Developing a method to map coconut agrosystems from high-resolution satellite images
Komba Mayossa Prune Christobelle, Coppens D’eeckenbrugge Geo, Borne Frederic, Gadal Sébastien, Viennois Gaëlle. 2015-08-23. .
CONFERENCEOBJECT, (2015-08-23 ) - PUBLISHEDVERSION - English (en-GB)
OPENACCESS -
info:eu-repo/semantics/OpenAccess.
Audience : OTHER
HAL CCSD
Sujet
coconut, VHR images, spectral analysis, textural analysis, oil palm, mapping, agrosystem, [SDE]Environmental Sciences, [SHS.GEO]Humanities and Social Sciences/Geography, [SDE.ES]Environmental Sciences/Environmental and Society, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Domaines
Agriculture, Sciences Sociales, Environnement, Informatique, Télédétection, Sciences humaines
Description
https://icaci.org/files/documents/ICC_proceedings/ICC2015/papers/38/fullpaper/T38-504_1427765394.pdf International audience 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 byTeina (2009), based on spectral analysis and watershed segmentation, which we simplified byusing the NDVI vegetation index. The second one is the semi-automatic classification based ontexture analysis (PAPRI method of Borne, 1990). These methods were tested in two differentenvironments: Vanua Lava (Vanuatu; heterogeneous landscape, very ancient plantations) andIvory 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 of0.60 vs. 0.40). Spectral signatures do not allow a sufficiently accurate mapping of coconut and donot differentiate it from oil palm, despite their different NDVI signatures. The PAPRI methoddifferentiates 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 treeswhen they are well spaced. To increase the method accuracy, we suggest (1) field surveys (forsmall scale studies) and/or finer image resolution, allowing a high precision in manual mappingwith a better discrimination between coconut and oil palm, thus limiting the proportion of mixedpixels. (2) A phenological monitoring could improve the distinction between coconut and oil palmagrosystems. (3) Hyper-spectral images should allow extracting more precisely the respectivesignatures of both species. Another possibility would be (4) an object-oriented analysis asproposed by the eCognition software. Finally, (5) coupling the Lidar system with watershedanalysis would allow a better characterization of coconut varietal types.
Mots-clés
Environment, Geography
Auteurs
Komba Mayossa, Prune Christobelle, Coppens D’eeckenbrugge, Geo, Borne, Frederic, Gadal, Sébastien, Viennois, Gaëlle
Contributeurs
Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS) ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Aix Marseille Université (AMU), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE) ; Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE) ; Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), International cartographic association - Association cartographique internationale, International cartographic association - Association cartographique internationale
Sources
27th International Cartographic Conference 16th General Assembly, https://hal-amu.archives-ouvertes.fr/hal-01270740, 27th International Cartographic Conference 16th General Assembly, International cartographic association - Association cartographique internationale, Aug 2015, Rio de Janeiro, Brazil, http://www.icc2015.org/
