TY - JOUR
T1 - A new fractal index to classify forest fragmentation and disorder
AU - Peptenatu, Daniel
AU - Andronache, Ion
AU - Ahammer, Helmut
AU - Radulovic, Marko
AU - Costanza, Jennifer K.
AU - Jelinek, Herbert F.
AU - Di Ieva, Antonio
AU - Koyama, Kohei
AU - Grecu, Alexandra
AU - Gruia, Andreea Karina
AU - Simion, Adrian-Gabriel
AU - Nedelcu, Iulia Daniela
AU - Olteanu, Cosmin
AU - Drăghici, Cristian-Constantin
AU - Marin, Marian
AU - Diaconu, Daniel Constantin
AU - Fensholt, Rasmus
AU - Newman, Erica A.
N1 - Correction: https://doi.org/10.1007/s10980-023-01781-0
Publisher Copyright:
© 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Forest fragmentation
KW - Hierarchically structured random maps
KW - Remote sensing
KW - Romanian Carpathian Mountains
KW - Rényi information dimension
KW - Spatial disorder
U2 - 10.1007/s10980-023-01640-y
DO - 10.1007/s10980-023-01640-y
M3 - Journal article
AN - SCOPUS:85152048772
VL - 38
SP - 1373
EP - 1393
JO - Landscape Ecology
JF - Landscape Ecology
SN - 0921-2973
ER -