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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
08/06/2022 |
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Actualizado : |
08/06/2022 |
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Autor : |
VANCUTSEM, C.; PEKEL, J.-F.; KAYITAKIRE F.; DUVEILLER, G.; MERONI, M.; BAETHGEN, W.; CECCATO, P. |
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Afiliación : |
C. VANCUTSEM, European Commission, Joint Research Centre, Institute for Environment and Sustainability, 1-21027 Ispra (VA), Via E. Fermi 2749, Italy; J.-F. PEKEL, European Commission, Joint Research Centre, Institute for Environment and Sustainability, 1-21027 Ispra (VA), Via E. Fermi 2749, Italy; F. KAYITAKIRE, European Commission, Joint Research Centre, Institute for Environment and Sustainability, 1-21027 Ispra (VA), Via E. Fermi 2749, Italy; G. DUVEILLER, European Commission, Joint Research Centre, Institute for Environment and Sustainability, 1-21027 Ispra (VA), Via E. Fermi 2749, Italy; M. MERONI, European Commission, Joint Research Centre, Institute for Environment and Sustainability, 1-21027 Ispra (VA), Via E. Fermi 2749, Italy; WALTER E. BAETHGEN, International Research Institute for Climate and Society (IRI), Earth Institute at Columbia University, Palisades, NY 10964-8000, 61 Route 9W, Monell Building, United States; P. CECCATO, International Research Institute for Climate and Society (IRI), Earth Institute at Columbia University, Palisades, NY 10964-8000, 61 Route 9W, Monell Building, United States. |
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Título : |
Mapping winter and summer crops in Uruguay using MODIS time series. [Conference paper]. |
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Complemento del título : |
2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013. August 12-16 2013. Code 101027 |
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Fecha de publicación : |
2013 |
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Fuente / Imprenta : |
Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 2013, pp. 292-295, doi: http://doi.rog/10.1109/Argo-Geoinformatics.2013.6621924 |
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ISBN : |
978-147990868-4 |
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DOI : |
10.1109/Argo-Geoinformatics.2013.6621924 |
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Idioma : |
Inglés |
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Contenido : |
ABSTRACT - Agricultural monitoring is a major concern to economies largely based on agriculture like Uruguay. In order to improve crop yield forecasts, identification of crop types must be performed early in the planting season. However, this task is generally difficult because of the spatial heterogeneity of the landscape, the different crop cycles, the spectral similarity with grassland, and the inter-annual variability due to climatic events and fallow periods. In collaboration with INIA, this study investigates remote sensing methods for dynamic mapping of cropland areas and for producing a map of winter and summer crops at 250m using MODIS time series. The originality of the approach consists of: (i) exploiting all the multi-spectral information using an adaptive compositing method for a better discrimination of cropland types and to better capture their spatio-temporal variability; (ii) a spatio-temporal analysis of various land use types prior to the classification for a better knowledge of crops behaviours and the selection of the most discriminating seasons in the classification; and (iii) combining NDVI profiles, multi-spectral composites with reference dataset, high resolution images and expert knowledge. The accuracy of the product is assessed based on a reference dataset of crop fields. The results confirm the relevance of MODIS time series in term of spatial and temporal resolutions for mapping cropland areas and characterizing the inter-annual variability. Thanks to a good reference dataset and an analysis of crops spectro-temporal behaviour, it was possible to identify cropland areas from other land use types and discriminate summer crops from winter crops. MenosABSTRACT - Agricultural monitoring is a major concern to economies largely based on agriculture like Uruguay. In order to improve crop yield forecasts, identification of crop types must be performed early in the planting season. However, this task is generally difficult because of the spatial heterogeneity of the landscape, the different crop cycles, the spectral similarity with grassland, and the inter-annual variability due to climatic events and fallow periods. In collaboration with INIA, this study investigates remote sensing methods for dynamic mapping of cropland areas and for producing a map of winter and summer crops at 250m using MODIS time series. The originality of the approach consists of: (i) exploiting all the multi-spectral information using an adaptive compositing method for a better discrimination of cropland types and to better capture their spatio-temporal variability; (ii) a spatio-temporal analysis of various land use types prior to the classification for a better knowledge of crops behaviours and the selection of the most discriminating seasons in the classification; and (iii) combining NDVI profiles, multi-spectral composites with reference dataset, high resolution images and expert knowledge. The accuracy of the product is assessed based on a reference dataset of crop fields. The results confirm the relevance of MODIS time series in term of spatial and temporal resolutions for mapping cropland areas and characterizing the inter-annual variability. Tha... Presentar Todo |
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Palabras claves : |
Agricultural monitoring; Cropland mapping; Land use; Mean compositing; MODIS time series; Phenology; Spatial and temporal resolutions; Uruguay. |
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Asunto categoría : |
A50 Investigación agraria |
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Marc : |
LEADER 02708nam a2200301 a 4500
001 1063255
005 2022-06-08
008 2013 bl uuuu u01u1 u #d
020 $a978-147990868-4
024 7 $a10.1109/Argo-Geoinformatics.2013.6621924$2DOI
100 1 $aVANCUTSEM, C.
245 $aMapping winter and summer crops in Uruguay using MODIS time series. [Conference paper].$h[electronic resource]
260 $aSecond International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 2013, pp. 292-295, doi: http://doi.rog/10.1109/Argo-Geoinformatics.2013.6621924$c2013
520 $aABSTRACT - Agricultural monitoring is a major concern to economies largely based on agriculture like Uruguay. In order to improve crop yield forecasts, identification of crop types must be performed early in the planting season. However, this task is generally difficult because of the spatial heterogeneity of the landscape, the different crop cycles, the spectral similarity with grassland, and the inter-annual variability due to climatic events and fallow periods. In collaboration with INIA, this study investigates remote sensing methods for dynamic mapping of cropland areas and for producing a map of winter and summer crops at 250m using MODIS time series. The originality of the approach consists of: (i) exploiting all the multi-spectral information using an adaptive compositing method for a better discrimination of cropland types and to better capture their spatio-temporal variability; (ii) a spatio-temporal analysis of various land use types prior to the classification for a better knowledge of crops behaviours and the selection of the most discriminating seasons in the classification; and (iii) combining NDVI profiles, multi-spectral composites with reference dataset, high resolution images and expert knowledge. The accuracy of the product is assessed based on a reference dataset of crop fields. The results confirm the relevance of MODIS time series in term of spatial and temporal resolutions for mapping cropland areas and characterizing the inter-annual variability. Thanks to a good reference dataset and an analysis of crops spectro-temporal behaviour, it was possible to identify cropland areas from other land use types and discriminate summer crops from winter crops.
653 $aAgricultural monitoring
653 $aCropland mapping
653 $aLand use
653 $aMean compositing
653 $aMODIS time series
653 $aPhenology
653 $aSpatial and temporal resolutions
653 $aUruguay
700 1 $aPEKEL, J.-F.
700 1 $aKAYITAKIRE F.
700 1 $aDUVEILLER, G.
700 1 $aMERONI, M.
700 1 $aBAETHGEN, W.
700 1 $aCECCATO, P.
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