1. Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project

2. Introduction Estimating biophysical characteristics of forested wetlands a hot topic in remote sensing. Estimating biophysical characteristics of forested wetlands a hot topic in remote sensing. Promising results have been found for measuring biomass, tree height, basal area, tree density, and forest class cover in previous studies. Promising results have been found for measuring biomass, tree height, basal area, tree density, and forest class cover in previous studies.

3. Introduction Roanoke River Floodplain, North Carolina Roanoke River Floodplain, North Carolina Sites consist of diverse assemblage of bottomland hardwoods and swamp forests that annually flood. Sites consist of diverse assemblage of bottomland hardwoods and swamp forests that annually flood.

4. Introduction Use of widely available satellite-borne synthetic aperture radar (SAR) sensors to determine if study of forests over broad geographic areas and complex environmental gradients is possible. Use of widely available satellite-borne synthetic aperture radar (SAR) sensors to determine if study of forests over broad geographic areas and complex environmental gradients is possible. This would provide important information about global change and represent the scientific basis for regional scale forest assessment. This would provide important information about global change and represent the scientific basis for regional scale forest assessment.

5. Introduction Benefits of SAR: Benefits of SAR: 1. It’s not attenuated by atmosphere 2. SAR backscatter is responsive to multiple structural elements of forest canopies.

6. Introduction Limitations: Limitations: 1. Previous studies data not widely available to most people. 2. SAR platforms produce single-band, single polarization imagery which exhibit strong relationships with forest bio- physical properties. 3. Variations in environmental conditions affect backscatter from forests, especially in flooded areas without in-situ data.

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

8. Objectives 1. How does sensitivity of SAR imagery to forest bio-physical properties differ for flooded and non-flooded forests? 2. Can multitemporal SAR imagery be used to estimate forest bio-physical attributes accurately? 3. Does integration of multispectral optical imagery with SAR data substantially improve the ability to detect forest properties? 4. What effect do other forest and surface properties have on radar backscatter from forested wetlands?

9. Methods 202 sites in the floodplain examined from 11 Radarsat, 2 ERS, and 1 JERS images. 202 sites in the floodplain examined from 11 Radarsat, 2 ERS, and 1 JERS images. Multitemporal data sets of Landsat TM for vegetation and soils from 116 of the sites were analyzed with SAR images. Multitemporal data sets of Landsat TM for vegetation and soils from 116 of the sites were analyzed with SAR images. Landsat TM images used for forest cover classification and integrated with SAR images. Landsat TM images used for forest cover classification and integrated with SAR images. SAR imagery was analyzed either flooded or non-flooded and by seasonality. SAR imagery was analyzed either flooded or non-flooded and by seasonality.

10. Methods In-situ 90x90m plots tested for density, basal area (BA), and leaf area index (LAI). In-situ 90x90m plots tested for density, basal area (BA), and leaf area index (LAI). Soil samples analyzed for organic matter and silt, sand, and clay %. Soil samples analyzed for organic matter and silt, sand, and clay %. All sites were collected with differentially corrected GPS coordinates. All sites were collected with differentially corrected GPS coordinates.

11. Statistical Analysis Multivariate linear statistical analysis to predict bio-physical properties from the SAR images. Multivariate linear statistical analysis to predict bio-physical properties from the SAR images. Analysis stratified by flooding status. Analysis stratified by flooding status. Three categories of analysis: Three categories of analysis: 1. Based on plots that were flooded on the same dates. 2. Based on plots that were not flooded on the same dates. 3. Based on plots that are flooded on some dates and not flooded on others.

12. Statistical Analysis Shapiro-Wilk test for normality of forest structure data. Shapiro-Wilk test for normality of forest structure data. Simple and Multiple regressions used to test relationship between forest structure and radar backscatter at P >0.05. Simple and Multiple regressions used to test relationship between forest structure and radar backscatter at P >0.05.

13. Results Relationships between forest structure and radar Scattering Flooded vs. Non-Flooded Correlations affected by stratification of data. Correlations affected by stratification of data. Correlations strongest for flooded areas, due to the high backscatter and double bounce scattering. Correlations strongest for flooded areas, due to the high backscatter and double bounce scattering.

14. Results Flooded vs. Non- Flooded Strong correlation between basal area and height in flooded sites and backscatter due to the flooding. Strong correlation between basal area and height in flooded sites and backscatter due to the flooding.

15. Results Leaf-on vs. Leaf-off Both conditions were responsive to forest structure especially for basal area. Both conditions were responsive to forest structure especially for basal area. Suggests seasonal differences may be predictable by using SAR. Suggests seasonal differences may be predictable by using SAR.

16. Results Wavelength, polarization, and incidence angle Incidence angle-very little difference Incidence angle-very little difference Polarization- CVV and CHH in all three sensors showed they were useful for detecting forest properties in different polarizations. Polarization- CVV and CHH in all three sensors showed they were useful for detecting forest properties in different polarizations. C and L bands showed strong responsiveness. C and L bands showed strong responsiveness.

17. Results Estimating forest structure using multitemporal, multisensor SAR LAI, crown depth, and crown closure better analyzed with optical imagery. LAI, crown depth, and crown closure better analyzed with optical imagery. Forest height in flooded conditions responsive. Forest height in flooded conditions responsive.

18. Results Estimating forest structure using multitemporal, multisensor SAR Responsiveness of SAR to BA under both flooded and non- flooded conditions with leaf on and leaf off. Responsiveness of SAR to BA under both flooded and non- flooded conditions with leaf on and leaf off. Responsive to estimate BA even in high BA plots. Responsive to estimate BA even in high BA plots.

19. Results Integration with optical data Landsat TM images with SAR images ran to see if improved model performance. Landsat TM images with SAR images ran to see if improved model performance. NDVI used to determine vegetation properties. NDVI used to determine vegetation properties. During leaf-on imagery, model improved slightly as with flooded times. During leaf-on imagery, model improved slightly as with flooded times.

20. Results Integration with optical data Canopy height improved the model unexpectedly, but only slightly. Canopy height improved the model unexpectedly, but only slightly. Overall model improved only slightly. Overall model improved only slightly.

21. Results Relationship between backscatter and other variables Land cover type Relationships were not strong, and very few classes exhibited statistically significant differences. Relationships were not strong, and very few classes exhibited statistically significant differences. Forest types are more closely related to the average environmental conditions. Forest types are more closely related to the average environmental conditions.

22. Results Relationship between backscatter and other variables Only Tupelo-Cypress forests have distinctly different backscatter responses, due to a high BA. Only Tupelo-Cypress forests have distinctly different backscatter responses, due to a high BA.

23. Results Soil properties Environmental factors other than flooding and vegetation affect backscatter from forests. Environmental factors other than flooding and vegetation affect backscatter from forests. Non-flooded areas only analyzed. Non-flooded areas only analyzed. Very few correlations were determined between soil properties and backscatter. Very few correlations were determined between soil properties and backscatter. Clay and organic matter had the highest correlations, due to high soil moisture content. Clay and organic matter had the highest correlations, due to high soil moisture content.

24. Conclusions SAR imagery offers some usefulness in measuring bio-physical properties of forested wetlands. SAR imagery offers some usefulness in measuring 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 than forest composition. SAR imagery more sensitive to forest structure than forest composition. Results also showed stratification of data between flooded and non-flooded sites is extremely important. Results also showed stratification of data between flooded and non-flooded sites is extremely important. Optical imagery only improved model slightly. Optical imagery only improved model slightly. Results indicate the necessity for caution when using SAR data to characterize forest properties over large and diverse areas. Results indicate the necessity for caution when using SAR data to characterize forest properties over large and diverse areas.

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