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dc.contributor.author
De Marzo, Teresa
dc.contributor.author
Gasparri, Nestor Ignacio
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Lambin, Eric F.
dc.contributor.author
Kuemmerle, Tobias
dc.date.available
2023-09-27T15:51:13Z
dc.date.issued
2022-04
dc.identifier.citation
De Marzo, Teresa; Gasparri, Nestor Ignacio; Lambin, Eric F.; Kuemmerle, Tobias; Agents of Forest Disturbance in the Argentine Dry Chaco; MDPI; Remote Sensing; 14; 7; 4-2022; 1-19
dc.identifier.issn
2072-4292
dc.identifier.uri
http://hdl.handle.net/11336/213269
dc.description.abstract
Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of the causes of disturbances. Here, we apply an approach for attributing agents of forest disturbance across large areas of tropical dry forests, based on the Landsat image time series. Focusing on the 489,000 km2 Argentine Dry Chaco, we derived metrics on the spectral characteristics and shape of disturbance patches. We then used these metrics in a random forests classification framework to estimate the area of logging, fire, partial clearing, riparian changes and drought. Our results highlight that partial clearing was the most widespread type of forest disturbance from 1990–to 2017, extending over 5520 km2 (±407 km2 ), followed by fire (4562 ± 388 km2 ) and logging (3891 ± 341 km2 ). Our analyses also reveal marked trends over time, with partial clearing generally becoming more prevalent, whereas fires declined. Comparing the spatial patterns of different disturbance types against accessibility indicators showed that fire and logging prevalence was higher closer to fields, while smallholder homesteads were associated with less burning. Roads were, surprisingly, not associated with clear trends in disturbance prevalence. To our knowledge, this is the first attribution of disturbance agents in tropical dry forests based on satellite-based indicators. While our study reveals remaining uncertainties in this attribution process, our framework has considerable potential for monitoring tropical dry forest disturbances at scale. Tropical dry forests in South America, Africa and Southeast Asia are some of the fastest disappearing ecosystems on the planet, and more robust monitoring of forest degradation in these regions is urgently needed.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
MDPI
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
DISTURBANCE AGENTS
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DISTURBANCE REGIMES
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FOREST DEGRADATION
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LAND USE
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LANDSAT TIME SERIES
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LANDTRENDR
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TROPICAL DRY FORESTS
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Silvicultura
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Agricultura, Silvicultura y Pesca
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CIENCIAS AGRÍCOLAS
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Ecología
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Ciencias Biológicas
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CIENCIAS NATURALES Y EXACTAS
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Ciencias Medioambientales
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
dc.title
Agents of Forest Disturbance in the Argentine Dry Chaco
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-07-06T12:39:33Z
dc.journal.volume
14
dc.journal.number
7
dc.journal.pagination
1-19
dc.journal.pais
Suiza
dc.description.fil
Fil: De Marzo, Teresa. Université Catholique de Louvain; Bélgica
dc.description.fil
Fil: Gasparri, Nestor Ignacio. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo; Argentina
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Fil: Lambin, Eric F.. Université Catholique de Louvain; Bélgica. University of Stanford; Estados Unidos
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Fil: Kuemmerle, Tobias. Humboldt-Universität zu Berlin; Alemania
dc.journal.title
Remote Sensing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/rs14071758
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