Key in ensuring the effectiveness of conservation efforts and maintaining ecosystem health, measuring biodiversity can benefit greatly when remote sensing data comes into the equation. A new EU BON related paper, published in the journal Ecological Indicators, proposes open source solutions for measuring the important Rao's Q index, when it comes to remote sensing data.
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.
Rocchini, D., Marcantonio, M., Ricotta, C. (2017). Measuring Rao's Q diversity index rom remote sensing: an open source solution. Ecological Indicators, 72: 234-238. [5years-IF: 3.649] DOI:10.1016/j.ecolind.2016.07.039