Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing
Over the recent past, there has been a growing concern on the need for mapping cropping practices in order to improve decision-making in the agricultural sector. We developed an original method for mapping cropping practices: crop type and harvest mode, in a sugarcane landscape of western Kenya using remote sensing data. At local scale, a temporal series of 15-m resolution Landsat 8 images was obtained for Kibos sugar management zone over 20 dates (April 2013 to March 2014) to characterize cropping practices. To map the crop type and harvest mode we used ground survey and factory data over 1280 fields, digitized field boundaries, and spectral indices (the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI)) were computed for all Landsat images. The results showed NDVI classified crop type at 83.3% accuracy, while NDWI classified harvest mode at 90% accuracy. The crop map will inform better planning decisions for the sugar industry operations, while the harvest mode map will be used to plan for sensitizations forums on best management and environmental practices.
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Subjects: | F08 - Systèmes et modes de culture, U30 - Méthodes de recherche, F01 - Culture des plantes, E90 - Structure agraire, Saccharum officinarum, télédétection, système de culture, cartographie de l'utilisation des terres, pratique culturale, récolte, Landsat, http://aims.fao.org/aos/agrovoc/c_6727, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_1971, http://aims.fao.org/aos/agrovoc/c_9000100, http://aims.fao.org/aos/agrovoc/c_2018, http://aims.fao.org/aos/agrovoc/c_3500, http://aims.fao.org/aos/agrovoc/c_36766, http://aims.fao.org/aos/agrovoc/c_4086, |
Online Access: | http://agritrop.cirad.fr/577980/ http://agritrop.cirad.fr/577980/1/remotesensing-07-14428.pdf |
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dig-cirad-fr-5779802024-01-28T23:00:49Z http://agritrop.cirad.fr/577980/ http://agritrop.cirad.fr/577980/ Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing. Mulianga Betty, Bégué Agnès, Clouvel Pascal, Todoroff Pierre. 2015. Remote Sensing, 7 (11) : 14428-14444.https://doi.org/10.3390/rs71114428 <https://doi.org/10.3390/rs71114428> Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing Mulianga, Betty Bégué, Agnès Clouvel, Pascal Todoroff, Pierre eng 2015 Remote Sensing F08 - Systèmes et modes de culture U30 - Méthodes de recherche F01 - Culture des plantes E90 - Structure agraire Saccharum officinarum télédétection système de culture cartographie de l'utilisation des terres pratique culturale récolte Landsat http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1971 http://aims.fao.org/aos/agrovoc/c_9000100 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_3500 http://aims.fao.org/aos/agrovoc/c_36766 Kenya http://aims.fao.org/aos/agrovoc/c_4086 Over the recent past, there has been a growing concern on the need for mapping cropping practices in order to improve decision-making in the agricultural sector. We developed an original method for mapping cropping practices: crop type and harvest mode, in a sugarcane landscape of western Kenya using remote sensing data. At local scale, a temporal series of 15-m resolution Landsat 8 images was obtained for Kibos sugar management zone over 20 dates (April 2013 to March 2014) to characterize cropping practices. To map the crop type and harvest mode we used ground survey and factory data over 1280 fields, digitized field boundaries, and spectral indices (the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI)) were computed for all Landsat images. The results showed NDVI classified crop type at 83.3% accuracy, while NDWI classified harvest mode at 90% accuracy. The crop map will inform better planning decisions for the sugar industry operations, while the harvest mode map will be used to plan for sensitizations forums on best management and environmental practices. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/577980/1/remotesensing-07-14428.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.3390/rs71114428 10.3390/rs71114428 info:eu-repo/semantics/altIdentifier/doi/10.3390/rs71114428 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.3390/rs71114428 |
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F08 - Systèmes et modes de culture U30 - Méthodes de recherche F01 - Culture des plantes E90 - Structure agraire Saccharum officinarum télédétection système de culture cartographie de l'utilisation des terres pratique culturale récolte Landsat http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1971 http://aims.fao.org/aos/agrovoc/c_9000100 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_3500 http://aims.fao.org/aos/agrovoc/c_36766 http://aims.fao.org/aos/agrovoc/c_4086 F08 - Systèmes et modes de culture U30 - Méthodes de recherche F01 - Culture des plantes E90 - Structure agraire Saccharum officinarum télédétection système de culture cartographie de l'utilisation des terres pratique culturale récolte Landsat http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1971 http://aims.fao.org/aos/agrovoc/c_9000100 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_3500 http://aims.fao.org/aos/agrovoc/c_36766 http://aims.fao.org/aos/agrovoc/c_4086 |
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F08 - Systèmes et modes de culture U30 - Méthodes de recherche F01 - Culture des plantes E90 - Structure agraire Saccharum officinarum télédétection système de culture cartographie de l'utilisation des terres pratique culturale récolte Landsat http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1971 http://aims.fao.org/aos/agrovoc/c_9000100 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_3500 http://aims.fao.org/aos/agrovoc/c_36766 http://aims.fao.org/aos/agrovoc/c_4086 F08 - Systèmes et modes de culture U30 - Méthodes de recherche F01 - Culture des plantes E90 - Structure agraire Saccharum officinarum télédétection système de culture cartographie de l'utilisation des terres pratique culturale récolte Landsat http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1971 http://aims.fao.org/aos/agrovoc/c_9000100 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_3500 http://aims.fao.org/aos/agrovoc/c_36766 http://aims.fao.org/aos/agrovoc/c_4086 Mulianga, Betty Bégué, Agnès Clouvel, Pascal Todoroff, Pierre Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing |
description |
Over the recent past, there has been a growing concern on the need for mapping cropping practices in order to improve decision-making in the agricultural sector. We developed an original method for mapping cropping practices: crop type and harvest mode, in a sugarcane landscape of western Kenya using remote sensing data. At local scale, a temporal series of 15-m resolution Landsat 8 images was obtained for Kibos sugar management zone over 20 dates (April 2013 to March 2014) to characterize cropping practices. To map the crop type and harvest mode we used ground survey and factory data over 1280 fields, digitized field boundaries, and spectral indices (the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI)) were computed for all Landsat images. The results showed NDVI classified crop type at 83.3% accuracy, while NDWI classified harvest mode at 90% accuracy. The crop map will inform better planning decisions for the sugar industry operations, while the harvest mode map will be used to plan for sensitizations forums on best management and environmental practices. |
format |
article |
topic_facet |
F08 - Systèmes et modes de culture U30 - Méthodes de recherche F01 - Culture des plantes E90 - Structure agraire Saccharum officinarum télédétection système de culture cartographie de l'utilisation des terres pratique culturale récolte Landsat http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1971 http://aims.fao.org/aos/agrovoc/c_9000100 http://aims.fao.org/aos/agrovoc/c_2018 http://aims.fao.org/aos/agrovoc/c_3500 http://aims.fao.org/aos/agrovoc/c_36766 http://aims.fao.org/aos/agrovoc/c_4086 |
author |
Mulianga, Betty Bégué, Agnès Clouvel, Pascal Todoroff, Pierre |
author_facet |
Mulianga, Betty Bégué, Agnès Clouvel, Pascal Todoroff, Pierre |
author_sort |
Mulianga, Betty |
title |
Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing |
title_short |
Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing |
title_full |
Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing |
title_fullStr |
Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing |
title_full_unstemmed |
Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing |
title_sort |
mapping cropping practices of a sugarcane-based cropping system in kenya using remote sensing |
url |
http://agritrop.cirad.fr/577980/ http://agritrop.cirad.fr/577980/1/remotesensing-07-14428.pdf |
work_keys_str_mv |
AT muliangabetty mappingcroppingpracticesofasugarcanebasedcroppingsysteminkenyausingremotesensing AT begueagnes mappingcroppingpracticesofasugarcanebasedcroppingsysteminkenyausingremotesensing AT clouvelpascal mappingcroppingpracticesofasugarcanebasedcroppingsysteminkenyausingremotesensing AT todoroffpierre mappingcroppingpracticesofasugarcanebasedcroppingsysteminkenyausingremotesensing |
_version_ |
1792498917772886016 |