Abstract Accurate representations of canopy cover are essential for directing natural resource management efforts targeted at issues such as carbon storage, habitat modeling, fire spread, water resources, and ecosystem services. A two-phase classification approach utilizing an iterative classification of high-resolution aerial imagery to develop training data for a regional-scale classification of percentage woody canopy cover (PWCC) using Sentinel-2 imagery is presented in this study, and is tested for a large portion of South Texas (9,200,000 ha). The modeled PWCC for the study area belonged to the respective classes as follows, PWCC0 = 26%, PWCC90 = 14%, PWCC10 = 12%, PWCC80 = 8%, PWCC20 = 7%, PWCC30 = 7%, PWCC70 = 6%, PWCC50 = 5%, PWCC40 = 5%, and PWCC60 = 5%. Statistics indicated that the overall weighted accuracy for the mapped PWCC classes (Aow) was 0.82 and that the overall weighted kappa ( k w) was 0.49. To demonstrate the usefulness of the PWCC mapping approach to produce reasonable canopy cover estimates, the relative accuracies of modeled PWCC and other similar canopy cover products (LANDFIRE, NLCD) for the study area were summarized. MAE and RSS values were calculated based on five sample areas of directly measured LiDAR canopy cover estimates. The PWCC mapping approach presented here exhibited significantly MAE values for 5 out of 5 sample areas, and lower RSS values for 4 of 5 sample areas. By class MAE and RSS values were lower for all percentage cover classes. Overall, comparisons of the mapping result with high-resolution aerial imagery and the quantitative assessments indicated that the approach presented here was effective for developing highly detailed canopy cover estimates that can be used for planning and modeling at multiple scales (e.g. regional or local). Additionally, this approach can be employed by individual researchers and is less time and resource consumptive when compared to other large scale approaches. To date, only a limited number of existing studies have focused on approaches that can be used to map tree canopy cover for large areas.

中文翻译：

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使用多传感器方法绘制高分辨率的冠层覆盖百分比

摘要 冠层覆盖的准确表示对于指导针对碳储存、栖息地建模、火势蔓延、水资源和生态系统服务等问题的自然资源管理工作至关重要。本研究提出了一种两阶段分类方法，利用高分辨率航空影像的迭代分类来开发训练数据，用于使用 Sentinel-2 影像的区域尺度木质冠层覆盖率 (PWCC) 分类，并针对南德克萨斯的大部分地区（9,200,000 公顷）。研究区的模拟 PWCC 属于各自的类别如下，PWCC0 = 26%，PWCC90 = 14%，PWCC10 = 12%，PWCC80 = 8%，PWCC20 = 7%，PWCC30 = 7%，PWCC70 = 6%， PWCC50 = 5%，PWCC40 = 5%，PWCC60 = 5%。统计数据表明，映射的 PWCC 类 (Aow) 的整体加权精度为 0.82，整体加权 kappa (kw) 为 0.49。为了证明 PWCC 制图方法对产生合理的冠层覆盖估计的有用性，总结了模型 PWCC 和其他类似冠层覆盖产品（LANDFIRE，NLCD）在研究区域的相对精度。MAE 和 RSS 值是根据直接测量的 LiDAR 冠层覆盖估计的五个样本区域计算的。此处介绍的 PWCC 映射方法显示出 5 个样本区域中的 5 个显着的 MAE 值，以及 5 个样本区域中的 4 个的较低 RSS 值。按类别划分，所有百分比覆盖类别的 MAE 和 RSS 值都较低。全面的，测绘结果与高分辨率航空影像和定量评估的比较表明，此处介绍的方法对于开发可用于多个尺度（例如区域或局部）的规划和建模的高度详细的冠层覆盖估计是有效的。此外，与其他大规模方法相比，这种方法可以由个人研究人员采用，并且消耗的时间和资源更少。迄今为止，只有少数现有研究关注可用于绘制大面积树冠覆盖的方法。与其他大规模方法相比，这种方法可以由个人研究人员采用，并且时间和资源消耗更少。迄今为止，只有少数现有研究关注可用于绘制大面积树冠覆盖的方法。与其他大规模方法相比，这种方法可以由个人研究人员采用，并且时间和资源消耗更少。迄今为止，只有少数现有研究关注可用于绘制大面积树冠覆盖的方法。