Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R² = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R² = 67.8% and persistence: R² = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.

中文翻译：

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使用基于对象的卷积神经网络（OB-CNN）绘制城市树木覆盖率变化图

城市树木可提供社会，经济，环境和生态系统服务，从而改善城市的宜居性并有助于个人和社区的福祉。因此，需要对城市树木进行有效的制图，监测和维护。遥感技术可以有效地绘制和监视城市树木的覆盖范围以及随时间的变化，这是基于现场的测量的高效，低成本的替代方法，这种方法既费时又费钱。自动提取具有高精度的城市土地覆盖特征是一项艰巨的任务，并且需要基于对象的人工智能工作流来提高效率和主题准确性。这项研究的目的是有效地绘制城市树木的覆盖变化，并对这种变化与社会经济变量的关系进行建模。通过使用卫星，Google Earth影像和光探测与测距（LiDAR）数据集绘制2005年至2015/16年之间城市树木的覆盖变化，说明了基于对象的卷积神经网络（CNN）方法。CNN模型的训练样本是通过基于对象的图像分析（OBIA）使用树冠高度模型（CHM）中的阈值和归一化植被指数（NDVI）生成的。使用OBIA进一步完善了CNN模型产生的树热图。提取树木覆盖率的损失，增加和持久性，并进行多元回归分析以建立与社会经济变量之间的关系模型。对于2005年的图像，树覆盖提取的整体准确性和kappa系数分别为96％和0.77，对于2015/16年图像，则为98％和0.93，表明基于对象的CNN技术可以有效地实现城市树木覆盖率的测绘和监测。所有郊区的树木覆盖率都有所下降。平均包裹大小和家庭收入中位数与树木覆盖率损失显着相关（R² = 58.5％）。树木的覆盖率和持久性与高等教育，地块大小和所有权变化有正相关关系（收益：R ² = 67.8％，持久性：R ² = 75.3％）。研究结果表明，具有智能处理功能的遥感数据可以促进城市树木覆盖率管理政策输入的发展。