1. Remote Sensing of Vegetation

2. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal photosynthetic activity

3. Significance of Vegetation Mapping Species and community distribution –land cover mapping –estimating biodiversity Phenological (growth) cycles Vegetation health Temporal variations (change detection) –land cover change –slow vs. fast changes

4. Physical Basis for Remote Sensing of Vegetation Photosynthesis Pigmentation Leaf structure Plant water content Canopy structure Phenological cycles

5. Photosynthesis and Spectral Characteristics Energy-storage in plants, powered by light absorption by leaves Leaf structures have adapted to perform photosynthesis, hence their interaction with electromagnetic energy has a direct impact on their spectral characteristics

6. Visible, NearIR and Middle IR Interactions

7. Cross-section through a hypothetical and real leaf revealing the major structural components that determine the spectral reflectance of vegetation

8. Near IR Interactions within the Spongy Mesophyll High leaf reflectance in the NIR results from scattering/reflectance from the spongy mesophyll This layer is composed of cells and air spaces (lots of scattering interfaces)

9. Reflectance, Transmittance, and Absorption Characteristics of Big Bluestem Grass

10. Multiple Scattering in the Plant Canopy

11. Imaging Spectrometer Data of Healthy Green Vegetation in the San Luis Valley of Colorado Obtained on September 3, 1993 Using AVIRIS 224 channels each 10 nm wide with 20 x 20 m pixels

12. Vegetation Indices A vegetation index is a simple mathematical formula Used to estimate the likelihood that vegetation was actively growing at the time of data acquisition Widely used over several decades New, more sensitive vegetation indices have been developed

13. Vegetation Indices Make use of the red vs. NIR reflectance differences for green vegetation Veg indices are associated with canopy characteristics such as biomass, leaf area index and percentage of vegetation cover

14. Normalized Difference Vegetation Index (NDVI)  red = Reflectance in red channel  NIR = Reflectance in NIR channel Healthy, dense vegetation has high NDVI Stressed, or sparse vegetation produces lower NDVI Bare rock, soil have NDVI near zero Snow produces negative values of NDVI Clouds produce low to negative values of NDVI

15. Global NDVI from the Advanced Very High Resolution Radiometer

16. NDVI as an indicator of drought:

17. Cautions about NDVI Saturates over dense vegetation Less information than original data Any factor that unevenly influences the red and NIR reflectance will influence the NDVI –such as atmospheric path radiance, soil wetness Pixel-scale values may not represent plant-scale processes Derivatives of NDVI (FAPAR, LAI) are not physical quantities and should be used with caution

18. Other vegetation indices: Soil-adjusted Vegetation Index (SAVI) Soil and Atmospherically-Resistant Vegetation Index (SARVI) Moisture Stress Index (MSI) Global Monitoring Environmental Index (GEMI) Enhanced Vegetation Index (EVI)

19. Compensates for atmospheric and soil effects  red = Reflectance in red channel  NIR = Reflectance in NIR channel  blue = Reflectance in blue channel C 1 = Atmospheric resistance red correction coefficient (C 1 = 6) C 2 = Atmospheric resistance red correction coefficient (C 2 = 7.5) L = Canopy background brightness correction factor (L = 1) G = Gain factor (G = 2.5) Enhanced Vegetation Index (EVI)

20. EVI vs NDVI The EVI is a modified NDVI with a soil adjustment factor, L, and two coefficients, C 1 and C 2 which are used to correct for atmospheric scattering The coefficients, C 1, C 2, and L, are empirically determined (from observations using MODIS data) The EVI has improved sensitivity to high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmospheric influences (Huete and Justice, 1999).

21. Middle IR Interactions with Water in the Spongy Mesophyll Plant water content absorbs middle IR radiation Middle IR plant reflectance increases as leaf moisture content decreases Middle IR reflectance can be used to monitor plant water stress

22. Reflectance response of a single Magnolia leaf (Magnolia grandiflora) to decreased relative water content

23. Thermal Emission and Plant Water Stress Measures of thermal emission can be used to derive surface temperature for a crop As water transpires from a plant, it’s leaves are cooled If a plant is stressed, transpiration is reduced and leaf temperature increases red=warmer blue=cooler Thermal IR image showing plots of irrigated cotton

24. Aquatic Plants Immersed aquatic plants absorb solar energy and emit thermal radiation (warmer than surrounding water) This can be detected in thermal imagery water hyacinth plumes in Lake Victoria

25. Angular Reflectance Properties of Vegetation Vegetation reflects light unevenly, in different directions (“anisotropic reflectance”) Depends on: –leaf shape –canopy height –vegetation density Described by “Bidirectional Reflectance Distribution Function” (BRDF)

26. Vegetation Structure from Lidar Waveform

27. Phenological Cycles Temporal characteristics of vegetation growth Depends on: –plant available water: rainfall/irrigation –land surface temperature –vegetation type (evergreen vs. deciduous) Crop cycles (depends on planting/harvesting cycle) Deciduous cycles (depends on seasonality of rainfall and temperature)

28. Phenological cycles of San Joaquin and Imperial Valley, California crops and Landsat Multispectral Scanner images of one field during a growing season