Abstract:The fraction of the surface under forest canopies that is visible from above, or the viewable gap fraction (VGF), influences a number of significant physical processes, such as the longwave radiation budget of the surface and the magnitude of diffuse irradiance. In addition, it has significant implications for the remote sensing of the surface. The VGF is dependent on canopy structure, topography and viewing geometry. Although it is difficult to map VGF using current operational remote sensing systems, it is possible to estimate VGF using models based on the three-dimensional structure of forest canopies.Results from hemispheric photographs taken in the field at Fraser Experimental Forest, Colorado, and a geometric optical (GO) model show a trend of rapid decrease in VGF as the view zenith angles diverges from nadir. Whereas there is general agreement between model estimates and the hemispheric photographs, the hemispheric photographs generally show higher VGF values for all view zenith angles. In particular, the higher values for VGF are apparent at high view zenith angles. Use of a more complicated GO radiative transfer model would add the effect of within-crown gaps to those modelled by the GO model and will be used in future studies.VGF maps estimated using the GO model for the Fool Creek intensive study area show a significant decrease in VGF when view zenith angle is increased from 0°(nadir) to 30°viewing from the east. To produce VGF maps in mountain areas, the effect of topography must be taken into account, as changes in slope angle and azimuth are similar to changes in the view zenith angle. Hence, topography can serve either to accentuate or to minimize view zenith angle effects, depending on the slope orientation relative to the viewing position.
The Geospatial Remote Assessment for Ingress Locations (GRAIL) efforts under the Army Terrestrial-Environmental Modeling and Intelligence System (ARTEMIS) program have made significant advances in the remote assessment of terrain and soils for locating potential landing zone and drop zone (LZ and DZ) sites for military operations. The project identified sources of high-quality geospatial data, defined preprocessing requirements to produce global datasets for analysis, and created the GRAIL Tools software. The GRAIL Tools algorithms analyze and filter geospatial datasets to search for areas suitable for aircraft ground operations. GRAIL Tools then applies geometric criteria to determine if the required LZ/DZ will fit within the areas of suitable terrain and displays the potential LZs and DZs superimposed over geospatial imagery. Verification of the GRAIL Tools concept at Fort Hunter Liggett, California, developed and trained the suitability filter algorithms with regard to vegetation, obstructions, and soil strength. Further work served to enhance the algorithms and develop the full toolkit. Future work at a variety of sites, including work in northern regions with snow, ice, and freezing/thawing soils, will evolve the GRAIL Tools to handle the full spectrum of global terrain conditions for military operations. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.
Single-day snow covered area (SCA) products are incomplete and often inadequate representations of ground conditions due to short term variation in cloud cover, snow cover, and sensor geometry. To mitigate these effects, we developed a by-pixel filtering algorithm to produce relatively cloud-free SCA products from 16 days of MODIS imagery. The algorithm uses previous days' data to estimate the current SCA value of each pixel and uses a simple persistence test to reduce the effects of spurious SCA/cloud classifications in the input products. To be positively identified as SCA, a pixel must be snow-covered in the two most recent, cloud-free scenes of the 16-day period. We applied this Time-Domain-Filtering (TDF) methodology to two single-day MODIS fractional snow cover products (MOD10A1 and MODSCAG) over the MODIS period of record (2000-present) and compared the outputs to the unfiltered products, to filtered maps generated using the cloud-gap-filled algorithm (CGF, Hall et al. 2010), and to historical snow assessment reports from the U.S. Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL). The CRREL reports were manually generated and quality-controlled by an analyst and are treated as ground truth. We find that, when applied to MODSCAG, the TDF algorithm successfully fills in gap pixels and limits the effects of snow/cloud confusion and produces a filtered product that is more consistent and accurate than the MODSCAG CGF product and comparable to the MOD10A1 CGF product.
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