Detecting canopy openings in logged-over forests: a multi-classifier analysis of PlanetScope imagery

Abstract

This study focused on the detection of forest canopy openings resulting from harvesting activities in hill tropical forests. Canopy openings, whether natural or human-induced, can have detrimental effects on forest ecosystems. Traditional ground surveys to assess the extent of canopy opening can be challenging and time-consuming. Therefore the study aimed to utilise satellite imagery, specifically PlanetScope data, to detect, map and measure canopy openings in logged-over forests. Three different classification algorithms, namely maximum likelihood classifier (MLC), support vector machine (SVM) and object-based image analysis (OBIA) were used and compared to identify canopy opening areas. The assessment was conducted in two stages: a preliminary assessment with three classes (forest, canopy opening and shadow) and a final assessment with two classes (forest and canopy opening). The overall accuracies of the classification algorithms were 82% for MLC, 91% for SVM and 90% for OBIA. Both SVM and OBIA surpassed the
accuracy threshold, with SVM being the most effective in detecting and extracting canopy openings in dense forests. Results demonstrated the potential of PlanetSope imagery and advanced classification algorithms to detect canopy openings in logged-over forests. The findings highlighted the importance of regular updates on canopy opening extent, particularly concerning sustainable forest assessment and minimising the negative impacts on forest ecosystems.