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• 方法与技术 • 上一篇    

基于GIS与RS的山东森林火险因子及火险区划

黄宝华1,2,3,4,张华1**,孙治军4,周利霞5   

  1. 1中国科学院烟台海岸带研究所, 山东烟台 264003; 2烟台市地理信息中心, 山东烟台 264003; 3中国科学院大学, 北京 100049; 4中国农业大学(烟台), 山东烟台 264670; 5烟台市自然博物馆,   山东烟台264000)
  • 出版日期:2015-05-10 发布日期:2015-05-10

Forest fire danger factors and their division in Shandong based on GIS and RS.

HUANG Bao-hua1,2,3,4, ZHANG Hua1**, SUN Zhi-jun4, ZHOU Li-xia5   

  1. (1Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, Shandong, China; 2Geographic Information Center of Yantai City, Yantai 264003, Shandong, China; 3University of Chinese Academy of Sciences, Beijing 100049, China; 4China Agricultural University (Yantai), Yantai 264670, Shandong, China; 5Yantai Museum of Natural History, Yantai 264000, Shandong, China)
  • Online:2015-05-10 Published:2015-05-10

摘要: 森林火灾是山东森林地区严重的环境问题之一。本研究采用山东2001—2010年MOD14A1每日1 km温度异常/火L3级产品与地形、植被、天气、人为和可访问性数据,分析评估了火灾发生原因;收集了林火发生/未发生相关的15个解释变量的空间数据,利用二项Logistic回归模型估计了解释变量的函数与林火存在的概率。结果表明,高火险区域主要集中在黄河三角洲、鲁西北平原,包括德州、菏泽、济宁、枣庄南部、临沂东南部;中火险主要在聊城、滨州、济南北部、淄博北部、潍坊东部、泰安、日照和青岛大部分地区(包括蒙山林区、沂山林区、五莲山林区、徂徕山林区、尼山林区、泰莱林区);低火险主要集中在济南南部、淄博南部、莱芜、青岛南部和胶东半岛(包括济南林区、崂山林区、鲁山林区、昆嵛山林区、牙山林区)。Logistic回归结果表明,影响火灾发生的因素依次是年均温度、CTI、TPI、人口密度、植被类型、年降水量、植被盖度、距道路距离、坡向、距居民地距离、农民纯收入指数、坡度、年平均相对湿度、DEM、年蒸发量。其中前7个EXP(B)都>1,对森林火灾发生的与否贡献较大。这些结果作为战略规划工具来更好预测森林火灾,也可作为一种战术指南帮助森林管理人员设计区域防火措施。

关键词: 大数据, 生态系统服务流, 时空尺度, 分布式生态模型, 非生物流

Abstract: Forest fire is one of the serious environmental problems in Shandong forest areas. MOD14A1 daily temperature anomaly/fire L3 level products of 2001-2010 and topography, vegetation, weather, anthropogenic and accessibility data in Shandong were used to evaluate fire causes. The spatial data of 15 variables that relate to forest fire/no fire were collected, and the functions of these variables and fire probability were estimated by using binomial Logistic regression model.
that high fire risk areas are mainly concentrated in Yellow River delta, Shandong northwest plain,include Heze, Jining, Zaozhuang south, Linyi southeast; moderate fire risk areas are mainly concentrated in Liaocheng, Binzhou south, Jinan north, Zibo northwest, Weifang east, Taian, Rizhao and Qingdao most areas (including Meng mountain forest region, Yi mountain forest region, Wulian mountain forest region, Culai mountain forest region, Ni mountain forest region, Tailai mountain forest region); Low fire risk areas mainly concentrated in Jinan south, Zibo south, Laiwu, Qingdao south and Shandong peninsula (including Jinan mountain forest area, Tai lai mountain forest area, Laoshan mountain forest area, Lu mountain forest area, Kunyu mountain forest region, Ya mountain forest region). Logistic regression results showed that factors influencing the fires were in order of annual average temperature, CTI, TPI, population density, vegetation type, annual precipitation, vegetation coverage, distance from the road, aspect, distance from the residents, farmers’ net income index, slope, annual average relative humidity, DEM, annual evaporation. The EXP (B) values of the top seven factors were greater than 1, having great contributions to forest fires. These results can be used as a strategic planning tool to better predict forest fire, and also be used as a tactical guide to help forest management personnel for fire protection area design.

Key words: ecosystem service flow, spatial and temporal scale, distributed ecosystem model, abiotic flow, big data