Estimating the Population of Large Animals in the Wild Using Satellite Imagery: A Case Study of Hippos in Zambia’s Luangwa River

摘要

Degradation of natural ecosystems as influenced by increasing human activity and climate change is threatening many animal populations in the wild. Zambia’s hippo population in Luangwa Valley is one example where declining forest cover from increased farming pressures has the potential of limiting hippo range and numbers by reducing water flow in this population’s critical habitat, the Luangwa River. COMACO applies economic incentives through a farmer-based business model to mitigate threats of watershed loss and has identified hippos as a key indicator species for assessing its work and the health of Luangwa’s watershed. The goal of this effort is to develop automated machine learning tools that can process fine resolution commercial satellite imagery to estimate the hippo population and associated characteristics of the habitat. The focus is the Luangwa River in Zambia, where the ideal time for imagery acquisition is the dry season of June through September. This study leverages historical commercial satellite imagery to identify selected areas with observable hippo groupings, develop an-image-based signature for hippo detection, and construct an initial image classifier to support larger-scale assessment of the hippo population over broad regions. We begin by characterizing the nature of the problem and the challenges inherent in applying remote sensing methods to the estimation of animal populations. To address these challenges, spectral signatures were constructed from analysis of historical imagery. The initial approach to classifier development relied on spectral angle to distinguish hippos from background, where background conditions included water, bare soil, low vegetation, trees, and mixtures of these materials. We present the approach and the initial classifier results. We conclude with a discussion of next steps to produce an imagebased estimate of the hippo populations and discuss lessons learned from this study.