Remote sensing and machine learning to improve aerial wildlife population surveys

• DOI: 10.3389/fcosc.2024.1416706
• Publication Date: 2024-06-05T05:23:30Z
• Authors: Rowan L. Converse; Christopher D. Li...; Mark D. Koneff; Timothy P. White; Benjamin G. Weins...; Richard Gibbons; David R. Stewart; Abram B. Fleishman; Matthew J. Butler; Steven E. Sesnie; Grant M. Harris
• Keywords: Aerial Survey

Technological and methodological advances in remote sensing machine learning have created new opportunities for advancing wildlife surveys. We assembled a Community of Practice (CoP) to capitalize on these developments explore improvements the efficiency effectiveness aerial monitoring from management perspective. The core objective CoP is organize development testing methods improve population surveys that support decisions. Beginning 2020, collaboratively identified natural resource decisions are informed by survey data with focus waterbirds marine wildlife. surveyed our membership establish 1) what they were using count inform; 2) how collected prior advent sensing/machine methods; 3) impetus transitioning framework; 4) challenges practitioners face this framework. This paper documents findings identifies research priorities moving toward operational sensing-based service management.