Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity
- Autores
- Peri, Pablo Luis; Gaitán, Juan José; Diaz, Boris Gastón; Almonacid, Leandro; Morales, Cristian; Ferrerer, Francisco; Lasagno, Romina; Rodriguez Souilla, Julian; Martínez Pastur, Guillermo José
- Año de publicación
- 2024
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making.
Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Santa Cruz. Universidad Tecnológica Nacional. Facultad Regional Santa Cruz. Centro de Investigaciones y Transferencia de Santa Cruz. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia de Santa Cruz; Argentina
Fil: Gaitán, Juan José. Universidad Nacional de Luján; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Diaz, Boris Gastón. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Almonacid, Leandro. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Morales, Cristian. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Ferrerer, Francisco. Universidad Nacional de la Patagonia Austral; Argentina
Fil: Lasagno, Romina. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Rodriguez Souilla, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentina
Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentina - Materia
-
RANGELAND
LIVESTOCK
PLANT BIODIVERSITY
ECOSYSTEM SERVICES - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/230669
Ver los metadatos del registro completo
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oai:ri.conicet.gov.ar:11336/230669 |
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Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversityPeri, Pablo LuisGaitán, Juan JoséDiaz, Boris GastónAlmonacid, LeandroMorales, CristianFerrerer, FranciscoLasagno, RominaRodriguez Souilla, JulianMartínez Pastur, Guillermo JoséRANGELANDLIVESTOCKPLANT BIODIVERSITYECOSYSTEM SERVICEShttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Santa Cruz. Universidad Tecnológica Nacional. Facultad Regional Santa Cruz. Centro de Investigaciones y Transferencia de Santa Cruz. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia de Santa Cruz; ArgentinaFil: Gaitán, Juan José. Universidad Nacional de Luján; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Diaz, Boris Gastón. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Almonacid, Leandro. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Morales, Cristian. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Ferrerer, Francisco. Universidad Nacional de la Patagonia Austral; ArgentinaFil: Lasagno, Romina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Rodriguez Souilla, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaMDPI2024-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/230669Peri, Pablo Luis; Gaitán, Juan José; Diaz, Boris Gastón; Almonacid, Leandro; Morales, Cristian ; et al.; Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity; MDPI; Sustainability; 16; 2025; 2-2024; 1-152071-1050CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/su16052025info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:05:56Zoai:ri.conicet.gov.ar:11336/230669instacron: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:34982025-09-29 10:05:57.192CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity |
title |
Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity |
spellingShingle |
Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity Peri, Pablo Luis RANGELAND LIVESTOCK PLANT BIODIVERSITY ECOSYSTEM SERVICES |
title_short |
Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity |
title_full |
Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity |
title_fullStr |
Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity |
title_full_unstemmed |
Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity |
title_sort |
Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity |
dc.creator.none.fl_str_mv |
Peri, Pablo Luis Gaitán, Juan José Diaz, Boris Gastón Almonacid, Leandro Morales, Cristian Ferrerer, Francisco Lasagno, Romina Rodriguez Souilla, Julian Martínez Pastur, Guillermo José |
author |
Peri, Pablo Luis |
author_facet |
Peri, Pablo Luis Gaitán, Juan José Diaz, Boris Gastón Almonacid, Leandro Morales, Cristian Ferrerer, Francisco Lasagno, Romina Rodriguez Souilla, Julian Martínez Pastur, Guillermo José |
author_role |
author |
author2 |
Gaitán, Juan José Diaz, Boris Gastón Almonacid, Leandro Morales, Cristian Ferrerer, Francisco Lasagno, Romina Rodriguez Souilla, Julian Martínez Pastur, Guillermo José |
author2_role |
author author author author author author author author |
dc.subject.none.fl_str_mv |
RANGELAND LIVESTOCK PLANT BIODIVERSITY ECOSYSTEM SERVICES |
topic |
RANGELAND LIVESTOCK PLANT BIODIVERSITY ECOSYSTEM SERVICES |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making. Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Santa Cruz. Universidad Tecnológica Nacional. Facultad Regional Santa Cruz. Centro de Investigaciones y Transferencia de Santa Cruz. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia de Santa Cruz; Argentina Fil: Gaitán, Juan José. Universidad Nacional de Luján; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Diaz, Boris Gastón. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Almonacid, Leandro. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Morales, Cristian. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Ferrerer, Francisco. Universidad Nacional de la Patagonia Austral; Argentina Fil: Lasagno, Romina. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Rodriguez Souilla, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentina Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentina |
description |
Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-02 |
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/11336/230669 Peri, Pablo Luis; Gaitán, Juan José; Diaz, Boris Gastón; Almonacid, Leandro; Morales, Cristian ; et al.; Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity; MDPI; Sustainability; 16; 2025; 2-2024; 1-15 2071-1050 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/230669 |
identifier_str_mv |
Peri, Pablo Luis; Gaitán, Juan José; Diaz, Boris Gastón; Almonacid, Leandro; Morales, Cristian ; et al.; Vegetation Type mapping in Southern Patagonia and its relationship with ecosystem services, soil carbon stock, and biodiversity; MDPI; Sustainability; 16; 2025; 2-2024; 1-15 2071-1050 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.3390/su16052025 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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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1844613901545635840 |
score |
13.070432 |