Seminar Topic ; Applications of Remote Sensing in Forest Ecology

Seminar Incharge ; Dr.H.P.Sankhyan

Student Name ; Bibi Nagaar

Adm.No. ; F-15-23-M

Course No ; FGR-591

Overview of The Seminar

Introduction to Remote sensing

Introduction to Forest ecology

Relationship between Remote Sensing and Forest Ecology

Applications of Remote Sensing in Forest Ecology.

Case Studies

Conclusions

REMOTE SENSING. The science of acquiring

information about an object, without entering

in contact with it, by sensing and recording

reflected or emitted energy and processing, analyzing, and applying

that information.

The processes of collecting information

about Earth surfaces and phenomena using sensors

not in physical contact with the surfaces and

phenomena of interest. There is a medium of

transmission involved i.e. Earth’s Atmosphere.

Introduction

PRINCIPLES OF REMOTE SENSING

Detection and discrimination of objects or surface features means detecting and recording of radiant energy reflected or emitted by objects or surface material. Different

objects return different amount of energy in different bands of the electromagnetic spectrum, incident upon it. This depends on the

Property of material (structural, chemical, and physical)

Surface roughness

Angle of incidence

Intensity

Wavelength of radiant energy.

Remote sensing

Aero-plane

Satellite

Remote Sensing Platforms

Ground-based Airplane-based Satellite-based

Active Remote Sensors Laser altimeter Lidar Radar

Passive Remote Sensors Accelometer Radiometer Spectrometer

Some Remote Sensors

Three main types of sensors used

Optical (Visible/IR)

Radar (Microwave)

LiDAR (Mostly NIR)

Remote sensing involves the use of

aircraft or satellites to collect

photographs or scanned images of

the Earth’s surface.

The origins of remote sensing date

back to a photograph taken from a balloon

in 1858. By World War I, the aeroplane had become the main platform from which aerial photography

was collected.

Balloon photography (1858)

Pigeon cameras (1903)

Kite photography (1890)

Aircraft (WWI and WWII)Space (1947)

Remote Sensing & GIS Applications Directorate

History of Remote Sensing

Remote Sensing Includes:

A) The mission plan and choice of sensors

B) The reception, recording, and

processing of the signal data

C) The analysis of the

resultant data.

Remote Sensing & GIS Applications Directorate

Remote Sensing Cycle

Energy Source or Illumination (A)

Radiation and the Atmosphere (B)

Interaction with the Target (C)

Recording of Enery by the Sensor (D) g

Transmission, Reception, and Processing (E)

Interpretation ad Annalysis (F)

Application (G) Source: Canadian Centre for Remote Sensing

Remote Sensing Process Components

Data acquisition

Processing

Analysis

Accuracy assessment

Information distribution to users

Remote Sensing Basic Processes

NOAA-AVHRR

GOES

MODIS

Landsat TM and ETM

SPOT

IKONOS

Quick bird

Remote Sensing & GIS Applications Directorate

Some known Satellites

ECOLOGY

The study of living organisms in the natural

environment

How they interact with one another

How they interact with their environment

© 2008 Paul Billiet ODWS

Artecology. Study of individual plants or

populationSynecology.

Ecological study of group of organsims or

communities interacting

with each other as will as with

the environmentHabitat ecology .

Study of growth and developments of plants in their

habitat.

System ecology which provide graphic and

verbal information

about an ecosystem.

Applied ecology. Deals

with the applied aspects of ecological principle in

order to manage and conserve the nature and

natural resources.

Division of ecology

Physical hierarchy Biological hierarchy

Ecological hierarchy

Levels of organization

Cellular levelTiss

ue levelOrg

an level Organ

system

level

Individual level

Biological hierarchy

Population

level

Com

munity

level

Ecosystem level

Biosphere level

Ecological hierarchy

Forest Ecology is the scientific study of the interrelated patterns, processes, flora, fauna

 and ecosystems in forests.

A forest ecosystem is a natural woodland unit consisting of all plants,

animals and micro-organisms (

Biotic components) in that area functioning together with all of the non-living physical (abiotic) factors

of the environment.

Forest Ecology

Forest ecology is the study of forest ecosystems.

Forests are ecosystems in which the major ecological

characteristics reflect the dominance of

ecosystem conditions and processes by

trees.

Ecosystems are ecological systems

that have the attributes of

Structure

Function

Interaction of the component parts

Change over time.

The key structural components of forest

ecosystems are plants, animals,

microbes, soils and the atmosphere.

The key functional aspects of forest ecosystems are

energy capturing and biomass

creation, nutrient cycling , regulation of atmospheric and water chemistr,and

important contributions to the

regulation of the water cycle.

Forest ecosystems are continually changing. This

change, initiated by external disturbance

factors but largely determined by

internal ecosystem processes, is vital

for the maintenance of many aspects of

biological diversity.

A variety of ecological applications require data from

broad spatial extents that cannot be collected using field based

methods.Remote sensing data and techniques address these needs, which include

identifying and detailing the characteristics of species habitats

Predicting the distribution of species

Spatial variability in species richness

Detecting natural and human-caused change at scales ranging

from individual landscapes to the entire world.

Ecologists and conservation biologists are finding new ways to approach their research with the powerful tools and data from

remote sensing.

Relationship between forest ecology and remote sensing

Human activities now affect most of the

terrestrial biosphere and are increasing in intensity and extent.

Ensuring habitat loss and degradation impair

ecosystem function and reduce the value of ecosystem services for

humans.Although ecologists are improving their

understanding of the factors limiting the

distribution of species , extinc- tion rates

continue to accelerate .

However, traditional field eco- logical data do not translate readily to

regional or global extents, and models derived purely from such local data are

unlikely to predict the global consequences of

human activities.

Therefore, ecologists and conservation

biologists are turning to the rapidly

developing discipline of remote sensing to

provide the techniques and data sources

necessary to prepare scientific responses to

environmental change.

Although the need for remote sensing is especially urgent for conservation-related science, satellite-based

earth observations are also being used for basic ecological

research.

1.Land cover classification .Satellite

remote sensing is used to estimate the

Variety

Type Extent of land cover throughout a

study region, meeting a fundamental need that is

common to many ecological applications.

Land cover data describe the physiographical characteristics of the

surface environ- ment, which can range from bare rock to tropical forest and that are usually derived by applying

statistical clustering methods to multispectral remote sensing data .

Remote sensing also assist in the development of land use data that

reflect human interactions with the physical environment.

Application of Remote Sensing in Forest Ecology

Depending on the remote sensing and field-based resources

available, land cover

classifications can identify very specific habitats.

Land cover data have proven

especially valuable for

predicting the distribution of both individual

species and species

assemblages across broad

areas that could not otherwise be

surveyed.

Forest succession.

The spatial and temporal patterns of forest succession can be studied using spatially referenced vegetation data from two or more dates.

Transition probabilities of forest succession pathways in northern Minnesota, USA, were calculated using classified MSS scenes from 1973 to 1983 (Hall et al. 1987).Transition probabilities of the managed areas differed from those of the wilderness areas primarily because of the influence of logging, which altered not only the rates of transition but also the possible types of transition.

In a second type, Walker et al. (1986) successfully used Landsat MSS data in Australian semi-arid eucalypt wood- lands to detect stage of seccession based on structural differences in 1 to 50 year old clearings.

In another forest succession study,

which utilized both image processing and GIS technology, the sta- bility and fate of abandoned pasture

patches in a mosaic of mountainous forest

were found to be significantly related to elevation (Graham et

al. 1987).

Assessment of Stand Structure

Satellite data have been used to quantify spatially such forest structure characteristics as crown cover, tree density, tree di- ameter, basal area, tree height, tree age, biomass, and leaf area index. Most studies have used spectral data generated from airborne sensors such as the thematic mapper simulator (TMS).

Spanner et al. (1984) used a classification approach to study the ability of TMS imagery to differentiate crown closure and tree size classes in a fir-dominated forest in Idaho, USA.

They found > 60% accuracy in classifying crown closure class- es of > 70%, 40-69%, and 10-39%.

Integrated Ecosystem Measurements

Unlike field-based measurements of ecosystem function, which cannot easily be converted to estimates of function

across entire ecosystems, remote sensing can provide simultaneous estimates of ecosystem functions

Net primary productivity (NPP) represents one aspect of inte grated ecosystem function.

Recently,the Moderate Resolution Imaging Spectro Radiometer (MODIS) NPP, based on a micrometeorological approach was developed by Rahman et al. (2004) to provide a consistent, continuous estimate of photosynthetic production (Heinsh et al., 2006) hereinafter referred to as the MODNPP model.

MODNPP = 0.5139(MODPRI × APAR) − 1.9818 where, MOD PRI is MODIS-derived photochemical reflectance index, APAR refers to absorbed photosynthetically active radiation by vegetation and 0.5139 and 1.9818 are constants

Landscape Metrics and Spatial Patterns

Landscape metrics viz., patch size, shape, connectivity, fragmentation, porosity and interspersion, juxtaposition are used as surrogate parameters to understand the patterns of biodiversity.

With the advent of multi scale spatial information, the development of spatial information on landscape metrics using spatial analysis tools like GIS has become a robust and time effective process.

Roy and Tomar (2001), in a comprehensive study, characterized the landscape biodiversity of north-east Meghalaya India using geospatial tools.

The landscape parameters-patch shape, patch size, number of patches, porosity, fragmentation and juxtaposition, and have been analyzed to delineate the disturbance regimes.

Roy et al. (2005) using vegetation type map of Andaman & Nicobar islands studied the patch characteristics in terms of patch size, number, shape, porosity and land cover diversity (IIRS, 2003).

Prasad et al. (2009) analyzed the levels of forest fragmentation and its effect on species diversity in north Andaman forest using satellite data and GIS-based fragmentation model.

Analysis of patch characteristics and species distribution showed high species richness in less fragmented evergreen forests.

Assessment of Forest Productivity

Satellite sensors accurately detect forest productivity, they provide cost and effort advantages over traditional field survey methods.

Productivity estimates based on satellite data have been produced with some success for agronomic ecosystems (Olang 1983)

Wetlands (Butera et al. 1984; Hardisky et al. 1984), and Shrublands (Strong et al. 1985).

In a study, predictive models of wood mean annual increment of volume in three regions of the United States (southern Illinois, eastern Ten- nessee, and northeast New York) were developed using CIS, TM data, and digital bio geographical data on forest productivity and soils, slope, solar radiation, and/or vegetation type (Cook et al. 1987; Cook et al. 1989).

In general, forest produc- tivity was more accurately predicted with a combi- nation of TM and biogeographical variables than with either data type alone.

Bio prospecting

The assessment of spatial and temporal organization of biodiversity at ecosystem, habitat and species level is considered as one of the best globally followed approaches towards conservation planning and management.The remote sensing-based vegetation type maps and species distribution maps help in prioritizing the areas of bioprospecting and mapping of target economically useful species (Joshi et al., 2006).

Vegetation type maps in conjunction with phytosociology, topography and soil are used in identifying areas of high economic value (Reddy et al., 2008).

The direct identification of economically important of species depends on the extent of distribution and resolution of satellite data.

Vegetation formations like pines, sal, teak, which grow gregariously under unique climatic and geological formations are easily identified using remote sensing and the quantitative estimates of timber and biomass are obtained using optimized ground inventories.

In addition, species like Taxus baccata (Behera et al., 2000), Calamus rotang (Reddy et al., 2008), Orchids (Gupta et al., 2004) growing under unique topographic, soil conditions can also be identified using remote sensing.

Ecological niche models integrating remote sensing habitat maps and ground-based location information are used to prepare potential distribution maps of economically important species .

Monitoring - Changing Biodiversity

Levin (1999) emphasized the problem of biodiversity prediction and mentioned the need to understand processes that add to or remove the species from an ecosystem by changing the surrounding environment of the ecosystem under study.

Predictions of biodiversity change is quite challenging because of large uncertainties associated with the complex dynamics of ecological systems.

By using remotely sensed data to describe the changes brought about in vegetated areas of Vindhyan hills over a period of 10 years as a result of fragmentation and its impact on biodiversity have been successfully described (Jha et al. 2005). A high degree of influence of land-use history, fire regimes and other disturbances impacts the vegetation and the biogeochemical characteristics of currently existing ecosystems (Compton and Boone, 2000; Goodale and Aber, 2001)

Assessment of Forest Damage

The assessment of forest damage is an important

use of remote sensing data.Many of the changes in

tree or foliage morphology resulting

from stress can be detected with remote

sensors (Jackson 1986).

Furthermore, the spectral signature of

stressed trees indicate not only the degree of stress but also the type of stress. For example,

TMS imagery of damaged red spruce

(Picea rubens) stands in Vermont shows a large

reduction in the shortwave-infrared

reflectance (Rock et al. 1986).

Field verification of the image revealed that the foliage of

the highly damaged spruce stands was

drier and less dense than that of

undamaged stands (Rock et al. 1986; Vogelmann and

Rock 1986).

Damage produced by insect defoliation has also been successfuly assessed from remotely sensed im- agery.

This type of damage is easily perceived by ex- amining scenes of an area before and after defolia- tion.

For example, areas of heavy gypsy moth defoliation in Pennsylvania, USA, were quite evi- dent in a foliage difference map created from June 1976 and July 1977 MSS data (Williams and Stauffer 1979).

The key to successful defoliation as- sessment is to use scenes that capture the period of heaviest defoliation (Dottavio and Williams 1983). Spectral imagery is used routinely by forest managers to detect and measure insect defoliation.

Detection of forest change

Changes in forest cover over time are important be- cause

of the role forests play in the global carbon cycle, in global climatic trends, and in

providing species habitat

By comparing digitized ground-based maps of Costa Rican forestland from 1940,

1950, and 1961 and MSS-derived forest cover maps of

1977 and 1983, Sader and Joyce (1988) found that forest cover had decreased from 67

to 17% between 1940 and 1983 with the most rapid rate of clearing be- tween 1977 and

1983.

Furthermore, four of the Costa Rican life zones had disappeared completely: the dry tropical, the moist premontane, the moist lower montane, and the wet

montane. They also demonstrated the close relationship between road building and deforestation by overlaying transpor- tation network maps with forest cover maps.

Deforestation in the Amazon basin of Brazil has been quantified by using AVHRR band 3 thermal data which, unlike the visible bands, can penetrate the ubiquitous cloudcover of the region .

Estimates of deforestation were obtained by using band 3 to detect both the fires associated with lines of active deforestation and the devegetated areas, which are warmer. The studies of Rondonia, Brazil, indicate that the deforested area increased from 4200 km’ in 1978 to 10,000 km’ in 1982 to 27,000 km’ in 1985 to over 35,000 km’ in 1987 (Malingreau and Tucker 1987; Malingreau and Tucker 1988).

Habitat lossSatellite measurements of

broad-scale trends in vege- tation provide direct estimates of

habitat loss, increasing the power of applied ecological studies to

detect changes in species distributions or extinction rates.

Defore- station in humid tropical forests, which house many terrestrial

biodiversity hotspots, is a globally leading cause of

species loss.

It has proven very difficult to estimate

accurately the extent of humid tropical

deforestation because of poor monitoring

infrastructure in many countries and

inconsistencies among existing monitoring

regimes.

Satellite data from the 1990s, based on AVHRR and SPOT4/Vegetation and supplemented by high-resolution Landsat and SPOT4/HRVIR (high resol- ution visible and infrared) data, have been integrated to generate the best estimates of rates of deforestation among remaining humid tropical forests .

Deforestation ‘hotspots’ could also be detected.

Fire, another leading source of change, especially extensive in areas

that have previously been

damaged by deforestation.

A combination of AVHRR, Landsat

TM (Thematic Mapper) and radar data were used to detect the impact

of deforestation on the burn like-

lihood of forests in East Kalimantan,

Indonesia .

Satellite remote sensing data has been extensively used to map forests of tropics whereas up to date data about spatial distribution are absent or lacking. In India, the initial attempt at national level has been on 1: 250,000 scale using visual interpretation of false color images.

National Remote Sensing Agency for the first time studied 1: 1 million images for the periods 1972-75 and 1980-82 and forests were classified into three categories .

Forest Monitoring in India

Closed

Mangrove

Open/degraded

Subsequently, Forest Survey of India (FSI) also used similar technique for the period of 1981-83 for forest mapping on 1: 250,000 scale.

The major vegetation area of

FRI was taken as a study area.

(Prabhat Kumar Rai 2013)

The blocks chosen for study were teak gate block, garden block,

champion block, riding school block, research block and canal block.

IKONOS (2001) image data of model system

was used for the present study. IKONOS image

data has been proved to be useful for vegetation

type and land use mapping (Kim et al.,

2008)

Case Studies

.

Spectral reflectance (DN) values of major vegetation types were plotted against

different wavelength bands. There was maximum

reflectance for Sal followed by Chir

pine, Teak and Chir pine gaps. Maximum

reflectance between R and NIR region was recorded for vegetation when compared with

settlement/built up features.

Chir pine, which was observed to be major vegetation type, has shown distinct blood red tone with medium texture and regular pattern while Chir pine mixed reflected reddish green tone with rough texture.

Populus nursery showed light red tone with rough texture.

Healthy Sal reflected dark pink tone with regular pattern and coarse texture while senescent or diseased Sal was light pink with greenish tinge.

Further, another important component of forest cover i.e. Teak has shown greenish blue tone with regular pattern and rough texture.

Bamboo has shown pink/gray tone (yellowish tinge) with irregular pattern and coarse texture.

Mixed vegetation has shown light red-red tone with irregular rough texture.

Forest cover and land use map of FRI campus . Total area of selected portion came out to be 138.60 ha.Chir pine covered major portion (37.478%) together with Chir pine mixed (13.131%) .

Mixed vegetations which were scattered throughout the selected portion of FRI comprised 15.468% of the total area under investigation.

Teak comprised (5.551%) followed by Sal (4.035%), Cupressus mixed (3.129%), Botanical garden-mixed (1.389%) and open area/grass

Study on mapping and sustainable forest

management in Rewari district was carried out to map the forest cover

areas, crown density analysis of reserved forests and potential afforestation sites. IRS data was used and

visual image analysis techniques were

employed. The study area covers 1559 sq. km and consists of

tropical thorn forest with some tropical dry

deciduous species.

REMOTE SENSING APPLICATIONS IN MAPPING OF FOREST COVER AND POTENTIAL AFFORESTATION SITES FOR SUSTAINABLE FOREST MANAGEMENT. A CASE STUDY OF REWARI DISTRICT, HARYANA, INDIA

Forests covers 3587 ha. area of the district. These are classified into three categories- closed forest, open forest, and block plantations.

The study concluded that area occupied by different potential afforestation sites was 13069 ha., which constitute 8.36% of total area.

These mainly comprise of scrubland, degraded pasture, degraded block plantation, sands and rocky/ gravelly lands.

Major causes identified for low forest cover are arid climatic conditions, scarcity of water and poor soils.

Man made reasons for forest degradation are over grazing, soil erosion, unauthorized cutting of trees.

To protect existing forest and social forestry plantations: fencing, effective watch and ward staff, causalty replacement and educating rural masses about forest are recommended.

Important tree species

recommended are 

Prosopis cineraria, 

Acacia arabica

Acacia tortilis  Zizyphus numulleria,  

Lasirus sindicus 

Cencherus ciliaris

S. No. Category Area (ha) % to total Forest cover

1 Closed Forest 424 14.21

2 Block Plantation 3163 85.79

  Total 3587 100.00

Area under forest cover, Rewari district Haryana

S. No. Category Area (ha) % to total Potential land

1 Degraded block plantation 1421 10.87

2 Scrub land 4993 38.20

3 Degraded pasture/ grazing land 3595 27.50

4. Sand 1691 12.93

5. Rocky land 1369 10.47

  Total 13069 100.00

Statistics of Potential Sites for Afforestation

The present study outlines an approach to classify forest density and to estimate canopy closure of the forest of the Andaman and Nicobar archipelago.

The vector layers generated for the study area using satellite data was validated with the field knowledge of the surveyed ground control points.

Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia –a case study

The methodology adopted in this present analysis is three-tiered.

First, the density stratification into ffive zones using visual interpretation for the complete archipelago.

In the second step, they identified two island groups from the Andaman to investigate and compare the forest strata density.

The third and final step involved more of a localised phytosociological module that focused on the North Andaman Islands.

The results based on the analysis of the high-resolution satellite data show that more than 75% of the mangroves are under high- to very high-density canopy class.

Remote sensing is indispensable for

ecological and con- servation biological

applications and will play an increas- ingly important role

in the future.

For many purposes, it provides the only means of measuring the characteristics of habitats across broad areas and

detecting environ- mental changes that occur as a result of human or natural

processes.

These data areincreasingly easy

to find and use. Although field and

remote sensing data are often collected at divergent spatial

scales, ecologists have begun to

recog- nize both the potential and the pitfalls of satellite

information.

Conclusions

Established remote sensing systems

provide opportunities to

develop and apply new measurements

of ecosystem function across

landscapes, regions and continents.

New efforts to predict the

consequences of ecosystem function

change, both natural and human-

induced, on the regional and global distributions and

abundances of species should be a

high research priority.

The full range of remote sensing techniques for

identifying land covers, measuring

the biophysical properties of

ecosystems and detecting

environmental change will need to be integrated with existing and new ecological data to

meet this ambitious challenge.

THANK YOU FOR

YOUR KIND ATTENTIO

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