Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Titre : Satellite imagery based canopy cover estimation in tropical dryland forest of Senegal.

Auteur : Serbina, Valeriya

Université de soutenance : University of Eastern Finland

Grade : Master’s thesis in forest science 2018

Résumé
The Thesis focuses on fin ding suitable methods for measuring canopy cover in the NiokoloKoba National Park, an area in Senegal part of a REDD+ piloting project. The methods utilised in the study were the Sparse Bayesian and the Zero-and-One Inflated Beta (ZOINBR) regression models. Set of 10 vegetation indexes were used as test indicators to identify the most efficient method of forest cover measurement using remote sensing techniques. Visual assessment was necessary for producing ground data for modeling. The resulting statistical analysis showed the applicability of remote sensing methods using satellite imagery from different dates during the leaf-on season in tropical arid forest, results which outlined that for producing reliable output, several images are required. However, the most reliable vegetation indexes are different for each individual case and season. To conclude, both methods demonstrated reliable results when indexes are derived from two images. During the statistical analysis of results, confidence intervals for different forest classes were found

Mots clés : Remote sensing, canopy cover estimation, forest degradation, REDD+, Senegal forestry, vegetation indexes

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