Developing a method to map coconut agrosystems from high-resolution satellite images

Abstract : Our study aims at developing a generalizable method to exploit high resolution satellite images (VHR) for mapping coconut-based agrosystems, differentiating them from oil palm agrosystems. We compared two methods of land use classification. The first one is similar to that described by Teina (2009), based on spectral analysis and watershed segmentation, which we simplified by using the NDVI vegetation index. The second one is the semi-automatic classification based on texture analysis (PAPRI method of Borne, 1990). These methods were tested in two different environments: Vanua Lava (Vanuatu; heterogeneous landscape, very ancient plantations) and Ivory Coast (Marc Delorme Research Station, monoculture, regular spacing, oil palm plantations); and their results were evaluated against manually digitized photo-interpretation maps. In both situations, the PAPRI method produced better results than that of Teina (global kappa of 0.60 vs. 0.40). Spectral signatures do not allow a sufficiently accurate mapping of coconut and do not differentiate it from oil palm, despite their different NDVI signatures. The PAPRI method differentiates productive coconut from mixed plantations and other vegetation, either high or low (70% accuracy). In both situations, Teina's method allows counting 65% of the coconut trees when they are well spaced. To increase the method accuracy, we suggest (1) field surveys (for small scale studies) and/or finer image resolution, allowing a high precision in manual mapping with a better discrimination between coconut and oil palm, thus limiting the proportion of mixed pixels. (2) A phenological monitoring could improve the distinction between coconut and oil palm agrosystems. (3) Hyper-spectral images should allow extracting more precisely the respective signatures of both species. Another possibility would be (4) an object-oriented analysis as proposed by the eCognition software. Finally, (5) coupling the Lidar system with watershed analysis would allow a better characterization of coconut varietal types.
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https://hal-amu.archives-ouvertes.fr/hal-01270740
Contributor : Prune Christobelle Komba Mayossa <>
Submitted on : Monday, February 8, 2016 - 2:23:43 PM
Last modification on : Tuesday, September 10, 2019 - 8:32:14 PM
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Prune Christobelle Komba Mayossa, Geo Coppens D’eeckenbrugge, Frédéric Borne, Sébastien Gadal, Gaëlle Viennois. Developing a method to map coconut agrosystems from high-resolution satellite images. 27th International Cartographic Conference 16th General Assembly, International cartographic association - Association cartographique internationale, Aug 2015, Rio de Janeiro, Brazil. ⟨10.1007/978-94-007-0671-2_12⟩. ⟨hal-01270740⟩

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