Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation
Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation
复制标题
在人工林中使用基于无人机的 RGB 图像量化林下和林上植被覆盖
DOI：
10.3390/rs12020298
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发表时间：
2020-01-01
期刊：
影响因子：
5
通讯作者：
Zhang, Wuming
中科院分区：
文献类型：
--
作者：
Li, Linyuan;Chen, Jun;Zhang, Wuming
植被覆盖度的估算为森林碳、水循环模型的建立和森林生态系统功能评价提供了有价值的信息。虽然以前的研究表明，光探测和测距（LiDAR）在三维（3D）表征的森林上层和下层社区的能力，高成本抑制其应用在频繁和连续的调查任务。安装在无人机（UAV）上的低成本商业红绿蓝（RGB）相机作为激光雷达的替代品，提供了同时量化上层树冠覆盖（OCC）和下层植被覆盖（UVC）的操作系统。我们开发了一种有效的方法，称为反投影的3D点云到超像素分割图像（BAPS）提取上层和森林地面像素使用3D结构从运动（SfM）点云和二维（2D）超像素分割。OCC估计从提取的上层树冠像素。半高斯拟合（HAGFVC）方法被用来分割绿色植被和非植被像素从提取的森林地面像素，并获得UVC。以北方塞罕坝国家森林公园种植园为研究对象，采集了8个样地的无人机RGB遥感影像数据和实地验证数据。BAPS法与基于冠层高度模型（CHM）的方法估算的OCC值一致（决定系数为0.7171），证明了BAPS法估算OCC的能力。林下植被的分割验证了监督分类（SC）方法。验证结果表明，OCC和UVC的估计值与参考值吻合良好，其中OCC（无单位）和UVC（无单位）的均方根误差（RMSE）分别达到0.0704和0.1144。低成本的无人机观测系统和新开发的方法有望提高对生态系统功能的理解，并促进生态过程建模。
Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red-green-blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.