A new fractal index to classify forest disturbance and anthropogenic change

Abstract

AbstractContextDeforestation remains one of the most pressing threats to biodiversity. Characterizing the resulting forest loss and fragmentation efficiently from remotely sensed data therefore has strong practical implications. Data are often separately analyzed for spatial fragmentation and disorder, but no existing metric simultaneously quantifies the shapes and arrangement of fragments.ObjectivesWe present a Fractal Fragmentation and Disorder Index (FFDI), which advances a previously developed fractal index by merging it with the Rényi information dimension. TheFFDIis designed to work across spatial scales, and efficiently reports the fragmentation of images and spatial disorder of those fragments.MethodsWe validate theFFDIwith four sets of synthetic Hierarchically Structured Random Map (HRM) multiscale images, characterized by increasing fragmentation and disorder but decreasing average size over multiple scales. We then apply theFFDIto the Global Land Analysis & Discovery Global Forest Change database satellite imagery of forest cover for 10 distinct regions of the Romanian Carpathian Mountains from 2000-2014.ResultsTheFFDIoutperformed the individual use of its two components in resolving spatial patterns of disorder and fragmentation among HRM classes. It offers a clear advantage when compared to the individual use of Fractal Fragmentation Index and the Rényi information dimension, and works in an application to real data.ConclusionsThis work improves on previous characterizations of landscape patterns. With theFFDI, scientists will be able to better monitor and understand forest fragmentation from satellite imagery. TheFFDIwill have broad applicability to biological fields where image analysis is used.