Forest phenoclusters for Argentina based on vegetation phenology and climate
Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based o...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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Wiley
2024
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/18874 https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.2526 https://doi.org/10.1002/eap.2526 |
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| author | Silveira, Eduarda M.O. Radeloff, Volker C. Martínez Pastur, Guillermo José Martinuzzi, Sebastián Politi, Natalia Lizarraga, Leonidas Rivera, Luis Gavier Pizarro, Gregorio Ignacio Yin, He Rosas, Yamina Micaela Calamari, Noelia Cecilia Navarro, María Fabiana Sica, Yanina Vanesa Olah, Ashley Bono, Julieta Pidgeon, Anna M. |
| author_browse | Bono, Julieta Calamari, Noelia Cecilia Gavier Pizarro, Gregorio Ignacio Lizarraga, Leonidas Martinuzzi, Sebastián Martínez Pastur, Guillermo José Navarro, María Fabiana Olah, Ashley Pidgeon, Anna M. Politi, Natalia Radeloff, Volker C. Rivera, Luis Rosas, Yamina Micaela Sica, Yanina Vanesa Silveira, Eduarda M.O. Yin, He |
| author_facet | Silveira, Eduarda M.O. Radeloff, Volker C. Martínez Pastur, Guillermo José Martinuzzi, Sebastián Politi, Natalia Lizarraga, Leonidas Rivera, Luis Gavier Pizarro, Gregorio Ignacio Yin, He Rosas, Yamina Micaela Calamari, Noelia Cecilia Navarro, María Fabiana Sica, Yanina Vanesa Olah, Ashley Bono, Julieta Pidgeon, Anna M. |
| author_sort | Silveira, Eduarda M.O. |
| collection | INTA Digital |
| description | Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018–2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest–nonforest in areas where the lack of detailed ecological field data precludes tree species–level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool
for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions. |
| format | Artículo |
| id | INTA18874 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | INTA188742024-08-09T10:30:38Z Forest phenoclusters for Argentina based on vegetation phenology and climate Silveira, Eduarda M.O. Radeloff, Volker C. Martínez Pastur, Guillermo José Martinuzzi, Sebastián Politi, Natalia Lizarraga, Leonidas Rivera, Luis Gavier Pizarro, Gregorio Ignacio Yin, He Rosas, Yamina Micaela Calamari, Noelia Cecilia Navarro, María Fabiana Sica, Yanina Vanesa Olah, Ashley Bono, Julieta Pidgeon, Anna M. Cluster Sampling Imagery Precipitation Climate Muestreo Cluster Imagen Precipitación Atmosférica Sentinel Plants Planta Centinela Argentina Clima Conservation Enhanced Vegetation Index Land Surface Temperature Landsat 8 Centinel 2 Indice de Vegetación Mejorado para la Conservación Temperatura de la Superficie Terrestre Centinela 2 Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018–2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest–nonforest in areas where the lack of detailed ecological field data precludes tree species–level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions. Instituto de Recursos Biológicos Fil: Silveira, Eduarda M.O. University of Wisconsin–Madison. Department of Forest and Wildlife Ecology. SILVIS Lab; Estados Unidos Fil: Radeloff, Volker C. University of Wisconsin–Madison. Department of Forest and Wildlife Ecology. SILVIS Lab; Estados Unidos Fil: Martínez-Pastur, Guillermo J. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina Fil: Martinucci, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina Fil: Politi, Natalia. Universidad Nacional de Jujuy. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Lizarraga, Leonidas. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ecoregiones Andinas (INECOA); Argentina Fil: Rivera, Luis. Universidad Nacional de Jujuy. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Gavier Pizarro, Gregorio Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina Fil: Yin, He. Kent State University. Department of Geography; Estados Unidos Fil: Rosas, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas (CADIC); Argentina Fil: Calamari, Noelia Cecilia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina Fil: Navarro, María Fabiana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina Fil: Sica, Yanina Vanesa. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Fil: Olah, Ashley. University of Wisconsin–Madison. Department of Forest and Wildlife Ecology. SILVIS Lab; Estados Unidos Fil: Bono, Julieta. Ministerio de Ambiente y Desarrollo Sostenible de la Nación, Dirección Nacional de Bosques, Buenos Aires, Argentina Fil: Pidgeon, Anna M. University of Wisconsin–Madison. Department of Forest and Wildlife Ecology. SILVIS Lab; Estados Unidos 2024-08-09T10:06:43Z 2024-08-09T10:06:43Z 2022-04-01 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/18874 https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.2526 1051-0761 https://doi.org/10.1002/eap.2526 eng info:eu-repo/semantics/restrictedAccess 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 Wiley Ecological Applications 32 (3) : e2526. (April 2022) |
| spellingShingle | Cluster Sampling Imagery Precipitation Climate Muestreo Cluster Imagen Precipitación Atmosférica Sentinel Plants Planta Centinela Argentina Clima Conservation Enhanced Vegetation Index Land Surface Temperature Landsat 8 Centinel 2 Indice de Vegetación Mejorado para la Conservación Temperatura de la Superficie Terrestre Centinela 2 Silveira, Eduarda M.O. Radeloff, Volker C. Martínez Pastur, Guillermo José Martinuzzi, Sebastián Politi, Natalia Lizarraga, Leonidas Rivera, Luis Gavier Pizarro, Gregorio Ignacio Yin, He Rosas, Yamina Micaela Calamari, Noelia Cecilia Navarro, María Fabiana Sica, Yanina Vanesa Olah, Ashley Bono, Julieta Pidgeon, Anna M. Forest phenoclusters for Argentina based on vegetation phenology and climate |
| title | Forest phenoclusters for Argentina based on vegetation phenology and climate |
| title_full | Forest phenoclusters for Argentina based on vegetation phenology and climate |
| title_fullStr | Forest phenoclusters for Argentina based on vegetation phenology and climate |
| title_full_unstemmed | Forest phenoclusters for Argentina based on vegetation phenology and climate |
| title_short | Forest phenoclusters for Argentina based on vegetation phenology and climate |
| title_sort | forest phenoclusters for argentina based on vegetation phenology and climate |
| topic | Cluster Sampling Imagery Precipitation Climate Muestreo Cluster Imagen Precipitación Atmosférica Sentinel Plants Planta Centinela Argentina Clima Conservation Enhanced Vegetation Index Land Surface Temperature Landsat 8 Centinel 2 Indice de Vegetación Mejorado para la Conservación Temperatura de la Superficie Terrestre Centinela 2 |
| url | http://hdl.handle.net/20.500.12123/18874 https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.2526 https://doi.org/10.1002/eap.2526 |
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