Estimation de la densite des ligneux avec des images drones dans les ecosystemes Saheliens du Senegal
Abstract
Les avancées récentes des drones et du traitement des données permettent aujourd’hui de produire des images hautes résolution et des modèles 3D utiles pour évaluer les attributs des arbres. Cette étude a été menée à Widou thiengoly dans la localité du Ferlo, Nord du Sénégal, avec comme objectif général d’appliquer une approche photogrammétrique pour la mesure de la densité des tiges (tiges/ha) avec des images drones. Une méthode de l’approche arbre basée sur un modèle numérique de hauteur d'une zone d'étude de 10 hectares a été mise en œuvre, ce modèle a été construit à partir d'images obtenues par des drones. Au total, 92 arbres de référence ont été comptés dans le cadre de cette étude et l'algorithme a détecté 75 arbres, ce qui donne une précision supérieure à 90 % (score F de 0,93). Dans l'ensemble, l'algorithme a manqué 10 arbres (erreurs d'omission) et a faussement détecté 3 arbres (erreurs de commission), ce qui donne un compte total de 88 arbres. Cette étude suggère que l'algorithme de filtrage des maxima locaux combiné avec des tailles de fenêtre optimale, appliqués sur un Modèle Numérique de Hauteur construit par photogrammétrie est capable d’effectuer des comptages d'arbres avec une précision acceptable (F > 0,90) dans la zone sahélienne.
Recent advances in drone technology and data processing now make it possible to generate high-resolution images and 3D models that are useful for assessing tree attributes. This study was conducted in Widou Thiengoly, in the Ferlo area of northern Senegal, with the overall objective of applying a photogrammetric approach to measure stem density (stems/ha) using drone imagery. A tree-based method was implemented on a Digital Height Model covering a 10-hectare study area, constructed from drone images. In total, 92 reference trees were counted during the study, and the algorithm detected 75 trees, resulting in an accuracy above 90% (F-score of 0.93). Overall, the algorithm missed 10 trees (omission errors) and falsely detected 3 trees (commission errors), giving a total count of 88 trees. This study suggests that the local maxima filtering algorithm, combined with optimal window sizes and applied to a photogrammetrically derived Digital Height Model, can perform tree counts with acceptable accuracy (F > 0.90) in the Sahelian zone.
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