Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method

• Published in: International journal of applied earth observation and geoinformation Vol. 103; p. 102475
• Main Authors: Wu, Hao, Lin, Anqi, Xing, Xudong, Song, Danxia, Li, Yan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021; Elsevier
• Subjects: case studies; Driving factor; Global land cover products; humans; land cover; land degradation; Land use change; Point of interest; Random forest; spatial data; topography; urbanization; vegetation cover; Random forest; Driving factor; Land use change; Global land cover products; Point of interest
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2021.102475

•Global land cover products and POIs were combined to reveal LUCC driving mechanism.•Human and biophysical factors were identified by a margin-based measure of RF model.•Vegetation change was mainly driven by business, transportation, and manufacture.•Transportation, instead of business, recently became the core human driver in Wuhan. Rapid urbanization at the expense of the environment led to a reduction in vegetation cover, and consequently aggravated land degradation, urban water logging, heat island effect and other effects. Revealing the driving mechanism behind urban land use change facilitates deeper insight into the human and biophysical effects in the urbanization process and thereby supports the sustainable urban development. This work proposed a margin-based measure of random forest for core driving factor identification of urban land use change, which mainly included urban vegetation change, constructed land, water bodies, etc., using multitemporal global land cover products and point-of-interest (POI) data. Taking the land use change in Wuhan from 2010 to 2020 as the case study, the proposed method was employed to measure and sort the driving forces of 24 biophysical and human factors. The results suggested that the margin-based method was more reliable and sensitive than the traditional importance measure of random forest when detecting the driving mechanism behind land use change. Meanwhile, both the importance values and the ranking orders of driving factors measured by the margin-based method were stable regardless of which similarity measure was chosen and applied. The findings also showed that topographic conditions persistently affected urban land use change, while transportation factors, instead of business services, gradually became the most important human driving factors in Wuhan in the last 10 years.