11-Vegetation

11-Vegetation - 5/12/2008 Spectral characteristics related...

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Unformatted text preview: 5/12/2008 Spectral characteristics related to leaf pigments and water ESM 266: Vegetation— ESM 266: Vegetation— properties Jeff Dozier Water vapor absorption bands: 0.97 μm 1.19 μm 1.45 μm 1.94 μm 2.70 μm 1 Chlorophyll b Chlorophyll a peak absorption is at 0.43 and 0.66 μm. Chlorophyll b peak absorption is at 0.45 and 0.65 μm. Optimum chlorophyll absorption windows are: 0.45 - 0.52 μm and 0.63 - 0.69 μm Absorption Efficiency Cross‐ Cross‐section through a leaf Absorption spectra of compounds Chlorophyll a Absorption Spectra of Chlorophyll a and b 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 a. violet blue green yellow red Wavelength, μm Phycocyanin Phycoerythrin Absorption Efficiency β-carotene 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 b. violet blue green yellow red Wavelength, μm 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. Seasonal variability in spectral reflectance (Sweetgum – Sweetgum – Liquidambar q styraciflua L.) ESM 266: Vegetation – properties, processes, biomass 1 5/12/2008 Spatial variability on the leaves (Blackjack oak) a. 3 Reflectance, absorption, and transmittance through canopy – through canopy – Big bluestem grass 1 a 2 b. b 4 c. 45 40 35 Percent Reflectance 30 25 20 15 10 5 Green leaf Yellow Red/orange Brown 1 2 3 4 d. 0 Blue (0.45 - 0.52 μm) G reen (0.52 - 0.60 μm) Red (0.63 - 0.69 μm) Near-Infrared (0.70 - 0.92 μm) “Tassled cap” – red & NIR reflectance “Tassled cap” – Reflectance increases as leaf dries AVIRIS image, Sullivan’s Island, Oct 1998 AVIRIS image of crops, San Luis Valley CO Sept 1993 ESM 266: Vegetation – properties, processes, biomass 2 5/12/2008 Species and stress mapped from AVIRIS, San Luis Valley Luis Valley CO Sept 1993 Temporal variability – Temporal variability – phenological cycle of winter wheat in Great Plains Winter Wheat Phenology snow cover SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG crop establishment greening up heading mature Harvest Dead ripe 50 10 14 26 108 days 14 28 14 21 34 13 29 25 21 47 9 5 Sow Tillering Emergence Dormancy Growth Jointing resumes Heading Boot Soft dough Maximum Coverage Hard dough Phenological cycle of some San Joaquin Valley crops Landsat TM imagery of Imperial Valley Band 1 (blue; 0.45 – 0.52 μm) Band 2 (green; 0.52 – 0.60 μm) Band 3 (red; 0.63 – 0.69 μm) Band 4 (near-infrared; 0.76 – 0.90 μm) Band 5 (mid-infrared; 1.55 – 1.75 μm) Band 7 (mid-infrared; 2.08 – 2.35 μm) Landsat Thematic Mapper Imagery of Imperial Valley, California, December 10, 1982 feed lot Sugarbeets fl Cotton Fallow Alfalfa Band 6 (thermal infrared; 10.4 – 12.5 μm) Ground Reference Landsat TM color composites, Imperial Valley Vegetation characteristics – leaf‐ Vegetation characteristics – leaf‐area index • LAI may be computed using a Decagon Accupar Ceptometer™ that consists of a linear array of 80 adjacent 1 cm2 photosynthetically active radiation (PAR) sensors along a bar. • Incident sunlight above the canopy, Qa, and the amount of direct solar energy incident to the ceptometer, Qb, when it is laid at the bottom of the canopy is used to compute LAI. ESM 266: Vegetation – properties, processes, biomass 3 5/12/2008 Field measurement of LAI Temporal variability of live and dead biomass 1500 1250 1000 750 500 250 0 Smooth Cordgrass (Spartina alterniflora) Live Biomass Dead Biomass Dry Weight Biom mass, g/ m2 J F M A M J J A S O N D Biomass estimated from spectral reflectance CAMS Bands 1,2,3 (RGB) CAMS Bands 6,4,2 (RGB) Biomass in a Portion of Murrells Inlet, SC Derived from 3 x 3 m Calibrated Airborne Multispectral Scanner (CAMS) Data Obtained on August 2, 1997 Total Biomass (grams/m TM Bands 5,3,2 (RGB) 500 - 749 750 - 999 1000 - 1499 1500 - 1999 2000 - 2499 2500 - 2999 2) NDVI (normalized difference vegetation index) • Characteristics of leaf reflectance (phenological stage, leafy biomass, greening, drying, etc.) NDVI = NIR − red NIR + red 22 NDVI time series for wet (1988) and dry years (1984) in Sahel Other vegetation indices (condensed from p. 363 in Jensen) 363 in Jensen) Infrared index II = NIRTM 4 − SWIRTM 5 NIRTM 4 + SWIRTM 5 SWIRTM 5 NIRTM 4 Moisture stress index MSI = Soil adjusted index SAVI = (1 + L)( NIR − red ) , where L ≈ 0.5 NIR + red + L ∗ ∗ pnir − pred Enhanced vegetation index EVI = ∗ (1 + L ) ∗ ∗ pnir + C1 pred − C2 pblue + L where the p∗ terms are atmospherically corrected reflectances 23 ESM 266: Vegetation – properties, processes, biomass 4 5/12/2008 The MODIS EVI, produced every 16 days MODIS vegetation index page, then click on VI gallery Gross primary productivity, South America, Jan 2001 NTSG (Numerical Terradynamic Simulation Group) University of Montana What remote sensing of vegetation has accomplished • Terrestrial biosphere now a dynamic component of Earth System • Global Ecology has emerged as a discipline Land Classification AVHRR-NDVI ESM 266: Vegetation – properties, processes, biomass 5 ...
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This note was uploaded on 08/06/2008 for the course ESM 266 taught by Professor Dozier during the Spring '08 term at UCSB.

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