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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]

Abstract: C-band Radarsat-2 data were used to develop multi-parameter algorithms to retrieve soil moisture across a chronosequence (recently burned, shrubby regrowth and mature forest) of fire-disturbed Alaskan boreal black spruce forests using polarized backscatter intensity, polarimetric decomposition and discriminator parameters as independent variables. To account for complex interactions of surface roughness, biomass and soil moisture, multiple polarimetric variables were evaluated empirically in algorithm development using a time series dataset that included the wet and dry extreme conditions. Results indicate that polarimetric discriminators, such as maximum degree of polarization, fractional polarization or coefficient of variation, may be instrumental in accounting for the type of scattering occurring or the complexity or heterogeneity of the distributed target. Using the maximum degree of polarization combined with one or more variables on scattered intensity (e.g. C-HV, C-HH, or maximum scattering intensity) improved multi-linear moisture retrieval algorithms by 27–33% over the single, dual and quad polarized backscatter intensity algorithms. For all sites combined, algorithms were developed with low SE (7.0% volumetric soil moisture), moderately high R2 (0.77) and low RMSE (6.7% volumetric soil moisture). However, the best models were produced by separating the recently burned forests (strongest algorithm: R2 0.94, SE 5.9, RMSE 7.4) from the shrubby and mature forests (best algorithm: R2 0.85, SE 4.7, RMSE 7.3). Results are limited to sites with biomass below 3kg/m2. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

Abstract: 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 22.2tha-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. [ABSTRACT FROM AUTHOR]

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. [Copyright &y& Elsevier]

Climate change is altering the water-table (WT) height and near-surface moisture conditions in northern peatlands, which in turn both increases the susceptibility to fire and reduces the carbon sink capacity of these ecosystems. To further develop remote sensing-based measurements of peatland moisture characteristics, we employed coincident surface reflectance and moisture measurements in two Sphagnum moss-dominated peatland sites. We applied the Mexican hat continuous wavelet transform to the measured spectra to generate wavelet features and coefficients across a range of scales. Overall, wavelet analysis was an improvement over the previously tested spectral indices at both the study sites. Linear mixed effect models for WT height using wavelet features accounted for more of the variance with both an improved marginal R^\mathrm 2 (29% greater) and a larger conditional R^\mathrm 2 (21% greater) compared to the best performing spectral index. While spectral indices performed similarly with wavelet coefficients for moisture content measured at 3 cm depth, they performed poorly for volumetric moisture content measured at 7 cm depth. The current study also revealed the advantage of selecting the best subsets of wavelet features based upon genetic algorithm over a more widely used technique that selects features based on correlation scalograms. It also provided new insights into the significance of various spectral regions to detect WT alteration-induced vegetation change. [ABSTRACT FROM PUBLISHER]

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]

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user's and producer's accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user's accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve. [ABSTRACT FROM AUTHOR]