Semi‐automated detection of eagle nests: an application of very high‐resolution image data and advanced image analyses to wildlife surveys. Issue 2 (19th January 2017)

• Record Type: Journal Article
• Authors: Andrew, Margaret E.; Shephard, Jill M.
• Editors: Rocchini, Duccio; Buchanan, Graeme
• Is Part Of: Remote sensing in ecology and conservation. Volume 3:Issue 2(2017)
• Page Start: 66
• Page End: 80
• Publication Date: 2017-01-19
• Subjects: eCognition -- Haliaeetus leucogaster -- image analysis -- Maxent -- nest surveys -- object‐based image analyses (OBIA) -- white‐bellied sea eagle; Remote sensing -- Periodicals; Ecology -- Research -- Periodicals; Ecology -- Methodology -- Periodicals; Ecology -- Remote sensing -- Periodicals; Nature conservation -- Methodology -- Periodicals; 577.0723
• DOI: 10.1002/rse2.38
• Languages: English
• ISSNs: 2056-3485

Abstract

Abstract: Very high‐resolution (VHR) image data, including from unmanned aerial vehicle (UAV) platforms, are increasingly acquired for wildlife surveys. Animals or structures they build (e.g. nests) can be photointerpreted from these images, however, automated detection is required for more efficient surveys. We developed semi‐automated analyses to map white‐bellied sea eagle ( Haliaeetus leucogaster ) nests in VHR aerial photographs of the Houtman Abrolhos Islands, Western Australia, an important breeding site for many seabird species. Nest detection is complicated by high environmental heterogeneity at the scale of nests (~1–2 m), the presence of many features that resemble nests and the variability of nest size, shape and context. Finally, the rarity of nests limits the availability of training data. These challenges are not unique to wildlife surveys and we show how they can be overcome by an innovative integration of object‐based image analyses (OBIA) and the powerful machine learning one‐class classifier Maxent. Maxent classifications using features characterizing object texture, geometry and neighborhood, along with limited object color information, successfully identified over 90% of high quality nests (most weathered and unusually shaped nests were also detected, but at a slightly lower rate) and labeled <2% of objects as candidate nests. Although this overestimates the occurrence of nests, the results can be visually screened to rule out all but the most likely nests in a process that is simpler and more efficient than manual photointerpretation of the full image. Our study shows that semi‐automated image analyses for wildlife surveys are achievable. Furthermore, the developed strategies have broad relevance to image processing applications that seek to detect rare features differing only subtly from a heterogeneous background, including remote sensing of archeological remains. We also highlight solutions to maximize the use of imperfect or uncalibrated image data, such as some UAV‐based imagery and the growing body of VHR imagery available in Google Earth and other virtual globes. Abstract : Very high‐resolution image data are increasingly acquired for wildlife surveys. It has been difficult to detect nests of the white‐bellied sea eagle ( Haliaeetus leucogaster ) from image data, but these challenges were overcome by an innovative integration of object‐based image analyses (OBIA) and the powerful machine learning one‐class classifier Maxent. The strategies developed have broad relevance to image processing applications that seek to detect rare features differing only subtly from a heterogeneous background, and can be applied to rigorous analyses of uncalibrated image data, for instance available in virtual globes.