Urban local climate region classification method based on multi-source data

Abstract

The invention discloses an urban local climate region classification method based on multi-source data, and the method comprises the following steps: 1, obtaining a Gaofen-1 image and urban 3D building data in a research region range, and carrying out preprocessing; 2, extracting parameters for local climate partitioning, including the building height, building surface score, normalized vegetationindex, vegetation coverage, permeable surface score, water surface score and impermeable surface score, establishing regular grids of different scales, and extracting LCZ partitioning parameters of corresponding spatial scales based on the regular grids of different scales; 3, establishing a random forest classification model; 4, based on the established random forest classification model, carrying out local climate region classification of different scales; and 5, selecting an optimal local climate partition scale through visual interpretation and quantitative precision evaluation. Accordingto the method, the advantages of two local climate partitioning methods are effectively combined, and the precision and high efficiency of local climate partitioning are improved.

Description

technical field [0001] The invention belongs to a climate zone classification method, in particular to a multi-source data-based urban local climate zone classification method. Background technique [0002] Rapid urbanization leads to changes in land cover, urban geometry and urban structure, thereby increasing urban heat absorption and changing local climate. Urban heat island is one of the consequences of urbanization, which refers to a phenomenon in which the temperature in the city is higher than that in the suburbs. Traditional research on urban heat island intensity mainly focuses on the calculation of the temperature difference between urban and rural areas, the calculation of the temperature difference between the permeable surface and the impermeable surface, or the calculation of the temperature difference between the vegetation area and the non-vegetation area, and there is a lack of unified standards for these. , The shortcoming of too strong subjectivity, becau...

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Problems solved by technology

This method is widely used due to its high efficiency, but because the input data of this method is Landsat data with medium resolution, and the input data lacks the building height information of the three-dimensional shape of urban buildings, the classification accuracy of this method is not high. and has been shown to be more suitable for local climate subregions in large regions

On the contrary, the GIS-based method uses the parameters extracted from a variety of high-precision urban GIS vector data, and performs LCZ classification through threshold division. Although the LCZ classification accuracy is high, it needs to input a large amount of urban GIS data, and these data are usually Difficult to access, as many urban GIS data are not available to the public

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