DOI: 10.57760/sciencedb.27667
title: Spatial Distribution Dataset of Natural Grassland Fire Prone Areas in Qinghai Province
title: 青海省自然草原火灾易发性空间分布数据集

description: This data is based on the historical fire data of forest, grassland, and emergency management departments at all levels of state government in Qinghai Province from 2000 to 2020, which have identified the causes of natural grassland fires. It is combined with historical precipitation data with a resolution of 1km in Qinghai Province from 2000 to 2020 NDVI、 By constructing a maximum entropy model based on data such as average wind speed, relative humidity, slope, aspect, vegetation type, distance from rivers, thunderstorm lightning, etc., the susceptibility of natural grassland fires in Qinghai Province is simulated and predicted. To improve the spatial partitioning accuracy, the Kvamme gain statistical model was used to validate the partitioning accuracy simulated by the maximum entropy model. The AUC value of the model accuracy was 0.933, and the G value was close to 1, indicating that the constructed natural grassland fire susceptibility evaluation model can effectively identify high-risk areas where natural grassland fires occur. The dataset has a resolution of 1km and is in GeoTIFF file format, where 1 represents low susceptibility areas, 2 represents medium susceptibility areas, and 3 represents high susceptibility areas. The dataset provides data support and scientific basis for grassland fire prevention and management in Qinghai Province, and can also serve as an important basis for financial institutions and insurance companies to optimize insurance pricing and design disaster bonds. 
description: This data is based on the historical fire data of forest, grassland, and emergency management departments at all levels of state government in Qinghai Province from 2000 to 2020, which have identified the causes of natural grassland fires. It is combined with historical precipitation data with a resolution of 1km in Qinghai Province from 2000 to 2020 NDVI、 By constructing a maximum entropy model based on data such as average wind speed, relative humidity, slope, aspect, vegetation type, distance from rivers, thunderstorm lightning, etc., the susceptibility of natural grassland fires in Qinghai Province is simulated and predicted. To improve the spatial partitioning accuracy, the Kvamme gain statistical model was used to validate the partitioning accuracy simulated by the maximum entropy model. The AUC value of the model accuracy was 0.933, and the G value was close to 1, indicating that the constructed natural grassland fire susceptibility evaluation model can effectively identify high-risk areas where natural grassland fires occur. The dataset has a resolution of 1km and is in GeoTIFF file format, where 1 represents low susceptibility areas, 2 represents medium susceptibility areas, and 3 represents high susceptibility areas. The dataset provides data support and scientific basis for grassland fire prevention and management in Qinghai Province, and can also serve as an important basis for financial institutions and insurance companies to optimize insurance pricing and design disaster bonds.