Estimating forest structure in Estimating forest structure in wetlands using multitemporal wetlands using multitemporal

SAR SAR by Philip A. Townsendby Philip A. Townsend

Neal SimpsonNeal Simpson

ES 5053ES 5053

Final ProjectFinal Project

IntroductionIntroduction

Estimating biophysical characteristics Estimating biophysical characteristics of forested wetlands a hot topic in of forested wetlands a hot topic in remote sensing.remote sensing.

Promising results have been found for Promising results have been found for measuring biomass, tree height, basal measuring biomass, tree height, basal area, tree density, and forest class area, tree density, and forest class cover in previous studies.cover in previous studies.

IntroductionIntroduction

Roanoke River Roanoke River Floodplain, North Floodplain, North CarolinaCarolina

Sites consist of Sites consist of diverse assemblage diverse assemblage of bottomland of bottomland hardwoods and hardwoods and swamp forests that swamp forests that annually flood.annually flood.

IntroductionIntroduction

Use of widely available satellite-borne Use of widely available satellite-borne synthetic aperture radar (SAR) sensors to synthetic aperture radar (SAR) sensors to determine if study of forests over broad determine if study of forests over broad geographic areas and complex geographic areas and complex environmental gradients is possible.environmental gradients is possible.

This would provide important information This would provide important information about global change and represent the about global change and represent the scientific basis for regional scale forest scientific basis for regional scale forest assessment.assessment.

IntroductionIntroduction

Benefits of SAR:Benefits of SAR:

1.1. It’s not attenuated by atmosphereIt’s not attenuated by atmosphere

2.2. SAR backscatter is responsive to SAR backscatter is responsive to multiple structural elements of multiple structural elements of forest canopies.forest canopies.

IntroductionIntroduction Limitations:Limitations:

1.1. Previous studies data not widely Previous studies data not widely available to most people.available to most people.

2.2. SAR platforms produce single-band, SAR platforms produce single-band, single polarization imagery which exhibit single polarization imagery which exhibit strong relationships with forest bio-strong relationships with forest bio-physical properties.physical properties.

3.3. Variations in environmental conditions Variations in environmental conditions affect backscatter from forests, affect backscatter from forests, especially in flooded areas without in-situ especially in flooded areas without in-situ data.data.

ObjectivesObjectives

Evaluate the capabilities of Evaluate the capabilities of multitemporal SAR from Radarsat, ERS, multitemporal SAR from Radarsat, ERS, and JERS for estimating bio-physical and JERS for estimating bio-physical properties of forested wetlands on the properties of forested wetlands on the lower Roanoke River floodplain, North lower Roanoke River floodplain, North Carolina.Carolina.

ObjectivesObjectives

1.1. How does sensitivity of SAR imagery to forest How does sensitivity of SAR imagery to forest bio-physical properties differ for flooded and bio-physical properties differ for flooded and non-flooded forests?non-flooded forests?

2.2. Can multitemporal SAR imagery be used to Can multitemporal SAR imagery be used to estimate forest bio-physical attributes estimate forest bio-physical attributes accurately?accurately?

3.3. Does integration of multispectral optical Does integration of multispectral optical imagery with SAR data substantially improve imagery with SAR data substantially improve the ability to detect forest properties?the ability to detect forest properties?

4.4. What effect do other forest and surface What effect do other forest and surface properties have on radar backscatter from properties have on radar backscatter from forested wetlands?forested wetlands?

MethodsMethods

202 sites in the floodplain examined from 202 sites in the floodplain examined from 11 Radarsat, 2 ERS, and 1 JERS images.11 Radarsat, 2 ERS, and 1 JERS images.

Multitemporal data sets of Landsat TM for Multitemporal data sets of Landsat TM for vegetation and soils from 116 of the sites vegetation and soils from 116 of the sites were analyzed with SAR images.were analyzed with SAR images.

Landsat TM images used for forest cover Landsat TM images used for forest cover classification and integrated with SAR classification and integrated with SAR images.images.

SAR imagery was analyzed either flooded SAR imagery was analyzed either flooded or non-flooded and by seasonality.or non-flooded and by seasonality.

MethodsMethods

In-situ 90x90m plots tested for In-situ 90x90m plots tested for density, basal area (BA), and leaf density, basal area (BA), and leaf area index (LAI).area index (LAI).

Soil samples analyzed for organic Soil samples analyzed for organic matter and silt, sand, and clay %.matter and silt, sand, and clay %.

All sites were collected with All sites were collected with differentially corrected GPS differentially corrected GPS coordinates.coordinates.

Statistical AnalysisStatistical Analysis Multivariate linear statistical analysis to Multivariate linear statistical analysis to

predict bio-physical properties from the predict bio-physical properties from the SAR images.SAR images.

Analysis stratified by flooding status.Analysis stratified by flooding status. Three categories of analysis:Three categories of analysis:1.1. Based on plots that were flooded on the Based on plots that were flooded on the

same dates.same dates.2.2. Based on plots that were not flooded on Based on plots that were not flooded on

the same dates.the same dates.3.3. Based on plots that are flooded on some Based on plots that are flooded on some

dates and not flooded on others.dates and not flooded on others.

Statistical AnalysisStatistical Analysis

Shapiro-Wilk test for normality of Shapiro-Wilk test for normality of forest structure data.forest structure data.

Simple and Multiple regressions used Simple and Multiple regressions used to test relationship between forest to test relationship between forest structure and radar backscatter at P structure and radar backscatter at P >0.05.>0.05.

ResultsResults

Relationships between forest Relationships between forest structure and radar Scatteringstructure and radar Scattering

Flooded vs. Non-FloodedFlooded vs. Non-Flooded Correlations affected by stratification of Correlations affected by stratification of

data.data.

Correlations strongest for flooded areas, Correlations strongest for flooded areas, due to the high backscatter and double due to the high backscatter and double bounce scattering.bounce scattering.

ResultsResults

Flooded vs. Non-Flooded vs. Non-FloodedFlooded

Strong correlation Strong correlation between basal area between basal area and height in and height in flooded sites and flooded sites and backscatter due to backscatter due to the flooding.the flooding.

ResultsResults

Leaf-on vs. Leaf-offLeaf-on vs. Leaf-off Both conditions were responsive to Both conditions were responsive to

forest structure especially for basal forest structure especially for basal area.area.

Suggests seasonal differences may Suggests seasonal differences may be predictable by using SAR.be predictable by using SAR.

ResultsResults

Wavelength, polarization, and Wavelength, polarization, and incidence angleincidence angle

Incidence angle-very little differenceIncidence angle-very little difference Polarization- CVV and CHH in all three Polarization- CVV and CHH in all three

sensors showed they were useful for sensors showed they were useful for detecting forest properties in different detecting forest properties in different polarizations.polarizations.

C and L bands showed strong C and L bands showed strong responsiveness. responsiveness.

ResultsResults

Estimating forest structure Estimating forest structure using multitemporal, using multitemporal, multisensor SARmultisensor SAR

LAI, crown depth, and crown LAI, crown depth, and crown closure better analyzed with closure better analyzed with optical imagery.optical imagery.

Forest height in flooded Forest height in flooded conditions responsive.conditions responsive.

ResultsResults

Estimating forest structure Estimating forest structure using multitemporal, using multitemporal, multisensor SARmultisensor SAR

Responsiveness of SAR to BA Responsiveness of SAR to BA under both flooded and non-under both flooded and non-flooded conditions with leaf flooded conditions with leaf on and leaf off. on and leaf off.

Responsive to estimate BA Responsive to estimate BA even in high BA plots.even in high BA plots.

ResultsResults

Integration with optical dataIntegration with optical data Landsat TM images with SAR images Landsat TM images with SAR images

ran to see if improved model ran to see if improved model performance.performance.

NDVI used to determine vegetation NDVI used to determine vegetation properties.properties.

During leaf-on imagery, model During leaf-on imagery, model improved slightly as with flooded improved slightly as with flooded times.times.

ResultsResults

Integration with optical dataIntegration with optical data Canopy height improved the model Canopy height improved the model

unexpectedly, but only slightly.unexpectedly, but only slightly. Overall model improved only slightly.Overall model improved only slightly.

ResultsResults

Relationship between backscatter Relationship between backscatter and other variablesand other variables

Land cover typeLand cover type Relationships were not strong, and very Relationships were not strong, and very

few classes exhibited statistically few classes exhibited statistically significant differences.significant differences.

Forest types are more closely related to Forest types are more closely related to the average environmental conditions.the average environmental conditions.

ResultsResults

Relationship between backscatter and Relationship between backscatter and other variablesother variables

Only Tupelo-Cypress forests have Only Tupelo-Cypress forests have distinctly different backscatter responses, distinctly different backscatter responses, due to a high BA.due to a high BA.

ResultsResults

Soil propertiesSoil properties Environmental factors other than flooding Environmental factors other than flooding

and vegetation affect backscatter from and vegetation affect backscatter from forests.forests.

Non-flooded areas only analyzed.Non-flooded areas only analyzed. Very few correlations were determined Very few correlations were determined

between soil properties and backscatter.between soil properties and backscatter. Clay and organic matter had the highest Clay and organic matter had the highest

correlations, due to high soil moisture correlations, due to high soil moisture content.content.

ConclusionsConclusions SAR imagery offers some usefulness in measuring SAR imagery offers some usefulness in measuring

bio-physical properties of forested wetlands.bio-physical properties of forested wetlands. Most effective for BA and Canopy crown.Most effective for BA and Canopy crown. SAR imagery more sensitive to forest structure SAR imagery more sensitive to forest structure

than forest composition.than forest composition. Results also showed stratification of data Results also showed stratification of data

between flooded and non-flooded sites is between flooded and non-flooded sites is extremely important.extremely important.

Optical imagery only improved model slightly.Optical imagery only improved model slightly. Results indicate the necessity for caution when Results indicate the necessity for caution when

using SAR data to characterize forest properties using SAR data to characterize forest properties over large and diverse areas.over large and diverse areas.

ConclusionsConclusions

The launch of multi-polarization The launch of multi-polarization satellite SAR system Envisat-1, will satellite SAR system Envisat-1, will offer cross-polarized imagery that offer cross-polarized imagery that hopefully will improve the ability to hopefully will improve the ability to map forest properties over large map forest properties over large areas using single-date SAR.areas using single-date SAR.