Archetypal temporal dynamics of arid and semi-arid rangelands

Autores
Bruzzone, Octavio Augusto; Easdale, Marcos Horacio
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The way in which temporal dynamics structure ecological systems under the influence of a changing environment has long interested ecologists. Tackling the hierarchical structure of complex temporal patterns is a necessary step towards a more complete description of the fundamental nature of temporal dynamics in ecosystems. In pursuance of this task, remote sensing data provide valuable information to classify and describe functional features of ecosystems across scales. To approach the temporal complexity of ecosystems, we proposed a stepwise procedure based on combinations of big data techniques and time series analyses applied to data series of the Normalized Difference Vegetation Index (NDVI). The aim was to classify the temporal patterns and identify the differences in temporal dynamics of vegetation, by means of the frequency-domain and time-frequency domain components of a 20-year period of NDVI time series, respectively. In addition, we analysed the influence of climate in the temporal dynamics of vegetation. In particular, we applied archetype analysis to fast Fourier transform coefficients, using pixels as analytical units and frequencies as variables, of a large study area from North Patagonia, Argentina. Then, the most representative pixels for each archetype were used to analyse the explained variance by climatic predictor variables (temperature and precipitation) using a multiplicative model, whereas NDVI temporal dynamics was also described by means of time-frequency domain components with wavelet analysis, respectively. Six archetypes of temporal dynamics of arid and semi-arid rangelands were identified, as well as the main patterns of their distinctive frequencies through time and spatial location, respectively. The differences in temporal dynamics among archetypes were partly associated with climate and spatial features such as topography. The procedure was sensitive to capturing these temporal patterns, even those with a low data representation or noisy series while synthesizing information for an easy interpretation. Results are discussed in the light of future opportunities to combine this kind of information with other sources of in- formation, aiming at the development of reliable land degradation and desertification assessment and monitoring tools
Estación Experimental Agropecuaria Bariloche
Fil: Bruzzone, Octavio Augusto.Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fuente
Remote Sensing of Environment 254 : Art: 11229 (March 2021)
Materia
Desertificación
Degradación de Tierras
Pastizales
Desertification
Land Degradation
Pastures
Región Patagónica
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/8680

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spelling Archetypal temporal dynamics of arid and semi-arid rangelandsBruzzone, Octavio AugustoEasdale, Marcos HoracioDesertificaciónDegradación de TierrasPastizalesDesertificationLand DegradationPasturesRegión PatagónicaThe way in which temporal dynamics structure ecological systems under the influence of a changing environment has long interested ecologists. Tackling the hierarchical structure of complex temporal patterns is a necessary step towards a more complete description of the fundamental nature of temporal dynamics in ecosystems. In pursuance of this task, remote sensing data provide valuable information to classify and describe functional features of ecosystems across scales. To approach the temporal complexity of ecosystems, we proposed a stepwise procedure based on combinations of big data techniques and time series analyses applied to data series of the Normalized Difference Vegetation Index (NDVI). The aim was to classify the temporal patterns and identify the differences in temporal dynamics of vegetation, by means of the frequency-domain and time-frequency domain components of a 20-year period of NDVI time series, respectively. In addition, we analysed the influence of climate in the temporal dynamics of vegetation. In particular, we applied archetype analysis to fast Fourier transform coefficients, using pixels as analytical units and frequencies as variables, of a large study area from North Patagonia, Argentina. Then, the most representative pixels for each archetype were used to analyse the explained variance by climatic predictor variables (temperature and precipitation) using a multiplicative model, whereas NDVI temporal dynamics was also described by means of time-frequency domain components with wavelet analysis, respectively. Six archetypes of temporal dynamics of arid and semi-arid rangelands were identified, as well as the main patterns of their distinctive frequencies through time and spatial location, respectively. The differences in temporal dynamics among archetypes were partly associated with climate and spatial features such as topography. The procedure was sensitive to capturing these temporal patterns, even those with a low data representation or noisy series while synthesizing information for an easy interpretation. Results are discussed in the light of future opportunities to combine this kind of information with other sources of in- formation, aiming at the development of reliable land degradation and desertification assessment and monitoring toolsEstación Experimental Agropecuaria BarilocheFil: Bruzzone, Octavio Augusto.Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaElsevier2021-02-18T12:13:20Z2021-02-18T12:13:20Z2021-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/8680https://www.sciencedirect.com/science/article/abs/pii/S00344257203065200034-4257https://doi.org/10.1016/j.rse.2020.112279Remote Sensing of Environment 254 : Art: 11229 (March 2021)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-11T10:23:37Zoai:localhost:20.500.12123/8680instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-11 10:23:38.239INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Archetypal temporal dynamics of arid and semi-arid rangelands
title Archetypal temporal dynamics of arid and semi-arid rangelands
spellingShingle Archetypal temporal dynamics of arid and semi-arid rangelands
Bruzzone, Octavio Augusto
Desertificación
Degradación de Tierras
Pastizales
Desertification
Land Degradation
Pastures
Región Patagónica
title_short Archetypal temporal dynamics of arid and semi-arid rangelands
title_full Archetypal temporal dynamics of arid and semi-arid rangelands
title_fullStr Archetypal temporal dynamics of arid and semi-arid rangelands
title_full_unstemmed Archetypal temporal dynamics of arid and semi-arid rangelands
title_sort Archetypal temporal dynamics of arid and semi-arid rangelands
dc.creator.none.fl_str_mv Bruzzone, Octavio Augusto
Easdale, Marcos Horacio
author Bruzzone, Octavio Augusto
author_facet Bruzzone, Octavio Augusto
Easdale, Marcos Horacio
author_role author
author2 Easdale, Marcos Horacio
author2_role author
dc.subject.none.fl_str_mv Desertificación
Degradación de Tierras
Pastizales
Desertification
Land Degradation
Pastures
Región Patagónica
topic Desertificación
Degradación de Tierras
Pastizales
Desertification
Land Degradation
Pastures
Región Patagónica
dc.description.none.fl_txt_mv The way in which temporal dynamics structure ecological systems under the influence of a changing environment has long interested ecologists. Tackling the hierarchical structure of complex temporal patterns is a necessary step towards a more complete description of the fundamental nature of temporal dynamics in ecosystems. In pursuance of this task, remote sensing data provide valuable information to classify and describe functional features of ecosystems across scales. To approach the temporal complexity of ecosystems, we proposed a stepwise procedure based on combinations of big data techniques and time series analyses applied to data series of the Normalized Difference Vegetation Index (NDVI). The aim was to classify the temporal patterns and identify the differences in temporal dynamics of vegetation, by means of the frequency-domain and time-frequency domain components of a 20-year period of NDVI time series, respectively. In addition, we analysed the influence of climate in the temporal dynamics of vegetation. In particular, we applied archetype analysis to fast Fourier transform coefficients, using pixels as analytical units and frequencies as variables, of a large study area from North Patagonia, Argentina. Then, the most representative pixels for each archetype were used to analyse the explained variance by climatic predictor variables (temperature and precipitation) using a multiplicative model, whereas NDVI temporal dynamics was also described by means of time-frequency domain components with wavelet analysis, respectively. Six archetypes of temporal dynamics of arid and semi-arid rangelands were identified, as well as the main patterns of their distinctive frequencies through time and spatial location, respectively. The differences in temporal dynamics among archetypes were partly associated with climate and spatial features such as topography. The procedure was sensitive to capturing these temporal patterns, even those with a low data representation or noisy series while synthesizing information for an easy interpretation. Results are discussed in the light of future opportunities to combine this kind of information with other sources of in- formation, aiming at the development of reliable land degradation and desertification assessment and monitoring tools
Estación Experimental Agropecuaria Bariloche
Fil: Bruzzone, Octavio Augusto.Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
description The way in which temporal dynamics structure ecological systems under the influence of a changing environment has long interested ecologists. Tackling the hierarchical structure of complex temporal patterns is a necessary step towards a more complete description of the fundamental nature of temporal dynamics in ecosystems. In pursuance of this task, remote sensing data provide valuable information to classify and describe functional features of ecosystems across scales. To approach the temporal complexity of ecosystems, we proposed a stepwise procedure based on combinations of big data techniques and time series analyses applied to data series of the Normalized Difference Vegetation Index (NDVI). The aim was to classify the temporal patterns and identify the differences in temporal dynamics of vegetation, by means of the frequency-domain and time-frequency domain components of a 20-year period of NDVI time series, respectively. In addition, we analysed the influence of climate in the temporal dynamics of vegetation. In particular, we applied archetype analysis to fast Fourier transform coefficients, using pixels as analytical units and frequencies as variables, of a large study area from North Patagonia, Argentina. Then, the most representative pixels for each archetype were used to analyse the explained variance by climatic predictor variables (temperature and precipitation) using a multiplicative model, whereas NDVI temporal dynamics was also described by means of time-frequency domain components with wavelet analysis, respectively. Six archetypes of temporal dynamics of arid and semi-arid rangelands were identified, as well as the main patterns of their distinctive frequencies through time and spatial location, respectively. The differences in temporal dynamics among archetypes were partly associated with climate and spatial features such as topography. The procedure was sensitive to capturing these temporal patterns, even those with a low data representation or noisy series while synthesizing information for an easy interpretation. Results are discussed in the light of future opportunities to combine this kind of information with other sources of in- formation, aiming at the development of reliable land degradation and desertification assessment and monitoring tools
publishDate 2021
dc.date.none.fl_str_mv 2021-02-18T12:13:20Z
2021-02-18T12:13:20Z
2021-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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info:ar-repo/semantics/articulo
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dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/8680
https://www.sciencedirect.com/science/article/abs/pii/S0034425720306520
0034-4257
https://doi.org/10.1016/j.rse.2020.112279
url http://hdl.handle.net/20.500.12123/8680
https://www.sciencedirect.com/science/article/abs/pii/S0034425720306520
https://doi.org/10.1016/j.rse.2020.112279
identifier_str_mv 0034-4257
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Remote Sensing of Environment 254 : Art: 11229 (March 2021)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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