1. Active Remote Sensing Systems March 2, 2005 Spectral Characteristics of Vegetation Temporal Characteristics of Agricultural Crops Vegetation Indices Biodiversity and Gap Analysis Remote Sensing of Vegetation Change Reminder: Read Chapter 11 (pp. 383-403) for Next Class Remote Sensing of Vegetation

2. Active Remote Sensing Systems –The dominant factors controlling leaf reflectance are: leaf pigments in the palisade mesophyll scattering of near-infrared energy in the spongy mesophyll amount of water in the plant –chlorophyll absorption bands are: 0.43 – 0.45 μm 0.65 – 0.66 μm –primary water absorption bands are: 0.97, 1.19, 1.45, 1.94, and 2.7 μm Spectral Characteristics of Vegetation

3. Dominant Factors Controlling Leaf Reflectance Jensen, 2000 Water absorption bands: 0.97 mm 1.19 mm 1.45 mm 1.94 mm 2.70 mm Water absorption bands: 0.97 mm 1.19 mm 1.45 mm 1.94 mm 2.70 mm

4. Litton Emerge Spatial, Inc., CIR image (RGB = NIR,R,G) of Dunkirk, NY, at 1 x 1 m obtained on December 12, 1998. Natural color image (RGB = RGB) of a N.Y. Power Authority lake at 1 x 1 ft obtained on October 13, 1997.

5. Hypothetical Example of Additive Reflectance from A Canopy with Two Leaf Layers Jensen, 2000

6. Distribution of Pixels in a Scene in Red and Near-infrared Multispectral Feature Space Distribution of Pixels in a Scene in Red and Near-infrared Multispectral Feature Space Jensen, 2000

7. Reflectance Response of a Single Magnolia Leaf (Magnolia grandiflora) to Decreased Relative Water Content Reflectance Response of a Single Magnolia Leaf (Magnolia grandiflora) to Decreased Relative Water Content Jensen, 2000

8. Imaging Spectrometer Data of Healthy Green Vegetation in the San Luis Valley of Colorado Obtained on September 3, 1993 Using AVIRIS Jensen, 2000 224 channels each 10 nm wide with 20 x 20 m pixels

9. Hyperspectral Analysis of AVIRIS Data Obtained on September 3, 1993 of San Luis Valley, Colorado Jensen, 2000

10. What is essential in identifying different vegetation types?

11. Active Remote Sensing Systems –Timing is essential in attempting to identify different vegetation types! –“Green is green is green,” noted a prominent remote sensing scientist, referring to the very similar spectral signatures of two very different crops maturing at the same time –therefore, it is essential to understand the phenological (growth) cycles of different crops –examples Temporal Characteristics of Agricultural Crops

12. What is the phenological cycle of burley tobacco here in WNC?

13. Phenological Cycle of Hard Red Winter Wheat in the Great Plains Jensen, 2000

14. Phenological Cycles of San Joaquin and Imperial Valley, California Crops and Landsat Multispectral Scanner Images of One Field During A Growing Season Jensen, 2000

15. Phenological Cycles of Soybeans and Corn in South Carolina Jensen, 2000 SoybeansSoybeans CornCorn

16. Phenological Cycles of Winter Wheat, Cotton, and Tobacco in South Carolina Jensen, 2000 Winter Wheat CottonCotton TobaccoTobacco

17. “The accuracy of a remote sensing derived crop classification map is always dependent upon there being a significant difference in the spectral response between the various crop types.” “The only way to identify when this maximum contrast among spectral response should take place is to evaluate phenological crop calendars and select the appropriate dates of imagery for analysis” (Jensen 2007, p. 382) Importance of Phenological Cycles

18. Phenological Cycle of Cattails and Waterlilies in Par Pond, S.C. Jensen, 2000

19. Active Remote Sensing Systems –Significant effort has gone into the development of what are known at vegetation indices, defined as: dimensionless, radiometric measures that function as indicators of relative abundance and activity of green vegetation –These indices also include other measures such as: –the leaf-area-index (LAI) –percentage green cover –chlorophyll content –green biomass Vegetation Indices

20. Active Remote Sensing Systems –To be effective and to allow comparison across different areas and times, a vegetation index should: maximize sensitivity to plant biophysical parameters normalize sun angle, viewing angle, and the atmosphere (external effects) normalize canopy background variations, including topography (slope and aspect), soil variations, and differences in nonphotosynthetic canopy components be coupled to some specific measurable biophysical parameter such as biomass or LAI as part of validation and quality control

21. Infrared/Red Ratio Vegetation Index The near-infrared (NIR) to red simple ratio (SR) is the first true vegetation index: It takes advantage of the inverse relationship between chlorophyll absorption of red radiant energy and increased reflectance of near-infrared energy for healthy plant canopies (Cohen, 1991). The near-infrared (NIR) to red simple ratio (SR) is the first true vegetation index: It takes advantage of the inverse relationship between chlorophyll absorption of red radiant energy and increased reflectance of near-infrared energy for healthy plant canopies (Cohen, 1991).

22. Normalized Difference Vegetation Index The generic normalized difference vegetation index (NDVI): has provided a method of estimating net primary production over varying biome types (e.g. Lenney et al., 1996), identifying ecoregions (Ramsey et al., 1995), monitoring phenological patterns of the earth’s vegetative surface, and of assessing the length of the growing season and dry-down periods (Huete and Liu, 1994). The generic normalized difference vegetation index (NDVI): has provided a method of estimating net primary production over varying biome types (e.g. Lenney et al., 1996), identifying ecoregions (Ramsey et al., 1995), monitoring phenological patterns of the earth’s vegetative surface, and of assessing the length of the growing season and dry-down periods (Huete and Liu, 1994).

23. Time Series of 1984 and 1988 NDVI Measurements Derived from AVHRR Global Area Coverage (GAC) Data for the Region around El Obeid, Sudan, in Sub-Saharan Africa Jensen, 2000

24. Active Remote Sensing Systems –Remote sensing and GIS technologies have proved extremely helpful in identifying biodiversity hotspots –Gap Analysis assesses the current status of biodiversity at all levels Biodiversity and Gap Analysis

25. Active Remote Sensing Systems –A typical Gap Analysis includes the creation and analysis of four primary GIS layers: distribution of actual vegetation-cover types from satellite and aircraft remotely sensed data and other ancillary sources land ownership land management status distribution of terrestrial vertebrates as predicted from the distribution of vegetation and in situ observations

26. Active Remote Sensing Systems –Change detection studies are very important in understanding processes of ecological succession –A number of MA theses have analyzed vegetation and land cover change in northwest North Carolina –Par Pond Reservoir Study Remote Sensing of Vegetation Change