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Remote Sens. 2015, 7(11), 14428-14444; doi:10.3390/rs71114428

Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing

1
Centre for International Research for Agricultural Development (CIRAD), Spatial Information and Analysis for Territories and Ecosystems (UMR TETIS), Maison de la Télédétection, 500 rue J-F. Breton, Montpellier F-34093, France
2
Kenya Agriculture and Livestock Research Organization-Sugar Research Institute, Kisumu-Miwani Road, P.O Box 44–40100, Kisumu, Kenya
3
CIRAD Agro-ecology and Sustainable Intensification of Annual Crops (UPR AIDA), Avenue Agropolis, Montpellier Cedex 5, Montpellier F-34398, France
4
CIRAD Agro-ecology and Sustainable Intensification of Annual Crops (UPR AIDA), Station de Ligne-Paradis, 7 chemin de l’IRAT, Saint-Pierre, Réunion F-97410, France
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 1 October 2015 / Revised: 23 October 2015 / Accepted: 26 October 2015 / Published: 30 October 2015
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Abstract

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. View Full-Text
Keywords: remote sensing; Landsat; harvest; sugarcane; cropping practices remote sensing; Landsat; harvest; sugarcane; cropping practices
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mulianga, B.; Bégué, A.; Clouvel, P.; Todoroff, P. Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing. Remote Sens. 2015, 7, 14428-14444.

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