A new fractal index to classify forest fragmentation and disorder
| Authors: | Daniel Peptenatu, Ion Andronache, Helmut Ahammer, Marko Radulovic, Jennifer K. Costanza, Herbert F. Jelinek, Antonio Di Ieva, Kohei Koyama, Alexandra Grecu, Andreea Karina Gruia, Adrian-Gabriel Simion, Iulia Daniela Nedelcu, Cosmin Olteanu, Cristian-Constantin Drăghici, Marian Marin, Daniel Constantin Diaconu, Rasmus Fensholt, Erica A. Newman |
| Year: | 2023 |
| Type: | Scientific Journal |
| Station: | Southern Research Station |
| DOI: | https://doi.org/10.1007/s10980-023-01640-y |
| Source: | Landscape Ecology |
Abstract
Context Forest loss and fragmentation pose extreme threats to biodiversity. Their efficient characterization 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 both the shape and arrangement of fragments. Objectives We present a fractal fragmentation and disorder index (FFDI), which advances a previously developed fractal index by merging it with the Rényi information dimension. The FFDI is designed to work across spatial scales, and to effciently report both the fragmentation of images and their spatial disorder.Methods We validate the FFDI with 12,600 synthetic hierarchically structured random map (HRM) multiscale images, as well as several other categories of fractal and non-fractal test images (4880 images). We then apply the FFDI to satellite imagery of forest cover for 10 distinct regions of the Romanian Carpathian Mountains from 2000–2021.
Results The FFDI outperformed its two individual components (fractal fragmentation index and Rényi information dimension) in resolving spatial patterns of disorder and fragmentation when tested on HRM classes and other image types. The FFDI thus ofers a clear advantage when compared to the individual use of fractal fragmentation index and the Information Dimension, and provided good classifcation performance in an application to real data.
Conclusions This work improves on previous characterizations of landscape patterns. With the FFDI, scientists will be able to better monitor and understand forest fragmentation from satellite imagery. The FFDI may also fnd wider applicability in biology wherever image analysis is used.