Monitoring of stand quality of coastal shelterbelts using synergistic drone multispectral and LiDAR point cloud data

Coastal shelterbelts serve as ecological safety barriers in coastal areas, and the effective evaluation of stand quality (SQ) is crucial for enhancing their development potential and supporting decision-making for sustainable management and environmental improvement. However, improving SQ and accurately monitoring coastal shelterbelts remains challenging due to factors such as limited soil resources, tree biological characteristics, and coastal storm surges. This study focuses on the typical coastal shelterbelt species Casuarina equisetifolia. Firstly, based on field survey data, using the Analytic Hierarchy Process, stand parameters such as mean tree height, diameter at breast height, crown width, canopy closure, and stand density were selected from both stand growth status and structure for the quantitative evaluation of SQ in Casuarina equisetifolia shelterbelts. Secondly, by combining drone-based multispectral and LiDAR point cloud data, the study explores the applicability of Individual Tree Integration and Stand Regression methods in the inversion of stand parameters for Casuarina equisetifolia shelterbelts. Ultimately, precise remote sensing monitoring of Casuarina equisetifolia SQ was achieved at the stand scale. The results of this study provide technical references and a theoretical basis for the efficient monitoring of SQ and management of coastal shelterbelts, which are significantly essential for their sustainable development.