Lecture 5 Remote sensing of vegetation

Remote sensing for agricultural applications: principles and methods (2013-2014)

April 8, 2014

Instructor: Prof. Tao Cheng (tcheng@njau.edu.cn). Nanjing Agricultural University

Image courtesy of NASA

Gong et al., 2013. IJRS data.ess.tsinghua.edu.cn

Many of remote sensing techniques developed for vegetation applications are generic in nature. We can use some techniques across a variety of fields: • Cropland • Forests • Rangeland • Wetland

Leaf optical properties

Absorption spectra of pigments

Chl a absorption peaks at 0.43 and 0.66 um. Chl b absorption peaks at 0.45 and 0.65 um. Chl a+b dominate during the green-up period, while carotenes and other pigments dominate during senescence or stress.

Jensen (2006)

Leaf internal structure

Leaf cross-sections

Hypothetical

Actual

Jensen (2006)

Spectral reflectance properties of sweetgum leaves under different stages

Jensen (2006)

1 = 𝜌𝜆 + 𝜏𝜆 + 𝛼𝜆 Hemispherical absorptance

Hemispherical reflectance

Hemispherical transmittance

Jensen (2006)

Leaf optical properties

More leaf layers in a healthy, mature canopy may lead to increased NIR reflectance.

Jensen (2006)

Hypothetical additive NIR reflectance from a canopy of two leaf layers

Leaf reflectance changes in response of decreased relative water content

Jensen (2006)

RCW

100%

75%

50%

25%

5%

Ref

lect

ance

(%

)

Bidirectional Reflectance Distribution Function (BRDF)

𝐵𝑅𝐹 𝜃𝑖 , 𝜑𝑖; 𝜃𝑟 , 𝜑𝑟; 𝜆 =𝑑𝐿𝑟 𝜃𝑖 , 𝜑𝑖; 𝜃𝑟 , 𝜑𝑟; 𝜆

𝑑𝐿𝑟𝑒𝑓 𝜃𝑖 , 𝜑𝑖; 𝜃𝑟 , 𝜑𝑟; 𝜆× 𝑅𝑟𝑒𝑓 𝜃𝑖 , 𝜑𝑖; 𝜃𝑟 , 𝜑𝑟; 𝜆

Jensen (2006)

𝐵𝑅𝐷𝐹 𝜃𝑖 , 𝜑𝑖; 𝜃𝑟 , 𝜑𝑟; 𝜆 =𝑑𝐿𝑟 𝜃𝑖 , 𝜑𝑖; 𝜃𝑟 , 𝜑𝑟; 𝜆

𝑑𝐸𝑖 𝜃𝑖 , 𝜑𝑖; 𝜆

Viewing directions

Sandmeier et al. (1998a)

Nadir

Forward viewing Backward viewing

Anisotropy factors

Sandmeier et al. (1998b) Sandmeier et al. (1998a)

𝐴𝑁𝐼𝐹 𝜃𝑖 , 𝜑𝑖; 𝜃𝑟 , 𝜑𝑟; 𝜆

=𝑅 𝜃𝑖 , 𝜑𝑖; 𝜃𝑟 , 𝜑𝑟; 𝜆

𝑅0 𝜃𝑖 , 𝜑𝑖; 𝜆

Nadir-normalized BRDF data.

ANIF of a Spectralon panel

505 nm

550 nm

675 nm

725 nm

Cheng et al. (2013)

An example of the BRDF effect

• A MASTER image acquired in the morning (11am local) looks brighter in the backward direction.

Flight path (S-N)

View angle effect

• Empirical correction:

– Modeling reflectance as a function of view zenith angle

– Correct for the cross-track brightness gradient

Before correction (morning image)

View zenith angle -9° 24° 0°

After correction (morning image)

Cheng et al. (2013)

Vegetation indices

• Simple Ratio - SR

– 𝑆𝑅 =𝜌𝑟𝑒𝑑

𝜌𝑛𝑖𝑟

• Normalized Difference Vegetation Index - NDVI

– 𝑁𝐷𝑉𝐼 =𝜌𝑟𝑒𝑑−𝜌𝑛𝑖𝑟

𝜌𝑟𝑒𝑑+𝜌𝑛𝑖𝑟

• SR and NDVI are one-to-one.

Jensen (2006)

Note the sensitivities of NDVI in high-biomass and low-biomass zones.

Pros and cons

• Pros: – NDVI can be used to monitor

vegetation changes in seasonal and inter-annual cycles.

– NDVI helps reduces multiplicative noise (illumination differences, cloud shadows, etc)

• Cons: – NDVI can be influenced by

additive noise effects (path radiance)

– NDVI is sensitive to LAI but tends to saturate when LAI is high (>3).

– NDVI is sensitive to canopy background (e.g., soil) variations

NDVI is still widely used and long term records of NDVI are available.

Global NDVI map

February 2014, 0.1 deg

Images by Reto Stockli, NASA's Earth Observatory Group, using data provided by the MODIS Land Science Team.

Frequency: 16 days or 1 month Data: Terra/MODIS

NDVI is used as a measure of greenness or vegetation vigor.

Figure from http://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD13A2_M_NDVI

Enhanced vegetation index - EVI

• Developed by a MODIS science team:

– 𝐸𝑉𝐼 =

2.5 ×𝜌𝑛𝑖𝑟−𝜌𝑟𝑒𝑑

𝜌𝑛𝑖𝑟+6.0×𝜌𝑟𝑒𝑑−7.5×𝜌𝑏𝑙𝑢𝑒+1.0

• has improved sensitivity to high-

biomass regions • is less sensitive to canopy

background and atmospheric influences

Jensen (2006)

Seasonal changes in EVI

Figure from: http://earthobservatory.nasa.gov/IOTD/view.php?id=2033

Averages of two months MODIS EVI data

MODIS NDVI vs EVI

• NDVI is sensitive to chlorophyll • EVI has less aerosol contamination problems • EVI is more sensitive to NIR reflectance and canopy structural variations (e.g., LAI, canopy

architecture)

March 5 through March 20, 2000

Image credit: University of Arizona

AVHRR NDVI archive

Source: https://lta.cr.usgs.gov/NDVI

September 17-30, 2013

AVHRR composite AVHRR NDVI

Long term archive: January 1989 to present. 1 km resolution.

Continuous remotely sensed data for phenology studies

Sensor Satellite Overpass/

Orbit Frequency Data Source (terrestrial data)

Data Record (years)

Spatial Resolution(s)

Processed Time Step

Latency

AVHRR NOAA series Daily USGS/EROS2 1989-present

1 km 1-week, 2-week

~24 hours

AVHRR NOAA series Daily Global Land Cover Facility

1982-2006 8 km Twice monthly

N/A

MSS Landsat 1-5 18 days USGS/EROS 1972-1992 79 m Distributed by scene

N/A

TM Landsat 4-5 16 days USGS/EROS 1982-2011 30 m Distributed by scene

N/A

ETM+ Landsat 7 16 days USGS/EROS 1999-present

30 m Distributed by scene

~1-3 days

Vegetation SPOT 1-2 days VITO 1999-present

1.15 km 10-day ~3 months

MODIS Terra 1-2 days LPDAAC 2000-present

250 m, 500 m, 1 km

8-day, 16-day

~7-30 days

MODIS Aqua 1-2 days LPDAAC 2002-present

250 m, 500 m, 1 km

8-day, 16-day

~7-30 days

eMODIS Terra/ Aqua 1-2 days USGS/EROS 2000-present

250 m, 500 m, 1 km

7-day ~15 hours, 7 days

Table from http://phenology.cr.usgs.gov/ndvi_avhrr.php

Further reading

• RSE book chapter 11.

• Sandmeier, St., Müller, Ch., Hosgood, B., and Andreoli, G. (1998a), Sensitivity analysis and quality assessment of laboratory BRDF data. Remote Sens. Environ. 64:176-191.

• Sandmeier, St., Müller, Ch., Hosgood, B., and Andreoli, G. (1998b), Physical mechanisms in hyperspectral BRDF data of grass and watercress. Remote Sens. Environ. 66:222-233.

Paper discussion 1:

• Collection of papers on Landsat legacy 1. Opening the archive: How free data has enabled the science and

monitoring promise of Landsat (Wulder et al., 2012) (Zhou/Li) 2. Forty-year calibrated record of earth-reflected radiance from

Landsat: A review (Markham et al., 2012) (Daniel/Li) 3. Generating global Leaf Area Index from Landsat: Algorithm

formulation and demonstration (Ganguly et al., 2012) (Zhou/He) 4. Monitoring gradual ecosystem change using Landsat time series

analyses: Case studies in selected forest and rangeland ecosystems (Vogelmann et al., 2012) (Zheng/Zeng)

5. Continuous monitoring of forest disturbance using all available Landsat imagery (Zhu et al., 2012) (Zhou/Wang)

6. Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data (Liu et al., 2005) (Liu/Wu)

7. Landsat-8: Science and product vision for terrestrial global change research (Roy et al., 2014) (Deng/Dai)

Evaluation of presentations

Were the main concepts presented in an understandable way?

Were the slides well prepared such as text font size and propose use of text and pictures?

Were the pace, fluency and duration of the presentation appropriate?

Were questions handled well and answered appropriately?

What is the overall impression of the presentation?

• Each presentation will be evaluated based on the criteria below on a 100-point scale. • Each group will be evaluated by the other six groups and the instructor. • The presentation weighs 20% of your final grade. 10% will be given to the average of

student evaluations and 10% to the instructor’s evaluation.