利用高分二号得出的城市绿地信息预测当地地表温度

IF 6 2区 环境科学与生态学 Q1 ENVIRONMENTAL STUDIES Urban Forestry & Urban Greening Pub Date : 2024-07-25 DOI:10.1016/j.ufug.2024.128463
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引用次数: 0

摘要

城市绿地(UGS)极大地影响着地表热量的分布,在调节地表温度方面发挥着至关重要的作用。然而,城市绿地与地表温度之间的定量关系尚不明确,需要进一步研究。本研究旨在根据高分二号卫星数据中的绿地信息预测地表温度。为此,研究人员利用高分二号卫星数据获取了新疆乌鲁木齐市的空间分布和植被生长状况。构建了支持向量机(SVM)、随机森林(RF)和梯度提升回归树(GBRT)等三种机器学习模型来预测地表温度。结果表明,利用 U-Net 语义分割模型从高分二号数据中提取的 UGS 信息成功地预测了地表温度。在三种机器学习模型中,GBRT 的预测精度最高,Radj2 为 0.81,RMSE 为 0.44,RPD 为 2.29,其次是 RF(Radj2 为 0.80,RMSE 为 0.45,RPD 为 2.22)和 SVM(Radj2 为 0.此外,变量重要性评估将原来的 44 个变量减少到 28 个,保持了 GBRT 模型的预测准确性,其 Radj2 为 0.81,RMSE 为 0.43,RPD 为 2.3。我们的研究表明,利用从高分二号卫星获取的植被信息来预测地表温度是有效的。该方法为城市地区的 UGS 布局提供了有价值的建议,为城市规划和房地产开发提供了全面的参考。
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Utilizing GaoFen-2 derived urban green space information to predict local surface temperature

Urban green spaces (UGS) significantly influence the distribution of surface heat and play a crucial role in regulating surface temperature. However, the quantitative relationship between UGS and surface temperature remains unclear, necessitating further research. This study aims to predict surface temperature based on green space information from GaoFen-2 satellite data. To achieve this, GaoFen-2 data were utilized to obtain spatial distribution and vegetation growth status in Urumqi, Xinjiang. Three machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Regression Tree (GBRT) were constructed to predict surface temperature. Results indicated that UGS information extracted from GaoFen-2 data using the U-Net semantic segmentation model successfully predicted surface temperature. Among the three machine learning models, GBRT exhibited the highest predictive accuracy with an Radj2 of 0.81, RMSE of 0.44, and RPD of 2.29, followed by RF (Radj2 of 0.80, RMSE of 0.45, and RPD of 2.22), and SVM (Radj2of 0.79, RMSE of 0.47, and RPD of 2.15), In addition, a variable importance assessment reduced the original 44 variables to 28, maintaining predictive accuracy with the GBRT model achieving an Radj2 of 0.81, RMSE of 0.43, and RPD of 2.3. Our study demonstrates the effectiveness of using vegetation information derived from GaoFen-2 to predict surface temperature. This approach provides valuable recommendations for the layout of UGS in urban areas and serves as a comprehensive reference for urban planning and real estate development.

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来源期刊
CiteScore
11.70
自引率
12.50%
发文量
289
审稿时长
70 days
期刊介绍: Urban Forestry and Urban Greening is a refereed, international journal aimed at presenting high-quality research with urban and peri-urban woody and non-woody vegetation and its use, planning, design, establishment and management as its main topics. Urban Forestry and Urban Greening concentrates on all tree-dominated (as joint together in the urban forest) as well as other green resources in and around urban areas, such as woodlands, public and private urban parks and gardens, urban nature areas, street tree and square plantations, botanical gardens and cemeteries. The journal welcomes basic and applied research papers, as well as review papers and short communications. Contributions should focus on one or more of the following aspects: -Form and functions of urban forests and other vegetation, including aspects of urban ecology. -Policy-making, planning and design related to urban forests and other vegetation. -Selection and establishment of tree resources and other vegetation for urban environments. -Management of urban forests and other vegetation. Original contributions of a high academic standard are invited from a wide range of disciplines and fields, including forestry, biology, horticulture, arboriculture, landscape ecology, pathology, soil science, hydrology, landscape architecture, landscape planning, urban planning and design, economics, sociology, environmental psychology, public health, and education.
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