Researchers at the USGS Fort Collins Science Center (FORT) evaluate landscape-scale characteristics for many natural resource questions. Landscape-scale characteristics can represent naïve statistics (which do not consider chance) of features (e.g., terrain ruggedness, percent cover of a vegetation community type, snow deposition) within a specified kernel size (Figure 1). In other words, kernels of varying scales are passed over remotely sensed or raster datasets, and a statistic is calculated for each cell describing all neighboring cells (Figure 1). These statistical measures are often used in linear and nonlinear models to predict landscape use and avoidance by a species. For example, a moving window applied to a percent cover of herbaceous plants may show species selection for moderate cover with a patch size of 100 meters (m), but that same species may avoid moderate cover with a patch size of 2 kilometers.
Applying moving windows to datasets can be an extremely long process, and this amount of time increases with increased spatial extents, spatial resolutions, and the kernel size. For example, a moving window of 6400 m, with a spatial resolution of 30 m, and a spatial extent of Wyoming, USA, can require up to a week of processing time using commercial off-the-shelf software. Usually about 4 scales of moving windows and hundreds of datasets are needed to evaluate habitat use. So, to evaluate relationships between a species and its habitat selection, a significant amount of processing time is required before any of the species predictive models can be evaluated for habitat selection.
We originally ran the analysis described above using GIS software, which required about 1100 hours of processing time. FORT then developed a C++ application that reduced the processing time to 100 hours. Our intent was then to use HTCondor1 and the C++ application to reduce the processing time to several hours. However, the GIS software we used was significantly enhanced in a newly released version, which reduced the processing time to 1 hour. Because we had not fully implemented HTCondor before completing the project requiring these analyses, we did not re-evaluate HTCondor execution times.
However, in the following more complex or data-intensive scenarios (or a combination of the two), HTC would be applicable:
1The use of any trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.