Field Spectroscopy, Hyperspectral Imaging, Applications in Vegetation and Soils Analysis Alexander F. H. Goetz University of Colorado and Analytical Spectral Devices Inc. goetz@cses.colorado.edu Beijing and NanJing, China June 28-29 and July 1-2, 2004 Lecture 2

Spectroscopy of Vegetation Brian Curtiss Analytical Spectral Devices, Inc. 5335 Sterling Drive, Suite A Boulder, CO 80301-2354, USA 303-444-6522 FAX: 303-444-6825 curtiss@asdi.com http://www.asdi.com/

Spectral Properties of Vegetation Unlike minerals, all vegetation is composed of a limited set of spectrally active compounds The relative abundances of these compounds, including water, are indicators of the condition of the vegetation and of the environment in which the vegetation is growing Vegetation architecture has a very strong influence on the overall characteristics of the reflectance spectrum The spatial scale of the reflectance measurement is important in determining the observed reflectance

Spectral Properties of Vegetation (cont.) Reflectance in the visible and near infrared region (350 to 800 nm) is dominated by absorption from chlorophyll and other accessory pigments Reflectance in the SWIR (800 to 2500 nm) is dominated by absorption from liquid water in the plant’s tissue Reflectance in the SWIR is modified by minor absorption features associated with C-H, N-H, and CH2 bearing compounds such as starches, proteins, oils, sugars, lignin and cellulose.

Scale Dependence of Vegetation Spectra Important at all scales: Viewing geometry Geometry and spectral characteristics of illumination source(s) Leaf / Needle scale: relative abundance of biochemicals cellular structure Branch scale: leaf / needle scale reflectance leaf / needle angle distribution leaf / needle shape and density

Scale Dependence of Vegetation Spectra (cont.) Crown scale: branch scale reflectance branch angle distribution crown geometry branch geometry and density background reflectance Canopy scale: crown scale reflectance crown density background reflectance (soil, understory, litter)

Information Content of Vegetation Spectra Leaf / Needle scale: leaf / needle age water and nutrient availability evidence of other environmental stress (both biotic and abiotic) Branch scale: branch scale architecture (relates to species) evidence of environmental stress Crown scale: tree age crown scale architecture (relates to species) Canopy scale: tree size distribution canopy scale architecture (relates to species assemblages)

Information Extraction from Vegetation Spectra Limitations of spectral libraries the multitude of factors affecting vegetation spectra makes it difficult to adequately populate a spectral library large within-species variability strong dependence on growing season climate scale dependence of vegetation spectra dependence of vegetation spectra on view and illumination geometry Successful use of spectral libraries crop type identification species identification in arid lands plant community identification

Information Extraction from Vegetation Spectra (cont.) Limitations of model-based approaches often requires extensive field and laboratory measurements often are season specific often are specific to a particular plant community or group of communities (e.g. C4 forage grasses) Successful use of model-based approaches quantification of canopy biochemicals tree crown size > stem diameter > timber volume stress across an environmental gradient crop yield

Similarity of Vegetation Spectra single leaf spectra — all have same set of absorption features but with varying depths

Major Spectral Features — Vegetation

Leaf Structure A Upper leaf cuticle B Palisade mesophyll cells containing the majority of chloroplasts C Spongy mesophyll cells with a large area of cell wall interfaces D Lower cuticle containing stomates Schematic cross-section of a typical broadleaf showing the basic photon interactions that occur when light strikes the leaf surface. A photon may be (1) specularly reflected, (2) diffusely reflected, (3) absorbed in leaf photosynthetic apparatus, (4) scattered from inside the leaf back in the general direction from which it originally entered, adding to the leaf reflectance, (5) transmitted or scattered out of the leaf in the same general direction as it originally traveled, adding to leaf transmittance.

Model-based Analysis of Vegetation Spectra Curve-fitting of absorption features more commonly applied to mineral spectra some success for chlorophyll and water features from ‘minor’ constituents (e.g. cellulose) found in the residual spectrum Regression-based chemometrics commonly used for determination of canopy biochemicals (e.g. lignin, cellulose, & nitrogen) requires many well characterized field samples collected near the time of over-flight

Model-based Analysis of Vegetation Spectra (cont.) Geometric optics models canopy as an aggregate of tree crowns image spectra are modeled as mixtures of sunlit and shaded crown, and, sunlit and shaded background the model derives the average crown size which is then related to stem diameter and timber volume Statistical classification supervised or unsupervised may be used in conjunction with a spectral library to aid in the definition of classes

Quantitative Reflectance Spectroscopy The Bouguer-Beer law is the fundamental relationship upon which many spectrometric techniques are based: -log R() = A() = () b() c R() Transmission as a function of wavelength A() Absorbance as a function of wavelength b() Absorption coefficient as a function of wavelength () Absorption path length as a function of wavelength c Concentration of the absorbing compound Solving for the concentration gives: c = -log R() / () b() Reflectance is the quantity that can be measured by remote sensing

Reflectance of Multiple Leaf Layers

Reflectance of Multiple Leaf Layers (cont.) Same water concentration, increasing path length

Wavelength Dependence of Path Length The amount of liquid water “seen” varies with wavelength

Single Leaf Spectra The liquid water absorption feature depths are a product of water concentration and path length — Pinyon pine has the lowest liquid water concentration, but the deepest absorption features at both 0.95µm and 1.2µm

Liquid Water Band Fitting — Single Leaf Laboratory Spectra

Liquid Water Band Fitting — AVIRIS Spectra

Liquid Water Band Fitting — AVIRIS Spectra (cont.) golf course Ponderosa pine

Liquid Water Band Fitting — AVIRIS Spectra (cont.) cellulose golf course Ponderosa pine

Chemometric Modeling

Chemometric Calibration Techniques Follows Beers Law Linear regression-based models calculations using log 1/Reflectance Calibration Calibration Image Samples  Model  Spectra Develop Apply

Chemometrics Model Development

Near Infrared Spectroscopy — Data Pre-treatments Log 1/Reflectance Spectral Filtering Smoothing Multiplicative Scattering Correction Spectral Transforms Derivatives

Near Infrared Spectroscopy — Chemometric Models Simple regression Single Wavelength Multiple Wavelengths Multivariate Regression Principal Component Regression Partial Least Squares Regression

Near Infrared Spectroscopy — Sample Selection Samples Must: span the range of variability found in the image variance for other scene variables must be independent be collected to be representative of the spatial pixel For Natural Samples: many (>100) samples are required for a good calibration 10-20 samples required for proof of concept

Calibration for Nitrogen, Lignin & Cellulose in Fresh Leaves

Fresh Leaf Calibration — Band Positions

Fresh Vs. Dry Predicted Nitrogen

AVIRIS Nitrogen PLS Calibration

AVIRIS Predicted Vs. Actual Nitrogen

Dry Leaf Band Depth Nitrogen Calibration

Band Depth Predicted Nitrogen Vs. Actual Nitrogen

Spectra of Vegetation Components Canopy biochemicals water woody components: lignin, cellulose nitrogen rich components: pigments, proteins other components: sugars, starch, oils, waxes Non-vegetative canopy elements wood bark Plant litter dry leaves decomposing leaves and woody material

Spectra of Cellulose & Lignin The spectra of most woody materials are dominated by lignins The spectra of leaves/needles show absorption by both lignin and cellulose Man-made cotton fabrics have no lignin absorption features

Spectra of Dry Plant Materials

Spectra of Dry Plant Materials (cont.)

Spectra of Other Vegetative Components

Wavelengths of Plant Biochemical Absorption Features Graphical plot of the major (boxed) and minor absorption peaks of canopy biochemicals overlaid with the location of the principal water absorption regions of the atmosphere as determined from AVIRIS spectra.

Wavelengths of Plant Biochemical Absorption Features (cont.) Absorption features in the Vis and NIR that have been related to particular foliar chemical concentrations.

Effects of Shadows on Vegetation Spectra The aspect ratio of canopy elements (in this case crowns) determines the relative amounts of illuminated and shaded crown and background.

Effects of “Hole” Shape on Vegetation Spectra (cont.) The aspect ratio of “holes” in the canopy (at all scales) determines the importance of shadows in the measured spectrum.

Viewing Geometry Effects — Soil

Viewing Geometry Effects — Grass

Viewing Geometry Effects — Juniper

Bi-Directional Reflectance Distribution Function illumination direction Red (680nm): Infrared (850nm) squares = pine; circles = broadleaf

Influence of View Geometry on Vegetation Spectra BRDF of a tallgrass prairie grassland community at two wavelengths. Solar zenith angle is 48°. The red band is centered at 662nm; the IR band is centered at 826nm.

Effects of Sensor Spatial Resolution on the Spectrum of a Vegetation Canopy The spatial variance of the amount of shaded canopy is an indicator of the average crown size. Crown size can then be related to trunk diameter and other parameters useful for forest management.

Effects of View Geometry on Reflectance of Vegetation Canopies Mean spectral reflectance of a mixed conifer stand from view angles of 0° and 15°, 30° and 45° in the backscatter direction and in the principal plane of solar illumination.

Time Variation of Vegetation Spectra Conifers retain needles for several years. Spectra of each years needles can be quite different. At the canopy level the number of years growth retained by a tree strongly influences the canopy scale spectral reflectance.

Time Variation of Vegetation Spectra (cont.) left: changes in NDVI for a Indian Grass below: reflectance spectra for three days in the growing season.

The “Red Edge” The plot to the left shows a “red edge” shift to blue wavelengths for “stressed” vegetation. Others have reported a shift to the red under “stressed” conditions.

Mechanisms for the “Red Edge” Shift Band broadening can be produced by chloroplast disruption. Band depth can be reduced by either chlorophyll loss or an architectural change that reduces the interaction of illumination with the chlorophyll in the leaf.

What Produces the Shift in the “Red Edge”? Anything that reduces the amount of chlorophyll seen by the sensor at either the leaf/needle scale or at the canopy scale actual changes in chlorophyll concentration canopy architecture changes branch-scale changes in architecture changes in vegetation cover Anything that disrupts the chloroplast membranes These two mechanisms compete under stressful conditions could get either a red or blue or no shift with stress

Effects of Chronic Stress on Vegetation In a natural ecosystem, chronic stress results in the establishment of individual trees and/or species that have adapted to the stress The chlorosis observed in seedlings grown under stress is generally not observed in a mature forest Architectural changes are a common response to chronic stress and result in changes in reflectance that are observable at leaf to canopy scales Chances in foliar reflectance may also result from chronic stress

Vegetation Hyperspectral Remote Sensing - Summary Step 1: A clearly defined objective; examples: delineation of dominant species assemblages mapping of ecosystem productivity crop type or yield mapping spatial distribution of heavy metal induced stress Step 2: Identify spectral changes or differences associated with process of interest; methods: examination of existing spectral libraries examination of study-specific field spectral correlation between spectral and non-spectral field or laboratory measurements

Vegetation Hyperspectral Remote Sensing - Summary (cont.) Step 3: Identify best analytical method based on available image, field, and laboratory resources; methods: spectral library based model based curve-fitting chemometics geometric optics classification