Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach
Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014–2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.
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dig-cirad-fr-5876852024-02-16T19:02:13Z http://agritrop.cirad.fr/587685/ http://agritrop.cirad.fr/587685/ Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach. Bellon De La Cruz Beatriz, Bégué Agnès, Lo Seen Danny, Lebourgeois Valentine, Evangelista Balbino Antônio, Simoes Margareth, Demonte Ferraz Rodrigo Peçanha. 2018. International Journal of Applied Earth Observation and Geoinformation, 68 : 127-138.https://doi.org/10.1016/j.jag.2018.01.019 <https://doi.org/10.1016/j.jag.2018.01.019> Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach Bellon De La Cruz, Beatriz Bégué, Agnès Lo Seen, Danny Lebourgeois, Valentine Evangelista, Balbino Antônio Simoes, Margareth Demonte Ferraz, Rodrigo Peçanha eng 2018 International Journal of Applied Earth Observation and Geoinformation F08 - Systèmes et modes de culture Brésil http://aims.fao.org/aos/agrovoc/c_1070 Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014–2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/587685/1/1-s2.0-S0303243418301004-main.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.jag.2018.01.019 10.1016/j.jag.2018.01.019 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jag.2018.01.019 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.jag.2018.01.019 info:eu-repo/grantAgreement/EC/FP7/603719//(EU) Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM/SIGMA |
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F08 - Systèmes et modes de culture http://aims.fao.org/aos/agrovoc/c_1070 F08 - Systèmes et modes de culture http://aims.fao.org/aos/agrovoc/c_1070 |
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F08 - Systèmes et modes de culture http://aims.fao.org/aos/agrovoc/c_1070 F08 - Systèmes et modes de culture http://aims.fao.org/aos/agrovoc/c_1070 Bellon De La Cruz, Beatriz Bégué, Agnès Lo Seen, Danny Lebourgeois, Valentine Evangelista, Balbino Antônio Simoes, Margareth Demonte Ferraz, Rodrigo Peçanha Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach |
description |
Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014–2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions. |
format |
article |
topic_facet |
F08 - Systèmes et modes de culture http://aims.fao.org/aos/agrovoc/c_1070 |
author |
Bellon De La Cruz, Beatriz Bégué, Agnès Lo Seen, Danny Lebourgeois, Valentine Evangelista, Balbino Antônio Simoes, Margareth Demonte Ferraz, Rodrigo Peçanha |
author_facet |
Bellon De La Cruz, Beatriz Bégué, Agnès Lo Seen, Danny Lebourgeois, Valentine Evangelista, Balbino Antônio Simoes, Margareth Demonte Ferraz, Rodrigo Peçanha |
author_sort |
Bellon De La Cruz, Beatriz |
title |
Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach |
title_short |
Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach |
title_full |
Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach |
title_fullStr |
Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach |
title_full_unstemmed |
Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach |
title_sort |
improved regional-scale brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach |
url |
http://agritrop.cirad.fr/587685/ http://agritrop.cirad.fr/587685/1/1-s2.0-S0303243418301004-main.pdf |
work_keys_str_mv |
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1792499477945253888 |