Mapping tree crown is critical for estimating the functional and spatial distribution of ecosystem services. However, accurate and up-to-date urban crown mapping remains a challenge due to the time-consuming nature of field sampling and spatial heterogeneity. Another challenge is the data cost, which is always a concern for low-cost processing of forest maps on large scales. Here, we developed a novel working framework by integrating an advanced deep learning technology, the Mask Region-based Convolutional Neural Network (Mask R-CNN) model with Google Earth images to detect tree crown cover in New York’s Central Park, which is a typical testbed for an urban forest area with highly heterogeneous tree crown cover. The results indicated that the tree number detection rate estimated by the Mask R-CNN crown detection model was 82.8% and the crown area detection rate was 81.8% for the entire study area. The model detected isolated trees and closed forest trees areas with a recall of 87.5% and 81.6% of the tree numbers, respectively. The analysis indicates that the tree crown detection model could accurately detect tree crowns under highly complex environments and demonstrates great potential to map urban tree crown covers.

绘制树冠图对于估计生态系统服务的功能和空间分布至关重要。然而，由于现场采样的耗时性和空间异质性，准确和最新的城市冠图仍然是一个挑战。另一个挑战是数据成本，这一直是大规模森林地图低成本处理的一个问题。在这里，我们通过将先进的深度学习技术、基于掩模区域的卷积神经网络 (Mask R-CNN) 模型与谷歌地球图像相结合，开发了一种新颖的工作框架，以检测纽约中央公园的树冠覆盖，这是典型的具有高度异质树冠覆盖的城市森林区域的试验台。结果表明，Mask R-CNN树冠检测模型估计的树数检测率为82。8%，整个研究区的冠区检出率为81.8%。该模型检测到孤立的树木和封闭的林木区域，分别召回了 87.5% 和 81.6% 的树木数量。分析表明，树冠检测模型可以在高度复杂的环境下准确检测树冠，并显示出绘制城市树冠覆盖图的巨大潜力。