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Monitoring spatial accuracy of oil palm cultivation mapping in southern Cameroon from Landsat series images

Abstract : Studying and mapping palm grove evolution allow understanding the impact related to its cultivation. Our study aims to map industrial palm grove using Landsat series images and measures the accuracy of the produced maps. It was carried out in SOCAPALM industrial plantation, located in southern of Cameroon. For the mapping and assessment of accuracy, perpixel classification and confusion matrix method were used, respectively. We obtained high correlated maps (Kappa =0.92 in 2001 vs 0.86 in 2015). However, some confusions were observed between vegetation and oil palm classes for the two periods, affecting the maps accuracy. These confusions are caused by the presence of mixed pixels resulting from the spatial and spectral characteristics of palm groves, the method used to map and validate the map, and uncertainty related to dada. To increase the accuracy, we suggest (1) to use another mapping method such as super-resolution mapping, (2) develop a classification system of cartographic products.
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https://hal-amu.archives-ouvertes.fr/hal-01352528
Contributor : Sébastien Gadal <>
Submitted on : Monday, August 8, 2016 - 12:49:21 PM
Last modification on : Tuesday, October 13, 2020 - 3:10:23 AM

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  • HAL Id : hal-01352528, version 1

Citation

Prune Christobelle Komba Mayossa, Sébastien Gadal. Monitoring spatial accuracy of oil palm cultivation mapping in southern Cameroon from Landsat series images. Spatial Accuracy 2016, Jul 2016, Montpellier, France. pp.358-365. ⟨hal-01352528⟩

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