Assessing spatial and temporal variations in surface soil moisture in fire-disturbed black spruce forests in Interior Alaska using spaceborne synthetic aperture radar imagery — Implications for post-fire tree recruitment

Recent studies [Bourgeau-Chavez, L.L., Kasischke, E.S., Riordan, K., Brunzell, S.M., Nolan, M., Hyer, E.J., Slawski, J.J., Medvecz, M., Walters, T., and Ames, S. (in press). Remote monitoring of spatial and temporal surface soil moisture in fire disturbed boreal forest ecosystems with ERS SAR imagery. Int. J. Rem. Sens.] demonstrated that ERS SAR imagery can be used to estimate surface soil moisture in recently burned black spruce forests in interior Alaska. We used this relationship to analyze the intra- and inter-annual variations surface soil moisture in two burned black spruce forests in Alaska. The results of this study showed distinct seasonal and longer-term trends in soil moisture in the two sites, with the site that burned in 1994 having higher soil moisture than the site that burned in 1999. The differences in soil moisture between the sites were related to landscape-scale variations in soil drainage and seasonal permafrost thawing. Finally, we found that the 1999 site had dramatically lower levels of tree recruitment (both aspen and black spruce) than the 1994 site as a result of the lower soil moisture levels. These results show that the ERS SAR and similar systems can be used to monitor a site characteristic that is important to understanding changes in the ecosystem community structure that result from variations in climate and the fire regime in the boreal region.     

The detection and mapping of Alaskan wildfires using a spaceborne imaging radar system

The study presented here focuses on using a spaceborne imaging radar, ERS-1, for mapping and estimating areal extent of fires which occurred in the interior region of Alaska. Fire scars are typically 3 to 6 dB brighter than adjacent unburned forests in the ERS-1 imagery. The enhanced backscatter from burned areas was found to be a result of high soil moisture and exposed rough ground surfaces. Fire scars from 1979 to 1992 are viewed in seasonal ERS-1 synthetic aperture radar (SAR) data obtained from 1991 to 1994. Three circumstances which influence the detectability of fire scars in the ERS-1 imagery are identified and examined; seasonality of fire scar appearances, fires occurring in mountainous regions, and fires occurring in wetland areas. Area estimates of the burned regions in the ERS-1 imagery are calculated through the use of a Geographic Information System (GIS) database. The results of this analysis are compared to fire records maintained by the Alaska Fire Service (AFS) and to estimates obtained through a similar study using the Advanced Very High Resolution Radiometer (AVHRR) sensor.     

Effects of soil moisture and water depth on ERS SAR backscatter measurements from an Alaskan wetland complex

We conducted a preliminary investigation of the response of ERS C-band SAR backscatter to variations in soil moisture and surface inundation in wetlands of interior Alaska. Data were collected from 5 wetlands over a three-week period in 2007. Results showed a positive correlation between backscatter and soil moisture in sites dominated by herbaceous vegetation cover (r = 0.74, p < 0.04). ERS SAR backscatter was negatively correlated to water depth in all open (non-forested) wetlands when water table levels were more than 6 cm above the wetland surface (r = − 0.82, p < 0.001). There was no relationship between backscatter and soil moisture in the forested (black spruce-dominated) wetland site. Our preliminary results show that ERS SAR data can be used to monitor variations in hydrologic conditions in high northern latitude wetlands (including peatlands), particularly sites with sparse tree cover.     

Use of multitemporal ERS-2 SAR images for identification of burned scars in south-east Asian tropical rainforest

Evaluation of an area severely affected by fires in 1998 using a multitemporal series of ERS-2 Synthetic Aperture Radar (SAR) images showed that fire induced changes of the vegetation cover strongly affected C-band radar backscatter. We investigated the changes in radar backscatter over a period of ten months in areas of interest that represented different land-cover types at a study site in East Kalimantan, Indonesia. The impact of fire was found to cause a strong decrease in backscatter (2-5 dB) for all land-cover classes while areas not affected by fire showed only slight variations in backscatter (maximum 0.5 dB). Ground and aerial evidence suggests that the marked decrease in backscatter can be attributed to the removal of the vegetation cover and subsequently higher contribution of backscatter from dry soil. After the onset of rain the radar backscatter increased to 5.5 dB in areas severely affected by fire while in unburned forests it returned to values similar to those before the drought. Burned scars could be identified visually in multitemporal principal component analysis-enhanced ERS SAR colour composites.     

Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

The effect of savanna fires on SAR backscatter in northern Australia

This paper investigates the potential for utilizing multi-frequency, quad-polarized Synthetic Aperture Radar (SAR) for delineation of fire scars in the tropical savannas of northern Australia. Fire regimes and regional land management in the study area are contentious issues for the multiple stakeholders. Thus, the ability to accurately map fire scars and assess impacts on the mosaic of vegetation community types is crucial for effective landscape management. In this study identification of a fire scar resulting from a late dry season fire was assessed using TopSAR data. The assessment was achieved through comparison with optical datasets acquired both pre-burn and post-burn. By examining five representative vegetation communities, it was concluded that only C-band SAR data were affected sufficiently by fire in this environment to detect and map fire scars. Despite the intensity of the fire event, the resistance of vegetation communities to fire damage resulted in insignificant changes to the L- and P-band SAR data. Further investigation is required to determine if this behaviour can be exploited to improve the above-ground woody biomass mapping currently reliant on optical data.     

Short contributions

Studies of ERS-1 synthetic aperture radar (SAR) imagery have shown that fire scars in Alaskan forests are significantly brighter (3–6 dB) than surrounding unburned forest. The signature varies seasonally and changes as vegetation re-establishes on the site over longer time periods (>5years). Additionally, it is known that soil water content typically increases following forest fires due to changes in evapotranspiration rates and melting of the permafrost.

The objective of this study was to understand the relation between soil water content and the ERS-1 SAR signature at fire-disturbed sites. To accomplish this objective, we compared soil water in six burned black spruce (Picea mariana (Mill.) B.S.P.) forest sites in interior Alaska to ERS-1 SAR backscalter measurements. The six sites are of various age since burn. Soil water was periodically measured at each site during the summer of 1992 and at one site in 1993 and 1994 when the ERS-1 imaging radar was scheduled to pass overhead. Results indicate that a positive linear relation exists between soil water content and the SAR backscatter coefficient in young burns ( < ～4years). Older burns do not show this relation, a result of vegetation establishment following the burn. This interaction between soil moisture condition and ERS-1 SAR backscatter shows great potential for measuring soil water content and monitoring seasonal variations in soil water content in black spruce sites recently disturbed by wildfire.     

Assessment of polarimetric SAR data for discrimination between wet versus dry soil moisture conditions

Radarsat-2 imagery from extreme dry versus wet conditions are compared in an effort to determine the value of using polarimetric synthetic aperture radar (SAR) data for improving estimation of fuel moisture in a chronosequence of Alaskan boreal black spruce ecosystems (recent burns, regenerating forests dominated by shrubs, open canopied forests, moderately dense forest cover). Results show strong distinction between wet and dry conditions for C-HH and C-LR polarized backscatter, and Freeman–Durden and van Zyl surface bounce decomposition parameters (35–65% change for all but the dense spruce site). These four SAR variables have high potential for evaluation of within site surface soil moisture, as well as for relative distinction between wet and dry conditions across sites for lower biomass and sparse canopy forested sites. However, for any given test site except the shrubby regrowth site, van Zyl volume, surface, and double bounce scattering all result in similar percentage increases from dry to wet soil condition. This indicates that for most of these test sites/cases moisture enhances the magnitude of the return for all scattering mechanisms evaluated. Thus, differences in scattering from the interaction of biomass, surface roughness, and moisture condition across sites remains an issue and backscatter due to surface roughness or biomass cannot be uniquely estimated. In contrast, the Cloude–Pottier C-band decomposition variables appear invariant to soil moisture, but may instead account for variations in ecosystem structure and biomass. Further investigation is needed, as results warrant future research focused on evaluation of multiple polarimetric parameters in algorithm development.     

An approach for assessing boreal forest conditions based on combined use of satellite SAR and multi-spectral data

The degree and spatial distribution of boreal forest ecosystem degradation in Russia are not well known. The objective of this study is to develop an interpretation basis for analysis of satellite remote sensing data using a set of indicators characterizing the ecological situation and the degree of industrial pollution. European Remote Sensing Satellite (ERS) Synthetic Aperture Radar (SAR) and Landsat Multi-Spectral Scanner (MSS) data are used in combination for this purpose, along with an exceptionally extensive in situ data set of ground measurements of spectral radiance of pine biocenose components, and the results of moss chemistry and bio-indicator studies from the ecologically stressed St Petersburg region. It is shown that ERS SAR images provide an assessment of forested area distribution and forest type classification. The main factors of variability in parameters such as Normalized Difference Vegetation Index (NDVI) that are most strongly related to in situ indicators reflecting the state of the forest are identified. A supervised classification of forest degradation was performed on the basis of the NDVI values from the Landsat images. The results obtained make it possible to specify the areas at a local level and perform regional assessments. The potential for multi-temporal ERS SAR and multi-spectral sensor observations to trace the dynamics of changes in forest ecosystems is evaluated.     

Radarsat data analysis for monitoring and evaluation of irrigation projects in the monsoon

The availability of multi-look angle HH polarization C band SAR data of different swaths and resolutions from Radarsat, in addition to VV polarization C band SAR data from ERS, has raised hopes for separating individual crops and for estimating soil moisture, enabling monitoring and evaluation of irrigation projects even under cloud cover conditions. Under the Radarsat ADRO Project, an attempt was made to identify irrigated crops in the Bhadra project command area, Karnataka state, India, using temporal, multi-look angle and dual polarization C-band SAR data from Radarsat and ERS. Preliminary analysis included data quality evaluation, rectification, speckle suppression and separability comparison of different land use and land cover classes. Evaluation of Radarsat data quality showed consistency in spatial resolution. Radarsat data acquired on different dates in shallow (S7 mode) and steep (S2 mode) angles and concurrent ERS-2 data were processed and analysed for possible discrimination of individual crops. It was observed that Radarsat data showed better separability of land use and land cover classes than ERS data. Principal Component Analysis was used on multi-look angle and dual polarization data from both ERS and Radarsat to reduce data dimensionality and the results were better with the first three components.     

Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS Wind Scatterometer and SAR data

This paper presents an original methodology to retrieve surface (<5 cm) soil moisture over low vegetated regions using the two active microwave instruments of ERS satellites. The developed algorithm takes advantage of the multi-angular configuration and high temporal resolution of the Wind Scatterometer (WSC) combined with the SAR high spatial resolution. As a result, a mixed target model is proposed. The WSC backscattered signal may be represented as a combination of the vegetation and bare soil contributions weighted by their respective fractional covers. Over our temperate regions and time periods of interest, the vegetation signal is assumed to be principally due to forests backscattered signal. Then, thanks to the high spatial resolution of the SAR instrument, the forest contribution may be quantified from the analysis of the SAR image, and then removed from the total WSC signal in order to estimate the soil contribution. Finally, the Integral Equation Model (IEM, [IEEE Transactions on Geoscience and Remote Sensing, 30 (2), (1992) 356]) is used to estimate the effect of surface roughness and to retrieve surface soil moisture from the WSC multi-angular measurements. This methodology has been developed and applied on ERS data acquired over three different Seine river watersheds in France, and for a 3-year time period. The soil moisture estimations are compared with in situ ground measurements. High correlations (R² greater than 0.8) are observed for the three study watersheds with a root mean square (rms) error smaller than 4%.     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km² in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m³/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.     

The effect of changing environmental conditions on microwave signatures of forest ecosystems: preliminary results of the March 1988 Alaskan aircraft SAR experiment

Abstract

In preparation for the first European Space Agency (ESA) Remote Sensing(ERS-I) mission,a series of multitemporal, multifrequency, multipolarization aircraft synthetic aperture radar (SAR) data sets were acquired over the Bonanza Creek Experimental Forest near Fairbanks, Alaska in March, 1988. P-, L- and C-band data were acquired with the NASA/JPL Airborne SAR on five differentdays over a period of two weeks. The airborne data were augmented with intensiveground calibration data as well as detailed, simultaneous in situ measurements of the geometric, dielectric and moisture properties of the snow and forest canopy. During the time period over which the SAR data were collected, the environmental conditions changed significantly; temperatures ranged from unseasonably warm (I to 9°C) to well below freezing (-8 to - 15°C), and the moisture content of the snow and trees changed from a liquid to a frozenstate. The SAR data clearly indicate the radar return is sensitive to these changing environmental factors and preliminary analysis of the L-band SAR data shows a 0·4 to 5·8dB increase (depending on polarization and canopy type) in the radar cross section of the forest stands under the warm conditions relative to the cold. These SAR observations are consistent with predictions from a theoretical scattering model. These preliminary results are presented to illustrate the opportunity afforded by the ERS-l SAR to monitor temporal changes in forest ecosystems.     

Constraints on using AVHRR composite index imagery to study patterns of vegetation cover in boreal forests

A wide range of techniques are being developed to map vegetation cover types using multi-date imagery from the Advanced Very High Resolution Radiometer. To date, these techniques do not account for severe constraints which exist for the world's boreal forest. Using composite AVHRR imagery collected over Alaska, we demonstrate how several factors influence the time-series normalized vegetation difference index (NDVI) signatures developed for the boreal forests in this region, including the effects of: (1) clouds and atmospheric haze; (2) climate variations on plant phenology; (3) fire on forest succession; and (4) forest stand patch size with respect to system resolution. Based on the analysis of AVHRR composite data from Alaska, the results of this study show: (1) clouds and haze have distinct effects on the intra-seasonal NDVI signature; (2) there are significant interseasonal variations in NDVI signatures caused by variations in the length of the growing season as well as variations in precipitation and moisture during the growing season; (3) disturbances affect large areas in interior Alaska and forest succession after fire results in significant variations in the inter-seasonal NDVI signatures; and (4) much of the landscape in interior Alaska consists of heterogeneous patches of forest which are much smaller than the resolution cell size of the AVHRR sensor, resulting in significant sub-pixel mixing. Based on these findings, the overall conclusion of this study is scientists using AVHRR to map land cover types in boreal regions must develop approaches which account for these sources of variation.     

Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data

Recent advances in instrument design have led to considerable improvements in wildfire mapping at regional and global scales. Global and regional active fire and burned area products are currently available from various satellite sensors. While only global products can provide consistent assessments of fire activity at the global, hemispherical or continental scales, the efficiency of their performance differs in various ecosystems. The available regional products are hard-coded to the specifics of a given ecosystem (e.g. boreal forest) and their mapping accuracy drops dramatically outside the intended area. We present a regionally adaptable semi-automated approach to mapping burned area using Moderate Resolution Imaging Spectroradiometer (MODIS) data. This is a flexible remote sensing/GIS-based algorithm which allows for easy modification of algorithm parameterization to adapt it to the regional specifics of fire occurrence in the biome or region of interest. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and therefore retains the variability of spectral response of the area affected by fire and has the potential to be used beyond binary burned/unburned mapping for the first-order characterization of fire impacts from remotely sensed data. The algorithm inputs the MODIS Surface Reflectance 8-Day Composite product (MOD09A1) and the MODIS Active Fire product (MOD14) and outputs yearly maps of burned area with dNBR values and beginning and ending dates of mapping as the attributive information. Comparison of this product with high resolution burn scar information from Landsat ETM+ imagery and fire perimeter data shows high levels of accuracy in reporting burned area across different ecosystems. We evaluated algorithm performance in boreal forests of Central Siberia, Mediterranean-type ecosystems of California, and sagebrush steppe of the Great Basin region of the US. In each ecosystem the MODIS burned area estimates were within 15% of the estimates produced by the high resolution base with the R² between 0.87 and 0.99. In addition, the spatial accuracy of large burn scars in the boreal forests of Central Siberia was also high with Kappa values ranging between 0.76 and 0.79.     

Predicting slow‐drying fire weather index fuel moisture codes with NOAA‐AVHRR images in Canada's northern boreal forests

Fire danger predicted by the Canadian Fire Weather Index, a system based on point‐source weather records, is limited spatially. NOAA‐AVHRR images were used to model two slow‐drying fuel moisture codes, the duff moisture code and the drought code of the fire weather index, in boreal forests of a 250,000 km² portion of northern Alberta and the southern Northwest Territories, Canada. Temporal and spatial factors affecting both codes and spectral variables (normalized difference vegetation index, surface temperature, relative greenness, and the ratio between normalized difference vegetation index and surface temperature) were identified. Models were developed on a yearly and seasonal basis. They were strongest in spring, but had a tendency to saturate. Drought code was best modelled (R ² = 0.34–0.75) in the spring of 1995 when data were categorized spatially by broad forest cover types. These models showed improved spatial resolution by mapping drought code at the pixel level compared to broadly interpolated weather station‐based estimates. Limitations and possible improvements of the study are also discussed.     

Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha^-1) and σ° (L-HH) and σ° (L-HV) for the data collected on each of the three dates, with the highest correlation found using the L-HV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band σ° and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur.     

Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery

A multi-year study was carried out to evaluate ERS synthetic aperture radar (SAR) imagery for monitoring surface hydrologic conditions in wetlands of southern Florida. Surface conditions (water level, aboveground biomass, soil moisture) were measured in 13 study sites (representing three major wetland types) over a 25-month period. ERS SAR imagery was collected over these sites on 22 different occasions and correlated with the surface observations. The results show wide variation in ERS backscatter in individual sites when they were flooded and non-flooded. The range (minimum vs. maximum) in SAR backscatter for the sites when they were flooded was between 2.3 and 8.9 dB, and between 5.0 and 9.0 dB when they were not flooded. Variations in backscatter in the non-flooded sites were consistent with theoretical scattering models for the most part. Backscatter was positively correlated to field measurements of soil moisture. The MIchigan MIcrowave Canopy Scattering (MIMICS) model predicts that backscatter should decrease sharply when a site becomes inundated, but the data show that this drop is only 1-2 dB. This decrease was observed in both non-wooded and wooded sites. The drop in backscatter as water depth increases predicted by MIMICS was observed in the non-wooded wetland sites, and a similar decrease was observed in wooded wetlands as well. Finally, the sensitivity of backscatter and attenuation to variations in aboveground biomass predicted by MIMICS was not observed in the data.The results show that the inter- and intra-annual variations in ERS SAR image intensity in the study region are the result of changes in soil moisture and degree of inundation in the sites. The correlation between changes in SAR backscatter and water depth indicates the potential for using spaceborne SAR systems, such as the ERS for monitoring variations in flooding in south Florida wetlands.     

Assessment of C-band synthetic aperture radar data for mapping and monitoring Coastal Plain forested wetlands in the Mid-Atlantic Region,U.S.A.

Multi-temporal C-band SAR data (C-HH and C-VV), collected by ERS-2 and ENVISAT satellite systems, are compared with field observations of hydrology (i.e., inundation and soil moisture) and National Wetland Inventory maps (U.S. Fish and Wildlife Service) of a large forested wetland complex adjacent to the Patuxent and Middle Patuxent Rivers, tributaries of the Chesapeake Bay. Multi-temporal C-band SAR data were shown to be capable of mapping forested wetlands and monitoring hydroperiod (i.e., temporal fluctuations in inundation and soil moisture) at the study site, and the discrimination of wetland from upland was improved with 10 m digital elevation data. Principal component analysis was used to summarize the multi-temporal SAR data sets and to isolate the dominant temporal trend in inundation and soil moisture (i.e., relative hydroperiod). Significant positive, linear correlations were found between the first principal component and percent area flooded and soil moisture. The correlation (r²) between the first principal component (PC1) of multi-temporal C-HH SAR data and average soil moisture was 0.88 (p = < .0001) during the leaf-off season and 0.87 (p = < .0001) during the leaf-on season, while the correlation between PC1 and average percent area inundated was 0.82 (p = < .0001) and 0.47 (p = .0016) during the leaf-off and leaf-on seasons, respectively. When compared to field data, the SAR forested wetland maps identified areas that were flooded for 25% of the time with 63–96% agreement and areas flooded for 5% of the time with 44–89% agreement, depending on polarization and time of year. The results are encouraging and justify further studies to attempt to quantify the relative SAR-derived hydroperiod classes in terms of physical variables and also to test the application of SAR data to more diverse landscapes at a broader scale. The present evidence suggests that the SAR data will significantly improve routine wooded wetland mapping.     

Monitoring leaf area of sugar beet using ERS-1 SAR data

This paper presents the results of field testing a radar model which relates leaf area index to radar backscatter for ERS-1 C-band VV polarization SAR data. Ground truth measurements of leaf area index and soil moisture content were made in selected sugar beet fields, with simultaneous acquisition of ERS-1 SAR image data. Radar backscatter coefficients were derived from the calibrated ERS-1 SAR data. The Leeuwen and Clevers expression of the water cloud model was fitted to determine the in situ relationship between radar back-scatter and leaf area index. The model can be inverted analytically to calculate leaf area index from radar backscatter. The results show considerable potential for the operational application of ERS-1 SAR data in crop monitoring.