2026-10-12T16:54:14Z https://www.scidb.cn/oai

10.57760/sciencedb.j00001.01624 2026-06-10T13:23:54Z

2026-06-10 A dataset of wildfire susceptibility in Pakistan 2012-2023 doi:10.57760/sciencedb.j00001.01624 en With the climate getting hotter and drier, the frequency and intensity of extreme wildfires are rising. Pakistan stands out as vital within China's "Belt and Road" structure and is increasingly exposed to wildfires risk under global warming. Therefore, assessing wildfire susceptibility is crucial for ensuring the safe development and operation of the China-Pakistan Economic Corridor. In this study, key fuel information, including fuel moisture content and fuel load, are retrieved from remote sensing data. These factors, together with meteorological, topographical, and human factors, are integrated to train a machine learning model for wildfire susceptibility assessment across Pakistan. Using the trained model, a 12-year (2012–2023) wildfire susceptibility data for Pakistan are generated.  Pakistan; Wildfire; Susceptibility assessment; Key fuel information Zhang Hongguo Hou Xue Fan Chunquan Chen Rui He Binbin PUBLIC https://creativecommons.org/licenses/by-nc-sa/4.0/ dataset http://www.doi.org/10.11922/11-6035.csd.2026.0043.zh Science Data Bank