Accurate estimation of canopy cover (CC) and its delineation in tree-grass ecosystems (TGE) such as savannas and silvopastoral systems are crucial to analyze and model the functioning of these systems and the role of trees at different scales. At large-scale, remote sensing is a key tool, and assessments of land use and landscape elements often rely on satellite and aircraft sensor imagery and light detection and ranging (LiDAR) data. This study addresses automatic mapping of CC in TGE using high-resolution infrared orthophotographs and low-density LiDAR data from the Spanish Aerial Orthophoto National Plan (PNOA). Canopy cover mapping was performed in two areas with different structural complexity (with or without shrub layer) by an object based image analysis (OBIA) approach applied on canopy height model (CHM) generated by the LiDAR point cloud, infrared imagery, and both sources combined (LiDAR-imagery fusion). Overall accuracy (OA) was more than 91% with the two separated methods and more than 95% combining them. The results show that low-density LiDAR data is not a reliable source for the automatic mapping of canopy of scattered tree in TGE, OBIA on high-resolution infrared orthophotographs allows a more accurate automatic delineation of tree canopy, and the combined approach was the only way to obtain acceptable mapping in shrub-encroached stands, where errors were still greater than 15% with single-source based methods.

中文翻译：

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使用低密度激光雷达数据和红外图像自动绘制散落林地中的树冠

准确估计冠层盖度 (CC) 及其在稀树草原和林牧系统等树-草生态系统 (TGE) 中的描绘对于分析和模拟这些系统的功能以及不同尺度上树木的作用至关重要。在大规模方面，遥感是一个关键工具，土地利用和景观要素的评估通常依赖于卫星和飞机传感器图像以及光探测和测距 (LiDAR) 数据。本研究使用来自西班牙航空正射影像国家计划 (PNOA) 的高分辨率红外正射影像和低密度 LiDAR 数据解决了 TGE 中 CC 的自动映射问题。通过应用于 LiDAR 点云生成的冠层高度模型 (CHM) 的基于对象的图像分析 (OBIA) 方法，在具有不同结构复杂性（有或没有灌木层）的两个区域进行冠层覆盖映射，红外图像，并且两个来源结合（激光雷达图像融合）。两种分离方法的总体准确度 (OA) 超过 91%，组合使用超过 95%。结果表明，低密度 LiDAR 数据不是 TGE 中散布树冠自动映射的可靠来源，OBIA 在高分辨率红外正射影像上可以更准确地自动描绘树冠，并且组合方法是唯一的在灌木侵占的林分中获得可接受的映射的方法，其中使用基于单源的方法的误差仍然大于 15%。