lecture10

ERS186:Environmental Remote Sensing: 

ERS186: Environmental Remote Sensing Lecture 10: Species Discrimination Using Remote Sensing

Slide2: 

Factor 1: Spectral scattering/absorbing properties of canopy components. (leaves, stems, flowers, fruit, soil, etc.) Factor 2: Canopy architecture. (above-ground biomass; leaf area index; arrangement of foliage in x,y,z,q,f space – for example, are all leaves vertical and located in one layer – or perhaps they are arranged in space like the area on a sphere; etc.) Factor 3: Directions of illumination and view. (Is the sun the only significant source – or does aerosol- or Rayleigh-scattered light provide hemispherical illumination; is direction of view toward the hot spot or nadir or …) Keep in mind . . . Three factors determine canopy reflectance.... and thus our ability to discriminate plant canopies

Overview: 

Overview Applications Ecology Agriculture Physical Principles Cellular absorption and scattering Non-selective scattering BRDF Sensors Hyperspectral Hyperspatial

The Question: 

The Question What plant species are present in a remote sensing image?

Species Identification: 

Species Identification Not all vegetation looks the same! We can use this to help identify different species using RS.

Species Identification: 

Species Identification Why do the spectra of different species vary? Cellular differences (protein, cellulose and lignin, water, pigments, etc…) {factor 1, scattering/absorbing properties of canopy components (leaves)} LAI, leaf angle, and leaf shape differences {factor 2, architecture} Trunk, stem and branch differences (size, number, color) {factor 2, architecture} Crown size and shape {factor 2, architecture}

Cellular Differences: 

Cellular Differences PROSPECT (Jacquemoud et al., 1996): models the light path through a simulated leaf with differing structural and chemical properties. Structural differences included rough, medium and smooth epidermis Chemical differences included differences in protein, cellulose and lignin, and water. The structural and chemical properties were derived from real leaves. Found differences in modeled reflectance with different properties, and these matched real-world reflectance curves. Factor 1: scattering/absorbing properties of canopy components (leaves)

Cellular Differences: Pigments: 

Cellular Differences: Pigments Pigments can and will vary between species, even closely related ones. Mature Valley vs. Live Oak reflectance and pigment contents: Ustin et al. 1998 Factor 1: scattering/absorbing properties of canopy components (leaves)

Cellular Differences: Water: 

Cellular Differences: Water Water absorption features can help determine the amount of water in a leaf. Water differences can indicate different species, or different stress levels within a species. Greenberg et al. 2001, healthy and water stressed cotton spectra. Factor 1: scattering/absorbing properties of canopy components (leaves)

Canopy Level Differences, LAI: 

Canopy Level Differences, LAI All things being equal, LAI intercepts light according to Beer’s Law in the visible. Detection of LAI usually requires indices or proxy variables: NDVI vs. LAI EWT vs. LAI (Roberts et al., in review) Factor 2: architecture

Canopy BRF & LAI Differences : 

Canopy BRF & LAI Differences The relationship between LAI and canopy reflectance depends on species, age/growth, scale of measurement, distribution of leaves in a crown, leaf angle distribution, and many other factors. ==>> Key Point: LAI is important, but differences in LAI do not necessarily mean differences in species nor differences in canopy reflectance — and vice versa. LAI vs. canopy species at WRCCF, Thomas and Winner 2000. Shading refers to different canopy strata. Factor 2: architecture

Canopy BRF & LAI Differences: 

Canopy BRF & LAI Differences Consider ‘pathological’ example A: Two ‘razor blade’ canopies... Factor 1, Same leaves (black), different soil (white/black) Factor 2, Same ‘LAI’ in each canopy. Factor 3, Same view/illumination directions Factor 2: architecture, hypotheical example A Sensor, nadir view soil, white Sun shining down the rows of razor blades illuminates soil View down the rows of razor blade leaves Large BRF (white) Sensor, nadir view soil, black Sun shining down the rows of razor blades illuminates soil View down the rows of razor blade leaves Small BRF (black) Same LAI One canopy LAI value corresponds to two canopy reflectances ==>> Conclusion: the relationship between BRF and LAI is not unique <<==

Canopy BRF & LAI Differences: 

Canopy BRF & LAI Differences Consider ‘pathological’ example B: Two ‘razor blade’ canopies... Factor 1, Same leaf color (black), same soil (white) Factor 2, Different ‘LAI’ in each canopy. Factor 3, Same view/illumination directions Factor 2: architecture, hypotheical example B Sensor, nadir view soil, white Sun shining down the rows of razor blades illuminates soil View down the rows of razor blade leaves Large LAI Sensor, nadir view soil, white Sun shining down the rows of razor blades illuminates soil View down the rows of razor blade leaves Small LAI Same BRF One canopy BRF corresponds to two canopy LAI values ==>> Conclusion: the relationship between BRF and LAI is not unique <<==

Canopy BRF & LAI Differences: 

Canopy BRF & LAI Differences Consider ‘pathological’ example C: Two ‘razor blade’ canopies... Factor 1, Same leaves (black), same soil (white) Factor 2, Same ‘LAI = 1.0 in each canopy but different leaf angle distribution Factor 3, Same view/illumination directions Factor 2: architecture, hypotheical example C Sensor, nadir view soil, white Sun shining down the rows of razor blades illuminates soil View down the rows of razor blade ‘leaves’ LAI=1.0 Sensor, nadir view soil, white Sun shining down the rows of razor blades illuminates soil Razor blade ‘leaves’ form contiguous horizontal layer above soil Different BRF (White/black) One canopy LAI value corresponds to two canopy reflectances ==>> Conclusion: the relationship between BRF and LAI is not unique <<== LAI=1.0

LAI and Ecosystems: 

LAI and Ecosystems Factor 2: architecture, examples

LAI and Ecosystems: 

LAI and Ecosystems Factor 2: architecture, examples

Definition of Leaf Area Index, LAI: 

Definition of Leaf Area Index, LAI One sided green leaf area per unit ground area Example: Total square meters of one side of green leaves above 1.0 square meter of soil LAI units: [m2 of leaf area]/[m2 of ground] e.g. dimensionless soil 1.0 m2 Green leaves Factor 2: architecture

Leaf Angles Distribution: 

Leaf Angles Distribution Plants can dynamically change the angle of their leaves to increase or decrease the amount of EMR (and increase or decrease the heat loading). Leaves range from planophile (horizontally oriented) to erectophile (vertically oriented). Leaf angle probability density function is approximately spherical in many canopies i.e. canopy leaf area is distributed in angle like the area on a sphere. The angle of incident solar radiation and the angle of the leaf affect the at-sensor reflectance. Factor 2: architecture

LAI/Leaf Angle and Spectra: 

LAI/Leaf Angle and Spectra Asner, 1998 Factor 2: architecture MLA is Mean Leaf Angle

Leaf Angle Differences: 

Leaf Angle Differences Asner, 1998 Factor 2: architecture, examples

Leaf Shape: 

Leaf Shape Conclusion: The shape of leaves can also affect reflectance. P. Cull D. Tortosa Factor 2: architecture

Woody Matter: 

Woody Matter Amount of woody matter can influence spectra, albeit slightly (Asner, 1998). SAI: Stem area index. Factor 2: architecture

Crown Shape: 

Crown Shape Gerard and North 1997: modeled forests to look at red and NIR reflectance under different canopy conditions. Found wide, flat crowns (typical of tropical trees) were more reflective across all wavelengths than tall, skinny crowns (typical of northern conifers). Factor 2: architecture

Crown Shape: 

Crown Shape The shape of crowns is diagnostic of certain species. Example: coniferous (conical) vs. deciduous (spherical) Hyperspatial imagery can be used to assess the actual shape. Factor 2: architecture

Mapping Invasive Species: 

Mapping Invasive Species DiPietro, 2002 Putting it all together....example 1

Mapping Crop Types: 

Mapping Crop Types Clark et al. 1995: used AVIRIS, Tricorder and reference spectrum to differentiate different CO crops. Putting it all together....example 2

Scaling in remote sensing:: 

Scaling in remote sensing: Atomic/molecular properties (Factor 1): Absorption, transmission, molecular scattering Microscopic and small particle properties (Factor 1): Scattering (cellular and particulate) Macroscopic structure properties (Factor 2): BRDF, geometric optics Landscape properties (Factor 2): Mixed pixels, ecosystem structure Putting it all together.... Large small

Putting it all together....: 

Putting it all together.... Most of the fundamental work on the mechanisms of remote sensing involves lab or field spectrometers, or modeling approaches and a good understanding of physics. Most of the work on the applications of remote sensing involves aerial or satellite sensors and a good understanding of statistics. The connection between the two scales is important, but is not well understood.