Developing an integrated remote sensing based biodiversity index for predicting animal species richness

Abstract

Many remote sensingmetrics have been applied in large-scale animal speciesmonitoring 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%). Themetricswith a high explained variance on the global scaleweremainly in the energy/productivity category. Themetrics 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. Threemachine learningmodels (i.e., support vectormachine, randomforests, and neural networks) were evaluated formetric integration, and the randomforestmodel 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 univariatemetrics.