| 摘要: |
| 局地气候区（Local Climate Zone,
LCZ）是依据城市地表特征进行分区的城市气候
定量分析方法。现有WUDAPT分类流程（World
Urban Database and Access Portal ToolsM,
WUDAPT）由于人为样本类别识别偏差造成建
成类型分类精度明显低于自然类型，本文以西安
主城区为例对这一分类流程进行优化，优化流
程采用GIS计算和归一化差异指数计算达到预
先识别建筑形态特征与自然覆盖属性的目的，
结果用于提取不同LCZ样本类型。采用大气校
正法反演得到研究区内地表温度数据，与LCZ分类地图叠置分析得到不同LCZ地表热环境差异与规律。从LCZ分类结果来看，优化的分类流
程制作的LCZ地图总体分类精度、建成类型分类精度和自然覆盖类型分类精度分别为79.76%、
79.97%和79.41%，相比常规流程下的精度分别提升17.4%、19.4%和9.5%，Kappa系数为0.78，一
致性达到显著水平。从地表温度分析结果来看，建成类型LCZ地表温度受建筑密度、高度、功能
等共同影响，建筑高度相近时密度越大地表温度越高，而建筑密度一定高度越高地表温度越低，
LCZ10受人为热影响而地表温度最高。自然覆盖类型地表温度整体低于建成类型，但仍表现出显
著的类间差异，如无植被覆盖的硬化地面与裸地均高于植被覆盖类型，低矮植被与稀疏林地均
高于稠密林地，水体地表温度最低。从局地气候区分类视角合理规划不同LCZ用地类型可达到
改善城市地表热环境的目的。 |
| 关键词: 局地气候区分类 GIS计算 归一化差异指数 分类精度 地表温度 |
| DOI：10.13791/j.cnki.hsfwest.20240517 |
| 分类号: |
| 基金项目:国家自然科学基金面上项目（52078407） |
|
| Optimization of local climate zone classification process and analysis of surface thermalenvironment characteristics by WUDAPT method: Taking the main city of Xi’an as anexample |
|
LIU Yan,LEI Chenxiao,YANG Liu,LI Qi,ZHAO Long
|
| Abstract: |
| Local Climate Zone (LCZ) is a quantitative analysis method for u rban climate based on
urban surface characteristics. The principle of LCZ is to delineate the response ability of urban
surface to thermal environment based on urban surface characteristics, and establish the connection
between urban surface properties and urban climate issues. It is widely used in the research of urban
climate related issues. At present, in the local climate region classification method, The WUDAPT
classification process (World Urban Database and Access Portal ToolsM, WUDAPT) relies on its
easily accessible remote sensing data (30 m resolution Landsat8 multispectral image) and Google earth
platform. LCZ classification mapping is realized with the help of random forest classifier in the open
source tool SAGA-GIS, which provides a clearer workflow for remote sensing image classification
and reduces the requirements of users’ remote sensing professional background, making this
method a mainstream method for the study of urban local climate area. In the standard WUDAPT
classification process, the classification of the training sample area and the verification sample area
refer to Google Earth images and judge the building form feature parameters (mainly building height
and density) and natural land cover information through artificial visual interpretation. However,
due to the lack of data support, the method of delineating LCZ training areas by visual interpretation
is more difficult to apply to urban built-up areas with highly heterogeneous spatial forms, and the
classification accuracy of built-up types is often significantly lower than that of natural types due
to the bias of human sample classification. In this paper, the main urban area of Xi’an was taken
as an example to optimize the classification process. GIS calculation and normalized differenceindex calculation were used to identify the architectural form characteristics and natural cover attributes in advance, and the identification results were
used to extract different LCZ sample types. The data used mainly include 3D building data, DEM elevation data and landsat8 multi-band image data. In
addition, atmospheric correction method was used to invert the surface temperature data in the study area, and the differences and rules of surface thermal
environment in different LCZ classification maps were obtained by overlay analysis. According to the LCZ classification results, the overall classification
accuracy, built type classification accuracy and natural coverage type classification accuracy of LCZ map produced by the optimized classification
process are 79.76%, 79.97% and 79.41% respectively, which is 17.4%, 19.4% and 9.5% higher than that under the conventional process. This shows that the
optimization process can improve the accuracy of LCZ classification of built type. The Kappa coefficient of LCZ obtained by this optimization process is 0.78,
and the consistency reaches a significant level. The field sampling results also show that the classification results of this process are in good agreement with
the real surface types, and this classification process is suitable for cities with complex architectural forms. According to the results of surface temperature
analysis, the surface temperature of built type LCZ is affected by the density, height and function of buildings. In LCZ, the surface temperature of the three
groups of buildings with close height is affected by the building density, and the higher the building density, the higher the surface temperature. On the
contrary, the building density is similar, and the higher the building height, the lower the surface temperature. In addition, the sparsely built area (LCZ9)
has the lowest surface temperature because most of them are flexibly distributed around “urban cold sources” such as vegetation and water as supporting
facilities for urban park services, while the heavy industrial area has the highest surface temperature due to the heat production of dense industrial equipment
and the heat release from human activities. The surface temperature of natural cover type LCZ was lower than that of built type, but showed significant
difference between classes. Except for water, the surface temperature of non-vegetation type is higher than that of vegetation type. For example, the hardened
ground and bare land without vegetation cover are higher than that of vegetation cover type. Among the vegetation types, the surface temperature of low
vegetation (LCZD) and sparse forest land (LCZB) was higher than that of dense forest land (LCZA), while the surface temperature of water body (LCZG)
was significantly lower than that of all LCZ types. From the surface temperature distribution, the cooling capacity of water body was also related to its area
size. From the perspective of local climate zone classification, the relations between different LCZ types and urban surface temperature distribution can
be quantitatively analyzed. Therefore, in urban planning, different LCZ land types can be rationally arranged from the perspective of local climate zone
classification to improve the urban surface thermal environment. |
| Key words: local climate zone classification GIS calculation difference index accuracy of classification the surface temperature |
