Characterization of Canopy Gaps identified with spatial GLAS/ICESat data in the Bolo-Est Classified Forest (Southwest Côte d'Ivoire)

The study presents an analysis of using the Geoscience Laser Altimeter System of Ice, Cloud, and land Elevation Satellite (GLAS/ICESat) for the detection and characterization of canopy gaps in the Bolo-Est classified forest. This classified forest is located in southwest C & ocirc;te d'Ivoire, a region facing increasing deforestation due to agriculture, in particular cocoa cultivation. The main objective of the study was to demonstrate the effectiveness of this Light Detection and Ranging (LiDAR) sensor for mapping canopy gaps, crucial for understanding the dynamics of tropical forest degradation. The methodology used integrates key steps: acquisition and pre-processing of GLAS/ICESat data, normalization of elevations to correspond to the World Geodetic System (WGS 84), calculation of canopy heights, detection of gaps via a thresholding process, and validation of results by field observations. The analysis revealed that 52% of the points analyzed corresponded to gaps, covering 19% of the study area, mainly caused by human activities such as agriculture and logging. The gaps identified vary in size from 50 m2 to 100 m2, indicating large canopy gap sizes in this forest. The results confirm that LiDAR GLAS/ICESat offers interesting accuracy and efficiency for detecting canopy gaps as a complement to traditional optical and radar remote sensing methods. The LiDAR approach thus provides a good understanding of degradation processes, offering useful data for environmental monitoring and sustainable forest management. This study demonstrates the potential of LiDAR remote sensing to support conservation initiatives such as the reducing emissions from deforestation and forest degradation (REDD+), contributing to the accurate assessment of anthropogenic canopy gaps, and proves a promising tool for the sustainable management of tropical forests on a national scale.