Protection of Coastal Shelter Forests Using UAVs: Individual Tree and Tree-Height Detection in Casuarina equisetifolia L. Forests

Abstract

Casuarina equisetifolia L. plays a significant role in sandy, coastal regions for sand stabilization and windbreaks. However, C. equisetifolia forests are susceptible to plant diseases and insect pests, resulting in mortality due to pure stands and a harsh natural environment. Mapping the distribution of C. equisetifolia and detecting its height can inform forest-management decisions. Unmanned aerial vehicle (UAV) imagery, coupled with the classical detection method, can provide accurate information on tree-level forest parameters. Considering that the accuracy of a forest-parameter estimation is impacted by various flight altitudes and extraction parameters, the purpose of this study is to determine the appropriate flight altitude and extraction parameters for mapping C. equisetifolia using UAV imagery and the local maxima algorithm in order to monitor C. equisetifolia more accurately. A total of 11 different flight altitudes and 36 combinations of circular smoothing window size (CSWS) and fixed circular window size (FCWS) were tested, and 796 trees with corresponding positions in the UAV image and ground–tree heights were used as reference. The results show that the combination of a 0.1 m CSWS and a 0.8 m FCWS for individual tree detection (ITD) and tree-height detection achieved excellent accuracy (with an F1 score of 91.44% for ITD and an estimation accuracy (EA) of 79.49% for tree-height detection). A lower flight altitude did not indicate a higher accuracy for individual tree and tree-height detection. The UAV image obtained within a flight altitude of 60 m–80 m can meet the accuracy requirements for the identification of C. equisetifolia tree-height estimation (F1 score > 85% for ITD; EA > 75% for tree-height estimation). This study provides a foundation for monitoring C. equisetifolia by using UAV imagery and applying the local maxima algorithm, which may help forestry practitioners detect C. equisetifolia trees and tree heights more accurately, providing more information on C. equisetifolia growth status.