Mapping local climate zones for a Japanese large city by an extended workflow of WUDAPT Level 0 method

Mapping local climate zones for a Japanese large city by an extended workflow of WUDAPT Level 0 method
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DOI:
10.1016/j.uclim.2020.100660
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发表时间:
2020-09
期刊:
影响因子:
6.4
通讯作者:
Xilin Zhou;Tsubasa Okaze;C. Ren;Meng Cai;Yasuyuki Ishida;A. Mochida
Xilin Zhou;Tsubasa Okaze;C. Ren;Meng Cai;Yasuyuki Ishida;A. Mochida
中科院分区:
工程技术2区
文献类型:
--
作者:
Xilin Zhou;Tsubasa Okaze;C. Ren;Meng Cai;Yasuyuki Ishida;A. Mochida

文献摘要

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世界数据库和访问门户工具 (WUDAPT) 0 级方法公布了绘制当地气候区 (LCZ) 的工作流程。然而,Level 0 的 LCZ 分类精度较低,特别是对于由类别识别和操作员偏差造成的建成区,正在成为 WUDAPT Level 1 和 2 进一步研究的障碍。由于日本的情况复杂,在划定训练区域时可能存在类别识别和操作员偏差。本文提出了 WUDAPT 的扩展工作流程,用于通过预设的类识别和参数分析来映射 LCZ。将建筑覆盖率(BCR)、建筑高度(BH)、透水面积率(PSF)与LCZ图相交,分析阐述预设的LCZ类别识别。鉴于 WUDAPT 工作流程的普遍性,提出了一种基于免费可用数据源导出建筑数据的卫星方法。仙台作为日本大城市的代表,被选为 LCZ 分类,为 WUDAPT 1 级和 2 级作出贡献。该研究不仅将为开发 LCZ 数据提供改进的方法,还将为日本中尺度气候建模和模拟提供新的城市形态数据集及其相应参数。
World Database and Access Portal Tools (WUDAPT) Level 0 method announced a workflow of mapping Local Climate Zones (LCZs). However, the low accuracy of LCZ classifications in Level 0 especially for the built-up areas caused by recognition of classes and operator bias is becoming an obstacle for further study in WUDAPT Level 1 and 2. Since the landscape in Japan is complicated, the recognition of classes and operator bias may exist for delineating training areas. This article argues an extended workflow of WUDAPT for mapping LCZs with pre-set recognition of classes and parameter analysis. The building coverage ratio (BCR), building height (BH), pervious surface fraction (PSF) were intersected with LCZ map for analysis and expound of the pre-set recognition of LCZ classes. Given the universality of WUDAPT workflow, a satellite method for deriving building data based on free available data sources was proposed. Contributing to WUDAPT level 1 and 2, a LCZ classification of Sendai, as a representative of Japanese large cities, was selected. The study will provide not only an improved methodology of development LCZ data, but also a new urban morphological dataset and its corresponding parameters for mesoscale climate modelling and simulations in Japan.