A new fractal index to classify forest fragmentation and disorder
| Autor: | Peptenatu Daniel, Andronache Ion, Ahammer Helmut, Radulović Marko, Costanza Jennifer K, Jelinek Herbert F., Di Ieva Antonia, Koyama Kohei, Grecu Alexandra, Gruia Andreea Karina, Simion Adrian-Gabriel, Nedelcu Iulia Daniela, Olteanu Cosmin, Drăghici Cristian-Constantin, Marin Marian, Diaconu Daniel Constantin, Fensholt Rasmus, Newman Erica A. |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Hierarchically structured random maps
Rényi information dimension fractal fragmentation and disorder index (FFDI) Ecology Romanian Carpathian Mountains Forest fragmentation Geography Planning and Development Remote sensing landscape patterns Spatial disorder spatial patterns biodiversity Nature and Landscape Conservation |
| Zdroj: | Peptenatu, D, Andronache, I, Ahammer, H, Radulovic, M, Costanza, J K, Jelinek, H F, Di Ieva, A, Koyama, K, Grecu, A, Gruia, A K, Simion, A-G, Nedelcu, I D, Olteanu, C, Drăghici, C-C, Marin, M, Diaconu, D C, Fensholt, R & Newman, E A 2023, ' A new fractal index to classify forest fragmentation and disorder ', Landscape Ecology, vol. 38, pp. 1373–1393 . https://doi.org/10.1007/s10980-023-01640-y |
| DOI: | 10.1007/s10980-023-01640-y |
| Popis: | Abstract 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 efficiently 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 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.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 find wider applicability in biology wherever image analysis is used. 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 efficiently 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 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.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 find wider applicability in biology wherever image analysis is used. |
| Databáze: | OpenAIRE |
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