A new fractal index to classify forest fragmentation and disorder

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

doi:10.1007/s10980-023-01640-y

A new fractal index to classify forest fragmentation and disorder

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^*

^*Corresponding author for this work

Macquarie Medical School

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

44 Downloads (Pure)

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 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.

Original language	English
Pages (from-to)	1373-1393
Number of pages	21
Journal	Landscape Ecology
Volume	38
Issue number	6
Early online date	7 Apr 2023
DOIs	https://doi.org/10.1007/s10980-023-01640-y
Publication status	Published - Jun 2023

Bibliographical note

Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

A correction exists for this article and can be found at doi: 10.1007/s10980-023-01781-0. The original article has been updated.

Keywords

Forest fragmentation
Hierarchically structured random maps
Remote sensing
Romanian Carpathian Mountains
Rényi information dimension
Spatial disorder

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10.1007/s10980-023-01640-yLicence: CC BY

Publisher version (open access)Final published version, 3.08 MBLicence: CC BY

Cite this

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, 38(6), 1373-1393. https://doi.org/10.1007/s10980-023-01640-y

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title = "A new fractal index to classify forest fragmentation and disorder",

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 R{\'e}nyi 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 R{\'e}nyi 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.",

keywords = "Forest fragmentation, Hierarchically structured random maps, Remote sensing, Romanian Carpathian Mountains, R{\'e}nyi information dimension, Spatial disorder",

author = "Daniel Peptenatu and Ion Andronache and Helmut Ahammer and Marko Radulovic and Costanza, {Jennifer K.} and Jelinek, {Herbert F.} and {Di Ieva}, Antonio and Kohei Koyama and Alexandra Grecu and Gruia, {Andreea Karina} and Adrian-Gabriel Simion and Nedelcu, {Iulia Daniela} and Cosmin Olteanu and Cristian-Constantin Dr{\u a}ghici and Marian Marin and Diaconu, {Daniel Constantin} and Rasmus Fensholt and Newman, {Erica A.}",

note = "Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. A correction exists for this article and can be found at doi: 10.1007/s10980-023-01781-0. The original article has been updated.",

year = "2023",

month = jun,

doi = "10.1007/s10980-023-01640-y",

language = "English",

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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, A-G, Nedelcu, ID, Olteanu, C, Drăghici, C-C, Marin, M, Diaconu, DC, Fensholt, R & Newman, EA 2023, 'A new fractal index to classify forest fragmentation and disorder', Landscape Ecology, vol. 38, no. 6, pp. 1373-1393. https://doi.org/10.1007/s10980-023-01640-y

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 - Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. A correction exists for this article and can be found at doi: 10.1007/s10980-023-01781-0. The original article has been updated.

PY - 2023/6

Y1 - 2023/6

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

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A new fractal index to classify forest fragmentation and disorder

Abstract

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Keywords

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