Methodology for Wildland–Urban Interface Mapping in Anning City Using High-Resolution Remote Sensing

Feng Jiang, Xinyu Hu, Xianlin Qin (), Shuisheng Huang and Fangxin Meng
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Feng Jiang: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Xinyu Hu: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Xianlin Qin: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Shuisheng Huang: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Fangxin Meng: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Land, 2025, vol. 14, issue 6, 1-19

Abstract: The wildland–urban interface (WUI) has been a global phenomenon, yet parameter threshold determination remains a persistent challenge in this field. In China, a significant research gap exists in the development of WUI mapping methodology. This study proposes a novel mapping approach that delineates the WUI by integrating both vegetation and building environment perspectives. GaoFen 1 Panchromatic Multi-spectral Sensor (GF1-PMS) imagery was leveraged as the data source. Building location was extracted using object-oriented and hierarchical classification techniques, and the pixel dichotomy method was employed to estimate fractional vegetation coverage (FVC). Building location and FVC were used as input for the WUI mapping. In this methodology, the threshold of FVC was determined by incorporating the remote sensing characteristics of the WUI types, whereas the buffer range of vegetation was refined through sensitivity analysis. The proposed method demonstrated high applicability in Anning City, achieving an overall accuracy of 88.56%. The total WUI area amounted to 49,578.05 ha, accounting for 38.08% of Anning City’s entire area. Spatially, the intermix WUI was predominantly distributed in the Taiping sub-district of Anning City, while the interface WUI was mainly concentrated in the Bajie sub-district of Anning City. MODIS fire spots from 2003 to 2022 were primarily clustered in the Qinglong sub-district, Wenquan sub-district, and Caopu sub-district of Anning City. Our findings indicated a spatial overlap between the WUI and fire-prone areas in Anning City. This study presents an effective methodology for threshold determination and WUI mapping, making up for the scarcity of mapping methodologies in China. Moreover, our approach offers valuable insights for a wise decision in fire risk.

Keywords: GF1-PMS; mapping methodology; sensitivity analysis; WUI; wildfire risk (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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