| 基于改进RRT算法的区域森林郁闭度无人机LiDAR分辨率识别 |
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| 引用本文：冯待飞，薛洪文，王永成，刘勐.基于改进RRT算法的区域森林郁闭度无人机LiDAR分辨率识别[J].计算技术与自动化,2026,(2):189-194 |
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| 中文摘要:在复杂多变的森林环境中，天气状况和地形特征往往变幻莫测。通过无人机路径优化，可有效降低因飞行过程中无人机的不稳定状态带来的数据误差，进而提升森林郁闭度识别精确度。因此，提出了基于改进RRT算法的区域森林郁闭度无人机LiDAR分辨率识别。采用双层启发RRT算法对传统RRT算法进行改进，根据规划路径利用搭载的LiDAR采集区域森林郁闭度信息，整合成冠层高度模型（CHM）影像，设定CHM影响分辨率，计算出区域森林郁闭度真值，结合卫星影像提取出的特征值，运用支持向量回归（SVR）技术构建预测模型，反演并绘制出区域森林的郁闭度图。实验分析结果显示，双层启发RRT算法相较于传统RRT算法的无人机路径规划更加平滑，有效避免不必要的转向动作。 |
| 中文关键词:RRT算法 区域森林 郁闭度 无人机 LiDAR 分辨率 |
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| Resolution Recognition of Regional Forest Canopy Density Using Unmanned Aerial Vehicle LiDAR Based on Improved RRT Algorithm |
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| Abstract:In complex and ever-changing forest environments, weather conditions and terrain features are often unpredictable. By optimizing the drone path, data errors caused by the unstable state of the drone during flight can be effectively reduced, thereby improving the accuracy of forest canopy recognition. Therefore, an improved RRT algorithm based regional forest canopy recognition LiDAR resolution for drones is proposed. The double-layer heuristic RRT algorithm is used to improve the traditional RRT algorithm. Based on the planned path, the forest canopy closure information in the area is collected using LiDAR, and integrated into a canopy height model (CHM) image. The CHM influence resolution is set to calculate the true value of regional forest canopy closure. Combined with the feature values extracted from satellite images, support vector regression (SVR) technology is used to construct a prediction model, invert and draw the canopy closure map of the regional forest. The experimental analysis results show that the dual layer heuristic RRT algorithm is smoother in UAV path planning compared to traditional RRT algorithms, effectively avoiding unnecessary turning actions. |
| keywords:RRT algorithm regional forest canopy closure UAV LiDAR resolving power |
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