Mapping soil organic carbon content in Patagonian forests based on climate, topography and vegetation metrics from satellite imagery
Soil organic carbon (SOC) content supports several ecosystem services. Quantifying SOC requires: (i) accurate C estimates of forest components, and (ii) soil estimates. However, SOC is difficult to measure, so predictive models are needed. Our objective was to model SOC stocks within 30 cm depth in...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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Multidisciplinary Digital Publishing Institute (MDPI)
2022
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| Online Access: | http://hdl.handle.net/20.500.12123/13417 https://www.mdpi.com/2072-4292/14/22/5702 https://doi.org/10.3390/rs14225702 |
| _version_ | 1855485165314244608 |
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| author | Martínez Pastur, Guillermo José Aravena Acuña, Marie Claire Silveira, Eduarda M.O. Von Müller, Axel La Manna, Ludmila González Polo, Marina Chaves, Jimena Elizabeth Cellini, Juan Manuel Lencinas, María Vanessa Radeloff, Volker C. Pidgeon, Anna Michle Peri, Pablo Luis |
| author_browse | Aravena Acuña, Marie Claire Cellini, Juan Manuel Chaves, Jimena Elizabeth González Polo, Marina La Manna, Ludmila Lencinas, María Vanessa Martínez Pastur, Guillermo José Peri, Pablo Luis Pidgeon, Anna Michle Radeloff, Volker C. Silveira, Eduarda M.O. Von Müller, Axel |
| author_facet | Martínez Pastur, Guillermo José Aravena Acuña, Marie Claire Silveira, Eduarda M.O. Von Müller, Axel La Manna, Ludmila González Polo, Marina Chaves, Jimena Elizabeth Cellini, Juan Manuel Lencinas, María Vanessa Radeloff, Volker C. Pidgeon, Anna Michle Peri, Pablo Luis |
| author_sort | Martínez Pastur, Guillermo José |
| collection | INTA Digital |
| description | Soil organic carbon (SOC) content supports several ecosystem services. Quantifying SOC requires: (i) accurate C estimates of forest components, and (ii) soil estimates. However, SOC is difficult to measure, so predictive models are needed. Our objective was to model SOC stocks within 30 cm depth in Patagonian forests based on climatic, topographic and vegetation productivity measures from satellite images, including Dynamic Habitat Indices and Land Surface Temperature derived from Landsat-8. We used data from 1320 stands of different forest types in Patagonia, and random forest regression to map SOC. The model captured SOC variability well (R2 = 0.60, RMSE = 22.1%), considering the huge latitudinal extension (36.4◦ to 55.1◦ SL) and the great diversity of forest types. Mean SOC was 134.4 ton C ha−1 ± 25.2, totaling 404.2 million ton C across Patagonia. Overall, SOC values were highest in valleys of the Andes mountains and in southern Tierra del Fuego, ranging from 53.5 to 277.8 ton C ha−1 for the whole Patagonia region. Soil organic carbon is a metric relevant to many applications, connecting major issues such as forest management, conservation, and livestock production, and having spatially explicit estimates of SOC enables managers to fulfil the international agreements that Argentina has joined. |
| format | Artículo |
| id | INTA13417 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
| publisherStr | Multidisciplinary Digital Publishing Institute (MDPI) |
| record_format | dspace |
| spelling | INTA134172022-11-15T10:52:28Z Mapping soil organic carbon content in Patagonian forests based on climate, topography and vegetation metrics from satellite imagery Martínez Pastur, Guillermo José Aravena Acuña, Marie Claire Silveira, Eduarda M.O. Von Müller, Axel La Manna, Ludmila González Polo, Marina Chaves, Jimena Elizabeth Cellini, Juan Manuel Lencinas, María Vanessa Radeloff, Volker C. Pidgeon, Anna Michle Peri, Pablo Luis Soil Organic Carbon Primary Forests Satellite Imagery Carbono Orgánico del Suelo Bosque Primario Imágenes por Satélites Landsat-8 Región Patagónica Dynamic Habitat Indices Bosques Nativos Soil organic carbon (SOC) content supports several ecosystem services. Quantifying SOC requires: (i) accurate C estimates of forest components, and (ii) soil estimates. However, SOC is difficult to measure, so predictive models are needed. Our objective was to model SOC stocks within 30 cm depth in Patagonian forests based on climatic, topographic and vegetation productivity measures from satellite images, including Dynamic Habitat Indices and Land Surface Temperature derived from Landsat-8. We used data from 1320 stands of different forest types in Patagonia, and random forest regression to map SOC. The model captured SOC variability well (R2 = 0.60, RMSE = 22.1%), considering the huge latitudinal extension (36.4◦ to 55.1◦ SL) and the great diversity of forest types. Mean SOC was 134.4 ton C ha−1 ± 25.2, totaling 404.2 million ton C across Patagonia. Overall, SOC values were highest in valleys of the Andes mountains and in southern Tierra del Fuego, ranging from 53.5 to 277.8 ton C ha−1 for the whole Patagonia region. Soil organic carbon is a metric relevant to many applications, connecting major issues such as forest management, conservation, and livestock production, and having spatially explicit estimates of SOC enables managers to fulfil the international agreements that Argentina has joined. EEA Esquel Fil: Martínez Pastur, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC). Laboratorio de Recursos Agroforestales; Argentina Fil: Aravena Acuña, Marie Claire. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC). Laboratorio de Recursos Agroforestales; Argentina Fil: Silveira, Eduarda M. O. University of Wisconsin. Department of Forest and Wildlife Ecology. SILVIS Lab.; Estados Unidos Fil: von Müller, Axel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agroforestal Esquel; Argentina Fil: La Manna, Ludmila. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: La Manna, Ludmila. Universidad Nacional de la Patagonia San Juan Bosco. Facultad de Ingeniería. Centro de Estudios Ambientales Integrados; Argentina Fil: González Polo, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: González Polo, Marina. Universidad Nacional del Comahue. Instituto de Investigaciones en Biodiversidad y Medioambiente (INIBIOMA); Argentina Fil: Chaves, Jimena E. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC). Laboratorio de Recursos Agroforestales; Argentina Fil: Cellini, Juan M. Universidad Nacional de La Plata. Laboratorio de Investigaciones en Maderas (LIMAD); Argentina Fil: Lencinas, María V. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC). Laboratorio de Recursos Agroforestales; Argentina Fil: Radeloff, Volker C. University of Wisconsin. Department of Forest and Wildlife Ecology. SILVIS Lab.; Estados Unidos Fil: Pidgeon, Anna M. University of Wisconsin. Department of Forest and Wildlife Ecology. SILVIS Lab.; Estados Unidos Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina. Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina. Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. 2022-11-15T10:36:43Z 2022-11-15T10:36:43Z 2022-11-11 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/13417 https://www.mdpi.com/2072-4292/14/22/5702 Martínez Pastur, G.; Aravena Acuña, M.-C.; Silveira, E.M.O.; Von Müller, A.; La Manna, L.; González-Polo, M.; Chaves, J.E.; Cellini, J.M.; Lencinas, M.V.; Radeloff, V.C.; et al. Mapping Soil Organic Carbon Content in Patagonian Forests Based on Climate, Topography and Vegetation Metrics from Satellite Imagery. Remote Sens. 2022, 14, 5702. https://doi.org/ 10.3390/rs14225702 2072-4292 https://doi.org/10.3390/rs14225702 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Multidisciplinary Digital Publishing Institute (MDPI) Remote Sensing 14 : 5702. (2022) |
| spellingShingle | Soil Organic Carbon Primary Forests Satellite Imagery Carbono Orgánico del Suelo Bosque Primario Imágenes por Satélites Landsat-8 Región Patagónica Dynamic Habitat Indices Bosques Nativos Martínez Pastur, Guillermo José Aravena Acuña, Marie Claire Silveira, Eduarda M.O. Von Müller, Axel La Manna, Ludmila González Polo, Marina Chaves, Jimena Elizabeth Cellini, Juan Manuel Lencinas, María Vanessa Radeloff, Volker C. Pidgeon, Anna Michle Peri, Pablo Luis Mapping soil organic carbon content in Patagonian forests based on climate, topography and vegetation metrics from satellite imagery |
| title | Mapping soil organic carbon content in Patagonian forests based on climate, topography and vegetation metrics from satellite imagery |
| title_full | Mapping soil organic carbon content in Patagonian forests based on climate, topography and vegetation metrics from satellite imagery |
| title_fullStr | Mapping soil organic carbon content in Patagonian forests based on climate, topography and vegetation metrics from satellite imagery |
| title_full_unstemmed | Mapping soil organic carbon content in Patagonian forests based on climate, topography and vegetation metrics from satellite imagery |
| title_short | Mapping soil organic carbon content in Patagonian forests based on climate, topography and vegetation metrics from satellite imagery |
| title_sort | mapping soil organic carbon content in patagonian forests based on climate topography and vegetation metrics from satellite imagery |
| topic | Soil Organic Carbon Primary Forests Satellite Imagery Carbono Orgánico del Suelo Bosque Primario Imágenes por Satélites Landsat-8 Región Patagónica Dynamic Habitat Indices Bosques Nativos |
| url | http://hdl.handle.net/20.500.12123/13417 https://www.mdpi.com/2072-4292/14/22/5702 https://doi.org/10.3390/rs14225702 |
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