Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
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Peptenatu, D; Andronache, I; Ahammer, H; Radulovic, M; Costanza, JK; Jelinek, HF; Di Ieva, A; Koyama, K; Grecu, A; Gruia, AK; Simion, AG; Nedelcu, ID; Olteanu, C; Draghici, CC; Marin, M; Diaconu, DC; Fensholt, R; Newman, EA.
A new fractal index to classify forest fragmentation and disorder
LANDSCAPE ECOL. 2023;
Doi: 10.1007/s10980-023-01640-y
Web of Science
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- Co-Autor*innen der Med Uni Graz
- Ahammer Helmut
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- Abstract:
- ContextForest 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.ObjectivesWe present a fractal fragmentation and disorder index (FFDI), which advances a previously developed fractal index by merging it with the Renyi information dimension. The FFDI is designed to work across spatial scales, and to efficiently report both the fragmentation of images and their spatial disorder.MethodsWe 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.ResultsThe FFDI outperformed its two individual components (fractal fragmentation index and Renyi information dimension) in resolving spatial patterns of disorder and fragmentation when tested on HRM classes and other image types. The FFDI thus offers a clear advantage when compared to the individual use of fractal fragmentation index and the Information Dimension, and provided good classification performance in an application to real data.ConclusionsThis 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 find wider applicability in biology wherever image analysis is used.
- Find related publications in this database (Keywords)
- Forest fragmentation
- Hierarchically structured random maps
- Remote sensing
- Renyi information dimension
- Romanian Carpathian Mountains
- Spatial disorder