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Object-based characterization of vegetation heterogeneity with sentinel images proves efficient in a highly human-influenced National Park of Côte d’Ivoire

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Abstract

Forest monitoring requires more automated systems to analyze high ecosystem heterogeneity. The traditional pixel-based detection method has proven to be less and less effective. A novel change detection method is therefore proposed to detect changes in forest cover using satellite images at very high spatial resolution. This is object-oriented classification, which groups pixels into interpreted objects, based on their spectral values, spatial, and textural properties. Using sentinel and Lansat images, we tested for the first time in the West African rainforest zone the effectiveness of this method for better detection, delineation, and analysis of land use and occupation types. The mean shift algorithm was used in both the segmentation and classification processes. Next, we compared the proposed object-oriented method with a pixel-based image classification detection method by implementing both methods under the same conditions. High detection accuracy (> 90%) and an overall Kappa greater than 0.90 were obtained by the object-oriented method, which is about 20% higher than the pixel-based method. The object-based method was free of salt and pepper effects and was less prone to image misregistration in terms of change detection accuracy and mapping results. This study demonstrates that the object-based classifier is a much better approach than the classical pixel-based classifier. In addition, it shows the problems of detecting heterogeneous landscapes and explains the observed confusions between the types of vegetation formations specific to tropical wetlands. The results obtained are encouraging and the contribution of high-resolution images and the object-based method to better discrimination of tropical wetland vegetation is discussed.

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Acknowledgements

The authors thank the “Sud Expert Plantes et Development Durable” (SEP2D) program for the material and financial support in carrying out this study. They thank the Ivorian Office of Parks and Reserves (OIPR) for authorizing access to the Azagny National Park, as well as the members of the Biodiversity and Valuation of ecosystem services research team (BioValse) for their support on the ground. They also thank the Institute of Environmental Geosciences (IGE) of the University of Grenoble-Alpes for its technical support and advice during the execution of the research work. Finally, we would like to thank the Cooperation and Cultural Action Service (SCAC) of the France Embassy in Côte d’Ivoire, for having funded our doctoral internship stay.

Funding

Our field work was funded by the Institut de Recherche et de développement (IRD) from the SEP2D-PRP3-45 project (Sud Expert Plante et Développement Durable). The training in data analysis methodology was funded by the cooperation and cultural action department of France Embassy in Côte d’Ivoire.

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Authors and Affiliations

  1. Laboratoire de Biodiversité et d’Ecologie Tropicale, UFR Environnement, Université Jean Lorougnon Guédé, BP 150, Daloa, Côte d’Ivoire

    Stéphanie Diane Konan Houphlet

  2. Institut d’Urbanisation et de Géographie Alpine, Université Grenoble-Alpes, CNRS, PACTE, 38100, Grenoble, France

    Pauline Dusseux

  3. Laboratoire de Botanique, UFR Biosciences, Université Félix Houphouët-Boigny, 22 BP 582, Abidjan, Côte d’Ivoire

    Adjo Estelle Geneviève Adiko & Constant Yves Adou Yao

  4. Laboratoire des Sciences et Technologie de l’Environnement, UFR Environnement, Université Jean Lorougnon Guédé, BP 150, Daloa, Côte d’Ivoire

    Arthur Brice Konan-Waidhet

  5. Laboratoire Interdisciplinaire de Physique, Université Grenoble-Alpes, 38041 Cedex 9, Grenoble, France

    François Munoz

  6. Institut des Géosciences de l’Environnement, Université Grenoble-Alpes, CNRS, IRD, Grenoble INP, 70 Rue de La Physique, 38400, Saint Martin d’Hères, France

    Stéphanie Diane Konan Houphlet & Sylvain Bigot

  7. Centre Suisse de Recherche Scientifiques en Côte d’Ivoire, 1 BP 1303, Abidjan, Côte d’Ivoire

    Constant Yves Adou Yao

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  1. Stéphanie Diane Konan Houphlet
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Houphlet, S.D.K., Dusseux, P., Adiko, A.E.G. et al. Object-based characterization of vegetation heterogeneity with sentinel images proves efficient in a highly human-influenced National Park of Côte d’Ivoire. Environ Monit Assess 195, 200 (2023). https://doi.org/10.1007/s10661-022-10792-4

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