Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes

Autores
Gaitan, Juan Jose; Bran, Donaldo Eduardo; Oliva, Gabriel Esteban; Ciari, Georgina; Nakamatsu, Viviana Beatriz; Salomone, Jorge Manuel; Ferrante, Daniela; Buono, Gustavo Gabriel; Massara Paletto, Virginia; Humano, Gervasio; Celdran, Diego Javier; Opazo, Walter Javier; Maestre, Fernando Tomás
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Assessing the spatial variability of ecosystem structure and functioning is an important step towards developing monitoring systems to detect changes in ecosystem attributes that could be linked to desertification processes in drylands. Methods based on ground-collected soil and plant indicators are being increasingly used for this aim, but they have limitations regarding the extent of the area that can be measured using them. Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. We tested whether we can predict ecosystem structure and functioning, as measured with a field methodology based on indicators of ecosystem functioning (the landscape function analysis, LFA), over a large area using spectral vegetation indices (VIs), and evaluated which VIs are the best predictors of these ecosystem attributes. For doing this, we assessed the relationship between vegetation attributes (cover and species richness), LFA indices (stability, infiltration and nutrient cycling) and nine VIs obtained from satellite images of the MODIS sensor in 194 sites located across the Patagonian steppe. We found that NDVI was the VI best predictor of ecosystem attributes. This VI showed a significant positive linear relationship with both vegetation basal cover (R2 = 0.39) and plant species richness (R2 = 0.31). NDVI was also significantly and linearly related to the infiltration and nutrient cycling indices (R2 = 0.36 and 0.49, respectively), but the relationship with the stability index was weak (R2 = 0.13). Our results indicate that VIs obtained from MODIS, and NDVI in particular, are a suitable tool for estimate the spatial variability of functional and structural ecosystem attributes in the Patagonian steppe at the regional scale.
Fil: Gaitan, Juan Jose. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina
Fil: Bran, Donaldo Eduardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina
Fil: Oliva, Gabriel Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina
Fil: Ciari, Georgina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; Argentina
Fil: Nakamatsu, Viviana Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; Argentina
Fil: Salomone, Jorge Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Ferrante, Daniela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina
Fil: Buono, Gustavo Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Massara Paletto, Virginia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Humano, Gervasio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina
Fil: Celdran, Diego Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Opazo, Walter Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; Argentina
Fil: Maestre, Fernando T. Universidad Rey Juan Carlos. Escuela Superior de Ciencias Experimentales y Tecnología. Departamento de Biología y Geología, Física y Química Inorgánica; España
Fuente
Ecological indicators 34 : 181-191. (Nov. 2013)
Materia
Desertificación
Desertification
Ecosystems
Vegetation
Remote Sensing
Spatial Distribution
Ecosistema
Vegetación
Teledetección
Distribución Espacial
Vegetation Indices
Landscape Function Analysis
Región Patagónica
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/1449
Acceder
id INTADig_e700e249735caf63c2a77aed483b7184
oai_identifier_str oai:localhost:20.500.12123/1449
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppesGaitan, Juan JoseBran, Donaldo EduardoOliva, Gabriel EstebanCiari, GeorginaNakamatsu, Viviana BeatrizSalomone, Jorge ManuelFerrante, DanielaBuono, Gustavo GabrielMassara Paletto, VirginiaHumano, GervasioCeldran, Diego JavierOpazo, Walter JavierMaestre, Fernando TomásDesertificaciónDesertificationEcosystemsVegetationRemote SensingSpatial DistributionEcosistemaVegetaciónTeledetecciónDistribución EspacialVegetation IndicesLandscape Function AnalysisRegión PatagónicaAssessing the spatial variability of ecosystem structure and functioning is an important step towards developing monitoring systems to detect changes in ecosystem attributes that could be linked to desertification processes in drylands. Methods based on ground-collected soil and plant indicators are being increasingly used for this aim, but they have limitations regarding the extent of the area that can be measured using them. Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. We tested whether we can predict ecosystem structure and functioning, as measured with a field methodology based on indicators of ecosystem functioning (the landscape function analysis, LFA), over a large area using spectral vegetation indices (VIs), and evaluated which VIs are the best predictors of these ecosystem attributes. For doing this, we assessed the relationship between vegetation attributes (cover and species richness), LFA indices (stability, infiltration and nutrient cycling) and nine VIs obtained from satellite images of the MODIS sensor in 194 sites located across the Patagonian steppe. We found that NDVI was the VI best predictor of ecosystem attributes. This VI showed a significant positive linear relationship with both vegetation basal cover (R2 = 0.39) and plant species richness (R2 = 0.31). NDVI was also significantly and linearly related to the infiltration and nutrient cycling indices (R2 = 0.36 and 0.49, respectively), but the relationship with the stability index was weak (R2 = 0.13). Our results indicate that VIs obtained from MODIS, and NDVI in particular, are a suitable tool for estimate the spatial variability of functional and structural ecosystem attributes in the Patagonian steppe at the regional scale.Fil: Gaitan, Juan Jose. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Bran, Donaldo Eduardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Oliva, Gabriel Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; ArgentinaFil: Ciari, Georgina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; ArgentinaFil: Nakamatsu, Viviana Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; ArgentinaFil: Salomone, Jorge Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; ArgentinaFil: Ferrante, Daniela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; ArgentinaFil: Buono, Gustavo Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; ArgentinaFil: Massara Paletto, Virginia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; ArgentinaFil: Humano, Gervasio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; ArgentinaFil: Celdran, Diego Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; ArgentinaFil: Opazo, Walter Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; ArgentinaFil: Maestre, Fernando T. Universidad Rey Juan Carlos. Escuela Superior de Ciencias Experimentales y Tecnología. Departamento de Biología y Geología, Física y Química Inorgánica; España2017-10-10T13:47:30Z2017-10-10T13:47:30Z2013-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/1449http://www.sciencedirect.com/science/article/pii/S1470160X130020331470-160Xhttps://doi.org/10.1016/j.ecolind.2013.05.007Ecological indicators 34 : 181-191. (Nov. 2013)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología AgropecuariaengPatagonia (general region)info:eu-repo/semantics/restrictedAccess2025-09-29T13:44:12Zoai:localhost:20.500.12123/1449instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:12.469INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes
title Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes
spellingShingle Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes
Gaitan, Juan Jose
Desertificación
Desertification
Ecosystems
Vegetation
Remote Sensing
Spatial Distribution
Ecosistema
Vegetación
Teledetección
Distribución Espacial
Vegetation Indices
Landscape Function Analysis
Región Patagónica
title_short Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes
title_full Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes
title_fullStr Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes
title_full_unstemmed Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes
title_sort Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes
dc.creator.none.fl_str_mv Gaitan, Juan Jose
Bran, Donaldo Eduardo
Oliva, Gabriel Esteban
Ciari, Georgina
Nakamatsu, Viviana Beatriz
Salomone, Jorge Manuel
Ferrante, Daniela
Buono, Gustavo Gabriel
Massara Paletto, Virginia
Humano, Gervasio
Celdran, Diego Javier
Opazo, Walter Javier
Maestre, Fernando Tomás
author Gaitan, Juan Jose
author_facet Gaitan, Juan Jose
Bran, Donaldo Eduardo
Oliva, Gabriel Esteban
Ciari, Georgina
Nakamatsu, Viviana Beatriz
Salomone, Jorge Manuel
Ferrante, Daniela
Buono, Gustavo Gabriel
Massara Paletto, Virginia
Humano, Gervasio
Celdran, Diego Javier
Opazo, Walter Javier
Maestre, Fernando Tomás
author_role author
author2 Bran, Donaldo Eduardo
Oliva, Gabriel Esteban
Ciari, Georgina
Nakamatsu, Viviana Beatriz
Salomone, Jorge Manuel
Ferrante, Daniela
Buono, Gustavo Gabriel
Massara Paletto, Virginia
Humano, Gervasio
Celdran, Diego Javier
Opazo, Walter Javier
Maestre, Fernando Tomás
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Desertificación
Desertification
Ecosystems
Vegetation
Remote Sensing
Spatial Distribution
Ecosistema
Vegetación
Teledetección
Distribución Espacial
Vegetation Indices
Landscape Function Analysis
Región Patagónica
topic Desertificación
Desertification
Ecosystems
Vegetation
Remote Sensing
Spatial Distribution
Ecosistema
Vegetación
Teledetección
Distribución Espacial
Vegetation Indices
Landscape Function Analysis
Región Patagónica
dc.description.none.fl_txt_mv Assessing the spatial variability of ecosystem structure and functioning is an important step towards developing monitoring systems to detect changes in ecosystem attributes that could be linked to desertification processes in drylands. Methods based on ground-collected soil and plant indicators are being increasingly used for this aim, but they have limitations regarding the extent of the area that can be measured using them. Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. We tested whether we can predict ecosystem structure and functioning, as measured with a field methodology based on indicators of ecosystem functioning (the landscape function analysis, LFA), over a large area using spectral vegetation indices (VIs), and evaluated which VIs are the best predictors of these ecosystem attributes. For doing this, we assessed the relationship between vegetation attributes (cover and species richness), LFA indices (stability, infiltration and nutrient cycling) and nine VIs obtained from satellite images of the MODIS sensor in 194 sites located across the Patagonian steppe. We found that NDVI was the VI best predictor of ecosystem attributes. This VI showed a significant positive linear relationship with both vegetation basal cover (R2 = 0.39) and plant species richness (R2 = 0.31). NDVI was also significantly and linearly related to the infiltration and nutrient cycling indices (R2 = 0.36 and 0.49, respectively), but the relationship with the stability index was weak (R2 = 0.13). Our results indicate that VIs obtained from MODIS, and NDVI in particular, are a suitable tool for estimate the spatial variability of functional and structural ecosystem attributes in the Patagonian steppe at the regional scale.
Fil: Gaitan, Juan Jose. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina
Fil: Bran, Donaldo Eduardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina
Fil: Oliva, Gabriel Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina
Fil: Ciari, Georgina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; Argentina
Fil: Nakamatsu, Viviana Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; Argentina
Fil: Salomone, Jorge Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Ferrante, Daniela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina
Fil: Buono, Gustavo Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Massara Paletto, Virginia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Humano, Gervasio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina
Fil: Celdran, Diego Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentina
Fil: Opazo, Walter Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; Argentina
Fil: Maestre, Fernando T. Universidad Rey Juan Carlos. Escuela Superior de Ciencias Experimentales y Tecnología. Departamento de Biología y Geología, Física y Química Inorgánica; España
description Assessing the spatial variability of ecosystem structure and functioning is an important step towards developing monitoring systems to detect changes in ecosystem attributes that could be linked to desertification processes in drylands. Methods based on ground-collected soil and plant indicators are being increasingly used for this aim, but they have limitations regarding the extent of the area that can be measured using them. Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. We tested whether we can predict ecosystem structure and functioning, as measured with a field methodology based on indicators of ecosystem functioning (the landscape function analysis, LFA), over a large area using spectral vegetation indices (VIs), and evaluated which VIs are the best predictors of these ecosystem attributes. For doing this, we assessed the relationship between vegetation attributes (cover and species richness), LFA indices (stability, infiltration and nutrient cycling) and nine VIs obtained from satellite images of the MODIS sensor in 194 sites located across the Patagonian steppe. We found that NDVI was the VI best predictor of ecosystem attributes. This VI showed a significant positive linear relationship with both vegetation basal cover (R2 = 0.39) and plant species richness (R2 = 0.31). NDVI was also significantly and linearly related to the infiltration and nutrient cycling indices (R2 = 0.36 and 0.49, respectively), but the relationship with the stability index was weak (R2 = 0.13). Our results indicate that VIs obtained from MODIS, and NDVI in particular, are a suitable tool for estimate the spatial variability of functional and structural ecosystem attributes in the Patagonian steppe at the regional scale.
publishDate 2013
dc.date.none.fl_str_mv 2013-11
2017-10-10T13:47:30Z
2017-10-10T13:47:30Z
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/20.500.12123/1449
http://www.sciencedirect.com/science/article/pii/S1470160X13002033
1470-160X
https://doi.org/10.1016/j.ecolind.2013.05.007
url http://hdl.handle.net/20.500.12123/1449
http://www.sciencedirect.com/science/article/pii/S1470160X13002033
https://doi.org/10.1016/j.ecolind.2013.05.007
identifier_str_mv 1470-160X
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv Patagonia (general region)
dc.source.none.fl_str_mv Ecological indicators 34 : 181-191. (Nov. 2013)
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
_version_ 1844619118197604352
score 12.559606

Publicaciones similares