Modelling the spatial distribution of the classifcation error of remote sensing data in cocoa agroforestry systems

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dc.contributor.authorTamga, Dan Kanmegne
dc.contributor.authorLatifi, Hooman
dc.contributor.authorUllmann, Tobias
dc.contributor.authorBaumhauer, Roland
dc.contributor.authorThiel, Michael
dc.contributor.authorBayala, Jules
dc.date.accessioned2023-02-15T06:17:57Z
dc.date.available2023-02-15T06:17:57Z
dc.date.issued2023
dc.description© The Author(s) 2022. This article is published with open access at Springerlink.com and is licensed under the Creative Commons Attribution 4.0 International License - https://creativecommons.org/licenses/by/4.0/ . The Version of Scholarly Record of this Article is published in Agroforestry Systems, 2023, available online at: https://link.springer.com/article/10.1007/s10457-022-00791-2 . Keywords: cocoa mapping; geographically weighted regression; Sentinel-1; Sentinel-2; Shannon entropy; spatial error assessment.
dc.description.abstractCocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in Côte d’Ivoire, close to the Taï National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user’s accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in Côte d’Ivoire.
dc.description.sponsorshipAcknowledgements: Authors acknowledge the support from the German Federal Ministry for Education and Research (BMBF) via the project carrier at the German Aerospace Center (DLR Projektträger) through the research project: WASCAL-DE-Coop (FKZ: 01LG1808A). Open Access funding enabled and organized by Projekt DEAL.
dc.identifier.citationKanmegne Tamga, D., Latifi, H., Ullmann, T., Baumhauer, R., Thiel, M., & Bayana, J. (2023). Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems. Agroforestry Systems, 97, 109–119. https://doi.org/10.1007/s10457-022-00791-2
dc.identifier.otherhttps://doi.org/10.1007/s10457-022-00791-2
dc.identifier.urihttps://hdl.handle.net/20.500.14096/280
dc.language.isoen
dc.publisherSpringer Nature
dc.titleModelling the spatial distribution of the classifcation error of remote sensing data in cocoa agroforestry systems
dc.typeArticle

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