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Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

1 and 1,2,*
State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 739;
Received: 2 March 2018 / Revised: 24 April 2018 / Accepted: 5 May 2018 / Published: 10 May 2018
PDF [2707 KB, uploaded 10 May 2018]


Many remote sensing metrics have been applied in large-scale animal species monitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). The metrics with a high explained variance on the global scale were mainly in the energy/productivity category. The metrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Three machine learning models (i.e., support vector machine, random forests, and neural networks) were evaluated for metric integration, and the random forest model showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes’ species richness on a global scale, with an explained variance that was 20% higher than any of the univariate metrics. View Full-Text
Keywords: biodiversity; remote sensing; species richness; metric comparison; metric integration biodiversity; remote sensing; species richness; metric comparison; metric integration

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wu, J.; Liang, S. Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness. Remote Sens. 2018, 10, 739.

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