High-resolution Mapping of Energy Infrastructure and Impacts on Mule Deer Movement

Submitted by atripp on

Mule deer are known to avoid human disturbances, including energy infrastructure and development. By combining remote sensing data, GIS modeling, and information on energy expenditure of mule deer, researchers developed a spatiotemporal model to map the minimum energy expenditure required for mule deer to traverse a landscape with increasing levels of oil and gas development on the northern Colorado Plateau.

State oil and gas energy databases in the study area of Utah, Colorado, and New Mexico contain point coordinates of locations that have been developed for energy extraction as well as many that have been assessed but not developed. Because oil and gas pads are relatively small (~1 ha), there are no regional map products depicting the extent of surface disturbances, and past mapping efforts have been limited to hand-digitizing small areas of interest from aerial imagery. USDA National Agriculture Imagery Program (NAIP) 1-meter resolution aerial photography in Google Earth Engine was used to identify and map oil and gas pads across the Colorado Plateau Ecoregion. The team implemented a feature extraction procedure based on NAIP red-band pixel reflectance that exploits the brightness of reflectance values occurring in areas disturbed by energy development. Rules were based on percentile calculations of the highest red band reflectance that captured brightness of disturbed pixels relative to lower brightness of undisturbed pixels within a 90-meter polygon surrounding oil and gas point locations. About 40,000 active well pads were mapped with high accuracy across the region at 1-meter resolution.

Well pad maps, combined with data on access roads, were used to develop density rasters that quantify the percent of developed area within a 750-meter moving window. Areas with greater than 3% development were classified as avoidance areas for mule deer. Using a 1-arc second DEM and past regression analyses of energy expenditure by deer weight, terrain slope, and slope direction, researchers developed a spatiotemporal model of the minimum energy expenditure required for mule deer to traverse a landscape. By incorporating avoidance areas, the team measured changes in energy expenditure with the increasing size of avoidance for five time series: pre-development, 2001 and prior, 2002–2006, 2007–2011, and 2012–2016. In addition, a terrain index was developed to analyze results not only by time but also by level of topographic ruggedness. Using a 1-kilometer grid system of potential starting points to measure expenditure from multiple directions, researchers could calculate average expenditures by terrain and time and estimate the impacts of oil and gas development on the bioenergetic requirements of mule deer.

The study confirmed, with spatial precision, that not only does steep terrain increase energy expenditure, but energy expenditure typically increases with travel distance to avoid development and by the limiting of less costly terrain. As the energy costs of movement correlate across multiple species of large mammals, this analysis can serve as a quantitative representation of the impacts of oil and gas development for not only mule deer but multiple species found in areas with oil and gas development—including those listed as threatened or endangered, such as the grey wolf, grizzly bear, polar bear, and wood bison.

 https://www.usgs.gov/centers/wgsc/science/remote-sensing-energy-development

https://doi.org/10.1007/s10980-022-01521-w

 A) 1-meter USDA National Agriculture Imagery Program (NAIP) imagery showing detail of energy development in the Book Cliffs region of eastern Utah, B) results of NAIP oil and gas pad extraction algorithm, C) polygon perimeters of mapped oil and gas pads with layer of access roads, and D) mule deer avoidance zones based on density of roads and pads, mapped for different time periods.

 

Sensor
Platform
Author Name
Miguel Villarreal; Sam Chambers
Author Email
mvillarreal@usgs.gov; schambers@usgs.gov