Data analyses bring challenges to understand not only ‘where’ attributes are located, but how they relate to what else is found in the same locations. In spatial analysis terms, we place interest in how and why components of natural resources are located near other components. We consider a wide array of feature analyses, multi-spectral image analysis, satellite imagery dissection, linear and non-linear multi-attribute regression analysis, Kriging, variography, geo-statistics determination, with custom map creation and printing.
Geospatial Resource Analysis is a uniquely qualified resource when considering environmental analysis techniques. While there are several analytical tools available to the spatial analyst, these tools must be fine-tuned in order to extract meaningful results that lead to improved management decisions. Resource mapping and “location logistics” have long been valued by forest resource managers: technological advances in GIS systems and value optimization techniques provide us with new abilities to reveal significant value.
LANDSAT 8 OLI satellite imagery. Although Pan-sharpened to 15-meter resolution, the multi-spectral imagery can be used to identify surfaces. This makes the identification of road surfaces more obvious where physical surface disruptions on hillshade rasters are not conclusive. We generate Landsat 8 OLI scenes (approximate scene size is 170 km north-south by 183 km east-west) for all areas of consideration. All scene themes are spatially projected images:
- Natural Color
- Color Infrared (vegetation)
- Geologic Material Reactivity
- Land/Water Boundary Sensitivity
Watch the collection of images displayed in the panel below, to see how the various layers are displayed, each giving evidence of juxtaposition-specific attributes. Watch where geologic materials are exposed in relation to where trees are growing, but not yet considered to be Free-to-Grow. Locating open-water is relatively simple, but watch the color patterns to reveal where sub-surface water content extends beyond the stream shorelines. These are typically where hyporheic zones are located, an attribute not easily found when using visual assessments from aerial photography.
More data layers are used, like digital elevation models (DEM), once collected as 90m resolution, then to 30m and 10m. LiDAR data brings this threshold to 1m resolution. We use those features to derive stream layers across watersheds to reveal more detailed physical site attributes.