Integrating urban morphology and land surface temperature characteristics for urban functional area classification. Issue 2 (3rd April 2022)

• Record Type: Journal Article
• Authors: Li, Bin; Liu, Yefei; Xing, Hanfa; Meng, Yuan; Yang, Guang; Liu, Xiaoding; Zhao, Yaolong
• Is Part Of: Geo-spatial information science. Volume 25:Issue 2(2022)
• Page Start: 337
• Page End: 352
• Publication Date: 2022-04-03
• Subjects: Urban function classification -- urban morphology -- random forest -- Land Surface Temperature (LST); Geographic information systems -- Periodicals; Cartography -- Data processing -- Periodicals; Surveying -- Data processing -- Periodicals; Remote sensing -- Periodicals; 526.0285
• DOI: 10.1080/10095020.2021.2021786
• Languages: English
• ISSNs: 1009-5020

Abstract

ABSTRACT: The classification of urban functional areas plays an important role in urban planning and resource management. Although previous studies have confirmed that different urban functional areas have different morphological structures and Land Surface Temperature (LST) characteristics, these two types of characteristics have rarely been fully integrated and used for functional area classification. In this paper, a new framework for classifying urban functional areas is proposed by combining urban morphological features and LST features. First, metrics are constructed from three levels, namely, building, road and region, which are used to portray urban morphology; LST is retrieved using thermal infrared remote sensing to reflect LST features with four metrics: the average temperature, maximum temperature, temperature difference and standard deviation of temperature. Then, the functional areas are classified into four categories: service/public land, commercial land, residential land and industrial land. A random forest algorithm is used to effectively fuse the features of these two categories and classify the functional areas. The effectiveness of the proposed framework is tested in the study area of Shenzhen City, Guangdong Province. The results show that the combined classification accuracy of the proposed classification method is 0.85, which is 0.26 higher than that of the classification model based on urban morphology and 0.1 higher than that of the classification model based on LST features. The proposed framework verifies that the integration of LST features into urban functional area classification is reliable and effectively combines urban morphology and LST features for functional area classification.