Fragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networks
Questions: How do fragment-level characteristics affect remnant grassland functioning in a highly transformed landscape? Are artificial neural networks [ANNs] a better statistical tool to model variations in grassland functioning compared to linear regression models [LRMs]? Location: Tandilia Range, Southern Pampa, Buenos Aires Province, Argentina. Methods: We characterized the dynamics of the vegetation functioning in 60 remnant grasslands using enhanced vegetation index [EVI] data provided by MODIS/Terra images from July 2000 to June 2005. First, we performed a principal components analysis [PCA] on the fragment mean monthly values of EVI in order to obtain synthetic measures [i.e. the PCA axes] of grassland functioning. Grassland fragments were also characterized by size, vegetation structure [abundance of the tall-tussock grass Paspalum quadrifarium] and physical environment [soil type - abundance of litholitic soils - elevation, aspect and slope]. The relationship between grassland functioning and these explanatory variables was explored using linear regression models [LRMs] and artificial neural networks [ANNs]. Results: The first and second PCA axes were related to the annual integral of EVI [EVI-I] and EVI seasonality [EVI-S], respectively; these explained jointly ca. 80 percent of total variability in mean EVI values. ANNs captured better than regression models the relationships among the proposed controls and the spatial variability of grassland functioning in Southern Pampa. Results showed that EVI-I variability was related to all independent variables except aspect. While fragment size, litholitic soils and slope were negatively related to EVI-I, the abundance of P. quadrifarium had a positive effect on the spectral index. Grasslands with high seasonality were large and had high slope and aspect, low abundance of P. quadrifarium and increased abundance of litholitic soils. Conclusions: Our results showed that grassland functioning in Southern Pampa, as estimated by EVI, depends on fragment size, vegetation structure and physical factors [soil type, aspect and slope]. Paspalum quadrifarium may have an important functional role in this grassland system.
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| Subjects: | ENHANCED VEGETATION INDEX, FRAGMENTATION, LANDSCAPE STRUCTURE, MODIS DATA, NEURAL NETWORKS, TALL-TUSSOCK GRASSLAND, PASPALUM, PASPALUM QUADRIFARIUM, , |
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ENHANCED VEGETATION INDEX FRAGMENTATION LANDSCAPE STRUCTURE MODIS DATA NEURAL NETWORKS TALL-TUSSOCK GRASSLAND PASPALUM PASPALUM QUADRIFARIUM ENHANCED VEGETATION INDEX FRAGMENTATION LANDSCAPE STRUCTURE MODIS DATA NEURAL NETWORKS TALL-TUSSOCK GRASSLAND PASPALUM PASPALUM QUADRIFARIUM |
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ENHANCED VEGETATION INDEX FRAGMENTATION LANDSCAPE STRUCTURE MODIS DATA NEURAL NETWORKS TALL-TUSSOCK GRASSLAND PASPALUM PASPALUM QUADRIFARIUM ENHANCED VEGETATION INDEX FRAGMENTATION LANDSCAPE STRUCTURE MODIS DATA NEURAL NETWORKS TALL-TUSSOCK GRASSLAND PASPALUM PASPALUM QUADRIFARIUM Herrera, Lorena P. Texeira, Marcos Paruelo, José María Fragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networks |
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Questions: How do fragment-level characteristics affect remnant grassland functioning in a highly transformed landscape? Are artificial neural networks [ANNs] a better statistical tool to model variations in grassland functioning compared to linear regression models [LRMs]? Location: Tandilia Range, Southern Pampa, Buenos Aires Province, Argentina. Methods: We characterized the dynamics of the vegetation functioning in 60 remnant grasslands using enhanced vegetation index [EVI] data provided by MODIS/Terra images from July 2000 to June 2005. First, we performed a principal components analysis [PCA] on the fragment mean monthly values of EVI in order to obtain synthetic measures [i.e. the PCA axes] of grassland functioning. Grassland fragments were also characterized by size, vegetation structure [abundance of the tall-tussock grass Paspalum quadrifarium] and physical environment [soil type - abundance of litholitic soils - elevation, aspect and slope]. The relationship between grassland functioning and these explanatory variables was explored using linear regression models [LRMs] and artificial neural networks [ANNs]. Results: The first and second PCA axes were related to the annual integral of EVI [EVI-I] and EVI seasonality [EVI-S], respectively; these explained jointly ca. 80 percent of total variability in mean EVI values. ANNs captured better than regression models the relationships among the proposed controls and the spatial variability of grassland functioning in Southern Pampa. Results showed that EVI-I variability was related to all independent variables except aspect. While fragment size, litholitic soils and slope were negatively related to EVI-I, the abundance of P. quadrifarium had a positive effect on the spectral index. Grasslands with high seasonality were large and had high slope and aspect, low abundance of P. quadrifarium and increased abundance of litholitic soils. Conclusions: Our results showed that grassland functioning in Southern Pampa, as estimated by EVI, depends on fragment size, vegetation structure and physical factors [soil type, aspect and slope]. Paspalum quadrifarium may have an important functional role in this grassland system. |
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ENHANCED VEGETATION INDEX FRAGMENTATION LANDSCAPE STRUCTURE MODIS DATA NEURAL NETWORKS TALL-TUSSOCK GRASSLAND PASPALUM PASPALUM QUADRIFARIUM |
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Herrera, Lorena P. Texeira, Marcos Paruelo, José María |
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Herrera, Lorena P. Texeira, Marcos Paruelo, José María |
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Herrera, Lorena P. |
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Fragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networks |
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Fragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networks |
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Fragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networks |
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Fragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networks |
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Fragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networks |
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fragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networks |
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http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46942 |
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AT herreralorenap fragmentsizevegetationstructureandphysicalenvironmentcontrolgrasslandfunctioningatestbasedonartificialneuralnetworks AT texeiramarcos fragmentsizevegetationstructureandphysicalenvironmentcontrolgrasslandfunctioningatestbasedonartificialneuralnetworks AT paruelojosemaria fragmentsizevegetationstructureandphysicalenvironmentcontrolgrasslandfunctioningatestbasedonartificialneuralnetworks |
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KOHA-OAI-AGRO:469422023-05-16T09:50:58Zhttp://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46942AAGFragment size, vegetation structure and physical environment control grassland functioning a test based on artificial neural networksHerrera, Lorena P.Texeira, MarcosParuelo, José Maríatextengapplication/pdfQuestions: How do fragment-level characteristics affect remnant grassland functioning in a highly transformed landscape? Are artificial neural networks [ANNs] a better statistical tool to model variations in grassland functioning compared to linear regression models [LRMs]? Location: Tandilia Range, Southern Pampa, Buenos Aires Province, Argentina. Methods: We characterized the dynamics of the vegetation functioning in 60 remnant grasslands using enhanced vegetation index [EVI] data provided by MODIS/Terra images from July 2000 to June 2005. First, we performed a principal components analysis [PCA] on the fragment mean monthly values of EVI in order to obtain synthetic measures [i.e. the PCA axes] of grassland functioning. Grassland fragments were also characterized by size, vegetation structure [abundance of the tall-tussock grass Paspalum quadrifarium] and physical environment [soil type - abundance of litholitic soils - elevation, aspect and slope]. The relationship between grassland functioning and these explanatory variables was explored using linear regression models [LRMs] and artificial neural networks [ANNs]. Results: The first and second PCA axes were related to the annual integral of EVI [EVI-I] and EVI seasonality [EVI-S], respectively; these explained jointly ca. 80 percent of total variability in mean EVI values. ANNs captured better than regression models the relationships among the proposed controls and the spatial variability of grassland functioning in Southern Pampa. Results showed that EVI-I variability was related to all independent variables except aspect. While fragment size, litholitic soils and slope were negatively related to EVI-I, the abundance of P. quadrifarium had a positive effect on the spectral index. Grasslands with high seasonality were large and had high slope and aspect, low abundance of P. quadrifarium and increased abundance of litholitic soils. Conclusions: Our results showed that grassland functioning in Southern Pampa, as estimated by EVI, depends on fragment size, vegetation structure and physical factors [soil type, aspect and slope]. Paspalum quadrifarium may have an important functional role in this grassland system.Questions: How do fragment-level characteristics affect remnant grassland functioning in a highly transformed landscape? Are artificial neural networks [ANNs] a better statistical tool to model variations in grassland functioning compared to linear regression models [LRMs]? Location: Tandilia Range, Southern Pampa, Buenos Aires Province, Argentina. Methods: We characterized the dynamics of the vegetation functioning in 60 remnant grasslands using enhanced vegetation index [EVI] data provided by MODIS/Terra images from July 2000 to June 2005. First, we performed a principal components analysis [PCA] on the fragment mean monthly values of EVI in order to obtain synthetic measures [i.e. the PCA axes] of grassland functioning. Grassland fragments were also characterized by size, vegetation structure [abundance of the tall-tussock grass Paspalum quadrifarium] and physical environment [soil type - abundance of litholitic soils - elevation, aspect and slope]. The relationship between grassland functioning and these explanatory variables was explored using linear regression models [LRMs] and artificial neural networks [ANNs]. Results: The first and second PCA axes were related to the annual integral of EVI [EVI-I] and EVI seasonality [EVI-S], respectively; these explained jointly ca. 80 percent of total variability in mean EVI values. ANNs captured better than regression models the relationships among the proposed controls and the spatial variability of grassland functioning in Southern Pampa. Results showed that EVI-I variability was related to all independent variables except aspect. While fragment size, litholitic soils and slope were negatively related to EVI-I, the abundance of P. quadrifarium had a positive effect on the spectral index. Grasslands with high seasonality were large and had high slope and aspect, low abundance of P. quadrifarium and increased abundance of litholitic soils. Conclusions: Our results showed that grassland functioning in Southern Pampa, as estimated by EVI, depends on fragment size, vegetation structure and physical factors [soil type, aspect and slope]. Paspalum quadrifarium may have an important functional role in this grassland system.ENHANCED VEGETATION INDEXFRAGMENTATIONLANDSCAPE STRUCTUREMODIS DATANEURAL NETWORKSTALL-TUSSOCK GRASSLANDPASPALUMPASPALUM QUADRIFARIUMApplied Vegetation Science |