Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach

Questions: Can herbaceous above‐ground net primary production (ANPP) be estimated from remote sensing when woody and herbaceous plants are intermingled? How does herbaceous ANPP change in space and time in an ecosystem dominated by woody species? What are the main controls of herbaceous ANPP to padd...

Descripción completa

Detalles Bibliográficos
Autores principales: Blanco, Lisandro Javier, Paruelo, José María, Oesterheld, Martin, Biurrun, Fernando Noe
Formato: Artículo
Lenguaje:Inglés
Publicado: 2018
Materias:
Acceso en línea:https://onlinelibrary.wiley.com/doi/abs/10.1111/jvs.12398
http://hdl.handle.net/20.500.12123/2749
https://doi.org/10.1111/jvs.12398
_version_ 1855483113801515008
author Blanco, Lisandro Javier
Paruelo, José María
Oesterheld, Martin
Biurrun, Fernando Noe
author_browse Biurrun, Fernando Noe
Blanco, Lisandro Javier
Oesterheld, Martin
Paruelo, José María
author_facet Blanco, Lisandro Javier
Paruelo, José María
Oesterheld, Martin
Biurrun, Fernando Noe
author_sort Blanco, Lisandro Javier
collection INTA Digital
description Questions: Can herbaceous above‐ground net primary production (ANPP) be estimated from remote sensing when woody and herbaceous plants are intermingled? How does herbaceous ANPP change in space and time in an ecosystem dominated by woody species? What are the main controls of herbaceous ANPP to paddock scale? Location: Native plant communities and buffelgrass roller chopped pastures of the Arid Chaco, western Argentina (28–32° S, 64–67° W; area: 100 000 km2). Methods: We decomposed normalized difference vegetation index (NDVI) data from MODIS (pixel size: 250 m × 250 m) into woody (W) and herbaceous (H) components. We calibrated the relationship between field estimates of herbaceous ANPP and the H component of NDVI using linear regression. The regression model fitted was applied to a 10‐yr MODIS database for four paddocks to estimate herbaceous ANPP. We analysed the relationship between herbaceous ANPP and watering point distance and growing season precipitation. Results: The annual integral of NDVI × proportion of the herbaceous component [H/(H + W)] explained 71% and 91% of herbaceous ANPP variation in native plant communities and buffelgrass roller chopped pastures, respectively. The regression model fitted, however, differed (P < 0.05) between the two types of system. The NDVI annual integral explained a higher proportion of herbaceous ANPP variations than the NDVI annual peak or the growing season (December–April) integral. For native plant communities, herbaceous production increased significantly (P < 0.05) with watering point distance, and marginally significantly (P < 0.10) with growing season precipitation. For buffelgrass roller chopped pastures, the herbaceous production increased significantly (P < 0.05) with growing season precipitation. Conclusion; Our model was able to estimate herbaceous ANPP from the decomposition of an NDVI time series that included woody components. Thus, the model provides the basis for more accurate monitoring of spatial and temporal variability of herbaceous ANPP in areas where herbaceous and woody plant components co‐exist. Applying our models, we detected clear spatial and temporal patterns of herbaceous ANPP. The possibility of describing in a spatially explicit way the past 14 yrs of herbaceous ANPP allows designing livestock management strategies and devise alternatives to control degradation processes in the Arid Chaco.
format Artículo
id INTA2749
institution Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina)
language Inglés
publishDate 2018
publishDateRange 2018
publishDateSort 2018
record_format dspace
spelling INTA27492018-10-17T13:05:39Z Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach Blanco, Lisandro Javier Paruelo, José María Oesterheld, Martin Biurrun, Fernando Noe Tierras de Matorral Zona Semiárida Producción Primaria Teledetección Indice de Vegetación Scrublands Semiarid Zones Primary Production Remote Sensing Vegetation Index Matorrales Questions: Can herbaceous above‐ground net primary production (ANPP) be estimated from remote sensing when woody and herbaceous plants are intermingled? How does herbaceous ANPP change in space and time in an ecosystem dominated by woody species? What are the main controls of herbaceous ANPP to paddock scale? Location: Native plant communities and buffelgrass roller chopped pastures of the Arid Chaco, western Argentina (28–32° S, 64–67° W; area: 100 000 km2). Methods: We decomposed normalized difference vegetation index (NDVI) data from MODIS (pixel size: 250 m × 250 m) into woody (W) and herbaceous (H) components. We calibrated the relationship between field estimates of herbaceous ANPP and the H component of NDVI using linear regression. The regression model fitted was applied to a 10‐yr MODIS database for four paddocks to estimate herbaceous ANPP. We analysed the relationship between herbaceous ANPP and watering point distance and growing season precipitation. Results: The annual integral of NDVI × proportion of the herbaceous component [H/(H + W)] explained 71% and 91% of herbaceous ANPP variation in native plant communities and buffelgrass roller chopped pastures, respectively. The regression model fitted, however, differed (P < 0.05) between the two types of system. The NDVI annual integral explained a higher proportion of herbaceous ANPP variations than the NDVI annual peak or the growing season (December–April) integral. For native plant communities, herbaceous production increased significantly (P < 0.05) with watering point distance, and marginally significantly (P < 0.10) with growing season precipitation. For buffelgrass roller chopped pastures, the herbaceous production increased significantly (P < 0.05) with growing season precipitation. Conclusion; Our model was able to estimate herbaceous ANPP from the decomposition of an NDVI time series that included woody components. Thus, the model provides the basis for more accurate monitoring of spatial and temporal variability of herbaceous ANPP in areas where herbaceous and woody plant components co‐exist. Applying our models, we detected clear spatial and temporal patterns of herbaceous ANPP. The possibility of describing in a spatially explicit way the past 14 yrs of herbaceous ANPP allows designing livestock management strategies and devise alternatives to control degradation processes in the Arid Chaco. EEA La Rioja Fil: Blanco, Lisandro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria La Rioja; Argentina Fil: Paruelo, José María. 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. Departamento de Métodos Cuantitativos y Sistemas de Información. Laboratorio de Análisis Regional y Teledetección; Argentina Fil: Oesterheld, Martin. 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. Departamento de Métodos Cuantitativos y Sistemas de Información. Laboratorio de Análisis Regional y Teledetección; Argentina Fil: Biurrun, Fernando Noe. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria La Rioja; Argentina 2018-07-10T14:37:13Z 2018-07-10T14:37:13Z 2016-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://onlinelibrary.wiley.com/doi/abs/10.1111/jvs.12398 http://hdl.handle.net/20.500.12123/2749 1100-9233 1654-1103 https://doi.org/10.1111/jvs.12398 eng info:eu-repo/semantics/restrictedAccess application/pdf Journal of Vegetation Science 27 (4) : 716-727 (July 2016)
spellingShingle Tierras de Matorral
Zona Semiárida
Producción Primaria
Teledetección
Indice de Vegetación
Scrublands
Semiarid Zones
Primary Production
Remote Sensing
Vegetation Index
Matorrales
Blanco, Lisandro Javier
Paruelo, José María
Oesterheld, Martin
Biurrun, Fernando Noe
Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach
title Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach
title_full Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach
title_fullStr Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach
title_full_unstemmed Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach
title_short Spatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approach
title_sort spatial and temporal patterns of herbaceous primary production in semi arid shrublands a remote sensing approach
topic Tierras de Matorral
Zona Semiárida
Producción Primaria
Teledetección
Indice de Vegetación
Scrublands
Semiarid Zones
Primary Production
Remote Sensing
Vegetation Index
Matorrales
url https://onlinelibrary.wiley.com/doi/abs/10.1111/jvs.12398
http://hdl.handle.net/20.500.12123/2749
https://doi.org/10.1111/jvs.12398
work_keys_str_mv AT blancolisandrojavier spatialandtemporalpatternsofherbaceousprimaryproductioninsemiaridshrublandsaremotesensingapproach
AT paruelojosemaria spatialandtemporalpatternsofherbaceousprimaryproductioninsemiaridshrublandsaremotesensingapproach
AT oesterheldmartin spatialandtemporalpatternsofherbaceousprimaryproductioninsemiaridshrublandsaremotesensingapproach
AT biurrunfernandonoe spatialandtemporalpatternsofherbaceousprimaryproductioninsemiaridshrublandsaremotesensingapproach