Open Access

ARTICLE

Assessing and Modeling the Vegetation Cover in the W and Pendjari National Parks and Their Peripheries from 1985 to 2030, Using Landsat Imagery and Climatic Data in Benin, West Africa

Abdel Aziz Osseni¹, Hubert Olivier Dossou-Yovo^2,*, Apollon D.M.T. Hegbe³, Muhammad Nauman Khan⁴, Brice Sinsin²

1 Horticultural Research and Management of Green Spaces Unit, Laboratory of Plant, Horticultural and Forest Sciences, School of Horticulture and Management of Green Spaces, National University of Agriculture, Kétou, P.O. Box 43, Benin
2 Faculty of Agronomic Sciences, Laboratory of Applied Ecology, University of Abomey-Calavi, Godomey, P.O. Box 1974, Benin
3 Research Unit in Tropical Mycology and Soil-Plants-Fungi Interactions, Laboratory of Ecology, Botany and Plant Biology, University of Parakou, Parakou, P.O. Box 123, Benin
4 Department of Botany, Islamia College Peshawar, Peshawar, 25120, Pakistan

* Corresponding Author: Hubert Olivier Dossou-Yovo. Email:

Revue Internationale de Géomatique 2025, 34, 209-234. https://doi.org/10.32604/rig.2025.061448

Received 25 November 2024; Accepted 13 March 2025; Issue published 14 April 2025

Abstract

Today, environmental studies based on satellite imagery are known as making valuable contributions to the dynamics and spatial prediction of sensitive or complex ecosystems such as wide protected areas and represent sustainable decision tools. The Pendjari and W Transboundary Reserves which constitute biodiversity reservoirs, habitats for wildlife conservation lack substantial investigations on the vegetation dynamics. Despite the protection measures they benefit from, these reserves remain dependent on climatic hazards that can influence their stability. The present study is innovative since it applied remote sensing techniques combined with climate records from the last thirty years to analyze the past dynamics of land use and climate changes to predict the future trends of the vegetation cover of the two national parks in Benin, as well as their peripheries. The methodology used remote sensing and Geographic Information System (GIS) techniques that allowed the supervised classification of Landsat images from 1985, 2000 and 2015. Climatic data were combined in R software to identify the break periods for climatic parameters. Finally, the predictive vegetation cover for the year 2030 was made by combining vegetation and climatic data in the “Land Change Modeler” extension. Results show ten land use and land cover classes which are the agglomerations, mosaics of fields and fallows, water bodies, dense forests, gallery forests, clear forests and wooded savannahs, swamp forests and shrubby wooded savannahs, saxicolous savannahs and bare ground. The natural vegetation decreased from 90.85% in 1985 to 83.54% in 2000 then to 79.56% in 2015, representing a decline of 11.39% over a study period of 30 years. The analysis of the climatic curves revealed the presence of a break, meaning drought frequencies. The predictive modeling showed that land use units projected up to the year 2030 are consistent with past trends, but with the continued expansion of fields and fallows (2%) instead of the natural vegetation. This study not only provides good insights useful in the sustainable management of the Biosphere Reserves but will also motivate many other researches towards such ecosystems.

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Keywords

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