PDF(Pubmed)
Abstract：
Forest canopy cover (FCC) is essential in forest assessment and management, affecting ecosystem services such as carbon sequestration, wildlife habitat, and water regulation. Ongoing advancements in techniques for accurately and efficiently mapping and extracting FCC information require a thorough evaluation of their validity and reliability. The primary objectives of this study are to: (1) create a large-scale forest FCC dataset with a 1-meter spatial resolution, (2) assess the regional spatial distribution of FCC at a regional scale, and (3) investigate differences in FCC areas among the Global Forest Change (Hansen et al., 2013) and U.S. Forest Service Tree Canopy Cover products at various spatial scales in Arkansas (i.e., county and city levels). This study utilized high-resolution aerial imagery and a machine learning algorithm processed and analyzed using the Google Earth Engine cloud computing platform to produce the FCC dataset. The accuracy of this dataset was validated using one-third of the reference locations obtained from the Global Forest Change (Hansen et al., 2013) dataset and the National Agriculture Imagery Program (NAIP) aerial imagery with a 0.6-m spatial resolution. The results showed that the dataset successfully identified FCC at a 1-m resolution in the study area, with overall accuracy ranging between 83.31% and 94.35% per county. Spatial comparison results between the produced FCC dataset and the Hansen et al., 2013 and USFS products indicated a strong positive correlation, with R2 values ranging between 0.94 and 0.98 for county and city levels. This dataset provides valuable information for monitoring, forecasting, and managing forest resources in Arkansas and beyond. The methodology followed in this study enhances efficiency, cost-effectiveness, and scalability, as it enables the processing of large-scale datasets with high computational demands in a cloud-based environment. It also demonstrates that machine learning and cloud computing technologies can generate high-resolution forest cover datasets, which might be helpful in other regions of the world.
摘要：
森林冠层覆盖(FCC)在森林评估和管理中至关重要，影响生态系统服务，如碳封存，野生动物栖息地,和水的调节。准确有效地映射和提取FCC信息的技术的不断进步需要对其有效性和可靠性进行全面评估。本研究的主要目标是：（1）创建具有1米空间分辨率的大规模森林FCC数据集，(2)在区域尺度上评估FCC的区域空间分布,和(3)调查全球森林变化中FCC区域的差异(Hansen等人。,2013)和阿肯色州各种空间尺度的美国森林服务树冠覆盖产品(即，县级和市级)。这项研究利用了高分辨率的航空图像和机器学习算法，使用GoogleEarthEngine云计算平台进行了处理和分析，以生成FCC数据集。使用从全球森林变化中获得的参考位置的三分之一验证了该数据集的准确性（Hansen等人。,2013)数据集和国家农业图像计划(NAIP)航空图像，空间分辨率为0.6米。结果表明，该数据集在研究区域中以1-m的分辨率成功识别了FCC，总体准确率在每个县83.31%至94.35%之间。产生的FCC数据集和Hansen等人之间的空间比较结果。,2013年和USFS产品显示出强正相关，县级和市级的R2值在0.94到0.98之间。该数据集为监测提供了有价值的信息，预测，和管理阿肯色州及其他地区的森林资源。本研究采用的方法提高效率，成本效益，和可扩展性，因为它可以在基于云的环境中处理具有高计算要求的大规模数据集。它还证明了机器学习和云计算技术可以生成高分辨率的森林覆盖数据集，这可能对世界其他地区有所帮助。
