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

Research output: Contribution to journal › Journal article › Research › peer-review

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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
Journal	Landscape Ecology
Volume	38
Pages (from-to)	1373–1393
ISSN	0921-2973
DOIs	https://doi.org/10.1007/s10980-023-01640-y
Publication status	Published - 2023

Bibliographical note

Correction: https://doi.org/10.1007/s10980-023-01781-0

Publisher Copyright:
© 2023, The Author(s).

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

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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, 1373–1393. https://doi.org/10.1007/s10980-023-01640-y

A new fractal index to classify forest fragmentation and disorder. / Peptenatu, Daniel; Andronache, Ion; Ahammer, Helmut; Radulovic, Marko; Costanza, Jennifer K.; Jelinek, Herbert F.; Di Ieva, Antonio; 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.

In: Landscape Ecology, Vol. 38, 2023, p. 1373–1393.

Research output: Contribution to journal › Journal article › Research › peer-review

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, pp. 1373–1393. https://doi.org/10.1007/s10980-023-01640-y

Peptenatu, Daniel ; Andronache, Ion ; Ahammer, Helmut ; Radulovic, Marko ; Costanza, Jennifer K. ; Jelinek, Herbert F. ; Di Ieva, Antonio ; 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. / A new fractal index to classify forest fragmentation and disorder. In: Landscape Ecology. 2023 ; Vol. 38. pp. 1373–1393.

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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.",

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note = "Correction: https://doi.org/10.1007/s10980-023-01781-0 Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

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

language = "English",

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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.

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 -