Informal Settlements Extraction and Fuzzy Comprehensive Evaluation of Habitat Environment Quality Based on Multi-Source Data

Zanxian Yang, Fei Yang (), Yuanjing Xiang, Haiyi Yang, Chunnuan Deng (), Liang Hong and Zhongchang Sun
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Zanxian Yang: Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Fei Yang: Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Yuanjing Xiang: Yunnan Yuanjing Planning and Research Institute (Group) Co., Ltd., Kunming 650051, China
Haiyi Yang: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Chunnuan Deng: Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Liang Hong: Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Zhongchang Sun: International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China

Land, 2025, vol. 14, issue 3, 1-29

Abstract: The United Nations Sustainable Development Goal (SDG) 11.1 emphasizes improving well-being, ensuring housing security, and promoting social equity. Informal settlements, one of the most vulnerable groups, require significant attention due to their dynamic changes and habitat quality. These areas limit the ability to comprehensively capture spatial heterogeneity and dynamic shifts in regional sustainable development. This study proposes an integrated approach using multi-source remote sensing data to extract the spatial distribution of informal settlements in Mumbai and assess their habitat environment quality. Specifically, seasonal spectral indices and texture features were constructed using Sentinel and SAR data, combined with the mean decrease impurity (MDI) indicator and hierarchical clustering to optimize feature selection, ultimately using a random forest (RF) model to extract the spatial distribution of informal settlements in Mumbai. Additionally, an innovative habitat environment index was developed through a Gaussian fuzzy evaluation model based on entropy weighting, providing a more robust assessment of habitat quality for informal settlements. The study demonstrates that: (1) texture features from the gray level co-occurrence matrix (GLCM) significantly improved the classification of informal settlements, with the random forest classification model achieving a kappa coefficient above 0.77, an overall accuracy exceeding 0.89, and F1 scores above 0.90; (2) informal settlements exhibited two primary development patterns: gradual expansion near formal residential areas and dependence on natural resources such as farmland, forests, and water bodies; (3) economic vitality emerged as a critical factor in improving the living environment, while social, natural, and residential conditions remained relatively stable; (4) the proportion of highly suitable and moderately suitable areas increased from 65.62% to 65.92%, although the overall improvement in informal settlements remained slow. This study highlights the novel integration of multi-source remote sensing data with machine learning for precise spatial extraction and comprehensive habitat quality assessment, providing valuable insights into urban planning and sustainable development strategies.

Keywords: informal settlements; MDI; entropy-weighted; fuzzy comprehensive evaluation; habitat environment quality (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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