T9_Vegetation_v1_3slides

T9_Vegetation_v1_3slides - Geography 333: Remote Sensing I...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Geography 333: Remote Sensing I Topic 9: Vegetation Remote Sensing Part I: Spectral Characteristics Readings This class: Topic 9 - Chapter 8 (Vegetation Transformations pg 301322) Next classes: Topic 10 - Spatial Filtering and Texture Analysis Required Texts Jensen, J. R., 2005: Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice Hall. 2 Cross-section Through A CrossHypothetical and Real Leaf Revealing the Major Structural Components that Determine the Spectral Reflectance of Vegetation 3 1 Dominant Factors Controlling Leaf Reflectance Water absorption bands: 0.97 m 1.19 m 1.45 m 1.94 m 2.70 m Jensen, 2000 4 Leaf Structure and Reflectance 5 Chlorophyll b Chlorophyll a Absorption Efficiency Absorption Spectra of Chlorophyll andb a Absorption Spectra of Chlorophyll a and b, -carotene, Phycoerythrin (red) and Phycocyanin (blue) Pigments Phycoerythrin is a red pigment in algae Phycocyanin – blue pigment in (light-harvesting) algae/ cyanobacteria lack of absorption a. 0.25 0.3 0.35 0.4 violet blue green yellow red Wavelength, m Phycoerythrin 0.45 0.5 0.55 0.6 0.65 0.7 Phycocyanin -carotene Chlorophyll a peak absorption is at 0.43 and 0.66 mm. Chlorophyll b peak absorption is at 0.45 and 0.65 mm. Optimum chlorophyll absorption windows are: 0.45 - 0.52 mm and 0.63 - 0.69 mm How are these absorption features represented in a wide spectral band?? 6 Absorption Efficiency 0.25 0.3 0.35 0.4 b. violet blue green yellow red Wavelength, m 0.45 0.5 0.55 0.6 0.65 0.7 2 TM Bands and Spectral Reflectance Curves of Common Targets 12 3 4 5 Soil 7 Vegetation Water 7 Litton Emerge Spatial, Inc., CIR image (RGB = NIR,R,G) of Dunkirk, NY, at 1 x 1 m obtained on Dec 12, 1998. Natural color image (RGB = RGB) of a N.Y. Power Authority lake at 1 x 1 ft obtained on October 13, 1997. 8 Liquidambar styraciflua L.) ((Liquidambarstyraciflua L.) Same leaf (species), different stage of development/decay Can you foresee any problem with this for remote sensing classifications? Spectral Reflectance Spectral Reflectance Characteristics of Characteristics of Sweetgum Leaves Sweetgum Leaves 9 3 3 1 a 2 a. Spectral Reflectance Spectral Reflectance Characteristics of Characteristics of Selected Areas of Selected Areas of Blackjack Oak Leaves Blackjack Oak Leaves b. 4 c. 45 40 35 Red/orange Percent Reflectance 30 25 20 15 10 5 Brown Green leaf Yellow 1 2 3 4 d. 0 Blue (0.45 - 0.52 m) G reen (0.52 - 0.60 m) Red (0.63 - 0.69 m) Near-Infrared (0.70 - 0.92 m) 10 Hypothetical Example of Additive Reflectance from A Canopy with Two Leaf Layers Leaf What happens to the light in a dense forest canopy? 11 Distribution of Pixels in a Scene in Red and Near-infrared Multispectral Feature Space 12 4 Reflectance Response of a Single Magnolia Leaf (Magnolia grandiflora) to Decreased Relative Water Content Signatures Change from 13 Tree and forest Classification Generally, if spatial resolution are appropriate and complete spectra over Vis, NIR, and SWIR regions of the EM spectrum are available, the discrimination of species can be accomplished with acceptable accuracy Note that the reflectance's for the several coniferous tree species (fir, pine, cedar) are lower than those for deciduous trees (maple, oak, aspen) Source: http://rst.gsfc.nasa.gov/Sect3/Sect3_5.html 14 15 5 • Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Data-cube of Sullivan’s Island Obtained on October 26, 1998 16 Imaging Spectrometer Data of Healthy Green Vegetation in the San Luis Valley of Colorado Obtained on September 3, 1993 Using AVIRIS Jensen, 2000 Jensen, 2000 224 channels each 10 nm wide with 20 x 20 m pixels 17 Hyperspectral Analysis of AVIRIS Data Obtained on September 3, 1993 of San Luis Valley, Colorado 18 6 Geography 333 Remote Sensing I Topic 8: Vegetation Remote Sensing Part II: Temporal (Phenological) Characteristics (Phenology is the study of periodic plant and animal life cycle events and how these are influenced by seasonal and interannual variations in climate) 19 Predicted Percent Cloud Cover in Four Areas in the United States 20 Phenological Cycle of Hard Red Winter Wheat in the 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 50 10 14 26 108 days Dormancy Sow Tillering Emergence 28 34 29 21 Dead 14 14 21 13 25 4 7 9 5 ripe Growth Jointing Heading resumes Boot Soft Hard dough dough Maximum Coverage 21 7 100 75 50 25 cm JAN FEB 50% MAR Winter Wheat Winter Wheat 100% snow cover ground cover APR MAY JUN Harvest JUL AUG SEP OCT NOV a. DEC Seed Tillering 150 125 100 75 50 Jointing Booting Head Dormant or multicropped Phenological Cycles of Winter Wheat, Cotton, and Tobacco in South Carolina Winter Wheat Phenology Cotton Cotton b. snow cover 25 cm height JAN FEB APR MAY JUN Seeding JUL 50% 100% ground cover MAR AUG Fruiting SEP Boll OCT NOV DEC Dormant or multicropped Maturity/harvest Pre-bloom 125 100 75 50 snow cover 25 cm height JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV 50% c. Tobacco Tobacco 100% DEC Dormant or multicropped Transplanting Development Maturity/harvest Dormant or multicropped Jensen, 2000 Topping 22 Phenological Cycles of San Joaquin and Imperial Valley, California Crops and Landsat Multispectral Scanner Images of One Field During A Growing Season 23 Band 1 (blue; 0.45 – 0.52 m) Band 2 (green; 0.52 – 0.60 m) Band 3 (red; 0.63 – 0.69 m) Landsat (4) Thematic Mapper Imagery of the Imperial Valley, California Obtained on December 10, 1982 30 m (MSS) 120 m (Thermal) Band 4 (near-infrared; 0.76 – 0.90 m) Band 5 (mid-infrared; 1.55 – 1.75 m) Band 7 (mid-infrared; 2.08 – 2.35 m) 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 m) Ground Reference 24 8 Landsat Thematic Mapper Color Composites and Classification Map of a Portion of the Imperial Valley, California 25 1500 1250 1000 750 500 250 0 J Smooth Cordgrass ( Spartina alterniflora ) Live Biomass DeadBiomass Dry Weight Biomass, g/ m2 Phenological Cycle Phenological Cycle of Smooth of Smooth Cordgrass Cordgrass ((Spartina Spartina alterniflora)) alterniflora Biomass in South Biomass in South Carolina Carolina FMAM J JASOND 26 Band 1 (blue; 0.45 – 0.52 m) Band 2 (green; 0.52 – 0.60 m) Band 3 (red; 0.60 – 0.63 m) Nine Bands of 3 x 3 m Calibrated Airborne Multispectral Scanner (CAMS) Data of Murrells Inlet, SC Obtained on August 2, 1997 Band 4 (red; 0.63 – 0.69 m) Band 5 (near-infrared; 0.69 – 0.76 m) Band 6 (near-infrared; 0.76 – 0.90 m) Band 7 (mid-infrared; 1.55 – 1.75 m) Band 8 (mid-infrared; 2.08 – 2.35 m) Band 9 (thermal-infrared; 10.4 – 12.5 m) 27 9 Calibrated Airborne Multispectral Scanner Data of Murrells Inlet, S.C. Obtained on August 2, 1997 Natural Color Natural Color Composite Composite (Bands 3,2,1 ==RGB) (Bands 3,2,1 RGB) Masked and Masked and Contrast Stretched Contrast Stretched Color Composite Color Composite 28 Calibrated Airborne Multispectral Scanner Data of Murrells Inlet, S.C. Obtained on August 2, 1997 Color Infrared Composite (Bands 3,2,1 = RGB) Masked and Masked and Contrast Stretched Contrast Stretched Color Composite Color Composite 29 In Situ Ceptometer Leaf-Area-Index Measurement Ceptometer Leaf- AreaLeaf Area Index (LAI) is the ratio of total upper leaf surface of vegetation divided by the surface area of the land on which the vegetation grows. of the LAI is a dimensionless value, typically ranging from 0 for bare ground to 6 for a dense forest (Source: Wikipedia – LAI) LAI may be computed using a Decagon Accupar Ceptometer™ 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 (Qa), direct solar energy incident to the ceptometer, (Qb), when it ceptometer, (Qb), was laid at the bottom of the canopy directly on the mud is used to compute LAI. 30 10 In Situ Ceptometer Ceptometer Leaf-Area-Index Leaf- AreaMeasurement 31 Relationship Between Calibrated Airborne Multispectral Scanner (CAMS) Band 6 Brightness Values and in situ in Measurements of Spartina Spartina alterniflora Total Dry 2) at Biomass (g/m 27 Locations in Murrells Inlet, SC Obtained on August 2 and 3, 1997 32 NASA Calibrated Airborne NASA Calibrated Airborne Multispectral Scanner Multispectral Scanner Imagery (3 m) and Imagery (3 m) and Derived Biomass Map of aa Derived Biomass Map of Portion of Murrells Inlet, Portion of Murrells Inlet, South Carolina on August South Carolina on August 2, 1997 2, 1997 CAMSBands 1,2,3 (RGB) CAMSBands 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 2 ) TM Bands 5,3,2 (RGB) 500 - 749 750 - 999 1000 - 1499 1500 - 1999 2000 - 2499 2500 - 2999 33 11 Total Above-ground AboveBiomass in Murrells Inlet, S. C. Extracted from Calibrated Airborne Multispectral Scanner Data on August 2, 1997 Total Biomass (grams/m2) 500 - 749 750 - 999 1000 - 1499 1500 - 1999 2000 - 2499 2500 - 2999 34 Readings This class: Topic 9 - Chapter 8 (Vegetation Transformations pg 301322) Next classes: Topic 10 - Spatial Filtering and Texture Analysis Required Texts Jensen, J. R., 2005: Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice Hall. 35 12 ...
View Full Document

This note was uploaded on 01/18/2011 for the course GEOG 331 taught by Professor Staff during the Fall '08 term at Kansas.

Ask a homework question - tutors are online