Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ

Autores
Gaffney, Rowan; Porensky, Lauren; Gao, Feng; Irisarri, Jorge Gonzalo Nicolás; Durante, Martín; Derner, Justin; Augustine, David
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Monitoring of aboveground net primary production (ANPP) is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Remote sensing of absorbed photosynthetically active radiation (APAR) can be used to predict ANPP, potentially offering an alternative means of quantifying ANPP at both high temporal and spatial resolution across broad spatial extents. The relationship between ANPP and APAR has often been quantified based on either spatial variation across a broad region or temporal variation at a location over time, but rarely both. Here we assess: (i) if the relationship between ANPP and APAR is consistent when evaluated across time and space; (ii) potential factors driving differences between temporal versus spatial models, and (iii) the magnitude of potential errors relating to space for time transformations in quantifying productivity. Using two complimentary ANPP datasets and remotely sensed data derived from MODIS and a Landsat/MODIS fusion data product, we find that slopes of spatial models are generally greater than slopes of temporal models. The abundance of plant species with different structural attributes, specifically the abundance of C4 shortgrasses with prostrate canopies versus taller, more productive C3 species with more vertically complex canopies, tended to vary more dramatically in space than over time. This difference in spatial versus temporal variation in these key plant functional groups appears to be the primary driver of differences in slopes among regression models. While the individual models revealed strong relationships between ANPP to APAR, the use of temporal models to predict variation in space (or vice versa) can increase error in remotely sensed predictions of ANPP.
Fil: Gaffney, Rowan. United States Department of Agriculture. Agricultural Research Service; Argentina
Fil: Porensky, Lauren. United States Department of Agriculture. Agricultural Research Service; Argentina
Fil: Gao, Feng. United States Department of Agriculture. Agricultural Research Service; Argentina
Fil: Irisarri, Jorge Gonzalo Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Durante, Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Concepción del Uruguay; Argentina
Fil: Derner, Justin. United States Department of Agriculture. Agricultural Research Service; Argentina
Fil: Augustine, David. United States Department of Agriculture. Agricultural Research Service; Argentina
Materia
ANPP
BIOMASS
LANDSAT
MODIS
NDVI
PLANT COMPOSITION
RADIATION USE EFFICIENCY
SPATIAL
TEMPORAL
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/96143
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/96143
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differGaffney, RowanPorensky, LaurenGao, FengIrisarri, Jorge Gonzalo NicolásDurante, MartínDerner, JustinAugustine, DavidANPPBIOMASSLANDSATMODISNDVIPLANT COMPOSITIONRADIATION USE EFFICIENCYSPATIALTEMPORALhttps://purl.org/becyt/ford/4.2https://purl.org/becyt/ford/4Monitoring of aboveground net primary production (ANPP) is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Remote sensing of absorbed photosynthetically active radiation (APAR) can be used to predict ANPP, potentially offering an alternative means of quantifying ANPP at both high temporal and spatial resolution across broad spatial extents. The relationship between ANPP and APAR has often been quantified based on either spatial variation across a broad region or temporal variation at a location over time, but rarely both. Here we assess: (i) if the relationship between ANPP and APAR is consistent when evaluated across time and space; (ii) potential factors driving differences between temporal versus spatial models, and (iii) the magnitude of potential errors relating to space for time transformations in quantifying productivity. Using two complimentary ANPP datasets and remotely sensed data derived from MODIS and a Landsat/MODIS fusion data product, we find that slopes of spatial models are generally greater than slopes of temporal models. The abundance of plant species with different structural attributes, specifically the abundance of C4 shortgrasses with prostrate canopies versus taller, more productive C3 species with more vertically complex canopies, tended to vary more dramatically in space than over time. This difference in spatial versus temporal variation in these key plant functional groups appears to be the primary driver of differences in slopes among regression models. While the individual models revealed strong relationships between ANPP to APAR, the use of temporal models to predict variation in space (or vice versa) can increase error in remotely sensed predictions of ANPP.Fil: Gaffney, Rowan. United States Department of Agriculture. Agricultural Research Service; ArgentinaFil: Porensky, Lauren. United States Department of Agriculture. Agricultural Research Service; ArgentinaFil: Gao, Feng. United States Department of Agriculture. Agricultural Research Service; ArgentinaFil: Irisarri, Jorge Gonzalo Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Durante, Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Concepción del Uruguay; ArgentinaFil: Derner, Justin. United States Department of Agriculture. Agricultural Research Service; ArgentinaFil: Augustine, David. United States Department of Agriculture. Agricultural Research Service; ArgentinaMDPI AG2018-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/96143Gaffney, Rowan; Porensky, Lauren; Gao, Feng; Irisarri, Jorge Gonzalo Nicolás; Durante, Martín; et al.; Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ; MDPI AG; Remote Sensing; 10; 9; 9-20182072-4292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/2072-4292/10/9/1474info:eu-repo/semantics/altIdentifier/doi/10.3390/rs10091474info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:22:46Zoai:ri.conicet.gov.ar:11336/96143instacron: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:34982025-10-15 15:22:46.544CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ
title Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ
spellingShingle Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ
Gaffney, Rowan
ANPP
BIOMASS
LANDSAT
MODIS
NDVI
PLANT COMPOSITION
RADIATION USE EFFICIENCY
SPATIAL
TEMPORAL
title_short Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ
title_full Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ
title_fullStr Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ
title_full_unstemmed Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ
title_sort Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ
dc.creator.none.fl_str_mv Gaffney, Rowan
Porensky, Lauren
Gao, Feng
Irisarri, Jorge Gonzalo Nicolás
Durante, Martín
Derner, Justin
Augustine, David
author Gaffney, Rowan
author_facet Gaffney, Rowan
Porensky, Lauren
Gao, Feng
Irisarri, Jorge Gonzalo Nicolás
Durante, Martín
Derner, Justin
Augustine, David
author_role author
author2 Porensky, Lauren
Gao, Feng
Irisarri, Jorge Gonzalo Nicolás
Durante, Martín
Derner, Justin
Augustine, David
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv ANPP
BIOMASS
LANDSAT
MODIS
NDVI
PLANT COMPOSITION
RADIATION USE EFFICIENCY
SPATIAL
TEMPORAL
topic ANPP
BIOMASS
LANDSAT
MODIS
NDVI
PLANT COMPOSITION
RADIATION USE EFFICIENCY
SPATIAL
TEMPORAL
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.2
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Monitoring of aboveground net primary production (ANPP) is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Remote sensing of absorbed photosynthetically active radiation (APAR) can be used to predict ANPP, potentially offering an alternative means of quantifying ANPP at both high temporal and spatial resolution across broad spatial extents. The relationship between ANPP and APAR has often been quantified based on either spatial variation across a broad region or temporal variation at a location over time, but rarely both. Here we assess: (i) if the relationship between ANPP and APAR is consistent when evaluated across time and space; (ii) potential factors driving differences between temporal versus spatial models, and (iii) the magnitude of potential errors relating to space for time transformations in quantifying productivity. Using two complimentary ANPP datasets and remotely sensed data derived from MODIS and a Landsat/MODIS fusion data product, we find that slopes of spatial models are generally greater than slopes of temporal models. The abundance of plant species with different structural attributes, specifically the abundance of C4 shortgrasses with prostrate canopies versus taller, more productive C3 species with more vertically complex canopies, tended to vary more dramatically in space than over time. This difference in spatial versus temporal variation in these key plant functional groups appears to be the primary driver of differences in slopes among regression models. While the individual models revealed strong relationships between ANPP to APAR, the use of temporal models to predict variation in space (or vice versa) can increase error in remotely sensed predictions of ANPP.
Fil: Gaffney, Rowan. United States Department of Agriculture. Agricultural Research Service; Argentina
Fil: Porensky, Lauren. United States Department of Agriculture. Agricultural Research Service; Argentina
Fil: Gao, Feng. United States Department of Agriculture. Agricultural Research Service; Argentina
Fil: Irisarri, Jorge Gonzalo Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Durante, Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Concepción del Uruguay; Argentina
Fil: Derner, Justin. United States Department of Agriculture. Agricultural Research Service; Argentina
Fil: Augustine, David. United States Department of Agriculture. Agricultural Research Service; Argentina
description Monitoring of aboveground net primary production (ANPP) is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Remote sensing of absorbed photosynthetically active radiation (APAR) can be used to predict ANPP, potentially offering an alternative means of quantifying ANPP at both high temporal and spatial resolution across broad spatial extents. The relationship between ANPP and APAR has often been quantified based on either spatial variation across a broad region or temporal variation at a location over time, but rarely both. Here we assess: (i) if the relationship between ANPP and APAR is consistent when evaluated across time and space; (ii) potential factors driving differences between temporal versus spatial models, and (iii) the magnitude of potential errors relating to space for time transformations in quantifying productivity. Using two complimentary ANPP datasets and remotely sensed data derived from MODIS and a Landsat/MODIS fusion data product, we find that slopes of spatial models are generally greater than slopes of temporal models. The abundance of plant species with different structural attributes, specifically the abundance of C4 shortgrasses with prostrate canopies versus taller, more productive C3 species with more vertically complex canopies, tended to vary more dramatically in space than over time. This difference in spatial versus temporal variation in these key plant functional groups appears to be the primary driver of differences in slopes among regression models. While the individual models revealed strong relationships between ANPP to APAR, the use of temporal models to predict variation in space (or vice versa) can increase error in remotely sensed predictions of ANPP.
publishDate 2018
dc.date.none.fl_str_mv 2018-09
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/11336/96143
Gaffney, Rowan; Porensky, Lauren; Gao, Feng; Irisarri, Jorge Gonzalo Nicolás; Durante, Martín; et al.; Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ; MDPI AG; Remote Sensing; 10; 9; 9-2018
2072-4292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/96143
identifier_str_mv Gaffney, Rowan; Porensky, Lauren; Gao, Feng; Irisarri, Jorge Gonzalo Nicolás; Durante, Martín; et al.; Using APAR to predict aboveground plant productivity in semi-aid rangelands: Spatial and temporal relationships differ; MDPI AG; Remote Sensing; 10; 9; 9-2018
2072-4292
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.mdpi.com/2072-4292/10/9/1474
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs10091474
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 AG
publisher.none.fl_str_mv MDPI AG
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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score 13.22299

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