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Abstract: The arid and semi-arid sagebrush–grass ecosystem occupies a substantial portion of rangelands in the western United States. Using remote sensing techniques to map the percent of sagebrush, grass/forb, and bare ground components is necessary for forage production estimation and natural resource management over large areas. However optical data have significant deficiencies in these ecosystems because of exposed bright soil, spectrally-indeterminate vegetation, and a large dead vegetation component. Radar data also have deficiencies caused by factors such as antenna pattern calibration, local incidence angle (LIA), soil moisture, and surface roughness. With the complementary vegetation information gained from optical data and radar data, these two datasets were fused to estimate 10-m sagebrush, grass, and bare ground percent cover in the non-forested areas of Yellowstone National Park, which is a representative native western rangeland ecosystem of the US. The datasets were processed to resolve the issues of antenna pattern calibration and LIA effect. Peak green Landsat, late fall Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AirSAR) data were fused in this analysis. AVIRIS, Landsat, AirSAR and elevation data were used to segment the study area into two main subcategories of “pure grass” and “mixed sagebrush and grass”. Landsat Tasseled Cap Greenness (LTCG) was used to retrieve bare land and grass percentages in pure grass areas. In the areas with mixed grass and sagebrush, standardized LTCG and radar C vv were used to derive the vegetation cover percentage, and the ratio of standardized LTCG and radar L hv was further used to calculate the relative abundance of sagebrush and grass. Comparison between the field and remote sensing estimations shows the correlation coefficients were 0.838, 0.746, and 0.830 for bare land, grass, and sagebrush, respectively. When grouped into three discrete categories of “low”, “medium”, and “high”, the overall accuracies were 79.4%, 75.9%, and 77.6%, respectively. Our study shows the potential for application of global spaceborne C- and L-band radar and optical data fusion for large-area rangeland monitoring. [Copyright &y& Elsevier]

Abstract: The Coarse Woody Debris (CWD) quantity, defined as biomass per unit area (t/ha), and the quality, defined as the proportion of standing dead logs to the total CWD quantity, greatly contribute to many ecological processes such as forest nutrient cycling, tree regeneration, wildlife habitat, fire dynamics, and carbon dynamics. However, a cost-effective and time-saving method to determine CWD is not available. Very limited literature could be found that applies remote sensing technique to CWD inventory. In this paper, we fused the wall-to-wall multi-frequency and multi-polarization Airborne Synthetic Aperture Radar (AirSAR) and optical Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to estimate the quantity and quality of CWD in Yellowstone post-fire forest ecosystem, where the severe 1988 fire event resulted in high spatial heterogeneity of dead logs. To relate backscatter values to CWD metrics, we first reduced the terrain effect to remove the interference of topography on AirSAR backscatter. Secondly, we removed the influence of regenerating sapling by quadratic polynomial fitting between AVIRIS Enhanced Vegetation Index (EVI) and different channels backscatters. The quantity of CWD was derived from P hh and P hv, and the quality of CWD was derived from P hh aided by the ratio of L hv and P hh. Two maps of Yellowstone post-fire CWD quantity and quality were produced. The calculated CWD quantity and quality were validated by extensive field surveys. Regarding CWD quantity, the correlation coefficient between measured and predicted CWD is only 0.54 with mean absolute error up to 29.1 t/ha. However, if the CWD quantity was discretely classified into three categories of “≤60”, “60–120”, and “≥120”, the overall accuracy is 65.6%; if classified into two categories of “≤90” and “≥90”, the overall accuracy is 73.1%; if classified into two categories of “≤60” and “≥60”, the overall accuracy is 84.9%. This indicates our attempt to map CWD quantity spatially and continuously achieved partial success; however, the general and discrete categories are reasonable. Regarding CWD quality, the overall accuracy of 5 types (Type 1—standing CWD ratio ≥40%; Type 2—15%≤standing CWD ratio <40%; Type 3—7%≤standing CWD ratio<15%; Type 4—3%≤standing CWD ratio <7%; Type 5—standing CWD ratio <3%) is only 40.32%. However, when type 1, 2, 3 are combined into one category and type 4 and 5 are combined into one category, the overall accuracy is 67.74%. This indicates the partial success of our initial results to map CWD quality into detailed categories, but the result is acceptable if solely very coarse CWD quality is considered. Bias can be attributed to the complex influence of many factors, such as field survey error, sapling compensation, terrain effect reduction, surface properties, and backscatter mechanism understanding. [Copyright &y& Elsevier]

Abstract: The density of lodgepole pine (Pinus contorta) sapling regeneration was mapped in areas burned during the 1988 wildfires across Yellowstone National Park (YNP), Wyoming, USA. Hyperspectral image analysis and field measurements were combined across the entire YNP extent. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) image data from 2006 were used to compute ten different vegetation indices (VI). The ten VIs were combined to build multiple regression models for predicting and mapping post-fire sapling density. Four different forms of regression modeling were applied to derive the highest possible prediction accuracy (correlation coefficient of R 2 =0.83). Pine sapling regeneration 19years after large-scale wildfires showed a high level of variability in patch density (ranging from 14/100ha to 57/100ha), whereas sapling density measured previously from the first decade following wildfire was more uniform (10/100ha to 21/100ha). The ecosystem-level clumpiness index showed major shifts in aggregation of different sapling density classes, and was consistent with an overall decrease in estimated sapling density of nearly 50% between 1998 and 2007. This analysis revealed important succession patterns and processes in post-fire forest regeneration for the Greater Yellowstone Area (GYA). [Copyright &y& Elsevier]