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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
26/09/2013 |
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Data da última atualização: |
19/12/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; LAMPARELLI, R. A. C.; RODRIGUES, L. H. A. |
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Afiliação: |
JOÃO FRANCISCO GONÇALVES ANTUNES, CNPTIA; JÚLIO CÉSAR DALLA MORA ESQUERDO, CNPTIA; RUBENS AUGUSTO CAMARGO LAMPARELLI, Nipe/Unicamp; LUIZ HENRIQUE ANTUNES RODRIGUES, Feagri/Unicamp. |
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Título: |
Analysis of the vegetation phenology from the Alto Paraguai basin throught the representation of harmonic cycles of EVI/MODIS time-series. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Geografia, Rio Claro, v. 38, p. 109-122, ago. 2013. Número especial. |
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Idioma: |
Inglês |
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Conteúdo: |
The Alto Paraguai Basin (BAP) is of strategic importance for Brazil, due to its ecological diversity of landscape, especially because it includes the Pantanal floodplain. The harmonic analysis can be used in remote sensing time-series data to study the cyclic behavior of vegetation indices. The visual representation of harmonic terms can hel image interpretation through the combination of colors in the HLS (Hue, Lightness, Saturation) space which provides a soft visual transition effect between the cycles. The objective of this study was to analyze the vegetation phenology of the BAP using the harmonic analysis applied to an EVI (Enhanced Vegetation Index) time-series data from MODIS (Moderate Resolution Imaging Spectroradiometer) during 10 hydrologic years from October 2001 to September 2011, considering the HLS representation of the harmonic terms. The results show that the vegetation phenology of BAP presents spatial patterns coherent with the vegetation development and consistent with the variability of the seasonal inundations in Pantanal, which determines the hydrologic conditions of the region, directly affecting the moment of maximum EVI. The HLs representation of harmonic terms indicates that it is an effective tool for the visual interpretation of vegetation cycles. |
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Palavras-Chave: |
Análise harmônica; Harmonic analysis; HLS transform; Processamento de imagens; Transformação HLS. |
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Thesagro: |
Fenologia; Sensoriamento Remoto. |
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Thesaurus Nal: |
Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/158528/1/ANALYSIS.pdf
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Marc: |
LEADER 02113naa a2200253 a 4500
001 1967222
005 2017-12-19
008 2013 bl uuuu u00u1 u #d
100 1 $aANTUNES, J. F. G.
245 $aAnalysis of the vegetation phenology from the Alto Paraguai basin throught the representation of harmonic cycles of EVI/MODIS time-series.
260 $c2013
520 $aThe Alto Paraguai Basin (BAP) is of strategic importance for Brazil, due to its ecological diversity of landscape, especially because it includes the Pantanal floodplain. The harmonic analysis can be used in remote sensing time-series data to study the cyclic behavior of vegetation indices. The visual representation of harmonic terms can hel image interpretation through the combination of colors in the HLS (Hue, Lightness, Saturation) space which provides a soft visual transition effect between the cycles. The objective of this study was to analyze the vegetation phenology of the BAP using the harmonic analysis applied to an EVI (Enhanced Vegetation Index) time-series data from MODIS (Moderate Resolution Imaging Spectroradiometer) during 10 hydrologic years from October 2001 to September 2011, considering the HLS representation of the harmonic terms. The results show that the vegetation phenology of BAP presents spatial patterns coherent with the vegetation development and consistent with the variability of the seasonal inundations in Pantanal, which determines the hydrologic conditions of the region, directly affecting the moment of maximum EVI. The HLs representation of harmonic terms indicates that it is an effective tool for the visual interpretation of vegetation cycles.
650 $aRemote sensing
650 $aFenologia
650 $aSensoriamento Remoto
653 $aAnálise harmônica
653 $aHarmonic analysis
653 $aHLS transform
653 $aProcessamento de imagens
653 $aTransformação HLS
700 1 $aESQUERDO, J. C. D. M.
700 1 $aLAMPARELLI, R. A. C.
700 1 $aRODRIGUES, L. H. A.
773 $tGeografia, Rio Claro$gv. 38, p. 109-122, ago. 2013. Número especial.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Amapá; Embrapa Amazônia Oriental. |
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Data corrente: |
18/11/2019 |
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Data da última atualização: |
07/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
TOURNE, D. C. M.; BALLESTER, M. V. R.; JAMES, P. M. A.; MARTORANO, L. G.; GUEDES, M. C.; THOMAS, E. |
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Afiliação: |
DAIANA C. M. TOURNE, USP; MARIA V. R. BALLESTER, USP; PATRICK M. A. JAMES, UNIVERSITY OF MONTRÉAL; LUCIETA GUERREIRO MARTORANO, CPATU; MARCELINO CARNEIRO GUEDES, CPAF-AP; EVERT THOMAS, BIOVERSITY INTERNATIONAL, REGIONAL OFFICE FOR THE AMERICAS. |
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Título: |
Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Ecology and Evolution, v. 9, n. 22, p. 12357-12960, Nov. 2019. |
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DOI: |
https://doi.org/10.1002/ece3.5726 |
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Idioma: |
Inglês |
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Conteúdo: |
Aim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model. MenosAim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Am... Mostrar Tudo |
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Palavras-Chave: |
Análise de componentes principais; Avaliação de modelo; Conhecimento especializado; Entropia máxima; Expert knowledge; Filtragem espacial; Maximum entropy; Model evaluation; Modelo de distribuição de espécie; Protected Amazonian species; Spatial filtering; Species distribution model. |
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Thesagro: |
Castanha. |
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Thesaurus NAL: |
Principal component analysis. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159297/1/Strategies-to-optimize.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/204836/1/CPAF-AP-2019-Strategies-to-optimize-modeling-habitat.pdf
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Marc: |
LEADER 03112naa a2200361 a 4500
001 2159297
005 2023-12-07
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1002/ece3.5726$2DOI
100 1 $aTOURNE, D. C. M.
245 $aStrategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia.$h[electronic resource]
260 $c2019
520 $aAim: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization: Amazon region, South America. Methods: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model.
650 $aPrincipal component analysis
650 $aCastanha
653 $aAnálise de componentes principais
653 $aAvaliação de modelo
653 $aConhecimento especializado
653 $aEntropia máxima
653 $aExpert knowledge
653 $aFiltragem espacial
653 $aMaximum entropy
653 $aModel evaluation
653 $aModelo de distribuição de espécie
653 $aProtected Amazonian species
653 $aSpatial filtering
653 $aSpecies distribution model
700 1 $aBALLESTER, M. V. R.
700 1 $aJAMES, P. M. A.
700 1 $aMARTORANO, L. G.
700 1 $aGUEDES, M. C.
700 1 $aTHOMAS, E.
773 $tEcology and Evolution$gv. 9, n. 22, p. 12357-12960, Nov. 2019.
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Embrapa Amazônia Oriental (CPATU) |
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