Spatial Analysis

Juxtapositional Investigations

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.

We treat GIS maps as databases of spatial information.
The familiar image shown in the adjoining panel shows an area northwest of Campbell River, Vancouver Island, British Columbia, Canada on July 8, 2013. This seemingly natural color aerial photograph is not what it seems to be. This image was captured from a satellite image collected by LANDSAT 8 OLI. By combining certain the spectral layers together we are able to create this seemingly natural color example. It is recorded at 15m resolution, not as fine as a typical aircraft flown photograph, but it can be collected on about a 4-day frequency and acquired at no cost.

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:

  1. Natural Color
  2. Color Infrared (vegetation)
  3. Geologic Material Reactivity
  4. 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.