Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas

Côte d’Ivoire and Ghana are the largest producers of cocoa in the world. In recent decades the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa plantations are essential to the implementation...

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Autores principales:	Itohan-Osa Abu, Zoltan Szantoi, Andreas Brink, Marine Robuchon, Michael Thiel
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Cocoa mapping Cash crops West Africa Sentinel-1 Sentinel-2 Protected areas Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/af09c1663f674e049a53a07833789c61
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id	oai:doaj.org-article:af09c1663f674e049a53a07833789c61
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spelling	oai:doaj.org-article:af09c1663f674e049a53a07833789c612021-12-01T04:54:43ZDetecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas1470-160X10.1016/j.ecolind.2021.107863https://doaj.org/article/af09c1663f674e049a53a07833789c612021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21005288https://doaj.org/toc/1470-160XCôte d’Ivoire and Ghana are the largest producers of cocoa in the world. In recent decades the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa plantations are essential to the implementation of sustainable cocoa practices and for the periodic and effective monitoring of forests. In this study, a method for cocoa plantation identification was developed based on a multi-temporal stack of Sentinel-1 and Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. The Normalized Difference Vegetation Index (NDVI) and second-order texture features were assessed for their importance in an RF classification, and their optimal combination was used as input variables for the RF model to identify cocoa plantations in both countries. The RF model-based cocoa map achieved 82.89% producer’s and 62.22% user’s accuracy, detecting 3.69 million hectares (Mha) and 2.15 Mha of cocoa plantations for Côte d'Ivoire and Ghana, respectively. The results demonstrate that a combination of an RF model and multi-feature classification can distinguish cocoa plantations from other land cover/use, effectively reducing feature dimensions and improving classification efficiency. The results also highlight that cocoa farms largely encroach into protected areas (PAs), as 20% of the detected cocoa plantation area is located in PAs and almost 70% of the PAs in the study area house cocoa plantations.Itohan-Osa AbuZoltan SzantoiAndreas BrinkMarine RobuchonMichael ThielElsevierarticleCocoa mappingCash cropsWest AfricaSentinel-1Sentinel-2Protected areasEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 107863- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cocoa mapping Cash crops West Africa Sentinel-1 Sentinel-2 Protected areas Ecology QH540-549.5
spellingShingle	Cocoa mapping Cash crops West Africa Sentinel-1 Sentinel-2 Protected areas Ecology QH540-549.5 Itohan-Osa Abu Zoltan Szantoi Andreas Brink Marine Robuchon Michael Thiel Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas
description	Côte d’Ivoire and Ghana are the largest producers of cocoa in the world. In recent decades the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa plantations are essential to the implementation of sustainable cocoa practices and for the periodic and effective monitoring of forests. In this study, a method for cocoa plantation identification was developed based on a multi-temporal stack of Sentinel-1 and Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. The Normalized Difference Vegetation Index (NDVI) and second-order texture features were assessed for their importance in an RF classification, and their optimal combination was used as input variables for the RF model to identify cocoa plantations in both countries. The RF model-based cocoa map achieved 82.89% producer’s and 62.22% user’s accuracy, detecting 3.69 million hectares (Mha) and 2.15 Mha of cocoa plantations for Côte d'Ivoire and Ghana, respectively. The results demonstrate that a combination of an RF model and multi-feature classification can distinguish cocoa plantations from other land cover/use, effectively reducing feature dimensions and improving classification efficiency. The results also highlight that cocoa farms largely encroach into protected areas (PAs), as 20% of the detected cocoa plantation area is located in PAs and almost 70% of the PAs in the study area house cocoa plantations.
format	article
author	Itohan-Osa Abu Zoltan Szantoi Andreas Brink Marine Robuchon Michael Thiel
author_facet	Itohan-Osa Abu Zoltan Szantoi Andreas Brink Marine Robuchon Michael Thiel
author_sort	Itohan-Osa Abu
title	Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas
title_short	Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas
title_full	Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas
title_fullStr	Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas
title_full_unstemmed	Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas
title_sort	detecting cocoa plantations in côte d’ivoire and ghana and their implications on protected areas
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/af09c1663f674e049a53a07833789c61
work_keys_str_mv	AT itohanosaabu detectingcocoaplantationsincotedivoireandghanaandtheirimplicationsonprotectedareas AT zoltanszantoi detectingcocoaplantationsincotedivoireandghanaandtheirimplicationsonprotectedareas AT andreasbrink detectingcocoaplantationsincotedivoireandghanaandtheirimplicationsonprotectedareas AT marinerobuchon detectingcocoaplantationsincotedivoireandghanaandtheirimplicationsonprotectedareas AT michaelthiel detectingcocoaplantationsincotedivoireandghanaandtheirimplicationsonprotectedareas
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Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas

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