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A multifrequency, multipolarization airborne SAR data set was utilized to examine the relationship between radar backscatter and the aboveground biomass in loblolly pine forests. This data set was also used to examine the potential of SAR to estimate aboveground biomass in these forests. The total aboveground biomass in the test stands used in this study ranged from

Investigations have shown that fires in boreal forests result in characteristic responses which are recorded on ERS-1 SAR imagery. Using one of the many fire signatures observed on ERS-1 SAR imagery, the analysis of the data revealed there is > 10 dB in variation in image intensity within the burned areas on the SAR images. Possible explanations for this variation include increases in soil moisture, increased surface roughness exposed to the incoming microwave radiation, and damage to the vegetation canopy by the fire.

Woodland vernal pools are important, small, cryptic, ephemeral wetland ecosystems that are vulnerable to a changing climate and anthropogenic influences. To conserve woodland vernal pools for the state of Michigan USA, vernal pool detection and mapping methods were sought that would be efficient, cost-effective, repeatable and accurate. Satellite-based L-band radar data from the high (10 m) resolution Japanese ALOS PALSAR sensor were evaluated for suitability in vernal pool detection beneath forest canopies. In a two phase study, potential vernal pool (PVP) detection was first assessed with unsupervised PALSAR (LHH) two season change detection (spring when flooded--summer when dry) and validated with 268, 1 ha field-sampled test cells. This resulted in low false negatives (14%-22%), overall map accuracy of 48% to 62% and high commission error (66%). These results make this blind two-season PALSAR approach for cryptic PVP detection of use for locating areas of high vernal pool likelihood. In a second phase of the research, PALSAR was integrated with 10 m USGS DEM derivatives in a machine learning classifier, which greatly improved overall PVP map accuracies (91% to 93%). This supervised approach with PALSAR was found to produce better mapping results than using LiDAR intensity or C-band SAR data in a fusion with the USGS DEM-derivatives. [ABSTRACT FROM AUTHOR]

Using the extensive archive of historical ERS-1 and -2 synthetic aperture radar (SAR) images, this analysis demonstrates that fire disturbance can be effectively detected and monitored in high northern latitudes using radar technology. A total of 392 SAR images from May to August spanning 1992-2010 were analyzed from three study fires in the Alaskan tundra. The investigated fires included the 2007 Anaktuvuk River Fire and the 1993 DCKN178 Fire on the North Slope of Alaska and the 1999 Uvgoon Creek Fire in the Noatak National Preserve. A 3 dB difference was found between burned and unburned tundra, with the best time for burned area detection being as late in the growing season as possible before frozen ground conditions develop. This corresponds to mid-August for the study fires. In contrast to electro-optical studies from the same region, measures of landscape recovery as detected by the SAR were on the order of four to five years instead of one. [ABSTRACT FROM AUTHOR]

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects. [ABSTRACT FROM AUTHOR]

This study evaluates the utility of synthetic aperture radar (SAR) imagery collected by the ERS-1 satellite for monitoring wetland vegetation communities in southwestern Florida. Two images were analyzed, one collected at the end of the dry season in April 1994 and one collected at the end of the wet season in October 1994. The range of imageintensity values from the different test sites varied by a factor of6.2 (7.9 dB) on the dry season ERS-1 SAR image and by a factor of 2.6 (4.1 dB) for the wet season ERS-1 SAR image. The results from the radar observations were found to be consistent with theoretical microwave scattering models that predict variations in backscatter as a function of vegetation structure, soil moisture, surface roughness, and the presence or absence of standing water. Both the radar data and models show that, in wetlands dominated by herbaceous vegetation, the presence of standing water results in a decrease in backscatter. Conversely, in wetlands with woody plants (trees and shrubs), the radar data and models show that the presence of water results in an increase in backscatter. The results of this study illustrate that radar imagery is uniquely suited to detect and monitor changes in soil moisture,flooding, and aboveground biomass in these wetland ecosystems. [ABSTRACT FROM AUTHOR]