ES 5053 Remote Sensing Final Project
Neal Simpson
Title
Estimating forest structure in wetlands using multi-temporal SAR
Journal
Remote Sensing of the Environment 79 (2002) 288-304
Author
Philip A. Townsend
Focus Categories
Multi-temporal radar, biophysical characteristics, forested wetlands, basal area, canopy
height
Abstract
Data from 202 forested plots on the Roanoke River floodplain in North Carolina
were used to asses the capabilities of multi-temporal radar imagery for estimating
biophysical characteristics of forested wetlands. The research was designed to determine
the potential for using data from current data from satellite SAR sensors to study forests
over a wide geographic areas and different environmental backgrounds. Data from
Radarsat, ERS-1, and JERS-1 was used. Sites were compared on common flood status
areas (flooded and non-flooded). The SAR imagery was compared to Landsat TM
imagery to determine forest biophysical properties, and if it was comparable for reliable
results. Statistical analysis including regression models were run to determine if the
possibility of the multi-temporal, multi-sensor data would be comparable to field tests
and optical imagery types collected. If this analysis is proven to be accurate, then this
could be used for future research for biophysical forest properties over vast geographic
regions with complex environmental gradients.
Introduction
Broad-scale estimation of the bio-physical properties of forests is a hot topic in
remote sensing. This type of research provides important information on global change
and scientific basis for regional scale forest assessment. Use of synthetic aperture radar
(SAR) has been interesting to detect forest properties. There are benefits and limitations
to the use of SAR sensors. The benefits are non-attenuation in the atmosphere and SAR
backscatter is responsive to multiple structural elements of forest canopies. Use of SAR
has shown promising results in analyzing biomass (Dobson et al., 1992), basal area
(Dobson et al., 1995), tree height (Dobson et al., 1995), tree diameter and density
(Hyyppa et al, 1997), and forest cover class (Dobson et al., 1995). SAR limitations
though fall into three categories: 1) Data that has been used in most studies is not widely
available or geographically extensive. 2) Widely available data that has been used is
produced from single-band, single-polarization imagery. This type of imagery has been
demonstrated that multi-channel, cross polarized data exhibit the strongest relationships
with forest bio-physical properties. 3) Variations in environmental conditions affect
backscatter from forests. Some surface properties sometimes exert a stronger influence
on backscatter than forest characteristics. This is especially dramatic in flooded forests,
where inundated areas exhibit higher backscatter than non-flooded areas due to increase
in double-bounce scattering. Effects of differences in soil and surface properties beneath
non-flooded forests are more subtle and are therefore are difficult to address without
detailed in-situ data. For broad scale analyses, detailed soil/surface properties are not
typically available.
Objectives
Goal was to evaluate the capabilities of multi-temporal SAR imagery from
Radarsat, ERS-1, and JERS-1 for estimating biophysical properties of forested wetlands
in the Lower Roanoke River floodplain in North Carolina. They attempted to address
several issues with using SAR data to study forest structure with the following:
1. How does the sensitivity of SAR imagery to forest biophysical properties differ for
flooded and non-flooded forests?
2. Can multi-temporal SAR imagery be used to estimate forest biophysical
characteristics accurately?
3. Does the integration of multi-spectral optical imagery with SAR data substantially
improve the ability to detect forest properties?
4. What effect do other forest and surface properties, such as species composition and
soil characteristics, have on radar backscatter from forested wetlands?
Materials and Methods
The study site for this project was the Roanoke River floodplain in North
Carolina. The area consists of wide range of forested wetlands, with various species of
hardwood trees and swamped forests from the North Carolina-Virginia Border down to
the Bay leading out to the Atlantic Ocean. The species of trees changes drastically from
higher elevation areas to flooded areas in the wetlands obviously due to the water content
in the soils. 202, 90 x 90m test plot sites located along the Roanoke River had field data
collected during 1995-6 time frame, including density, basal area, composition data using
the Bitterlich variable plot method. In the 90m plots five points were sampled for basal
areas of the various species. Also, canopy closure was assessed in almost all plots for all
species. In 39 of the plots forest canopies were measured for top and bottom of canopy to
determine canopy depth In 51 of the plots pictures were taken of the forest canopy using
a fish eye lens to measure leaf area index, using a video digitizing system the leaf area
index was calculated from the negatives. All 202 plots had their coordinates calculated
by using differentially corrected GPS. A second data set was collected from 116 forest
plots in which cover, density, and basal area were calculated, as well as the top 10cm of
the soil underneath the litter of the trees. The soil samples were analyzed for organic
matter content, and percentage of sand, silt, and clay. All field data was integrated with
an image based vegetation classification of the study area.
SAR data of the field sites was acquired from ERS-1, Radarsat, and JERS-1
satellites. All images were slant-to-ground range corrected and geo-referenced to UTM
coordinates. For all of the images, the 12.5m pixel size was used for the spatial
resolution, but the actual resolution was closer to 30m after analysis. For all of the three
satellites, imagery was collected at different times of the season, and different stages of
flooding. With the different times of the season, different stages of flooding, and
differences in phrenology, statistical analysis had to take these differences into account.
Imagery was collected between 1993 and 1998, but none of the sampled plots had any
changes (cutting or diseases), so they had valid use for the imagery and field data. Soil
moisture data was not available for the non-flooded areas, which may have substantially
influenced data in radar scattering.
Hydrologic state of each plot is stratified for each image acquisition date. For
JERS-1 and Radarsat imagery forested areas were classified as flooded or non-flooded
after speckle reduction was calculated. Flood classification was validated by wells
located throughout the plot areas. For ERS-1 imagery flood classification was not used
due to ERS-1 imagery inability to reliably to detect inundation beneath forest canopies,
but was indirectly incorporated into flood status.
Landsat TM imagery of seven of the plots was also used to develop the forest
cover classification, and compared to SAR data to determine the forest biophysical
properties. Landsat imagery was collected from Mar – Aug of 1993 and represents
phenological changes in the plots. The images were geo-referenced to UTM coordinates
and have a 30m spatial resolution. The imagery had dark object corrections conducted
and atmospheric scattering through haze removal. These images were used to determine
the NDVI of bands 3 and 4.
Multivariate statistical analysis was used to test hypotheses that multi-temporal,
multi-sensor SAR could be used to predict biophysical properties of the forests in the
study areas. Multi-temporal backscatter measurements were used as independent
variables, while the stand characteristics were use as the dependent variables in the
statistical analysis. Forest properties used in the tests were basal area, percent cover, leaf
area index, top and bottom height of canopy, and canopy depth. Major differences in
radar scattering from these forests are due to differences in flood inundation, so the
statistical analyses of the plot data were stratified by flood status. So, three categories of
analysis were derived: 1) analysis based on plots that were all flooded on the same dates,
2) analysis based on non-flooded plots on the same dates, and 3) analysis based on plots
that are flooded on the same dates and not flooded on others.
These three categories allowed complete coverage of the study area to attain the
model forest biophysical attributes spatially. Basal area and cover were split randomly
into 136 plots for model development, and 66 plots for model validation. For leaf area
index and canopy heights, the stratification of analysis meant that there were not enough
plots to split the sample into both test and validation sets, so cross validation was
accomplished by jackknifing the data. Normality of forest structure was accomplished by
the Shapiro-Wilk statistics. Collinearity was analyzed between forest properties and
radar backscatter, and to examine relationships between soil properties and radar
backscatter. Simple and multiple linear regressions were used to test the relationships
between forest properties and radar backscatter. Ratios between the calibrated scenes
were calculated as interaction variables in the models. Multiple linear regressions were
used to predict forest stand characteristics as a function of the multi-temporal, multisensor data. Separate regression models were used for high basal area stands (BA >
55m2/ha), due to sensitivity of radar at higher biomass levels. Generalized linear models
were used to test whether differences in radar backscatter could be attributed to
differences in vegetation community composition for the Landsat TM imagery.
Results
There were two main things analyzed in this experiment, 1) relationships between
forest structure and radar scattering, and 2) relationships between backscatter and other
variables. The relationships between forest structure and radar scattering was broke
down into smaller categories to determine which part of forest structures were able to be
determined by using SAR sensors. Stratification of data between flooded imagery and
non-flooded imagery was used to determine if there would be any difference between the
two data sets. The data sets were stratified in flooded plots, non-flooded plots, and for all
plots. There was apparent relationship among stratification of the data sets. Basal area
showed strong correlations between backscatter and forest structure in all plots, but were
stronger in the flooded plots due to the double-bounce backscatter. Tupelo-Cypress
swamps showed the best correlations with basal area, because they are typically flooded
swamps. Other forest variables appear strongest for plots classified as flooded, especially
basal area and height, with the strongest correlations with backscatter. These can be
explained by the presence of continuous flooding which minimizes backscatter
differences, and the increased responsiveness of SAR to structural components in flooded
forests from the proportionally higher level of trunk-ground and crown-ground
interactions that occur as consequence of double-bounce scattering from a flooded
surface.
Leaf-on versus leaf-off correlations were run to determine if seasonal changes
could be used to determine forest structural properties by comparing them against the
satellite backscatter. Basal area showed a strong correlation with forest structure
properties (especially basal area) for both leaf-on and leaf-off conditions, which suggests
differences between scattering during seasons could be used for predicting forest
structures by using SAR sensors.
They also looked at what affect the wavelength, polarization, and incidence angle
may have in determining forest structure properties. Incidence angle had only slight
differences to detect forest properties, although the smallest incidence angle exhibited the
lowest sensitivity, while the steepest incidence angle had the strongest sensitivity. Both
C and L bands and CVV and CHH polarizations appeared to be useful in detecting forest
properties because backscatter was responsive to the forest variables. Since all of these
sensors exhibited association with forest properties indicates potential for using
multitemporal, multisensor approach to estimate forest structure. Cost for these images is
the problem and may be cost prohibitive to do this type of analysis.
How does multitemporal, multisensor SAR compare in estimating forest structure
properties. Leaf area index, canopy depth, and crown closure was determined to be better
analyzed with optical imagery (Landsat TM), so analysis with SAR was not conducted
for these. Basal area was the strongest of correlations, especially in flooded forest plots.
The highest correlation model was for all plots flooded and non-flooded, which indicates
both plot areas can be used to determine basal area for forests. Leaf on images showed
correlations for a few of the plots with basal area, showing the C-band could determine
canopy foliage. The ERS-1 CVV image though showed it was useful in the early spring
images, but not in the late summer images from attenuation of foliage. The JERS-1 Lband penetrated the canopy and could estimate the sub-canopy forest structure, especially
basal area in high basal area plots. This shows the longer wavelength SAR for measuring
structure under high biomass areas. The ground truth basal area was run against SAR
determined an error of about 5m2/ha, suggesting good results but needs improvement.
Forest height was also responsive for the SAR sensors, especially in flooded
areas. For non-flooded forests summer images were the best results, while for flooded
areas spring and fall images were the best correlated. Non-flooded forests only were
useful in determining height to the bottom of the canopy, which was unexpected.
Once the SAR imagery was analyzed, it was wondered if integration of optical
imagery could improve the SAR only models. NDVI was used because it’s strong
relationship with vegetation properties. For basal area, the early spring images showed
the greatest improvement for the models, due to the small leaves on the trees which are
not incorporated into the basal area. Neither leaf-on nor leaf-off scenes improved the
model substantially. An improvement to the overall model was less than 0.1, therefore
showing integration of optical imagery did not help with analysis of high basal areas. It
was not expected that the Landsat TM data would improve the overall model for tree
height variables, although surprisingly some of the optical imagery slightly improved
canopy height for non-flooded areas. So, although improvements of the overall models
for basal area and tree height were slight, the use of optical imagery as supplemental data
can be helpful.
Once the forest structure properties were analyzed in the SAR images, what was
the effect on backscatter against other variables? Land cover type was the first variable
that was examined. Forest biophysical properties were not stratified by forest types since
most of the sites were strongly dominated by deciduous forests. Tree types may produce
important differences in backscatter between the different types of forests. Vegetation
classification was determined using NDVI into 20 different classes to see if backscatter
would differ between the forest class types. The only classes which backscatter differed
were expected to be flooded on each date, and those that were not flooded, which shows
the need for stratification of flooded and non-flooded dates. Even when classes were
stratified the correlations were weak, except for Tupelo-Cypress swamps. The TupeloCypress swamps have distinctly different backscatter measurements to the other forest
types, probably due to the high basal area. Analysis showed that the reason for the
difference in Tupelo-Cypress swamps was from the basal area and not from the
backscatter. Differences between forest types are more closely related to the average
environmental conditions of the type rather than differences in composition.
Modeling studies have indicated that environmental factors other than flood
inundation and vegetation structure substantially affect backscatter response from forests.
So, they also analyzed the soil properties of the sample sites to determine what affect they
would have on backscatter of the SAR sensors. Previous studies have shown the
sensitivity of SAR to surface parameters is most pronounced for co-polarized data,
especially for L-band backscatter. No a lot of surface data was available for the soils at
the sample sites, except for soil texture and organic matter content in the soils. Only nonflooded sites were used in this analysis because the flooded plots were thought to have
little interactions between the soil properties and backscatter. Soil moisture is highly
correlated with soil texture and organic matter content, so correlations were ran against
the soil properties and backscatter. Few correlations were found to be significant
between the soil properties and backscatter. The most frequent correlations were found
between backscatter and organic matter and clay content, due to the ability of these to
collect water strongly. The correlations were weak overall, but it shows that not only
empirical studies control the backscatter response of SAR sensors in forests. Field tests
would be important to cross validate the results of the SAR backscatter against physical
properties of the soils.
Conclusions
The results of this experiment demonstrates the SAR data of satellite SAR sensors
offer utility for measuring biophysical properties of forested wetlands. The most
effective correlations were found to be with basal area, canopy height to the top and
bottom of the canopy crown. Integration of optical imagery (Landsat TM) was found to
be helpful in improving the SAR model, but the addition in correlation of the model was
minimal, especially for the cost of the imagery. It was also determined that forest
structure is more sensitive to SAR sensors than that of forest composition. In forest with
predominantly broadleaf deciduous trees, there was very little difference in backscatter
among the species. The only forest type that showed difference in backscatter was
Tupelo-Cypress, but this was a result of basal area more than backscatter. In this type of
analysis it was demonstrated that stratification of plots between flooded and non-flooded
was necessary for accurate results. The results showed that correlations between forest
bio-physical properties and backscatter were stronger in flooded areas, due to doublebounce backscatter. Non-flooded areas also showed correlations with the bio-physical
properties and backscatter, but were not as strong. Environmental conditions may be
difficult to analyze especially without in-situ data over large geographic regions. This
research shows the need for data that is a mix of leaf-on and leaf-off, and flooded and
non-flooded data to accurately show accurate results for forested wetlands. The results
were encouraging but also show the need for more comprehensive satellite SAR systems
to obtain accurate data to analyze bio-physical properties in forested wetlands.
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