Remote sensing and machine learning to improve aerial wildlife population surveys
Rowan Converse, Christopher D. Lippitt, Mark D. Koneff, Timothy P. White, Benjamin G. Weinstein, Richard Gibbons, David R. Stewart, Abram B. Fleishman, Matthew J. Butler, Steven E. Sesnie, Grant Harris
Abstract
Technological and methodological advances in remote sensing and machine learning have created new opportunities for advancing wildlife surveys. We assembled a Community of Practice (CoP) to capitalize on these developments to explore improvements to the efficiency and effectiveness of aerial wildlife monitoring from a management perspective. The core objective of the CoP is to organize the development and testing of remote sensing and machine learning methods to improve aerial wildlife population surveys that support management decisions. Beginning in 2020, the CoP collaboratively identified the natural resource management decisions that are informed by wildlife survey data with a focus on waterbirds and marine wildlife. We surveyed our membership to establish 1) what management decisions they were using wildlife count data to inform; 2) how these count data were collected prior to the advent of remote sensing/machine learning methods; 3) the impetus for transitioning to a remote sensing/machine learning methodological framework; and 4) the challenges practitioners face in transitioning to this framework. This paper documents these findings and identifies research priorities for moving toward operational remote sensing-based wildlife surveys in service of wildlife management.