Measuring Rao's Q diversity index from remote sensing: An open source solution

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• 作者：Duccio Rocchini^a ; ¹ ; ^{duccio.rocchini@fmach.it" class="auth_mail" title="E-mail the corresponding author} ; Matteo Marcantonio^a ; ^b ; ¹ ; Carlo Ricotta^c
• 关键词：Biodiversity ; Heterogeneity ; Landscape metrics ; Rao's Q ; Remote sensing ; Spatial ecology ; Shannon's entropy
• 刊名：Ecological Indicators
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：72
• 期：Complete
• 页码：234-238
• 全文大小：1456 K

文摘

Measuring biodiversity is a key issue in ecology to guarantee effective indicators of ecosystem health at different spatial and time scales. However, estimating biodiversity from field observations might present difficulties related to costs and time needed. Moreover, a continuous data update for biodiversity monitoring purposes might be prohibitive. From this point of view, remote sensing represents a powerful tool since it allows to cover wide areas in a relatively low amount of time. One of the most common indicators of biodiversity is Shannon's entropy H′, which is strictly related to environmental heterogeneity, and thus to species diversity. However, Shannon's entropy might show drawbacks once applied to remote sensing data, since it considers relative abundances but it does not explicitly account for distances among pixels’ numerical values. In this paper we propose the use of Rao's Q applied to remotely sensed data, providing a straightforward R-package function to calculate it in 2D systems. We will introduce the theoretical rationale behind Rao's index and then provide applied examples based on the proposed R function.