Incorporating terrain roughness into helicopter landing zone site selection by using the Geomorphic Oscillation Assessment Tool (GOAT) v1.0

The Geomorphic Oscillation Assessment Tool (GOAT) quantifies terrain roughness as a mechanism to better explain forward arming and refueling point (FARP) suitability for Army aviation. An empirically driven characteristic of FARP consideration, surface roughness is a key discriminator for site utility in complex terrain. GOAT uses a spatial sampling of high-resolution elevation and land cover data to construct data frames, which enable a relational analysis of component and aggregate site suitability. By incorporating multiple criteria from various doctrinal sources, GOAT pro-duces a composite quality assessment of the areal options available to the aviation commander. This report documents and demonstrates version 1.0 of the GOAT algorithms developed by the U.S. Army Engineer Research and Development Center (ERDC). These details will allow users familiar with R to implement it as a stand-alone program or in R Studio.

Szorstkość pokrycia terenu jako źródło błędu metody SfM zastosowanej do rekonstrukcji zasięgu pokrywy śnieżnej = Terrain roughness as a source of error with the SfM method applied to the reconstruction of snow cover extent

A comparison between errors associated with snow-cover reconstruction performed by processing aerial imagery acquired by a visible-light camera mounted on board unmanned aerial vehicles, one the one hand; and average terrain roughness, on the other, revealed a dependent relationship between these variables. A stronger correlation is noted for two of the studied test areas (Polana Izerska and Krobica, both located in SW Poland), as opposed to the remaining site (Drożyna, SW Poland). In particular, correlations are noticeable where the analysis is performed in moving windows. It is typical for terrain where depth of snow cover is reconstructed with severe errors to reveal a high degree of roughness caused by single trees, clumps of trees or buildings. Ambiguous results are obtained for the Drożyna research field. While the character of the dependent relationship there seems consistent with results for the remaining sites, the strength is low. The lower values for the correlation coefficient were driven by observations for which errors were found to be high while values for the Topographic Ruggedness Index were at the same time low. This effect can be explained by reference to the specific nature of the area reconstructed, which is much transformed by human activity. It proves difficult to reconstruct the depth of snow cover on roads properly, as these are either partially cleared or snow or characterised by its loss in the course of melting. Low thickness of snow cover is thus found to be a constrained when it comes to the generation of accurate reconstructions of the depth of snow cover. This is in fact a finding in agreement with what has been reported by other authors.

Effects of environmental and anthropogenic landscape features on mule deer harvest in Nebraska

Understanding the habitat use of wildlife species is important for effective management. Nebraska has a variety of habitat types, with the majority being covered by rangeland and cropland. These habitat types likely influence the harvest of mule deer (MD; Odocoileus hemionus) in Nebraska, but their specific effects are unknown, and moreover, harvest may also be influenced by the accessibility of deer habitats for hunters. We modeled which environmental and anthropogenic landscape features influenced harvest densities. Spatial analysis in a Geographic Information System was used to determine the mean values of environmental and anthropogenic landscape features at the county level. We then used a generalized linear model to determine which of those factors influenced MD harvest from 2014–2016. We found that NDVI amplitude, hunter effort, road density, terrain roughness, and canopy cover influence MD harvest in Nebraska. According to our model, MD harvest densities are significantly greater areas with NDVI amplitude ∼38, increasing hunter effort, road densities near 1,750 m/km2, increasing terrain roughness, and decreasing canopy cover. Understanding increased harvest densities of MD can be beneficial for wildlife managers, allowing for more efficient allocation of efforts and expenses by managers for population management.