Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data
- Autores
- Baumann, Matthias; Levers, Christian; Macchi, Leandro; Bluhm, Hendrik; Waske, Björn; Gasparri, Nestor Ignacio; Kuemmerle, Tobias
- Año de publicación
- 2018
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Tropical dry forests and savannas provide important ecosystem services and harbor high biodiversity, yet are globally under pressure from land-use change. Mapping changes in the condition of dry forests and savannas is therefore critical. This can be challenging given that these ecosystems are characterized by continuous gradients of tree and shrub cover, resulting in considerable structural complexity. We developed a novel approach to map, separately, continuous fields of tree cover and shrub cover across the South American Gran Chaco (1,100,000 km2), making full use of the Landsat-8 optical and Sentinel-1 synthetic aperture radar (SAR) image archives. We gathered a large training dataset digitized from very-high resolution imagery and used a gradient-boosting framework to model continuous fields of tree cover and shrub cover at 30-m resolution. Our regression models had high to moderate predictive power (85.5% for tree cover, and 68.5% for shrub cover) and resulted in reliable tree and shrub cover maps (mean squared error of 4.4% and 6.4% for tree- and shrub cover respectively). Models jointly using optical and SAR imagery performed substantially better than models using single-sensor imagery, and model predictors differed strongly in some regions, especially in areas of dense vegetation cover. Mapping tree and shrub cover separately allowed identifying distinct vegetation formations, with shrub-dominated systems mainly in the very dry Chaco, woodlands with large trees mainly in the dry Chaco, and tree-dominated savannas in the wet Chaco. Our tree and shrub cover layers also revealed considerable edge effects in terms of woody cover away from agricultural fields (edge effects extending about 2 km), smallholder ranches (about 1.2 km), and roads and railways (about 1.4 and 0.9 km, respectively). Our analyses highlight both the substantial footprint of land-use on remaining natural vegetation in the Chaco, and the potential of multi-sensoral approaches to monitor forest degradation. More broadly, our approach shows that mapping canopy structure and distinct layers of woody vegetation in dry forest and savannas is possible across large areas, and highlights the value of the growing Landsat and Sentinel archives for doing so.
Fil: Baumann, Matthias. Universität zu Berlin; Alemania
Fil: Levers, Christian.
Fil: Macchi, Leandro. 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. Universität zu Berlin; Alemania
Fil: Bluhm, Hendrik. Universität zu Berlin; Alemania
Fil: Waske, Björn. Universität zu Berlin; Alemania
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. Universität zu Berlin; Alemania
Fil: Kuemmerle, Tobias. Universität zu Berlin; Alemania - Materia
-
CANOPY STRUCTURE
ESSENTIAL BIODIVERSITY VARIABLES
FOREST DEGRADATION
GRADIENT BOOSTING REGRESSION
SAVANNAS
SENSOR FUSION
TROPICAL DRY FORESTS
VEGETATION CONTINUOUS FIELDS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR)
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/90415
Ver los metadatos del registro completo
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Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 dataBaumann, MatthiasLevers, ChristianMacchi, LeandroBluhm, HendrikWaske, BjörnGasparri, Nestor IgnacioKuemmerle, TobiasCANOPY STRUCTUREESSENTIAL BIODIVERSITY VARIABLESFOREST DEGRADATIONGRADIENT BOOSTING REGRESSIONSAVANNASSENSOR FUSIONTROPICAL DRY FORESTSVEGETATION CONTINUOUS FIELDShttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Tropical dry forests and savannas provide important ecosystem services and harbor high biodiversity, yet are globally under pressure from land-use change. Mapping changes in the condition of dry forests and savannas is therefore critical. This can be challenging given that these ecosystems are characterized by continuous gradients of tree and shrub cover, resulting in considerable structural complexity. We developed a novel approach to map, separately, continuous fields of tree cover and shrub cover across the South American Gran Chaco (1,100,000 km2), making full use of the Landsat-8 optical and Sentinel-1 synthetic aperture radar (SAR) image archives. We gathered a large training dataset digitized from very-high resolution imagery and used a gradient-boosting framework to model continuous fields of tree cover and shrub cover at 30-m resolution. Our regression models had high to moderate predictive power (85.5% for tree cover, and 68.5% for shrub cover) and resulted in reliable tree and shrub cover maps (mean squared error of 4.4% and 6.4% for tree- and shrub cover respectively). Models jointly using optical and SAR imagery performed substantially better than models using single-sensor imagery, and model predictors differed strongly in some regions, especially in areas of dense vegetation cover. Mapping tree and shrub cover separately allowed identifying distinct vegetation formations, with shrub-dominated systems mainly in the very dry Chaco, woodlands with large trees mainly in the dry Chaco, and tree-dominated savannas in the wet Chaco. Our tree and shrub cover layers also revealed considerable edge effects in terms of woody cover away from agricultural fields (edge effects extending about 2 km), smallholder ranches (about 1.2 km), and roads and railways (about 1.4 and 0.9 km, respectively). Our analyses highlight both the substantial footprint of land-use on remaining natural vegetation in the Chaco, and the potential of multi-sensoral approaches to monitor forest degradation. More broadly, our approach shows that mapping canopy structure and distinct layers of woody vegetation in dry forest and savannas is possible across large areas, and highlights the value of the growing Landsat and Sentinel archives for doing so.Fil: Baumann, Matthias. Universität zu Berlin; AlemaniaFil: Levers, Christian.Fil: Macchi, Leandro. 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. Universität zu Berlin; AlemaniaFil: Bluhm, Hendrik. Universität zu Berlin; AlemaniaFil: Waske, Björn. Universität zu Berlin; AlemaniaFil: 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. Universität zu Berlin; AlemaniaFil: Kuemmerle, Tobias. Universität zu Berlin; AlemaniaElsevier Science Inc2018-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/90415Baumann, Matthias; Levers, Christian; Macchi, Leandro; Bluhm, Hendrik; Waske, Björn; et al.; Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data; Elsevier Science Inc; Remote Sensing of Environment; 216; 10-2018; 201-2110034-4257CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0034425718303249info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2018.06.044info:eu-repo/semantics/openAccessAtribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR)https://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:20:56Zoai:ri.conicet.gov.ar:11336/90415instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:20:56.907CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data |
title |
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data |
spellingShingle |
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data Baumann, Matthias CANOPY STRUCTURE ESSENTIAL BIODIVERSITY VARIABLES FOREST DEGRADATION GRADIENT BOOSTING REGRESSION SAVANNAS SENSOR FUSION TROPICAL DRY FORESTS VEGETATION CONTINUOUS FIELDS |
title_short |
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data |
title_full |
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data |
title_fullStr |
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data |
title_full_unstemmed |
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data |
title_sort |
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data |
dc.creator.none.fl_str_mv |
Baumann, Matthias Levers, Christian Macchi, Leandro Bluhm, Hendrik Waske, Björn Gasparri, Nestor Ignacio Kuemmerle, Tobias |
author |
Baumann, Matthias |
author_facet |
Baumann, Matthias Levers, Christian Macchi, Leandro Bluhm, Hendrik Waske, Björn Gasparri, Nestor Ignacio Kuemmerle, Tobias |
author_role |
author |
author2 |
Levers, Christian Macchi, Leandro Bluhm, Hendrik Waske, Björn Gasparri, Nestor Ignacio Kuemmerle, Tobias |
author2_role |
author author author author author author |
dc.subject.none.fl_str_mv |
CANOPY STRUCTURE ESSENTIAL BIODIVERSITY VARIABLES FOREST DEGRADATION GRADIENT BOOSTING REGRESSION SAVANNAS SENSOR FUSION TROPICAL DRY FORESTS VEGETATION CONTINUOUS FIELDS |
topic |
CANOPY STRUCTURE ESSENTIAL BIODIVERSITY VARIABLES FOREST DEGRADATION GRADIENT BOOSTING REGRESSION SAVANNAS SENSOR FUSION TROPICAL DRY FORESTS VEGETATION CONTINUOUS FIELDS |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 https://purl.org/becyt/ford/4.1 https://purl.org/becyt/ford/4 |
dc.description.none.fl_txt_mv |
Tropical dry forests and savannas provide important ecosystem services and harbor high biodiversity, yet are globally under pressure from land-use change. Mapping changes in the condition of dry forests and savannas is therefore critical. This can be challenging given that these ecosystems are characterized by continuous gradients of tree and shrub cover, resulting in considerable structural complexity. We developed a novel approach to map, separately, continuous fields of tree cover and shrub cover across the South American Gran Chaco (1,100,000 km2), making full use of the Landsat-8 optical and Sentinel-1 synthetic aperture radar (SAR) image archives. We gathered a large training dataset digitized from very-high resolution imagery and used a gradient-boosting framework to model continuous fields of tree cover and shrub cover at 30-m resolution. Our regression models had high to moderate predictive power (85.5% for tree cover, and 68.5% for shrub cover) and resulted in reliable tree and shrub cover maps (mean squared error of 4.4% and 6.4% for tree- and shrub cover respectively). Models jointly using optical and SAR imagery performed substantially better than models using single-sensor imagery, and model predictors differed strongly in some regions, especially in areas of dense vegetation cover. Mapping tree and shrub cover separately allowed identifying distinct vegetation formations, with shrub-dominated systems mainly in the very dry Chaco, woodlands with large trees mainly in the dry Chaco, and tree-dominated savannas in the wet Chaco. Our tree and shrub cover layers also revealed considerable edge effects in terms of woody cover away from agricultural fields (edge effects extending about 2 km), smallholder ranches (about 1.2 km), and roads and railways (about 1.4 and 0.9 km, respectively). Our analyses highlight both the substantial footprint of land-use on remaining natural vegetation in the Chaco, and the potential of multi-sensoral approaches to monitor forest degradation. More broadly, our approach shows that mapping canopy structure and distinct layers of woody vegetation in dry forest and savannas is possible across large areas, and highlights the value of the growing Landsat and Sentinel archives for doing so. Fil: Baumann, Matthias. Universität zu Berlin; Alemania Fil: Levers, Christian. Fil: Macchi, Leandro. 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. Universität zu Berlin; Alemania Fil: Bluhm, Hendrik. Universität zu Berlin; Alemania Fil: Waske, Björn. Universität zu Berlin; Alemania 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. Universität zu Berlin; Alemania Fil: Kuemmerle, Tobias. Universität zu Berlin; Alemania |
description |
Tropical dry forests and savannas provide important ecosystem services and harbor high biodiversity, yet are globally under pressure from land-use change. Mapping changes in the condition of dry forests and savannas is therefore critical. This can be challenging given that these ecosystems are characterized by continuous gradients of tree and shrub cover, resulting in considerable structural complexity. We developed a novel approach to map, separately, continuous fields of tree cover and shrub cover across the South American Gran Chaco (1,100,000 km2), making full use of the Landsat-8 optical and Sentinel-1 synthetic aperture radar (SAR) image archives. We gathered a large training dataset digitized from very-high resolution imagery and used a gradient-boosting framework to model continuous fields of tree cover and shrub cover at 30-m resolution. Our regression models had high to moderate predictive power (85.5% for tree cover, and 68.5% for shrub cover) and resulted in reliable tree and shrub cover maps (mean squared error of 4.4% and 6.4% for tree- and shrub cover respectively). Models jointly using optical and SAR imagery performed substantially better than models using single-sensor imagery, and model predictors differed strongly in some regions, especially in areas of dense vegetation cover. Mapping tree and shrub cover separately allowed identifying distinct vegetation formations, with shrub-dominated systems mainly in the very dry Chaco, woodlands with large trees mainly in the dry Chaco, and tree-dominated savannas in the wet Chaco. Our tree and shrub cover layers also revealed considerable edge effects in terms of woody cover away from agricultural fields (edge effects extending about 2 km), smallholder ranches (about 1.2 km), and roads and railways (about 1.4 and 0.9 km, respectively). Our analyses highlight both the substantial footprint of land-use on remaining natural vegetation in the Chaco, and the potential of multi-sensoral approaches to monitor forest degradation. More broadly, our approach shows that mapping canopy structure and distinct layers of woody vegetation in dry forest and savannas is possible across large areas, and highlights the value of the growing Landsat and Sentinel archives for doing so. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/90415 Baumann, Matthias; Levers, Christian; Macchi, Leandro; Bluhm, Hendrik; Waske, Björn; et al.; Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data; Elsevier Science Inc; Remote Sensing of Environment; 216; 10-2018; 201-211 0034-4257 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/90415 |
identifier_str_mv |
Baumann, Matthias; Levers, Christian; Macchi, Leandro; Bluhm, Hendrik; Waske, Björn; et al.; Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data; Elsevier Science Inc; Remote Sensing of Environment; 216; 10-2018; 201-211 0034-4257 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0034425718303249 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2018.06.044 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR) https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR) https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science Inc |
publisher.none.fl_str_mv |
Elsevier Science Inc |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.22299 |