaluation Data type(s) Spatial

ResolutionSpectral

Resolution Task

% cover shrubs <5m O

A-Ph, A-Vd, A-Ms, S-MsF, S-

HsF to M Ms to Hs

Classification/ Change detection

(except when covered by

overstorey trees)

% cover understory U A-Ls EF to F. -

Assuming that understorey not

visible due to tree cover above-

% cover herbs U - - -

Assuming that understorey not

visible due to tree cover above-

% cover of floodplain O

A-Ph, A-Vd, A-Ms, S-MsF,

S-Hs F to M Ms to Hs Classification/

Change detection



Comments: Classify from imagery, if shrubs are the over-story. Where shrubs are the understorey, structural information may be derived from the laser data, but depending on the density of the tree cover above. Seasonally, the habitat indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Vegetation identification as part of transects across floodplain

Confidence: Medium

Constraints: Requires vegetation to be spectrally and structurally distinct and visible from sensor above.

Table 7: Riparian Vegetation Density Component

Measurement variables Evaluation Data

type(s) Spatial

Resolution Spectral

Resolution Task

Basal area of dominant

speciesL A-Ls EF to M -

Relate detectable

crown size to basal area

Stem density of dominant

speciesO A-Ls EF - F Ms to Hs

Manual crown counting, or

via computer-based crown-

counting methods

Comments: Modelled tree crown size as a function of height. These indicators can be measured effectively any time of year. While optical systems are recommended at present, high resolution imaging radar data may become a viable option for mapping basal area in the near future.

Field Work: Vegetation transects to determine allometric relationships

Confidence: Medium

Constraints: Crown size, to ‘diameter at breast height’ (dbh) relationships are not well defined for many eucalypt species, especially in multi-stem and clustering species.



Table 8: Riparian Vegetation Width Component


Evaluation Data type(s)

Spatial Resolution

Spectral Resolution

Task

Distance from edge of

channel to cleared/

developedland

O

A-Ph, A-Vd, A-Ms,

S-MsF, S-

MsM,S-Hs

F to M Ms to Hs Classification & GIS Analysis

Channel width O A-Ls, VF to M - Classification & GIS Analysis

Width of floodplain O

A-Ph, A-Vd, A-Ls,A-Ms,

S-MsF, S-

MsM,

F to M - GIS Analysis

Density of floodplain

‘vegetation’ O

A-Vd, A-Ms,

S-MsF, S-

MsM,S-Hs

F to M Ms to Hs

Vegetation cover (NDVI) analysis or crown counting

Comments: Depends on the definition of density. Classify from imagery. Structural information and percentage foliage cover can also be derived from laser. When high resolution image data is available, crown counting software can be used to determine average crown density. Seasonally, the floodplain vegetation density indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects. In particular with laser systems, other floodplain metrics can be mapped at other times as well.

Field Work: Not really needed

Confidence: High

Constraints:



Table 9: Riparian Habitat Fragmentation Component



Spatial Resolution

Spectral Resolution

Task

Length of bank with

vegetation >5 m wide

OA-Ph, A-

Vd, A-Ms, S-MsF,

EF to M L to Hs Classification/ GIS Analysis

Vegetatedstreamlength (eg % per unit streamlength)

O

A-Ph, A-Vd, A-Ms, S-MsM, S-

Hs


Number of gaps O


Hs

EF to M L to Hs

Classification/ GIS Analysis, or from laser height

data

Average patch size O


Hs


Patch size O


Hs


Length of gaps O


Hs


Riparianconnectivity F


Hs

EF to M L to Hs

Calculate from Classification

using metric(s) appropriate to

scale and process

Comments: Most metrics can be derived using GIS techniques or image analysis routines. Seasonally, the vegetation indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Vegetation transects

Confidence: High

Constraints: In some cases, requires vegetation to be spectrally and structurally distinct



Table 10: Riparian Canopy Complexity Component


type(s) Spatial

Resolution Spectral

Resolution Task

% cover of trees > 5m O

A-Ph, A-Vd, A-Ms, A-Ls,

S-MsF, S-Hs

F to M L to Hs

Classification and

identification of trees via their

crown size

% cover of shrubs O

A-Ph, A-Vd, A-Ms, A-Ls,

S-MsF, S-Hs

EF to F L to Hs

Classification and

identification of shrubs via their

crown size

% cover of understorey U - - - -

% cover of ground

vegetation U - - - -

Comments: Structural information can be derived from laser profilers, depending on density of vegetation. In very open canopies some detection of the understorey cover is possible – especially if imaged during a growth flush. Seasonally, the vegetation density indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Vegetation transects

Confidence: Medium

Constraints: Requires shrub vegetation to be spectrally and structurally distinct, and not be overshadowed by trees.



Table 11: Riparian Canopy Demography Component


ResolutionSpectral

Resolution Task

Proportion of individuals of each species of ‘major riparian

plants’ (overstorey

only) in each age class

F A-Ls, A-Hs EF to F Hs Classification

Comments: Discriminating between the major species is possible using hyper-spectral data. Lidar-derived structural data must be of sufficient resolution to reveal internal structure. There would be a high degree of site dependence. Seasonally, these vegetation indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Vegetation transects, field radiometry and species identification

Confidence: Medium

Constraints: Resolving age distribution is an R&D issue. Requires vegetation to be spectrally and structurally distinct

Table 12: Standing Litter Component



Spatial Resolution

Spectral Resolution

Task

Depth and percentage

cover of litter in quadrats

U - - - R&D



Comments: The possibility exists to estimate photosynthetic versus woody vegetation using hyper-spectral data. However this is still in the R&D realm.

Field Work: Transects and field radiometry

Confidence: Currently low

Constraints: The Litter signal may be masked by standing pasture and canopy layers.

Table 13: Vegetation Overhang Component


Resolution Spectral

Resolution Task

Distance of canopy from

channel O A-Ph, A-Ms, A-

Ls, S-MsF, VF to M L to Hs GISoperation

Comments: Very high resolution needed to discriminate canopy elements and to accurately measure distance to channel. Seasonally, these vegetation indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: none

Confidence: High to Medium

Constraints: Access to high resolution image data that is accurately geo-coded.

Table 14: Vegetation Vigour Component



Spectral Resolution

Task

Spectral vegetation

indices O

A-Ms, A-Hs, S-MsF, S-MsM,

S-Hs VF to M Ms to Hs Band

Math



Comments: Calculate from imagery (basic greenness index – NDVI, or hyper-spectrally derived chlorophyll/carotene/anthocyanin contents can be calculated, but vary among species). Seasonally, these vegetation indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Vegetation transects to derive relationships between foliar chemistry and field radiometry

Confidence: High

Constraints: Requires target vegetation groups to be spectrally distinct

Table 15: Emergent aquatic macrophyte species richness and diversity Component


Resolution Spectral

Resolution List of species

(eg rushes, sedges)

L A-Ms, A-Hs, S-Hs VF to M Ms to Hs

Relative abundance of each species

L A-Ms, A-Hs-Ms, S-Hs VF to M Ms to Hs

% native macrophyte

speciesU - - - R&D

Comments: Where macrophytes are emergent (not if submerged or floating or under a canopy) presence/absence can be detected, species can be determined if spectrally or texturally distinct. Seasonally, these vegetation indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Vegetation transects, field radiometry and species identification

Confidence: Low

Constraints: Overhanging canopy



Table 16: Emergent aquatic macrophyte area and relative abundance Component



Spatial Resolution

Spectral Resolution

Task

Cover of aquatic

macrophytes F

A-Ms, A-Hs, S-MsF,

S-MsM VF to M Ms to Hs Classification

% macrophyte cover within

patches F A-Ms, A-

Hs, S-MsF VF to M Ms to Hs Classification

% macrophyte area F

A-Ph, A-Ms,

A-Hs S-MsF, S-MsM

VF to M Ms to Hs Classification

Stem density of aquatic

macrophytes U A-Ms, S-

MsF VF to M Ms to Hs Classification

Comments: Where emergent (not if submerged or floating or under a canopy) presence/absence can be detected, species can be determined if spectrally or texturally distinct. The Stem density variable could only be inferred through determining a relationship with leaf area index (LAI). Seasonally, these vegetation indicators ought to be mapped during the late spring or early summer when solar elevation is highest, to minimise shadowing effects from neighbouring land vegetation.

Field Work: Field radiometry, species identification and structural metrics

Confidence: Medium to low

Constraints:

Table 17: Channel Form Component



Spectral Resolution

Task

Deviation from U-shape F A-Ls, A-Lads VF to F - GIS analysis

form a DEM

Location of large woody

debrisF A-Ph, A-Ms, S-

Ms VF to M Ms to Hs Classification

Location of macrophytes F A-Ph, A-Ms, A-

Hs, S-MsF VF to M Ms to Hs Classification



Amount of organic matter (particulate eg

leafpacks)

LA-Ph, A-Ms, A-Ms, S-MsF, S-

MsM, S-Hs VF to M Ms to Hs Classification

Sediment type (of the bottom) F

A-Ms, A-Ms, S-MsF, S-MsM,

S-Hs VF to M VF to M Classification

Channel complexity (eg

benches at different flood

heights)

F A-Ls VF to M - DEM analysis

Comments: Where emergent (not if submerged or floating or under a canopy) presence/absence can be detected; Where above waterline (eg during low flows) and can be derived from Digital Elevation Model (DEM) with 1m cell size. No seasonal constraints for mapping these indicators.

Field Work: Survey and vegetation transects and field radiometry and species identification

Confidence: Medium to low

Constraints: Benthic mapping determined by the water clarity

Table 18: River Reach Component


type(s) Spatial

Resolution Spectral

Resolution Task

Depth (Coefficient of

Variation) F A-Lds VF to F - -

Current (Coefficient of

Variation) U - - - R&D



Comments: Seasonally, these river depth and current indicators ought to be mapped at times of low flow and low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Hydrographic survey and metrics

Confidence: Low

Constraints: Laser based depth sounding requires very clear water to be effective. Depth measurements limited to 1.3 * Secchi depth

Table 19: Pool assessment Component



Spectral Resolution

Task

Pool length O A-Ph, A-Ms, S-MsF VF to M Ms to Hs GIS analysis

Pool width O A-Ph, A-Ms, S-MsF VF to M Ms to Hs GIS analysis

Pool depth F A-Ms, S-MsF, A-Lads VF to F - -

Comments: All variables are flow volume dependent. Seasonally, these river indicators ought to be mapped at times of low flow and low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Survey

Confidence: Medium to high

Constraints: For depth mapping with multi-spectral or laser sounding, the water needs to be optically clear and then measurements are limited to 1.3 * Secchi depth. The amount of riparian vegetation and overhang will be a hindrance.

Table 20: Meso-habitat diversity Component



Spectral Resolution

Task

"Proportion of pool, riffle,

run, backwater"

F A-Ph, A-Ms, A-Hs, S-MsF VF to M Ms to Hs Classification



Comments: River flow and sensor resolution dependant. Uses spectral information for the different water surface patterns to classify the river types. Seasonally, these river indicators ought to be mapped at times of low flow and low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Ground survey of sample sites covered by the imagery to ‘train’ the classification process.

Confidence: Medium

Constraints: Would require EF-level resolution for narrow river channels and user interpretation. Some R&D would be needed to develop more automated procedures such as using ‘textural’ rather than spectral information for consitent classification of these indicators

Table 21: Snag assessment Component



Spatial Resolution

Spectral Resolution

Task

Snag number LA-Ph, A-Ms, S-MsF


"Snag type (eg rootball,

branches, telegraph pole)"

L A-Ph, A-Ms, S-MsF

EF to M Ms - Hs Classification

Snag diameter U - - - -

Snag water column position U - - - -

Snagdistribution L

A-Ph, A-Ms, S-MsF

VF to M Ms to Hs Classification,

Bathymetry mapping

Comments: For automated classification, woody materials need to spectrally separable from the surrounding materials. Seasonally, these river snag indicators ought to be mapped at times of low flow and low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects

Field Work: Survey

Confidence: Low

Constraints: Water clarity and amount of algal growth on snags



Table 22: Proportion of clay, silt, sand, gravel, cobble, boulders, bedrock, and detritus Component



Spatial Resolution

Spectral Resolution

Task

Proportion of bed material F A-Hs VF to M Hs Benthic

mapping

Comments: Benthic mapping in optically clear water is well posed. Seasonally, these river bed indicators ought to be mapped at times of low flow and low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Determine the in-water optical properties. Survey of benthos can be sensitive to benthic variations. Field radiometry.

Confidence: Low to medium

Constraints: Water clarity and amount of vegetation overhang.

Table 23: Embeddedness Component


type(s) Spatial

Resolution Spectral

Resolution Task

Embeddedness (i.e. amount of

fine material around cobbles)

F A-Hs VF Hs Classification

Comments: Benthic mapping in optically clear water is well posed though the amount algal growth on the substrate will be an issue – especially if the same biofilm covers both rock and silts. Seasonally, these river material indicators ought to be mapped at times of low flow and low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Determine the in water optical properties. Survey of benthos and soundings (echo sound can be sensitive to benthic variations). Field radiometry

Confidence: Low

Constraints: Water clarity, biofilms and amount of vegetation overhang.



Table 24: Cover of algae/periphyton/biofilm Component


Resolution Spectral

Resolution Task

Proportion of surface

covered by algal

categories

L A-Ms, A- Hs VF Ms to Hs Classification

Proportion of surface

covered by fine silt

L A-Hs VF Hs R&D

Type of biofilm L A-Hs VF Ms to Hs Classification

Thickness of biofilm U - - - R&D

Comments: Limited to where the surface is emergent and not overhung and the pigment is spectrally distinct eg cyanobacteria or not. Where the surface (of the snags, boulders) is emergent and not overhung and the chlorophyll or chemical characteristics are spectrally distinct. Seasonally, these indicators ought to be mapped at times of low flow and low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Determine the in water optical properties. Survey of benthos and field radiometry.

Confidence: Low

Constraints: Water clarity and amount of vegetation overhang.

Table 25: Riparian regeneration Component


type(s) Spatial

Resolution Spectral

Resolution Task

Expectedfuture

proportion of individuals of

‘large and common

species’ in each age class

LA-Ms, A-Hs, S-MsF

VF to M Ms to Hs Classification and change detection



Comments: Mapping change in the overall community through time is well posed though predicting the change in proportions of individuals requires an understanding and modelling of ecology and the successional dynamics of the communities. Seasonally, baseline mapping of vegetation distribution indicators ought to be mapped preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects.

Field Work: Vegetation transects and field radiometry and species identification

Constraints:

Confidence: Medium

Table 26: Potential input of large woody debris Component



Spatial Resolution

Spectral Resolution

Task

Snagrecruitment

per unit area of bank

FA-Ls, A-Ms,

A-Hs

S-MsF VF to M Ms to Hs Classificati

on

Comments: Estimates of riparian cover would need to be classified into woody vs. green cover to derive a surrogate for potential recruitment. Seasonally, these indicators ought to be mapped at times of low flow and low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects

Field Work: Vegetation transects and field radiometry


Constraints: R&D required using airborne hyperspectral imagery to develop the best way to separate green vegetation from woody materials, at high spatial resolution.



Table 27: Hydrologic Connectivity Component



Spectral Resolution

Task

Presence of levees

(upstream & downstream)

F A-Ph, A-Ls, A- Ms, S-MsF VF to M Ms to Hs

Classification of high

resolution laser terrain

data

Comments: Derive from Digital Elevation Model (DEM) or from high resolution imagery where levee is emergent and not overhung (may be more readily visible in flood). There are no seasonal constraints on assessment of this indicator.

Field Work: Survey

Confidence: Medium

Constraints: Access to digital terrain data

Table 28: Lateral and longitudinal: part of Connectivity Component



Spectral Resolution

Task

Distance to the nearest weir O

A-Ph, A-Sc,

S-MsM , S-MsF F to M L to Ms GIS

Number of barriers

(upstream & downstream eg

1m high artificial barriers)

F A-Ls, S-MsM, S-MsF VF to M L to Ms GIS

Longitudinal connectivity – Cumulative

height of barriers

upstream

L A-Ls VF to M

- GIS

Longitudinal connectivity - Cumulative

height of barriers

downstream

LA-Ls VF to M

- GIS



Lateralconnectivity -

Extent of floodplain alienation

FA-Ls, S-MsF VF to M

- GIS

Return period of bankful discharge

U - - - Hydrology R&D

Comments: Most of these measures can be derived from high resolution Digital Elevation Model (DEM) where levee is emergent and not overhung (may be more readily visible in flood. There are no seasonal constraints on the assessment of these indicators.

Field Work: Survey


Constraints: The quality and resolution of the airborne laser imagery needs to be high.

Table 29: Sediment regime (and grazing) Component


Resolution Spectral

Resolution Task

Stock density U - - - R&D

Stock access to riparian areas L

A-Ph,

A-Ms, A-Ls VF to F Ms to Hs Classification

and GIS

Stock watering points L A-Ph, A-Ms,

A-Ls VF to F Ms to Hs Classification and GIS

Channel movement,

area of gullying

LA-Ph, A-Vd,

A-Sc, A-Ls, S-MsF, S-MsM

VF to M Ms to Hs

Classification of multi-temporal

data

erosion (sheet or gullying) L A-Ls, A-Ms,

A-Hs, S-MsF, VF to M Hs -

% sediment patch (large

areas of sediment on

the bottom or edge)

LA-Ph, A-Ms,

A-Hs,

S-MsF, S-MsM VF to M Ms to Hs Classification



Sediment load FA-Ms, A-Hs,

S-MsF,S-MsM EF to M Ms to Hs -

Comments: Watering points are often detected in high resolution imagery by path convergence, accentuated erosion patterns and noticeable breaks in the riparian zone and increase in foraging (< NDVI). The ability to map sheet erosion is related to the spectral variation of the subsoil and regolith layers. Large in-channel depositional features are easily mapped but deeper sand and silt layers are less certain. Most of these indicators, except for sediment load estimation can be characterised any time of year. Sediment loading should preferably be assessed immediately after peak rain periods when climatic conditions are suitable.

Field Work: Survey transects and field radiometry


Constraints: Water clarity needs to be high for in-water sediment transport mapping

Table 30: Waterbody type Component



Spatial Resolution

Spectral Resolution

Task

stream,channel,

floodplain etc. F

A-Ph, A-Vd,

A-Ms, A-Hs,

A-Ls, S-MsF,

S-MsM,

VF to M Ms to Hs Classification and GIS analysis



Comments: Flow volume dependent. Seasonally this assessment can be undertaken whenever flow conditions are suitable.

Field Work: Survey and visual assessment

Confidence: Medium to High

Constraints:

Table 31: Water Processes - primary indicators Component


type(s) Spatial

Resolution Spectral

Resolution Task

Pelagic Chl-a O

A-Sc,

S-MsF,

S-MsM,

S-Hs;

VF to M Ms to Hs Spectral

Indices and Classification

Comments: Suspended chl to secchi depth. See examples in Figures 5.12 and – 5.14. Seasonally, this indicator ought to be mapped at times of low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects

Field Work: Field radiometry and water sampling

Confidence: High

Constraints: High water turbidity levels may reduce accuracy of the quantitative chlorophyll estimate



Table 32: Water Processes - ancillary indicators Component



Spatial Resolution

Spectral Resolution

Task

DissolvedOrganic Matter O

A-Sc, S-MsF,

S-MsM,S-Hs


Turbidity O

A-Sc, S-MsF, S-MsMSat-Hs


Secchi O

A-Sc, S-MsF,

S-MsM,S-Hs


Temperature O

A-Sc, S-MsF,

S-MsM,S-Hs


Comments: Suspended chl to secchi depth. Seasonally, these water quality indicators ought to be mapped at extreme times of low/high flow, but low turbidity, and preferably during the late spring or early summer when solar elevation is highest, to minimise shadowing effects

Field Work: Field radiometry and water sampling

Confidence: High

Constraints: Turbidity levels



5 Methodologies, Operational Requirements and Sample Applications

Overall, remote sensing integration with on-ground environmental assessments require an ongoing dialogue between river and catchment management agencies, and the remote sensing methodology developers. This way it can be established which are the most suitable and feasible indicators (or to prioritize indicators) to be detected and monitored by remote sensing.

Once the sensor specifications and project details discussed in Chapter 2 have been decided, the initial stages of a remote sensing assessment project involve fieldwork to establish a baseline spectral library or a collection of vegetation classes which are to be used in the image classification and map generation processes. In the case of development of new application for remotely sensed data, this includes some time on the development of a new protocol methodology and the development of faster processing methodologies to reduce the costs of multi-date monitoring programs.

These developments require specific types of scientific integration skills often found at institutions such as Geography Departments at Universities, CSIRO, or private expert consultants. Once developed, however, and depending on the level of sophistication in the requirements, routine processing could be done by skilled GIS or laboratory staff within the client’s workplace.

5.1 Infrastructure requirements

Rigorous remote sensing and image processing applications are heavy users of computer technologies. Advances in sensor design have led to larger data sets, which in turn require faster and more powerful computing systems. Image processing software and GIS, is needed and these have on-going maintenance costs ($5,000 - $10,000 p.a.). Field equipment - specifically radiometers are expensive to purchase and require specialist technical care and maintenance. Finally, the costs associated with travel and fieldwork can be substantial.

Accordingly, highly trained scientific and technical staff are initially required. It may be best to outsource this stage and specific tasks of the mapping projects, at least until the methodology and procedures have become routine and technical staff from within the client’s organisation can be trained.

In the face of such outlays, data acquisition costs are a minor component of a full scale remote sensing project budget. Below is a summary of the personnel and IT and hardware resources that would be needed to develop and implement the sort of remote sensing driven mapping and monitoring identified by the SRA.

Technical Expert Staffing

Image Analysis & GIS Specialists – field work and image processing. BSc or MSc in the field of natural resource management with coursework in GIS or remote sensing. The number of



staff needed with this level of expertise depend on the size of the mapping projects. As a starting point, it would be suggested that two image analysts be involved in processing, GIS integration and reporting for the proposed pilot study below, where many image data types will be evaluated and routine methodologies developed in conjunction with senior remote sensing experts and external reference groups. After a first year, this staff could be expanded accordingly with the routine use of remote sensing across all pilot sites or ‘whole-of-river’ projects.

Senior Remote Sensing Expert: Required especially in the start-up, development and reporting phases – but when operational, their input can be limited to requirement definition, project development and quality assurance.

IT Resources

Note: The hardware and software tools listed below are examples that have the necessary functionality. CSIRO does not endorse any specific manufacturer as other products may also serve these purposes.

Large file sever (min 500GB storage) and backup device (DLT tape),

100 mb/sec LAN or better

Image processing workstations (one for each image analyst) containing:

• Twin screens

• Fast CPU

• 512-1025 Giga-bytes RAM

• 100 GB hard disk storage

• DVD reader and writer.

Image processing software, ENVI and/or TNT MIPS

Image Segmentation software; ‘eCognition’ is being currently evaluated by CSIRO

GIS software Arcview, Arcinfo, MapInfo or TNT MIPS

A4 colour printers and large format plotter

Field Equipment to Support Remote Sensing Projects*

• Spectroradiometer VIS-NIR system or VIS-SWIR system

• In water spectroradiometer VIS-NIR

• Canvas or plastic reflection targets (bright and dark)

• Water sampling equipment

• Vegetation sampling equipment



• Portable meteorological station

• Cryogenic sampling materials (liquid nitrogen dewars, safety equipment etc)

• Misc: ladders, tapes, pegs

* A number of agencies own such equipment and can be hired to perform this type of analysis under contract.

Field Data Analysis*

• Laboratory analysis of water samples

• HPLC plant tissue analysis

• Soils and sediments analysis

• X-Ray diffraction testing for soils and sediment tracing.

* A number of agencies will perform this type of analysis under contract.

5.2 Considerations on Remote Sensing Project Costs

The costs of implementing remote sensing derived information within a management organisation can be split into 4 main components:

• Image data acquisition and field/laboratory measurement costs

• Image pre-processing and processing to variables such as macrophytes and tree species

• Costs associated with IT infrastructure implementation (e.g. image analysis system and GIS workstation)

• Integration of spatially explicit environmental information into knowledge system (e.g. GIS) of end-user management authority.

Image data acquisition and processing costs are highly dependent on the type of result required. While most of the costs cited in Table 2 represent raw image data acquisition costs as supplied by the difference vendors, a full remote sensing project also requires a budget for image analysis and field verification. The real costs to map a specific indicator naturally depend on the indicator type, and are also highly dependent on the image analyst’s experience, software used, as well as the volume of data and baseline quality of the raw data used. Finally, reporting costs are an often overlooked and underestimated (in time and funding), component of these projects.



The following rules-of-thumb are based on previous remote sensing work undertaken by this CSIRO group, and apply primarily to one-off mapping projects. Obviously there will be some variations, depending on the routine nature of the tasks and the required processing steps:

Fixed Data Provider Costs:

• Image data purchase represents about 20-25% of the full cost of the project, although discounts apply for large areas or repeated acquisitions

• Image radiometric correction and geo-rectification represent about 5% of the total cost

Variable costs:

• Generation of seamless mosaics represent about 5-20% of the data acquisition costs (depending on the native accuracy of the raw image product)

• Traditional classification and vegetation type mapping with field survey and validation: 20-35% of the total cost

• Geophysical remote measurement (e.g. maps of chlorophyll concentration in water, plant species) with field survey and validation: 30% of the total cost.

This cost breakdown does not necessarily represent additional savings and changes in the distribution of costs for multi-year monitoring programs, where the relative propositions of the different cost fractions would be substantially reduced for a number of steps. Figure 3 shows the estimated relative cost reductions for multi-temporal monitoring projects, and especially when a quantitative measurement approach is used (e.g. water chlorophyll concentrations), which requires minimal field measurement and validation after an initial detailed field measurement campaign.



Remote Sensing Project Cost Breakdown

0

10

20

30

40

50

60

70

80

90

100

First Mapping ofSelected Area

First Repetition ofSame Area

N-th Repetition ofSame Area

Arb

itrar

y U

nits

Geophysical Remote MeasurmentTraditional classificationSeamless MosaickingImage Radiometric Correction and RectificationImage Purchase

Figure 3: Remote sensing project costs breakdown

Raw Image Data Acquisition Costs

Some general considerations and rules exist for the costs of different types of remote sensing data acquisition:

Comparison of platforms

As stated in Section 2, satellite data are comparatively low cost per unit area for fine to coarse spatial resolution data, relative to airborne image acquisition. When considering the collection of a time series of images, it is important to note that satellite data is often discounted for historical time-series over a particular area.

Satellite data processing costs are also often low per unit area compared to airborne remote sensing techniques, as there is generally lower data volume per area. The new generation of high spatial resolution, mid-spectral resolution sensors such as ‘Quickbird’ and ‘IKONOS’ are intermediate in costs between airborne hyperspectral and satellite multispectral data. However, satellite data are taken at regular intervals (once per day for coarse, 1 km resolution data, or every 16 days for medium 30 m data) and at the same time of day, but for the same reason are less flexible then airborne data.



For airborne data acquisition, aircraft and pilot costs are about the same, regardless of the sensor on-board. Also, aircraft based mapping has maximum flexibility in targeting of different sites in the same day. Advanced airborne sensor data (e. g. hyperspectral) acquisition has in the past been expensive due to the need to recover the high sensor development costs , but with time it is rapidly becoming increasingly competitive in cost with air photography and digital camera (ADAR) or multispectral video data (DMSI).

Airborne data processing, analysis and ‘value-adding’ costs also vary depending on levels of processing, atmospheric correction, data volume and level of spatial accuracy (3 metre RMS, or better) required. In selected applications such as benthic mapping where cost:benefit analysis was estimated, hyperspectral scanner data have been shown to be more cost-effective and objective than air photo interpretation and standard field sampling, which often involve higher costs in nested field sampling (Mumby et al. 1999). In addition due to its high spectral information content, one hyperspectral image may be used to map many more simultaneous indicators than traditional air photo survey or 4-band digital camera systems, thus the cost per indicator per unit area may come down as well.

Cost-sharing

On a more general note, it is advisable to seek various simultaneous customers for image data acquisition purchases within organizations and between organizations, since shared data-acquisition for multiple purposes can significantly decrease the cost per indicator per area.

5.3 Sample Pilot Project Costs

Wallis Lake Seagrass Mapping for Great Lakes Council and NSW Dept. of Land and Water Conservation

In order to present an example of a typical one-off remote sensing study, a benthic mapping application of Wallis Lake (NSW) is described below.

This mapping project involved two image data types:

• An airborne hyperspectral data collection (the example is for a 2 m resolution dataset, similar calculations can be done for any spatial resolution between 0.4 and 10 m)

• Quickbird satellite data (2.4 m resolution multispectral bands similar to Landsat)

These two types of data have complementary capabilities and were considered to cover many indicators of interest to the managers of this coastal lake and associated estuary.

As discussed elsewhere in this report, airborne hyper-spectral data (such as CASI and HYMAP) are high spatial resolution, flexible and capable of delivering the most indicators at the highest level of confidence, due to their high spectral and radiometric resolution. Conversely, satellite multispectral data (such as Quickbird and Ikonos) have very high



spatial resolution (0.6 m black & white; 2.4 m multi-spectral) data. These are attractive datasets, since the cost per square kilometre is intermediate between high resolution airborne data and medium resolution satellite imagery. This sensor suite also has a similar capacity of indicator discrimination as the Landsat TM system.

The costs for acquisition of airborne imaging spectrometry are relatively high (Table 33 and Figures 4-6) although becoming more competitive with other types of airborne data.

Mapping all of the 85 km2 of the Wallis Lake area would cost approximately $34,000 at 1 m resolution, but here significant savings were achieved by collecting the data at 10 m resolution, since the spatial resolution required for this application does not require 1 m resolution. For CASI (Compact Airborne Imaging Spectrometer) data for example, the width of the imaged area ranges from 500 m wide at 1 m resolution to 5000 m wide at 10 m resolution. Therefore the number of flight lines required to cover the study area of interest was also drastically reduced when the required spatial resolution became coarser.

In addition, in this study, only those sites identified as being of ecological or environmental importance were flown with this system, thus further decreasing the costs substantially down to about ~$8,500. The equivalent calculation of costs of acquisition using the Quickbird satellite data are lower than for the airborne imaging spectrometry example (Figure 5). In this case, an 85km2 area would have cost $3652. However, the company allows end-users to select any shape of the collage of images covering an area, as long as the minimal width is 5 km. The limitation here is that Quickbird data is multi-spectral only, and can thus not resolve as many indicators as airborne imaging spectrometry.

Table 33. Cost comparison of different image data sources for the Wallis Lake study.

SensorSpectral

Resolution Spatial

Resolution Cost $AUD

Landsat TM 7+ 8 bands (4VNIR,

2SWIR + 1TIR)

M

30m (15m panchromatic)

$560 for 25km2 ($22.40/km2)

$1500 185km2 ($8.11/km2)

ALI

10 bands

(1 Pan

6 VNIR

3 SWIR)

M

30m (10m panchromatic)

$3400 for 1554km2 (37x42km strip

$2.20/km2)

$5100 for 6845km2 (7.5x185km strip

$0.75/km2)

If bundled with Hyperion data AL



Additional $1,400 (for the 42 or 185

Spot 5 1 Pan 3VNIR 1 SWIR

EF to F

2.5 or 5m

Panchromatic

10m VNIR

20m SWIR

$1900 for 400km2 (Standard programmed level) 10m colour ($4.70/km2)

$4360 for 169km2 (Precision 2B)

2.5m colour archive data

($26/km2)

Up to $6060 for 169km2 (orthorectified programmed level 3) 2.5m colour ($36/km2)

Quickbird 4VNIR

EF to F

0.61mpanchromatic

2.5m multi-spectral

$3660 for 2.4m multi-spectral

($43 per km2)

$4400 for bundled 1m pan & 2.4m

spectral data or 4-band pan sharpe

data ($51 per km2)

Promotional prices often up to 40% off.

Ikonos 4VNIR

EF to F

1mpanchromatic

4m multi-spectral

$3700 for 100km2 for 4m multi-spe

($37/km2)

$5000 for 100km2 for bundled pan

4m multi-spectral data



($50/km2)

Hyperion 224 bands M

30m

$3400 for 315km2 (7.5x42km strip)

($11/km2)

CASI Up to 256 bands VIS-NIR EF to F

$17000-34000

($200-400/km2)

HYMAP 126 bands

VIS-SWIR EF to F

$8500-34000

($100-400/km2)

Theoretical analysis of this type of information (see Figures 4–6) suggests that overall, remote sensing is most cost-effective if it is applied over larger areas, and for increasingly higher numbers of indicators, with significant discounts also applied by vendors for repeated acquisitions over the same area.

Costs (approximate) of Airborne Imaging Spectrometry per unit area per resolution of pixels

0

50

100

150

200

250

300

350

400

450

0 100 200 300 400 500 600 700 800 900 1000Area surveyed km2

cost

s (th

ousa

nds

of A

UD

)

1 m2 m4 m10 m

Figure 4: Simulated cost scenarios for different mapping sizes with airborne spectrometry, at different spatial resolutions



Costs of Quickbird 2.4 m satellite data (note that polygons may be acquired thus avoiding purchase of areas not wanted)

0

10

20

30

40

50

60

70

0 100 200 300 400 500 600 700 800 900 1000

Area surveyed km2

cost

s (th

ousa

nds

of A

UD

)

2.4m

Figure 5: Simulated cost scenarios for Quickbird satellite data .

Price per indicator for a 2 m resolution hyperspectral airborne campaign

0

20

40

60

80

100

120

0 100 200 300 400 500 600 700 800 900 1000km2

Pric

e pe

r ind

icat

or (i

n th

ousa

nds

AU

D)

2 indicators4 indicators6 indicators8 indicators10 indicators12 indicators14 indicators16 indicators18 indicators20 indicators

Figure 6: Simulated cost per indicator for a hyperspectral airborne remote sensing application.

The images below show Wallace Lake as seen by the Landsat (Figure 7) and Quickbird (Figure 8) satellites. The inset box in Figure 7 denotes the location and spatial extent of the Quickbird image were high definition patterns and texture of the macrophytes become visible whereas they are not at the Landsat (25m) data resolution.



Figure 7 : The Landsat September 2002 image with the white box indicating the spatial extent of the Quickbird image.

Figure 8: A subset of the Quickbird image of Wallis Lake showing spatial details of



macrophytes unavailable in Landsat.

Moreover, the 1.5 pixel RMS or spatial accuracy that applies to the Landsat data translates on the ground to a 45m geolocation uncertainty, while the Quickbird has a similarly defined RMS – because of it’s smaller pixel size this translates to only 3.6 m geolocation uncertainty.

5.4 Additional Applications

A number of researchers are applying remote sensing to improve the understanding of process and function within the riparian zone. Lymburner [2001] is studying the function of riparian zones of the Nogoa catchment - a sub-catchment of the Fitzroy River of central Queensland. Lymburner states that to make informed decisions about riparian zones, information from remote sensing, terrain analysis and geographic information systems (GIS) are required. Past studies have included some of these data sources, but have not always identified riparian vegetation type, the individual function or the land use within the riparian zone. Lymburner has quantified the riparian zone vegetation (RZV) functions from the literature and catchment indices, to allow their application to integrated data sets of remote sensing, GIS and terrain data.

The functions of RZV are important at the catchment level to maintain the ecological health, diversity and integrity of the system. To cover the existing gap between local studies and the spatial distribution, RZV functions can be quantified through the use of functional indices. Lymburner [2001] describes a number of indices (via literature) that can be used to define certain RZV functions;

• Runoff interception

• Sediment trapping

• Pollutant trapping

• Large woody debris production

• Denitrification

• Flood Attenuation

• Bank Re-enforcement and

• Provision of terrestrial habitat

Many of these indices can be transformed to obtain the desired SRA indicators, for example, the SRA indicator ‘ Woody Debris Potential’ can be obtained from the Large Woody Debris Loading Index (LWDLI) . The LWDLI is based on data collected from 6 Australian rivers with varying riparian vegetation types. This index when applied to other rivers obtained a high correlation (R2=0.91) when compared to field observations.

Lymburner [2001] describes existing remote sensing techniques using the ASTER sensor on the NASA Terra platform (see Figure 16 and Appendix 3) to classify vegetation types and extrapolating some parameters across a landscape. Some of these existing techniques and



their results have been published by Herron and Hairsine [1998] for runoff interception, Hairsine and Rose [1992] for sediment trapping, Palis et al [1990] and Ghadiri and Rose [1993] for pollutant trapping, Marsh et al [2001] for woody debris and Pickup and Marks [2001] on flood attenuation . To enhance the number and accuracy of riparian assessments, Lymburner [2001] describes a number of new techniques which are still experimental and results have yet been obtained, but would be suitable to test with a number of remotely sensed datasets.

The relationship between spectral and spatial resolution when mapping the morphology of a 4th order stream was examined by Legleiteir et al [2001]. They found that spectral resolution (in this context hyper-spectral versus multi-spectral), was more important than spatial or radiometric precision for the classification accuracy of in stream habitats. It must be noted the spatial resolutions compared in this study were both of the very fine range - 1 and 2.5 metres.

Apan et al [2002] quantified changes of the type and magnitude of riparian landscape changes with the Lockyer Valley catchment in south-east Queensland between 1973 and 1997. Landsat MSS data from 1973 and Landsat TM data from 1997 (both Landsat satellite sensors medium spectral and spatial resolution) were classified to yield 5 broad classes: woody vegetation, pasture, crops, settlement and water. These data were then integrated with a digital elevation model (DEM), catchment boundaries and a digital cadastral database (DCDB). To quantify riparian landscape structure and its temporal change, Apan et al [2002] used metrics or indices that describe the landscape configuration and composition at the patch, class and landscape levels. Changes in the areas of riparian zone were mapped and analysed and regions of vegetation change were integrated with land tenure, stream order and slope maps. These 3 characteristics were thought to be of the most ecological important in determining land use and its effect on riparian vegetation.

The results of Apan et al [2002], concluded that the riparian landscape had changed significantly within the 24 year period showing significantly more fragmentation and the proliferation of much smaller, less connected vegetation patches. This data could be used to identify problem areas and potential riparian reserves.

The riparian vegetation of the Lower Rio Grange Valley (240km of river in Southern Texas, USA) has been the focus of a multi-temporal assessment by researchers at the University of Texas Pan American, the University of Texas at Austin Centre for Space Research, and the University of Texas – San Antonio (http://www.csr.utexas.edu/projects/rs/valley.html) . Landsat TM data from 1996 was used to calculate riparian vegetation coverage over the entire 240km and provided a baseline for subsequent data acquisitions. Aerial videography was collected over eight test sites together with field data (transects run from the banks of the river to 50-100 meters inland). The classification accuracy for the vegetation species were generally very high (>85%).



5.5 Image Examples of River Systems using Different Sensor Types

Airborne Digital Photography (~ 1 m resolution): Pitt River, September 2002

Figure 9 illustrates aerial photography that was acquired over sections of the Pitt River (in northern California) with GIS information overlain. In order to map riverine health and river characteristics (such as benthic cover, chlorophyll-a, dissolved organic matter concentrations and turbidity), the California-based Pacific Gas and Electric Company have requested hyperspectral (HYMAP) imagery over 48km of the river with an extensive field campaign. Water quality mapping for this project is being carried out by CSIRO Land and Water. Terrestrial and bathymetric LIDAR data will be integrated with the HYMAP imagery.

Figure 9: Pitt River California; mosaicked aerial photography with elevation contours and GIS data overlaid, the figure on the right is the detail of the black box outlined on the

left. Image courtesy of Pacific Gas and Electric CA USA.



Airborne Hyperspectral Scanner CASI (1m resolution): Devon Downs Lagoon, March 1999.

The Devon Downs lagoon (along the Murray River in South Australia) water is used as part of Adelaide reticulated water supply (Figure 10). Management of these reservoirs requires occasional drawdowns to occur at times of low algal concentration and turbidity. Cyanobacteria detection and spatial distribution of these was successfully mapped using hyperspectral data (CASI) to detect spectral characteristics of algae and particularly of cyanobacteria. In addition different levels of turbidity were mapped across the lagoon and reaches of the nearby Murray River. This technique complements in situ point based sampling and remote sensing does not replace these tools but augments the existing system by providing a spatial and spectral context.

Figure 10: Devon Downs Lagoon on the lower Murray River. This fine resolution (1m pixel) CASI multi spectral image has been enhanced to show patterns of water greenness

within the lagoon proper.



Airborne Laser (2.4 m resolution): Toupna Creek, Millewa State Forest

Figure 11 shows a colour coded elevation image over the Toupna Creek area in the Millewa State Forest estimated from an airborne laser scanner. This data provided sub-meter height measurements at an average spacing of one point in every 2.4m. This density of height data enables the generation of high precision elevation surfaces, which can then be used in hydraulic modeling, an important floodplain management tool. This particular image shows higher elevations in light grey, red through to lower elevations in the cyans and blues. Other remote sensing data types are developing the capacity to provide users with digital elevation data, see Appendix 4 for more details.

Figure 11: Toupna Creek Area ( Millewa State Forest).

Source: Christina Ratcliff, Land Information Group. NRE Victoria



Airborne Laser Scanning and Digital Video Data (~ 2.5 m resolution): Brisbane River, QLD.

Witte et al (2001) details a cost-effective methodology for the assessment of riparian zones in sections of the Brisbane River and Lockyer Creek in south eastern Queensland, using a combination of an airborne laser scanner and a digital video system to not only map the riparian vegetation structure, but also provide some information on the type and species of the riparian vegetation.

Highly accurate digital elevation models (with an accuracy of less than 30cm for 80% of the data) were produced with this methodology, and were integrated with the video data and classified based on common vegetation types for each height class, Figure 12.

The methodology was shown to provide accurate maps of the riparian zone and terrain models, which are suitable for flood and bank erosion monitoring and erosion risk assessment.

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Airborne (Helicopter) Video (sub. 1 m)

The ‘Gyrovision’ helicopter video system provides a running image acquisition along riparian corridors and an accurate GPS system ‘stamping’ the video frames with accurate geographic positions. Specialised computer software allows different image frames to be ‘captured’ as static images (e.g. Figure 13) and used for mapping purposes by a trained interpreter.

Figure 13: Screen-capture product derived from a video camera system, operated out of helicopter following river systems. Image courtesy: Gyrovision



Satellite MultiSpectral: SPOT 5 (10 m resolution colour, 2.5 panchromatic): Burrowa

The new SPOT satellite SPOT-5 has improved spatial and spectral resolution compared to the previous SPOT satellites. A 10m resolution multispectral data can be obtained, and by merging the HRG (high resolution 2.5 m geometric) sensor(s) images with the multispectral, the resolution can be reduced to 5 or even 2.5m.

SPOT-5 provides a precise and reliable datasets for analysis. Depending on the level of processing requested, the locational accuracy for Level 1A is better than 50m or less than 15m for level 3 (ortho) processing. Level 3 (ortho) processing is obtained by acquiring a pair of stereo images with the HRG sensors to produce a DEM layer.

SPOT-5 data can be produced at the scale of 1:10000 over large areas which would require significantly less image processing compared with smaller datasets which would require mosaicking and colour balancing. SPOT-5 revisits the same site on average every couple of days, but with the use of the other SPOT satellites, it is possible for 3 SPOT satellites to cover one site on any given day. SPOT satellites have been covering Australia since 1986 and the scenes over Australia from May 1990 until January 2003 can be viewed at the Geoscience Australia (formerly ACRES) archive. After this date Raytheon Australia will need to be contacted (see contact details below). This company is also implementing direct reception facilities in Australia and have indicated a considerable reduction on costs of this data.

Figure 14: Sample SPOT-5 image collected near Burrowa. Courtesy: Raytheon Australia



Satellite Multi-spectral ASTER Data (15 m resolution): Murray River near Yarawonga and Corowa

This ASTER image (Figure 15, right) along the Victorian/NSW border was acquired to illustrate the spatial coverage obtained from satellite sensors at a resolution of 15 metres. It is interesting to note that although the river is only a small component of the entire image, it is placed in context with the adjacent land use. The ASTER data is displayed as a false colour image (i.e. the near infrared band has been used with 2 visible bands). The brighter the red colour corresponds to the greenness of the vegetation. This sample data illustrates the spectral variation within the scene and when integrated with DEM information (which can also be acquired by ASTER) makes this data useful for riparian assessment. Image data from this satellite sensor can be obtained from NASA directly (see Appendix 1 for contact details).

Figure 15: Some SRA riparian test sites are located within the map on the left, and on the right is a subset of an ASTER image covering the sites, with a 15m pixel resolution in the visible and near-infrared. An additional backward-looking near-infrared band

provides stereo coverage, from which a DEM can be extracted (with a horizontal accuracy of 30m).



Multi-spectral ASTER Satellite Data (15 m resolution): Nogoa Catchment – Queensland.

The top panel of the image shows a basic false colour infra red image of parts of the catchment with dense riparian zones in red. Below is a classified version (after Lymburner, 2002) of the same image where three distinguishable riparian vegetation types have been highlighted in different colours.

Figure 16: Top; A floodplain on the Nogoa, showing 3 different types of riparian vegetation. Centre; the three different types of riparian vegetation as discriminated by

the crossplot. Bottom; a NIR vs. SWIR crossplot, showing the location of the 3



different types of riparian vegetation - after Lymburner (2002).

Airborne Hyperspectral CASI (1 m resolution): Murrumbidgee River: Mapping Willow and Casuarinas in Riparian Zones.

This example image shows the utility of high spectral resolution, and high spatial resolution image data, which was used for spectrally mapping two distinct tree types. In this case, field work was conducted for collection of relevant spectral information for these species with a hand-held spectrometer. Subsequently, the CASI imagery was corrected and spectrally analysed to show the location of these species (see below).

Figure 17: Willow-tree classification (in red) and casuarina (green), derived from spectral analysis of airborne hyperspectral image analysis, combined with ground

collected measurements of the characteristic reflectance of this tree type.

Source: White & Lymburner, CLW internal report.



EO-1 NASA Satellite Hyperspectral Imaging - Hyperion (30 m resolution)

Figure 18 is an illustration of a ‘hyperspectral data cube’ derived from a 220-band Hyperion scene taken near Tumut, NSW. The 3-dimensional depiction is used to highlight the spectral richness of this type of satellite data. Riparian zones are clearly distinguishable at this coarse resolution, while this particular satellite system has been used primarily as a technology demonstrator, it paves the way for follow-up systems (currently being designed by the European Space Agency and in the US), capable of routine mapping and better spectral discrimination of various riparian features.

Coops et al (2001) have utilised Hyperion’s hyperspectral imagery to assess the biochemistry of eucalypt trees.

Figure 18: Sample 3-D depiction of the spatial coverage and spectral richness (220 bands) of Hyperion data from the NASA EO-1 demonstration satellite.



Multispectral Satellite - QuickBird (2.4 m colour, 0.6 m panchromatic): McLarenVale South Australia.

Imagery from this highest resolution commercial satellite system is by request and is taken whenever the satellite passes over the area and cloud cover conditions are suitable.

Figure 19 below is a very small subset of a larger image covering the grape growing region of MacLaren Vale in South Australia. It also shows the effect called ‘Panchromatic sharpening” where the 2.4 m resolution colour image has been ‘sharpened’ using the 60 cm pixel resolution black and white panchromatic image of the same area.

Figure 19: Sample QuickBird image subset for a riparian area near MacLaren Vale, South Australia. Image Courtesy SKM and DigitalGlobe



Water Quality Assessments: Airborne Hyperspectral Scanner CASI (2.5 m resolution): Hawkesbury River, 24th February 1993, 4th and 23rd February 1994

Figures 21 & 22 illustrate examples of products developed for the Hawkesbury Nepean Catchment Management Trust. In 1993 and 1994, Australian Water Technology (AWT) and CSIRO conducted a demonstration using a CASI sensor (in spatial mode) mapping algal species and producing quantitative assessments of water quality parameters such as chlorophyll, turbidity (organic and inorganic) and pigment (specifically phycocyanin of blue-green algae). The spatial distributions and concentration results were successfully compared with data obtained from in situ field measurements, Jupp et al. 1994 and Jupp et al 1996.

Figure 21: Chlorophyll map of the Hawkesbury and Colo Rivers. In 1994 this region was imaged using a CASI multi-spectral sensor in February as part of the Optical

Water Quality research program.



Figure 22: Turbidity map of the Hawkesbury and Colo Rivers. In 1994 this region was imaged using a CASI multi-spectral sensor in February as part of the Optical Water

Quality research program.



Satellite Multi-spectral ASTER (15 m resolution): Wagga Wagga, NSW - Water Quality Assessment

Figure 23 is an ASTER image acquired over the Wagga Wagga region and contains a section of the Murrumbidgee River. An algorithm applied to the 0.78-0.86 micrometer band of this sensor was applied to this image to obtain only relative levels of suspended sediment concentration.

Figure 23: Aster satellite image of Wagga Wagga area, New South Wales. Relative suspended matter levels are indicated in colour and are derived from the 0.78 - 0.86

band of the Aster data set. Pixel size is 15 m.



5.6 Floodplain Modelling Examples

Figures 24 and 25 illustrate outputs from combinations of digital elevation data and hydrologic modelling software. In this case, the authors [Pickup & Marks, 2001] have used the methodology to highlight potential flood extents for different flooding scenarios. The active flow zones, backwater zones and floodplain limits have been identified and this information is utilised in hydrologic models to calculate the floodplain flow.

Hydrologic modelling can estimate runoff, flood-risk assessment and transport of non-point source pollution and sediment [Slatton et al 2002]. The coarser scale DEM can often be improved by fusing with multiple datasets of finer resolution. Pickup & Marks [2001] found that one of the principal sources of inaccuracy included inability of DEM to distinguish small variations in topography, therefore finer resolution DEMs reduce the inaccuracy of these models.

Figure 24: Output of floodplain model (Pickup and Marks, 2001) on Aster satellite image background. The red area is representative of active flood movement while the

blue and green areas indicate slackwater.



Figure 25: The background of this graphic is a scanned image of floodplain extent for the Wagga Wagga area of New South Wales. The red vector lines are the boundaries

for flood events of 1 in 10 year flood (inner ones) and a 1 in 50 year flood (outer ones). The coloured modelling output [Pickup and Marks, 2001] is for a 1 in 25 year event and

is intermediate on the right hand side of the graphic. The floodplain modelling is based on a 25 m digital elevation model



Figure 26: A Landsat TM image of part of the Suttor River catchment, Queensland. The black lines delineate modelled 25-year flood extents. The image has been transformed

(using the LS-FIT algorithm in the ENVI image processing package) to find pixels containing features characteristic of clay minerals (Research Systems, 2000). The red

band shows the LS-FIT clay residual value (with brighter values suggesting higher clay content), green shows TM band 2, and blue shows TM band 1.



Figure 27: The 25 year flood extents overlaid on the 9 second DEM (grey scale, 250 m cell) in the Logan River catchment, Queensland. Blue and orange indicate active flow zone and slackwater areas respectively obtained by modelling the 9 second DEM. The yellow lines show the boundaries of the inundated zone obtained by modelling using

a 25 m grid cell DEM



6 Proposed SRA Pilot Study

To broaden discussion on the utility of remote sensing for SRA assessment and to provide better estimates of the associated costs, a pilot study focussing on one or a small number of the SRA sites is recommended.

This exercise would help confirm the originally recommended sensor suite (CASI or Hymap, combined with Spot5, QuickBird or Ikonos) or suggest an alternative set to compliment to the current ground-based riparian health assessments. It would also assist in development of more customised field sampling protocols.

For the selected pilot site(s), we would suggest an expanded suite of image data acquisitions of satellite (Landsat, QuickBird, Hyperion, ASTER) data, and of airborne (Hymap, CASI, DMSI) data for a 10- 25 km long stretch of the River Murray, covering one or more SRA permanent monitoring sites.

In order to keep data costs down, the selection of pilot study sites could also be governed by the pre-availability of data sets. For example, if SRA pilot sites were located in the Barmah-Millewa forests along the Murray, or sites along the Murrumbidgee River, these have been imaged during the course of prior studies, and by a number of technologies such as Landsat MSS, Landsat TM over the years and most recently by Airborne Lidar. The Barmah-Millewa area for example had been the focus of remote sensing studies and River Red Gum health assessments by Dr. Laurie Chisholm (University of Wollongong).

A 10-25 km river length for the pilot study would be suitable for the expected size of both Landsat7 and QuickBird satellite frames and can easily be covered by aircraft hyperspectral aircraft scanners such as Hymap or CASI. To minimise aircraft flying costs and glint problems, a length of river should be chosen which runs in the N-NE to S-SW, or N-NW to S-SE general directions. Additional savings may be possible, since many image data vendors are quite willing to provide discounts for such demonstration studies.

Two mayor field campaigns would be required to both collect the relevant spectral information (e.g. reflectances of tree species, macrophytes, etc.) and water samples, as well as for post-classification validation purposes. Some of the field components and image analysis could possibly be done by CSIRO and/or University of Wollongong staff from Dr. Chisholm’s laboratory in close collaboration with MDBC and SRA experts with good local knowledge.

The total estimated costs of such a pilot study are in the order of $150,000 - $170,000, where the image data acquisition costs for all these sensors would cost about $50,000 - $80,000, and other components such as field campaigns (2) and commissioned image analysis would cost collectively about $80,000 to $100,000.



7 References

Adams, J.B., Sabol, S., Kapos, V., Almeida-Filho, R., Roberts, D.A., Smith, M.O. & Gillespie, A.R. [1995]. Classification of multispectral images based on fractions of endmembers: Application to land cover change in the Brazillian Amazon. Remote Sensing of Environment, 52, 137-154.

Adams, M., Hook, S., and Bhaskar R. [2002]. Aster Users Handbook Version 2 NASA JPL)

Apan, A.A., Raine, S.R. and Paterson, M.S. [2002] Mapping and analysis of changes in the riparian lanscape structure of the Lockyer Valley catchment, Queensland, Australia. Landscape and Urban Planning 59, 43-57.

Blair, J.B., Rabine, D.L., Hofton, M.A. [1999] The Laser Vegetation Imaging Sensor (LVIS): A medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS Journal of Photogram. And Remote Sensing, 54:115-122.

Blackburn, G.A. [1998]. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. Int. J. Remote Sensing 19:657-675.

Bricaud, A. Morel, A. and Barale, V. [1999], “MERIS Potential for Ocean Colour Studies in the Open Ocean,” International Journal of Remote Sensing, 20, pp. 1757-1769.

Bukata, R. P., Jerome, J. H., Kondratyev, K. Y. and Pozdnyakov, D. V. [1995], Optical Properties and Remote Sensing of Inland and Coastal Water, Boca Raton, USA, CRC Press.

Bukata, R. P., Jerome, J. H., Kondratyev, K. Y., Pozdnyakov, D. V. and Kotykhov, A. A. [1997], “Modeling the Radiometric Color of Inland Waters; Implications to a) Remote Sensing and b) Limnological Color Scales,” Journal of Great Lakes Research, 23, pp. 254-269.

Carter, G.A.., [1998] Reflectance wavebands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sensing.of Environment. 63:61-72.

Clark, R.N., Trude, V.V., King, V.V., ager, C., Swayze, G.A. [1995]. Initial vegetation species and senescence/stress indicator mapping in the San Luis Valley, Colorado, using imaging spectrometer data. Pp. 35-38. In:. Proc. Fifth Airborne/Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop. January, 1995.

Close, D.C., Beadle, C.L., Brown, P., and Holz, G.K. [2000]. Cold-induced photoinhibition affects establishment of Eucalyptus nitens Deane and Maiden Maiden and Eucalyptus globulus Labill. Structure and Function. 15:32-41.

Coops, N.C., Ryan, P.J., Bishop, A.P. [1998]. Investigating CASI responses to soil properties and disturbance across an Australian eucalypt forest. Can. J. Rem. Sens.24:153-168.



Coops, N.C., Smith, M.L., Martin, M.F., Ollinger, S.V., Held, A., and Dury, S.J. [2001]. Assessing the Performance of HYPERION in Relation to Eucalypt Biochemistry: Preliminary Project Design and Specifications. Proceedings IGARSS 2001, Sydney Australia.

Curran, P.J. [1994]. Imaging spectrometry –its present and future role in environmental research. In Imaging Spectrometry – a Tool for Environmental Observations. Joachim Hill & Jacques Megier (eds.). Kluwer Academic Publishers, Dotrecht. EU., pp. 1-24.

Curran, P., J., Kupiec, J.A., Smith, G.M. [1997] Remote sensing the biochemical composition of a slash pine canopy. IEEE Transactions on Geoscience and Remote Sensing. 35:415-420.

Curtiss, B. and A. F. H. Goetz [1994]. Field Spectrometry: Techniques and Instrumentation. pp1-10. International Symposium on Spectral Sensing Research.

Dekker, A. G., Malthus, T. J. M. and Hoogenboom, H. J. [1995], “The Remote Sensing of Inland Water Quality,” In F. M. Danson and S. E. Plummer (eds.), Advances in Environmental Remote Sensing, Chichester (UK), John Wiley & Sons, pp. 123-142.

Dekker, A. G. and Bukata, R. P. [2002], “Remote sensing of inland and coastal waters,” in W. R. Stone (Ed.), Review of Radio Science 1999-2002, John Wiley & Sons , New York, USA. pp. 519-534.

Dekker, A. G., V. E. Brando, J. M. Anstee, N. Pinnel, T. Kutser, H. J. Hoogenboom, R. Pasterkamp, S. W. M. Peters, R. J. Vos, C. Olbert, and T. J. Malthus [2001], “Applications of Imaging Spectrometry in Inland, Estuarine, Coastal and Ocean Waters,” In F. van der Meer (ed.), Imaging Spectrometry: Basic Principles And Prospective Applications", Dordrecht, Kluwer Academic Publishers.

Dobson, M. C., F. T. Ulaby, and L. E. Pierce, Land-Cover Classification and Estimation of Terrain Attributes Using Synthetic Aperture Radar. [1995]. Remote Sensing of Environment, 51:199-214, Jan. 1995.

Doerffer, R. and Murphy, D. [1989], “Factor Analysis and Classification of Remotely Sensed Data for Monitoring Tidal Flats,” Helgolander Meeresuntersuchungen, pp. 276-293.

Durand, D., Pozdnyakov, D. V., Sandven, S., Cauneau, F. and Wald, L. [1999], “Characterisation of Inland and Coastal Water with Space Sensors,” Nansen Environmental and Remote Sensing Centre (NERSC) Technical Report No. 164, European Commission-Centre For Earth Observations (CEO), Solheimsviken, Norway.

Ghadiri, H. and Rose, C.W. [1993] Water erosion processes and the enrichment of sorbed pesticides .1. Enrichment mechanisms and the degradation of applied pesticides. Journal of Environmental Management 37, 23-35.



Gittelson., A.A., Merzlyak, M.N., Lichtenhaler, K. [1996]. Detection of red edge and chlorophyll content by reflectance measurements near 700 nm. J. Plant Physiol.148:501-508.

Goetz, A. F. H. [1992]. Imaging Spectrometry for Earth Remote Sensing. Imaging Spectroscopy: Fundamentals and Prospective Applications. F. Toselli &. J. Bodechtel, Eds. Brussels and Luxembourg, 1-19.

Green, E.P., Mumby, P.J., Edwards, A.J. and Clark, C.D. [1996] A review of remote sensing for the assessment and management of tropical coastal resources. Coastal Management, 24, 1-40.

Guzzi, R. Rizzi, R. and Zibordi, G. [1987], “Atmospheric Correction of Data measured by a Flying Platform over the Sea: Elements of a Model and its Experimental Verification,” Applied Optics, 26, pp. 3043-3051.

Hairsine, P.B. and Rose, C.W. [1992] Modeling Water Erosion Due to Overland Flow Using Physical Principles 1. Sheet Flow. Water Resources Research 28, 237-243.

Harrison, B.A. and Jupp, D.L.B. [1989]. Introduction to Remotely Sensed data. Part One: Resource manual. CSIRO Publications, Melbourne, Australia.

Held, A. and Billings, S. [1998]. Automatic tree crown recognition and counting from high resolution casi data. Proceedings: 9th Australasian Remote Sensing and Photogrammetry Conference, July 20-24 1998, The University of New South Wales, Sydney, Australia. Vol1, pp 3307-3311.

Herron, N.F. and Hairsine, P.B. [1998] A scheme for evaluating the effectiveness of riparian zones in reducing overland flow to streams. Australian Journal of Soil Research 36, 683-698.

Hilton, J. [1984], “Airborne Remote Sensing for Freshwater and Estuarine Monitoring,” Water Research 18, pp. 1195-1223.

Horler, D.N.H., Dockray, M., Barber, J. [1983] The red-edge of plant leaf reflectance. Intl. J. Remote Sensing, 4:273-289.

Hunt, G.R. and Ashley, R.P., [1979]. Spectra of altered rocks in the visible and near-IR. Economic Geology 74:1613-1629.

Jensen, J. R., Hodgson, M.E., Christensen, E., Mackey, H.E., Tinney, L.R. and R. Sharitz [1986]. “Remote sensing inland wetlands: a multispectral approach.” Photogrammetric Engineering and Remote Sensing 52: 87-100.

Jerome, J. H., Bukata, R. P. and Miller, J. R. [1996], “Remote Sensing Reflectance and its Relationship to Optical Properties of Natural Waters,” International Journal of Remote Sensing, 17, pp. 3135-3155.



Jupp, D. L. B., J. T. O. Kirk, and Harris, G.P. [1996]. “Detection, identification and mapping of cyanobacteria using remote sensing to measure the optical quality of turbid inland waters.” Australian Journal of Marine and Freshwater Research 45: 801-828.

Jupp, D.L.B., Byrne, G., McDonald, E., Anstee, J., Harris, G. and Smith, J. [1994] Monitoring the Optical Water Quality of the Hawkesbury River Using Remote Sensing. CSIRO Division of Water Resources Consultancy Report 94/11

Kirk, J. T. O. [1983], Light and Photosynthesis in Aquatic Ecosystems, Cambridge, UK, Cambridge University Press.

Kirk, J. T. O. [1994], Light and Photosynthesis in Aquatic Ecosystems, Cambridge, UK, Cambridge University Press.

Kneizys, F. X., Shettle, E. P . Gallery, W. O., Chetwynd, J. H., Abreu, L. W., Selby, J. E. A., Clough, A. and Fenn, R. W. [1983], “Atmospheric Transmittance/Radiance: Computer Code Lowtran6,” Air Force Geophysics Lab., Hanscom AFB Environmental Research Report AFGL-TR-83-0187.

Kondratyev, K. Y., Pozdnyakov, D. V. and Petterson, L. H. [1998], “Water Quality Remote Sensing in the Visible Spectrum International Journal of Remote Sensing, 19, pp. 957-979.

Kumar, L., Schmidt, S., Dury, S. Skidmore, A. [2001]. Chapter 5: Imaging spectrometry and vegetation science. 2001. In: Imaging Spectrometry: Basic Principles and Prospective Applications. F.D. van der Meer & S.M. de Jong (Eds.). Kluwer Academic Publishers. Dotrecht

Leckie, D.G. [1990] Advances in remote sensing technologies for forest surveys and management. Can. J. For. Res. 20:464-483.

Lefsky, M., Cohen, W., Acker, S., Parker, G., Spies, T. and Harding, D., [1999]. Lidar remote sensing of biophysical properties and canopy structure of forests of Douglas-fir and western hemlock. Remote Sensing of Environment 70, pp. 339¯361.

Legleiter, C.J., Marcus, W.A., Lawrence, R.L. [2002]. Effects of Sensor Resolution on Mapping In-Steam Habitats. Photogrammetric Engineering and Remote Sensing 68: No8pp 801-807

Lindell, L. T., Pierson, D., Premazzi, G. and Zillioli, E. [1999], Manual For Monitoring European Lakes Using Remote Sensing Technique, Luxembourg, European Communities, 164 p.

Lymburner, L. [2001]. Estimating riparian vegetation functions in the Nogoa catchment of the Fitzroy river basin using remote sensing and spatial analysis. Confirmation Report. Department of Civil and Environmental Engineering University of Melbourne and CRC Catchment Hydrology CSIRO Land and Water.



MacLeod, W., Aiken, J.A. & Borstad, G. [1995]. Intertidal habitat mapping in British Colombia using an airborne imaging spectrometer. In: Proceedings of the Third Thematic Conference on Remote Sensing for Marine and Coastal Environments, Sept 18-20, Seattle, Vol.1, 687-692.

Marsh, N. Rutherfurd, I. and Jerie, K. [2001] 'Predicting Pre-disturbance Loading and Distribution of Large Woody Debris' Third Australian Stream Management Conference. Rutherfurd, I. Sheldon, F. Brierley, G. and Kenyon, C. (Editors). Brisbane, 27-29th August, 2001

Merton, R. [1999]. Monitoring community hysteresis using spectral shift analysis and the red-edge vegetation stress index. Proceedings, 8th AVIRIS Earth Science Workshop, JPL, Pasadena, California.

Miller, J.R., Jiyou Wu, Boyer, M.G., Belanger, M. and Hare, E.W. [1991]. Seasonal patterns in leaf reflectance red-edge characteristics. Int. J. Remote Sens. 12:1509-1523.

Mumby, P.J., Green, E.P., Edwards, A.J. and Clark, C.D. [1999]. The cost-effectiveness of remote sensing for tropical coastal resources assessment and management. Journal of Environmental Management 55:157-166.

Nieke, J., Schwarzer, H. , Neumann, A. and Zimmermann, G. [1997], “Imaging Spaceborne and Airborne Sensor Systems in the Beginning of the Next Century,” in European Symposium on Aerospace Remote Sensing (IEE), London, UK, pp. 3221-3371.

Peñuelas, J. and Filella, I. [1998]. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3:151-156.

Palis, R.G., Okwach, G., Rose, C.W. and Saffigna P.G. [1990] Soil-Erosion Processes And Nutrient Loss .1. The Interpretation Of Enrichment Ratio And Nitrogen Loss In Runoff Sediment. Australian Journal Of Soil Research 28, 623

Phinn, S.R. [1997]. “Remote sensing and spatial analytic techniques for monitoring landscape structure in disturbed and restored coastal environments". Ph.D. Dissertation, Departments of Geography, San Diego State University and University of California at Santa Barbara, 444p.

Phinn, S.R. [1998] A Framework for Selecting Appropriate Remotely Sensed Data Dimensions For Environmental Monitoring and Management. International Journal of Remote Sensing, 19(17): 3457-3463.

Phinn, S.R., Hess, L. and Finlayson, C.M. [1999]. “An assessment of the usefulness of remote sensing for wetland inventory and monitoring in Australia." In: Finlayson, C.M and Speirs, A.G. (eds.) Techniques for enhanced wetland inventory and modelling, Supervising Scientist Report 147, Supervising Scientist, Canberra, 44-83.

Phinn, S., Stanford,M., Held,A. and Ticehurst, C. [2001]. Evaluating the feasibility of remote sensing for monitoring the State of the Wet Tropics Environmental Indicators. Cooperative Research Centre for Tropical Rainforest Ecology and Management. Cairns.



Pickup, G. and Marks, A. [2001]. Identification of Floodplains and Estimation of Floodplain Flow Velocities for Sediment Transport Modelling: National Land and Water Audit Sediment Project. CSIRO Land and Water Technical Report 14/01

Richards, J.A. [1994] Remote Sensing Digital Image Analysis, Berlin: Springer-Verlag .

Roberts, D.A., Brown, K.J., Green, K.J., Ustin, S., Hinckley, T. [1998]. Investigating the relationship between liquid water and leaf area in clonal populus. Proceedings 7th JPL Airborne Earth Science Workshop. January 1998, JPL- Publication 97-21, Pasadena, California.

Rock, B.N., Vogelman, J.E., Williams, D.L., Vogelman, A.F., Hoshizaki, T. [1986]. Remote detection of forest damage. BioScience 36:439-445.

Rodriguez, E., Imel, D.A., Madsen, S.N. [1996]. The accuracy of airborne interferometric SARs. IEEE Trans. Aerosp. Electron. Syst.

Slatton, K.C, Crawford, M.M. and Teng, L. [2002] Multiscale Fusion of INSAR Data for Improved Topographic Mapping.

Stow, D., A. Hope, A. Nguyen, S. Phinn and C. Benkelman, [1996]. Monitoring Detailed Land Surface Changes from an Airborne Multispectral Digital Camera System, IEEE Transactions on Geoscience and Remote Sensing, 34: 1991-1202.

Tucker, C.J. [1979]. Red and photographic infrared linear combinations monitoring vegetation. Remote Sensing of Environment, 8:127-150.

Treuhaft, R.N., Madsen, S.N., Moghaddam, M., van Zyl, J. [1996]. Vegetation characteristics and underlying topography from interferometric radar. Radio Science 31:1449-1485.

Ustin, S.L., Smith, M.O. and Adams, J.B. [1993]. Remote sensing of ecological processes: A strategy for developing and testing ecological models using spectral mixture analysis. In: Scaling Physiological Processes: Leaf to Globe. Academic Press Inc.

Vane, G. (ed.) (1993). Imaging spectrometry of terrestrial environment. In: Proc. Soc. Photo-Optical Instrum. Eng. 1937, Bellingham, WA.

Walker, J., Veitch, S., Dowling, T., Braaten, R., Guppy, L. and Herron, N. [2002]. Assessment of catchment condition. The intensive land use zone in Australia. CSIRO Land and Water. Canberra.

Wallace, J.F. and Campbell, N.A. [1998], Evaluation of the Feasibility of Remote Sensing for Monitoring National State of The Environment Indicators, Australia: State of the Environment Technical Paper Series (Environmental Indicators), Department of the Environment, Canberra.



Wessman, C.A. [1989]. Evaluation of Canopy Biochemistry. In: Remote Sensing of Biosphere Functioning. R.J. Hobbs and H.A. Mooney (Eds.). Springer Verlag, New York. pp. 65-86.

Wilen, B. O. and M. K. Bates. [1995]. The U.S. Fish and Wildlife Service's National Wetlands Inventory project. Vegetation 118:153-169.

Witte, C., Dowling, R., Weller, D., Denham, R. and Rowland, T. [2001]. Quantifying riparian vegetation and stream bank form through the use of airborne laser scanning and the digital video data. Proceedings, 10th Australian Photogrammetry and Remote Sensing Conference, Adelaide, August 2000.

Wrigley, R. C., Spanner, M. A., Slye, R.E., Pueschel, R. F. and Aggarwal, H. R. [1992], “Atmospheric Correction of Remotely-Sensed Image Data by a Simplified Model,” Journal of Geophysical Research. Special FIFE Issue, 97, pp. 18797-18814.



Appendix 1: Data Suppliers

Aerial Photography

NSW Dept. Land and Property Inf. www.lpi.nsw.gov.au

Airborne Multi-spectral and Hyperspectral

Digital Video Specterra Systems www.specterra.com.au

University of New England

David Lamb [[email protected]]

Daedalus 1268 Air Target Services www.airtargets.com.au

CASI DSTO (sensor currently operated by SKM in Adelaide)

Hymap Hyvista Corporation www.hyvista.com.au

Video Gyrovision www.gyrovision.com.au

ADAR University of Queensland

Stuart Phinn [[email protected]]

Airborne LIDAR

LIDAR AAM Geoscan http://www.aamsurveys.com.au/

Satellite Multi and Hyper-spectral

Landsat ETM+ ACRES www.auslig.gov.au

SPOT ACRES www.raytheon.com.au

ASTER NASA http://asterweb.jpl.nasa.gov/

QuickBird Digitalglobe http://www.digitalglobe.com/

IKONOS Space Imaging http://www.raytheon.com.au

MODIS NASA http://modarch.gsfc.nasa.gov/

Hyperion/ALI NASA http://eo1.gsfc.nasa.gov/Technology/Hyperion.html



Appendix 2: Main Remote Sensing Technologies

Aerial Photography

Lower altitude photographs provide greater spatial resolution, down to scales of 1: 1000 (eg 0.235 km and 0.05 km2) for examining individual stands or trees, and can extend to 1:50 000 high altitude photographs, that provide regional coverage (eg 11.75 km by 11.75 km, 138 km2). Different film types add a spectral dimension, enabling panchromatic (black and white) or colour photos of visible wavelengths, and black and white near-infrared and colour infrared (green, red and NIR). Photographic prints or transparencies may be scanned (at a suitable resolution, eg 200 microns) to produce digital format images, able to be geometrically corrected and subjected to image processing operations.

Digital multi-spectral cameras are now commercially available and being used extensively for airborne imaging operations in Australia, United States and Europe (Stow et al. 1996). If processed appropriately these systems have the geometric integrity of aerial photographs and the spectral and radiometric capabilities of multi-spectral image data.

Digital camera images may be subject to radiometric processing operations commonly limited to digital satellite data. Image data can be acquired by these systems for cell sizes down to 0.5 m up to 5.0 m. Individual frames can be processed to provide a seamless mosaic for an area.

The main purpose of camera systems has been to collect analogue data for use in manual interpretation work that may later be digitised as a vector coverage or scanned in as raster. Such operations provide a basis for discriminating different surface cover types, vegetation communities or landforms, mapping structural classes and disturbance features, based on established interpretation cues at specific scales.

Aerial photography is: time consuming to process; insensitive to structural and sub-canopy properties; has limited application for quantitative estimates of biophysical properties or their change over time; and is not considered cost effective for a regional scale inventory and monitoring (Dobson et al. 1995, Wilen and Bates 1995, Taylor et al.1995, Stow et al. 1996). The spatial resolution of new satellite sensors approach the resolution achieved in aerial photography.

Digital video systems offer high spatial resolution coupled with filter selectable broad band passes.

Hand-Held Instruments (radiometers and spectrometers)

A radiometer is any instrument recording the strength of electromagnetic radiation (EMR) incident upon its collection optics. "Radiometer" normally refers to broad-band radiometer, which can be fitted with various interference or absorption filters to determine the wavelengths of light incident on the sensor. "Spectral radiometers" or "spectrometers" are narrow band radiometers, recording the strength of reflected EMR from 10 to 256 narrow bandwidths. If the response of a sensor can be calibrated to a known source of EMR at different levels, output can be produced in spectral radiance and reflectance for targets.



Radiometers are used to acquire information on the spectral reflectance characteristics (radiance or reflectance) of surface cover types in the field or in the laboratory (Curtiss & Goetz 1994). This enables acquisition of spectral reflectance information under controlled atmospheric and surface conditions.

Collecting ground radiometric data provides an initial assessment of the utility of remotely sensed data to discriminate between objects of interest (Ustin et al. 1993) and provides information necessary to fine- tune remotely sensed investigations. By measuring atmospheric conditions at the time of data acquisition the effect of varying amounts of cloud cover, water vapour and illumination geometry on the spectral reflectance characteristics of different surface cover types can be established. Acquiring spectra at different viewing angles enables the effect of off-NADIR views and interaction with illumination geometry and surface cover type to be established. Acquiring reflectance spectra from pure and mixed cover types provides a basis to test the spectral band(s) in which they exhibit significant differences. Repeated visits to the same site in the field over a day or growing season may help to determine the time to best acquire image data to maximise the potential for discriminating different cover types or estimating a biophysical property. Finally, by acquiring radiometer or spectrometer data coincident with airborne or spaceborne imaging of a site, ground data provide a basis for atmospheric correction and calibration of image data. Hand-held radiometry and spectrometry is a fully operational activity, with several different types of radiometers and spectrometers being made commercially (eg Curtiss & Goetz 1994). Disadvantages associated with this approach pertain to the small area covered on the ground and the difficulty of scaling measurements made at this scale to minimum sample units in satellite imaging systems.

Airborne Imaging Sensors – Optical/Passive (relying on reflected sunlight)

Airborne platforms including piloted aircraft, remotely piloted vehicles, helicopters and balloons contain a scanning or framing sensor, capable of acquiring images with cell sizes as small as 10-20cm up to 30m, over areas 1-100's km2, in a number of spectral bands (multispectral and hyperspectral). A scanning sensor utilises a laterally oscillating field of view (FOV) to provide across flight line coverage and platform movement provides along flight path movement. Multispectral capability is provided by different sensor elements for each pixel. In framing sensors an array of CCD's instantaneously acquires an image line and is displaced to the next line by movement along a flight path.

Scanners provide high to medium spatial resolution multi-hyper spectral image data in visible, short wavelength infrared (IR) and thermal IR bands. Image data are processed using ground information and laboratory tests to produce radiance and reflectance images. With geometric and radiometric processing these data may be joined together to produce image mosaics for larger areas then subject to image processing algorithms to delineate cover types or examined in other ways to estimate biophysical and biogeochemical properties (eg macrophyte production in Jensen et al. 1986 and projective foliage cover in Phinn et al. 1997).

A similar set of criticisms may be established for airborne scanner systems, as were identified for aerial photography. Specifically, the spatial and spectral resolution of the data able to be achieved by these sensors will soon be available from the next generation of commercial small satellites. In addition, the new satellites will provide much larger area coverage, and permit construction of regional to national scale mosaics.



Advantages of airborne scanner data include: scale specificity for smaller area applications; possibility of mobilisation at short notice; an ability to obtain data when requested and when suitable atmospheric (cloud or smoke) conditions become available; minimal atmospheric interference; data acquisition under cloud, and a capability for calibration to ground reference data as a basis for scaling between plant/patch/ community/regional scales and multi-temporal analyses.

Inherent problems with the scanning geometry and "hotspot" effects limit the geometric and radiometric utility of these sensors for producing mosaics of larger sites. These sensors portray canopy structure, chemical and moisture content and provide limited ability to penetrate the canopy to establish volumetric information or sub-canopy information.

Satellite Imaging Sensors - Optical /Passive (relying on reflected sunlight)

Digital imaging systems on polar orbiting satellite platforms provide regional to global scale coverage at repeat cycles from twice daily to approximately once monthly. Landsat multispectral scanner (MSS) and Thematic Mapper (TM), SPOT-MSS and Indian Resource Satellite (IRS-1C) deliver medium (10-30 m) to coarse (30-80 m) spatial resolution multispectral image data in visible, short wavelength IR and thermal IR bands.

High spatial resolution (cellsize <= 15m), large area coverage, multi- to hyper-spectral sensors with good radiometric precision are designed to provide high quality image data for environmental monitoring applications on a global scale. High spatial resolution satellite data may still not be able to separate ground features, such as some vegetation species (with similar spectral responses) but delimiting smaller patches and structures (< 1 ha) will be possible. These sensors will provide image data down to the scales able to be obtained from aerial photography with the added benefits of large area coverage and regular acquisition.

Image data are processed using ground information, satellite ephemeral data and atmospheric conditions to correct for geometric and atmospheric distortions to the spatial and radiometric integrity of the data. As with airborne sensors these data are then subject to image processing algorithms to delineate cover types or examined in other ways to estimate biophysical and biogeochemical properties.

Airborne and Satellite Hyperspectral Imaging Sensors - Optical /Passive (relying on reflected sunlight)

Imaging spectrometer systems are now carried on aircraft and satellites. These systems operate in the same mode as optical sensors discussed in the previous sections, but collect reflected and emitted EMR in at least 20 narrow spectral bandwidths. The large number of spectral bandwidths enables a complete spectral signature to be established for each pixel element within an image. Hence, detailed analyses can be conducted on the atmospheric column constituents of each pixel, surface composition and surface biogeochemical elements (Goetz 1992, Vane 1993, Curtiss & Goetz 1994). Data sets from imaging spectrometers occupy much larger volumes, as image cubes, i.e. instead of having 4-8 spectral bands per pixel there may be up to 240 spectral bands. Geometric distortions are similar to other scanning and solid state sensor systems, and may be corrected from aircraft/satellite ephemeral data and ground control points (GCPs).



Radiometrically, image values may be converted to sensor and to surface radiance and reflectance using modelled atmospheric parameters (to extract interference absorption/scattering, eg MODTRAN) (Vane 1993). Due to the increased data dimensionality, different image processing and analysis procedures have been applied to hyper-spectral data sets (compared with multispectral). The most commonly applied algorithms are spectral-unmixing (described later), to provide information on the type(s) of feature present and the fractional cover of each element within each pixel (Roberts et al.1993, Adams et al. 1995).

With the launch of satellites such as Hyperion and MERIS, high spectral resolution, multi-temporal hyperspectral data is becomes available over more geographic areas and is readily accessible.

The majority of hyperspectral data Australia have been collected using the, Hyvista Corporation ‘HYMAP’ and the Itres Inc. CASI (compact airborne spectrographic imager). Occasional visits by other hyperspectral instruments such as the NASA-AVIRIS (airborne visible and infra-red imaging spectrometer) sensor have also occurred. The AVIRIS sensor is limited to pre-scheduled flights, mainly in the continental USA, and typically acquires images with 20 m pixel. The CASI sensor provides images with pixels 0.5 m and up to 10 m, but only for narrow width images, but has been used in a variety of environments (MacLeod et al. 1995, Held et al. 1998, Green et al. 1996). The Hymap sensor collects hyperspectral image data also in the short-wave infra-red (1000 – 2500 nm) spectral range.

Although widely used for mineral exploration and geological mapping (e.g. Hunt and Ashley, 1979), the use of hyperspectral sensors for studies of vegetation dynamics (e.g. Miller et al., 1991; Ustin et al., 1993), vegetation biochemical composition (e.g. Wessman, 1989; Kumar et al., 2001), stress detection (Rock et al., 1996; Merton 1999) and for plant species discrimination (e.g. Clark et al., 1995), is still an active area of research.

Airborne LIDAR – Active (does not require sunlight)

LIDAR systems are optical sensors available from commercial operators which permit very detailed terrain mapping as well as forest structure mapping over thousands of hectares per day. These systems are based on rapidly pulsating laser systems (1 laser pulse per nanosecond or more), scanning like brooms across the landscape as the aircraft passes over. Built on the basis of the airborne laser systems designed for topographical mapping, an experimental airborne LIDAR system developed by NASA, called LVIS (Blair et al. 1999), has also been designed to collect information on forest heights and structure (see also Leckie, 1990 and Lefsky et al., 1999).

Airborne and Satellite Radar – Active (does not require sunlight)

Synthetic aperture radars (SARs) are active sensors operating in the microwave region (roughly 1 mm to 1 m in wavelength). Unlike passive sensors which measure radiation from natural sources such as reflected sunlight, SARs both transmit and receive pulses of specific wavelength and polarization; they thus operate independently of solar illumination. Operating at much longer wavelengths than optical sensors, imaging radars can penetrate clouds and smoke and are sensitive to structural elements of the surface encountered eg vegetation canopies such as leaves, branches, and boles. The following



sections will briefly review SAR data sources, microwave scattering mechanisms, and results of SAR studies in Australia and elsewhere.

SAR system characteristics

SAR instruments operate from both airborne and spaceborne platforms and are characterized by their band and polarization. Bands refer to wavelength: X (3 cm), C (5.6 cm), L (23.5 cm), and P (65 cm). Radars transmit plane-polarized waveforms, oriented either horizontally (H) or vertically (V), and then receive one or both polarizations. Satellite SAR sensors are currently limited to single-frequency, single-polarization systems, either C or L-band; airborne systems also operate at X and P-band. Most satellites record a single polarization, either HH (horizontal send, horizontal receive) or VV. Horizontal send, vertical receive (HV) is currently available only from airborne SARs. Incidence angle refers to the imaging geometry of the radar. It is equal to the angle between the radar beam and a line perpendicular to the ground surface, and may be fixed or variable. Nominal resolution is generally 1.5 to 2.5 times larger than pixel spacing. Airborne SAR systems are too numerous to list; the Jet Propulsion Lab AIRSAR is given as an example.

After pulses transmitted by a SAR sensor are reflected, scattered, and/or absorbed at the earth's surface, the intensity and timing of the energy scattered back toward the sensor (backscattering) are received and recorded. The brightness of an object in a SAR image corresponds to its radar backscattering coefficient σ°. Because of the large dynamic range of SAR systems, the unitless σ° is normally expressed in decibels (σ° dB = 10 log σ°linear). The signal detected by SAR is the coherent sum of signals from randomly distributed scatterers within an image pixel. Random constructive and destructive interference in the addition of these signals causes variability in among pixels, even for homogeneous targets. The resulting salt-and-pepper appearance, called speckle, poses problems in digital classification due to the high within-class variance of targets. Speckle is reduced during signal processing by multiple-look summing and can be further reduced during image processing by median or other filters.

Interferometry

Here the traditional radar system is enhanced with an additional antenna, and the results from the two channels are combined in a coherent fashion, and the phase difference between the radar returns are calculated adding the ability to resolve height differences. This technique is used for the creation of Digital Elevation Models (DEM’s). A comprehensive global DEM, at spatial resolutions of 90 m or better, is being derived from radar data using interferometric techniques, and is to be released by NASA in the near future*?. New analysis techniques are now also using precision interferometric radar for canopy structure mapping (Rodriguez et al., 1996; Treuhaft, et al., 1996).



Appendix 3: ASTER details

The ASTER sensor onboard the NASA ‘Terra’ platform has a number of very appealing characteristics, which make it suitable for baseline mapping at the more regional scale, but still providing sufficient detail for identification and delineation of most riparian zones (see image examples above).

One disadvantage of this system is that is has not been designed and operated to be a routine mapping system, which can be used from year-to-year over the same area.

Table 34 Characteristics of the 3 ASTER Sensor Systems

Note: The 3N and 3B VNIR bands with the same spectral range are distinguished by their respective look angles. N = nadir and B = backward looking (27.60 off nadir). These bands can be used in the creation of 15 metre DEM’s

Subsystem Band No.

Spectral Range (µm) Spatial Resolution, m

Quantization Levels

1 0.52-0.60 2 0.63-0.69

3N 0.78-0.86 VNIR

3B 0.78-0.86

15 8 bits

4 1.60-1.70 5 2.145-2.185 6 2.185-2.225 7 2.235-2.285 8 2.295-2.365

SWIR

9 2.360-2.430

30 8 bits

10 8.125-8.475 11 8.475-8.825 12 8.925-9.275 13 10.25-10.95

TIR

14 10.95-11.65

90 12 bits



Figure 28: Comparison of Spectral Bands between ASTER and Landsat-7 Thematic Mapper. (Source: Adams and Hook (2002) Aster Users Handbook Version 2, NASA,

JPL)



Appendix 4: Remotely Sensed Digital Elevation Model Data Sources

Catchment delineation and hydrologic modelling requires good knowledge of the terrain characteristics. A digital elevation model (DEM) is a computer-based representation of topography in the form of values of elevation of geographically coded points that make up a grid pattern, in turn representing the landscape terrain. DEMs come in a variety of grid densities (resolution) and have different levels of accuracy in the estimation of height differences. Such information is very useful for hydrological studies of water recharge and water movement across catchments, including identification of erosion potential.

While traditionally such height information was gathered by specialist surveyors using total stations and global positioning systems, a number of remote sensing techniques are now customarily used to generate such elevation maps.

Airborne Laser Scanning

High precision DEMs are being acquired by various companies which operate airborne laser scanning survey systems. These systems have a rapid acquisition rates (150 km/hr), high vertical accuracy (0.15 m RMS) and can measure through moderate tree canopies. The estimated horizontal accuracy is better than 0.55 m.

Aerial photography

Precision DEMs are being acquired by the use of soft photogrammetric digital workstations running software such as HELAVA, OrthoBase Pro and PCI. These packages use stereo aerial photographs for generating the DEM and orthoimages. An example of this: using 1:25,000 scale photography a 2 m grid can be achieved with a vertical resolution of 0.75 m.

Spot Pan

SPOT satellite panchromatic stereo pairs can be used to produce DEMs by using stereo plotting with automatic correlation. The stereo pair can be acquired with a minimum time delay of 2 days between scenes and 0 to 1 base-to-height ratio can be obtained. If imagery is used with minimal time difference between capture and an optimal base-to-height ratio (0.7) then the resultant DEM will have horizontal and vertical errors of 7 to 12 m RMS.

ASTER

The Advanced Spaceborne Thermal Emission and Reflectance Radiometer is an instrument mounted on the TERRA platform which was launched in December 1999. This instrument captures with images with pixel resolutions from 15 to 90 m. Each data



capture has a nadir and backward view at 27.6 degrees; base-to-height ratio of 0.6. Absolute DEMs requiring user supplied ground control points are routinely produced with a cell size of 30 m and a vertical resolution of 12.5 to 25 m. The image data is of a resolution that will allow DEMs of 15 m cell size to be produced by user supplied software.

QuickBird

QuickBird was launched late in 2001 and has the highest resolution for a commercial satellite. The resolution at nadir is 0.61 m and at 25 deg the resolution is 0.72 m. Production of DEMs with this data will be similar to that of the SPOT products but at this stage the costing of the stereo pairs has not been publicised. The announcement is due early 2003.

Table 35: DEM data sets; costing and mapping scale

DEM source Posting Vertical Accuracy Costing Scale of Mapping

AUSLIG 9 sec 250m 25m $100 1:250,000

SPOT 1,2,3,4 10m 10m $7,000 a 1:100,000

SPOT 5 10m 10m Contact

Raytheon Australia

1:100,000

ASTER 15/30m 12.5 to 25m $1,300 a 1:100,000

QuickBird 0.7 m 1.5 m Contact SKM for release date 1:5,000

Aerial Photography 0.5/10m 0.2 to 2.0m $84,000 b 1:2,500

Airborne Laser Scanning 2.1 m 0.15 m $170,000 c 1:1,000

a per 60 x 60 km b per 60 x 40 km c per 60 x 40 km



Appendix 5: Spectral Reflectance of Vegetation

As light strikes a leaf, some of this light is absorbed, some is transmitted through the leaf and some is reflected. It is mostly the reflected signal which is analysed to provide information on foliar chemistry and physiological condition. While the characteristic Green canopy reflectance is detectable from broad-band imaging sensors, such as the Landsat TM or SPOT HRV satellites, there are a number of important features of leaf reflectance, which are not measured with these sensors, and can only be detected in measurements made with high spectral resolution (hyperspectral) sensors.

Figure 29: Typical reflectance spectrum of vegetation, in this case a Eucalyptus minifera tree, measured in late autumn with a hand-held full-range (400 - 2500 nm) spectroradiometer. For comparison, the corresponding Landsat 7-band signature (displaced by 0.05 reflectance units) is displayed as horizontal lines. Discontinuities in the spectral signatures are caused by strong atmospheric water vapour absorption.

In the visible spectrum (400 – 700 nm), leaf reflectance is quite low (< 10%), due to the absorption of photons by photosynthetic pigments (mainly chlorophylls and carotenoids). The prominent rise in reflectance, typical for green vegetation between 680 and 750 nm, is commonly termed the ‘red edge’, and is caused mainly by the combination of strong chlorophyll-a absorption and leaf internal light scattering properties. This sharp increase in reflectance has been key for the development of ‘greenness indices’ such as the ‘Normalised Difference Vegetation Index’ (NDVI) (Tucker 1979), or the Simple Ratio (SR), as they are composed of reflectances measured in the red (670 – 690 nm) and near infra red (750 – 800 nm), where the contrast, caused by chlorophyll absorption in the 550 – 690 nm range, is very high. A number of studies have also used changes in other parts of the



reflectance spectrum, associated with other leaf pigments to derive specialised image transformations or ‘indexes’ that enhance these differences between the different plant types. One such transformation is the calculation of the red-edge the inflection point as an indicator of plant stress (e.g. Rock et al., 1996; Merton 1999). In wavelengths above 700 nm, reflectance is mostly dominated by internal light scattering and light absorption by water, cellulose, lignin and leaf proteins.

Figure 30: Spectral Reflectance curves for three Eucalyptus tree canopies. Important foliar chemical absorption regions are shown. Discontinuities in the spectral

signatures are caused by strong atmospheric water vapour absorption.

At the whole plant level, characteristic leaf reflectance features are also influenced by canopy structure, sun illumination geometry, and by branch, background soil and litter spectral features. For this reason, special care is required, when making comparisons between sites or collecting multi-temporal data over a single site, with different sensor-target-sun illumination geometries, as the reflected radiation and its spectral characteristics will be different from different directions. At the canopy level, spectral indices such as NDVI or the SR exhibit a curvilinear relationship with increasing leaf area due to the overlapping nature of leaves in canopies. Although dependent on the leaf area index, leaf angle distribution and chlorophyll concentration, the NDVI for most vegetation types saturates above a LAI of 3-4. This poses a problem in detection of early stress or phenological change via broad-band greenness indexes in dense forests, where LAI is often as high as 10, since NDVI or the SR would not show a significant change until the canopy would have lost considerable leaf area due to the stress. However with the advent of hyperspectral imaging systems, a number of case studies have shown that it is now possible to measure changes in concentrations of some other pigments and other leaf chemicals more directly, suggesting that this technology might be of use in detection of early signs of stress or phenological changes even before changes in satellite data can be observed from traditional broad-band spectral indices.



A number of studies have shown that stress or phenological changes have an effect on the relative concentrations of chlorophyll-a, chlorophyll-b, anthocyanins and carotenoids in leaves, before noticeable changes are detected with the standard greenness indexes (e.g. Curran 1994; Rock et al., 1986; Blackburn, 1998; Carter 1998). Many non-deciduous Eucalyptus tree species in Australia for instance, when exposed to cold temperatures and high light levels, produce a range of accessory pigments (e.g. anthocyanins) in current-year or emerging leaves, which are considered to be involved in protection of the light harvesting systems from excess photochemical energy (Close et al., 2000). In addition, it has been observed, that as chlorophyll concentrations are reduced due to early stress or phenological change, a ‘blue-shift’ of the red-edge towards the shorter, blue spectral region occurs (e.g. Blackburn, 1998; Horler et al., 1983; Merton, 1999).

Leaf chlorophyll concentration can be inferred from spectral reflectance, through measurement of the inflection point position of the red edge (e.g. Curran, et al., 1997; Gittelson et al., 1996). Others have found high correlations between chlorophyll concentration and narrow-band reflectance indexes, mostly relating the reflectance observed in the 680 – 700 nm spectral range and normalised by a reference band located near 750 nm (e.g. Carter, 1998; Gittelson et al., 1996; Peñuelas & Filella, 1998). As chlorophyll concentration increases, the red-edge feature in plants tends to deepen and broaden, moving the actual red-edge towards the longer wavelengths. Some stresses, diseases or natural leaf senescence cause changes in the relative proportions of accessory pigments (carotenoids, anthocyanins), relative to chlorophyll. Such changes in canopy colour are clear candidates for early detection by specially designed narrow-band indexes which such as SIPI (Peñuelas & Filella, 1998), which is sensitive to relative changes in reflectance due to carotenoids (445 nm) and chlorophyll-a (680 nm). These ‘spectral indexes’ can be calculated from hyperspectral image data (e.g. CASI, Hymap, Hyperion sensors) and used to map aspects of canopy surface chemistry, ,which in turn provides additional information for species differentiation methodologies.



Appendix 6: Remote Sensing of Water Quality

Remote sensing is a suitable and valuable technique for large-scale monitoring of inland and coastal water quality, providing synoptic views of the spatial distribution of the biological, chemical and physical variables of both the water and if visible, the bottom surface. Therefore, recent years have seen increasing interest and research in remote sensing of water quality of inland and coastal waters [Doerffer and Murphy, 1989; Jerome et al., 1996; Bukata et al., 1997; Kondratyev et al., 1998]. A review of satellite and airborne remote sensing of aquatic ecosystems was given by Kirk [1983] summarily updated in Kirk [1994]. Hilton [1984] gave a review of airborne remote sensing. Dekker et al. [1995] wrote a comprehensive review of satellite and airborne remote sensing of inland waters, including imaging spectrometry. Bukata et al. [1995] presented a sound treatise on remote sensing of inland and coastal waters, where the emphasis of the applications is on the Laurentian Great Lakes along the Canada-USA border. Lindell et al. [1999] reviewed the literature on satellite remote sensing of lakes and Durand et al. [1999] presented a review of satellite remote sensing of inland and coastal waters. Dekker et al. [2001] present a review of imaging spectrometry theory and applications for inland and coastal waters including coral reefs.

Remote sensing of water colour as a determinant of water quality was initially developed for oceans, the optical properties of which are determined solely by phytoplankton and its breakdown products. These optically relatively simple waters are known as Case 1 waters and a few spectral bands in the blue to green spectral areas are invariably sufficient to infer chlorophyll concentrations with adequate precision for most oceanographic-biological purposes.

All other waters whose optical properties are determined by components in addition to or other than phytoplankton are currently referred to as Case 2 waters. These other optical components are usually a composite of dissolved organic matter from terrestrial origin, dead particulate organic matter and particulate inorganic matter. Also, if bottom reflectance influences the water leaving radiance signal, a water body is considered as Case 2 irrespective of other organic and/or inorganic colorants. Phytoplankton is a composite term incorporating a multi-species population of aquatic biota. Due to the single-colorant nature of Case 1 waters, workers such as Bricaud et al. [1999] realize the potential of imaging spectrometry from space for deriving other algal pigments than chlorophyll a from oceanic waters. The optical properties of algal blooms such as the cyanobacterium Trichodesmium, however, require remote sensing via additional spectral bands at longer wavelengths. Thus, relatively simple band ratio algorithms applicable to clear ocean waters are inappropriate not only for Case 2 inland and coastal waters, but also for some algal bloom situations in Case 1 ocean waters. In this review we will describe water in terms of its optically significant properties and the aquatic matter responsible for those properties.

Research in remote sensing of inland and coastal waters has evolved as a synergistic admixture of theoretical and applied modeling, sensor development, and calibration/validation based on aquatic optics. Many inland and coastal waters are



highly affected by anthropogenic influences. In combination with the complex hydrological situation, highly contrasted structures evolve in time and space within these aquatic environments. Obviously, a water system with a variety of optically active substances that display temporal and spatial variations is more complex and requires more sophisticated models for remotely sensing the water constituents than mid-ocean waters containing only one component. Thus, an important development in the past ten years has been the increased availability of hyperspectral airborne sensors that enable monitoring of continuous wavelength ranges in the optical wavelength ranges between 400 and 1000 nm at resolutions from 16 to 1.3 nm.

From 1972 to 1984 remote sensing of inland and near coastal waters has relied on Landsat MSS. Subsequent to 1984 remote sensing of inland waters has largely relied on the collection of data from satellite-based sensors such as Landsat Thematic Mapper and SPOT-HRV (and to a lesser degree IRS –LISS series, CZCS, NOAA-AVHRR) and airborne remote sensing using instruments varying from multispectral scanners to line spectrometers and imaging spectrometers such as the CASI, AISA, AVIRIS, HYMAP and ROSIS. The CASI, AISA and ROSIS (and in a lesser degree HYMAP and AVIRIS) systems are not each one sensor with fixed capabilities. They are a family of sensors, whereby there is a progression in sophistication of the sensor with each new model developed. Notice must be taken that the results of e.g. a CASI flown in 1990 are not the same as the results for a CASI flown in 2000 due to the increased performance of each modified instrument. Inter-comparing CASI data encounters additional complications since CASI is a programmable imaging spectrometer, meaning that each application may have a unique spectral band set applied. Ground-based surface and subsurface spectral measurements may serve as surface calibration and as the link between the remotely sensed signal and the inherent optical properties. Interested readers are referred to the reviews by Dekker et al. [1995], Bukata et al. [1995], Nieke et al. [1997], Lindell et al. [1999], Durand et al. [1999] and Dekker et al. [2001].

We shall focus on imaging spectrometry as the research tool for monitoring of inland and estuarine aquatic environments. This in no way implies that the operational broader band sensors are of no consequence. In fact, due to their having been operational since 1972 (Landsat MSS), 1984 (Landsat TM) and 1986 (SPOT), vast data archives of remotely-sensed data exist that may potentially be exploited for purposes of trend detection. These broad spectral band satellite sensors are especially good at mapping levels of suspended matter in the water column. However, a discussion of the science of remote sensing is more logical in the context of hyperspectral sensors.

Satellite sensing of aquatic resources is essentially the monitoring of one attenuating medium (water) through another attenuating medium (the atmosphere), each producing comparable yet unique challenges to remote sensing. The atmosphere will not be considered in-depth here as it warrants separate review by worker(s) expert in atmospheric physics. Dekker et al. [2001] present an overview of theory and application of hyperspectral remote sensing applied to coastal and inland waters.

Introduction to the theory

The colour of natural water is a complex optical feature, formed by scattering and absorption processes as well as emission by the water column and of reflectance by the substrate. Substrate reflectance (from seagrass, macro-algae, corals, sand, mud, benthic



micro-algae etc.) is a function of absorption and scattering and to a lesser degree emission of the substrate materials.

Variations in colour are determined by the content of particulate and dissolved substances that absorb and scatter sky and solar radiation penetrating the water surface. The colours, or more correctly, the water leaving spectral radiances, are masked by the reflection of sun and skylight at the water surface and by extinction and scattering processes in the atmosphere. This exposes bottlenecks in the processing of remote sensing data into water quality maps. To address this bottleneck a careful and precise simulation (e.g., using statistical Monte Carlo photon propagation tracking methods or radiative transfer numerical models) is required of the radiative transfer in the water, at/through the water-atmosphere interface, and in the atmosphere. These simulations are a prerequisite for the improvement or development of algorithms for retrieving the concentrations of selected water constituents. Therefore, the relationship between the in-water optical properties and their concentrations must be known, as well as the density of the substrate for substrate mapping.

Optically active substances can be split into distinct classes based on their optical behaviour. If the inherent optical properties of these substances are sufficiently known, it becomes possible to determine their contribution to water column colour leading to an estimate of their concentration.

For substrates, there is insufficient information on how the optical properties influence the reflectance of substrate materials. Therefore, it is practice to determine the reflectance of the substrate rather than the concentration-dependent absorption and scattering. Water colour carries spectral information regarding the composition of the water column and, if measurable, of the substrate. For the retrieval of different water constituents, as well as substrate cover, from a remotely sensed hyperspectral signal a suite of inversion methods are available, ranging from the often used, but less precise regression methods, through to physics-based inverse methods.

It is possible to model the colour, or spectral reflectance, of a water body once the relationship between inherent and apparent optical properties and concentrations is known. Coupling such information with simulations of the radiative transfer through water and atmosphere leads to simulation of the at-sensor measured radiance. Inversion of this forward simulation model leads to assessment of concentrations and substrate cover. Analytical inversion methods produce better results than empirical, or semi-empirical methods that use simple correlation or reasonable band ratios instead of sophisticated optical models. However, the exploitation of information derived from water colour has been impeded by the incapability to deal with the optical behaviour and complexity of water constituents.

Water column optical properties that may be estimated from an optical remote sensing signal are: suspended matter, vertical attenuation coefficients of downwelling and upwelling light, transparency, coloured dissolved organic matter, chlorophyll a contents, and even red tides and blue-green algal blooms. If the water column is sufficiently transparent and the substrate is within the depth where a sufficient amount of light reaches the bottom and is reflected back out of the water body maps may be made of macrophyte species, sand and sandbanks, coral reefs, and other bottom features [Dekker and Bukata, 2002].



Atmospheric effects and atmospheric correction

Although the physics of atmospheric correction of remote sensing data over waters is essentially the same as for terrestrial targets, there are a few practical differences that need to be addressed. For any water body it is the signal coming from within the water that is the desired signal. On land it is the surface reflected signal that is of interest. For water bodies the surface reflected signal is a signal that is considered as noise, and is composed of the reflected component of diffuse skylight and of the direct sunlight impinging on the water surface. Water bodies generally reflect, as subsurface irradiance reflectance, in a range of about 1 to 15% of downwelling irradiance. The majority of these waters reflect between 2 and 6% of downwelling irradiance. Thus to obtain (say) 40 levels of irradiance reflectance in the range of 2 to 6% reflectance we need a minimal accuracy of atmospheric correction to 0.1% reflectance.

Many remote sensing ventures have been stymied by atmospheric intervention resulting from the intractable patchiness of atmospheric aerosols. Various models [see discussions in Kneizys et al, 1983; Guzzi et al., 1987; Wrigley et al., 1992] have attempted to extract the unwanted atmospheric signal from water-leaving radiances. These models ranged from overly-simplistically attributing all the atmospheric return to a single nearby infrared wavelength band (incorrectly assuming the water reflectance to be zero) to generation of “generic” atmospheres diligently generated by practical combinations of air temperature, solar azimuth, land elevation, aerosol density, day of year, cloud profiles, rainfall, atmospheric composition, wind speed, and other obligatory/optional atmospheric parameters such as visibility and meteorological range. The sad fact is that, despite user preferences, there is no one atmospheric correction technique that has as yet emerged as a clear “winner” and more often-than-not successful remote sensing ventures become very local in the sense that site measurements of atmospheric conditions become an essential component of the venture. A recent development is the use of image-derived information from wavelength bands in the nearby infrared that enables estimation of water vapour in the atmospheric column beneath the sensor. Some aircraft are now fitted with incident-light sensors that measure downwelling irradiance simultaneously. This irradiance data is often noisy due to the effects of aircraft movement. The incident-light data are useable for trend detection of changing downwelling irradiance intensities at scales of kilometres.

Concluding remarks

The field of remote sensing/imaging spectrometry is currently at a cross-roads: up until now all results for imaging spectrometry were carried out from aircraft, as there were no imaging spectrometry sensors in space. The successful launch of the EO-1 platform by TRW/NASA in November 2000, with the imaging spectrometer Hyperion on board, marks the dawn of a new era- imaging spectrometry from space. Therefore, this review has been based on the current state-of-the-art for aquatic systems imaging spectrometry prior to the arrival of data from the new hyperspectral space sensors. With the advent of imaging spectrometers that were suitable for water related investigations (the PMI, the AVIRIS and the CASI in particular) at the end of the eighties, this field of research commenced. First missions and associated research were explorative rather than operational. From the mid-nineties onwards, operational examples of multi-temporal deployments of airborne imaging spectrometry systems over, mainly, inland water targets started to happen in The Netherlands, Germany, and Scandinavia. These studies over inland waters were able to deal with the optically deep waters quite well and



produced meaningful results for optical water quality variables such as chlorophyll, cyanophycocyanin, coloured dissolved organic matter, total suspended matter, vertical attenuation coefficients and water transparency. The combination of these results into ecological assessment and monitoring is gathering speed rapidly. The bio-optical models for these waters are becoming more sophisticated as well as the instruments for measuring the IOPs and AOPs. Airborne sensors such as the Compact Airborne Spectrographic Imager (CASI) and others are well-suited for water quality monitoring, due to their flexibility in platform and programmable band sets. In this field we see developments towards more complete models but also towards methods to compute an inversion of an imaging spectrometry scene using either analytical 1 to 3 band inversions, look up tables, using matrix inversion schemes or using neural networks. For turbid estuarine remote sensing, less use has been made of airborne imaging spectrometers due to the very dynamic nature and the often large size of the estuaries. Most of these studies were intended as illustrations or experiments in preparation of using satellite sensors to monitor these systems. As spaceborne imaging spectrometers become available this field of application is likely to evolve very fast.

In the optically shallow waters, developments have been somewhat different as the bio-optical or physical model describing the interaction of light in the water column and on the substrate is more complex than for optically deep waters and is less easily inverted. This inversion is required to produce meaningful maps of water variables or substrate variables. Similar to the developments in the inland waters, but increasingly complex due to the effect of the substrate, more sophisticated inversion schemes are being proposed for bathymetry assessment and for seagrass. For coral reefs most work has been done to characterize the AOPs by establishing spectral libraries of coral reef reflectance. Very few imaging spectrometry data sets are available that were analysed for coral reef cover, health or species discrimination. Most applications were of a qualitative nature.

The past decade has seen slow but deliberate development towards fuller understanding of the physics that governs the interaction of spectral irradiance with the water column and the substrate. Simultaneously the remote sensing sensors have advanced towards systems with higher sensitivity and more available spectral bands. Concurrent increase in computing power has led to a situation, (together with space imaging spectrometers being launched) where we anticipate an expanding field of research, development, demonstration, operationalisation and commercialization of imaging spectrometry of aquatic ecosystems.

As bio-optical and physics based models become more accurate, inversion schemes can become more sophisticated. Simulations of the reflectance spectrum from waters dramatically reduce the requirement for in situ measurements in the long term. They also enable pre-flight determination of optimal spectral band configurations for specific tasks. As in situ detection and monitoring becomes more expensive (due to rising labour costs) and does not provide spatially comprehensive information, a remote sensing based approach is beginning to make more and more economical sense. It will be necessary to have available local airborne imaging spectrometry systems or the availability of data from space sensors. The fact that the water column is an ever changing medium, both in space and time as well as in reflectance signature, indicates that imaging spectrometry will be the remote sensing instrument of choice for the future for detection and monitoring of optical water quality and substrate variables.

Water in all its phases is of prime concern to society. Remote sensing (from aircraft and satellites) and related environmental measurement methods offer unprecedented



capabilities for synoptic measurement of many geophysical parameters. The scale of the measurements may vary from local (e.g. local water management authority level) to global (e.g. primary productivity of oceans).

Remote sensing is an emerging technology with respect to water quality detection and monitoring. The real problem in getting an emerging technology such as high spectral and radiometric resolution remote sensing accepted and implemented, lies in making it clear to end-users that application of the technique is beneficial to them in their work. For this purpose it is necessary provide the end-user with adequate water quality information from remote sensing at the right time, in the right format, at a competitive price (as compared to alternative methods). Thus the emerging technology needs the associated tools to “make it work for you”.

determination of sra habitat indicators by remote sensing · very high for future inclusion in the...

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