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国土资源遥感  2018, Vol. 30 Issue (3): 196-203    DOI: 10.6046/gtzyyg.2018.03.27
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基于阈值比值法的森林冰雪冻害遥感评估——以湖南省为例
王学成1,2, 杨飞1(), 高星1, 张英慧1,2
1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2. 中国科学院大学,北京 100049
Assessment of forest damage due to ice-snow disaster based on the method of threshold ratio:A case study of Hunan Province
Xuecheng WANG1,2, Fei YANG1(), Xing GAO1, Yinghui ZHANG1,2
1. Institute of Geographic Sciences and Natural Resources Research, CAS, State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 

冰雪冻灾是森林生态系统的主要生态干扰之一,对森林结构及生态系统功能有重要影响,快速准确评估森林资源受损程度,对灾后森林修复和森林生态系统管理有重要意义。根据2001—2007年归一化植被指数(normalized difference vegetation index,NDVI)数据提取灾前植被NDVI参考值和植被正常生长变化范围,利用灾后同时段NDVI数据,提取湖南省2008年森林冰雪受灾范围。在图像阈值法基础上,提出阈值比值法,评估森林冰雪受灾区域森林资源受损程度。与图像阈值法相比,阈值比值法评估结果与人工调查数据更加接近,标准误差为0.95,不足图像阈值法的1/3。阈值比值法评估结果显示,全省森林资源受损严重,森林重度受灾率高达53.69%,森林中度受灾率达27.50%,森林轻度受灾率仅为18.81%。与海拔、坡向等地形因子叠加分析,发现高海拔森林受损程度比低海拔森林严重,800 m以上的森林重度受损率高达58.91%,处于阴坡的森林受损程度比阳坡森林严重。
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王学成
杨飞
高星
张英慧
关键词 ： 阈值比值法,  森林,  冰雪冻灾,  湖南省    
Abstract：

Ice-snow disasters are one of the main disruptions to forest ecological systems, causing the loss of forest structure and degeneration of ecological system's functions. Rapid and accurate assessment of forest resource loss has an important significance for starting the process post-disaster recovery and forest ecosystem management. Using MODIS Normalized Difference Vegetation Index (NDVI) images during 2001—2007, the authors extract pre-disaster reference value of plant NDVI and growth change threshold. In combination with post-disaster NDVI images, the range of Hunan forest ice-snow disaster is extracted by pre-disaster reference value and growth change threshold in 2008. The method of threshold ratio is proposed on the basis of method of image threshold, which is used to assess the loss of forest resources within the region of forest ice-snow disaster. A comparison with the method of image threshold shows that the assessing results from the method of threshold ratio are more close to the manual survey results, and its standard error is only 0.95, which is less than 1/3 of that of the image threshold method. The assessing results show that forest resources of the whole province have suffered serious losses: severe damaged rate, moderate damaged rate and mild damaged rate are 53.69%, 27.50% and 18.81%, respectively. What’s more, the analysis combined with topographic factors shows that forests in high altitudes are more severely affected than in low altitudes, and forests located in shady aspect are especially more severely affected.
Key words： method of threshold ratio    forest    ice-snow disaster    Hunan Province
收稿日期: 2017-01-06      出版日期: 2018-09-10
:  TP79  
基金资助:国家自然科学基金“冰雪冻灾干扰后亚热带森林生态系统恢复力的动态诊断”(41301607);资源与环境信息系统国家重点实验室青年人才培养基金项目“森林生态系统恢复力的遥感监测”;西藏生态专项课题“西藏生态环境大数据规范与制图展示”;中国工程科技知识中心建设项目“防灾减灾知识服务系统”;中国科学院数字地球先导专项(19040305)
通讯作者: 杨飞
作者简介: 王学成(1991-),男,博士研究生,主要从事基于RS和GIS的森林资源调查方面研究。Email: wangxc.15s@igsnrr.ac.cn。
引用本文:   
王学成, 杨飞, 高星, 张英慧. 基于阈值比值法的森林冰雪冻害遥感评估——以湖南省为例[J]. 国土资源遥感, 2018, 30(3): 196-203.
Xuecheng WANG, Fei YANG, Xing GAO, Yinghui ZHANG. Assessment of forest damage due to ice-snow disaster based on the method of threshold ratio:A case study of Hunan Province. Remote Sensing for Land & Resources, 2018, 30(3): 196-203.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.03.27      或      https://www.gtzyyg.com/CN/Y2018/V30/I3/196
时段 R<0.3所占比例 R<0.4所占比例
2005049 0.953 1 0.982 9
2006049 0.913 9 0.940 6
2007049 0.566 1 0.627 3
2005065 0.984 7 0.993 1
2006065 0.987 3 0.994 4
2007065 0.990 8 0.997 5
Tab.1  灾前森林像元NDVI变化率统计结果
Fig.1  森林雪灾损失评估流程
Fig.2  湖南省2008年森林冰雪受灾区域空间分布
Fig.3  森林雪灾损失评估结果

地点
类别		 图像阈值法 阈值比值法
重度受灾 中度受灾 轻度受灾 重度受灾 中度受灾 轻度受灾

湖南省		 面积/104hm2 219.59 108.06 88.32 223.35 114.39 78.24
占比/% 52.79 25.98 21.23 53.69 27.50 18.81

道县		 面积/104hm2 3.85 2.21 1.10 3.85 2.19 1.12
占比/% 53.79 30.81 15.40 53.78 30.61 15.61

江永县		 面积/104hm2 3.01 1.94 1.09 3.65 1.65 0.75
占比/% 49.80 32.18 18.02 60.38 27.23 12.40

新田县		 面积/104hm2 0.53 0.43 0.55 0.57 0.54 0.40
占比/% 34.83 28.77 36.40 38.01 35.59 26.40

浏阳市		 面积/104hm2 2.08 3.42 4.56 3.88 3.81 2.37
占比/% 20.68 34.00 45.33 38.56 37.85 23.59
Tab.2  森林雪灾损失评估统计结果
方法 类别 重度受灾 中度受灾 轻度受灾

图像阈值法		 面积/104hm2 0.53 0.43 0.55
占比/% 34.83 28.77 36.40

阈值比值法		 面积/104hm2 0.57 0.54 0.40
占比/% 38.01 35.59 26.40

人工调查法		 面积/104hm2 0.28 0.22 1.01
占比/% 18.50 14.53 66.97
Tab.3  新田县森林雪灾损失评估统计结果
Fig.4  验证单元空间分布
地点 重度受灾 中度受灾 轻度受灾
江永县 2.96 1.72 1.37
浏阳市 4.00 3.69 2.36
Tab.4  江永县和浏阳市人工调查数据

海拔
类别		 图像阈值法 阈值比值法
重度受灾 中度受灾 轻度受灾 重度受灾 中度受灾 轻度受灾

400 m以下		 面积/104hm2 107.81 46.69 29.59 89.30 55.49 39.30
占比/% 58.56 25.37 16.07 48.51 30.14 21.35

400~800 m		 面积/104hm2 83.82 43.37 37.51 94.47 41.92 28.32
占比/% 50.89 26.33 22.77 57.36 25.45 17.19

800 m以上		 面积/104hm2 27.91 18.01 21.26 39.58 16.98 10.62
占比/% 41.55 26.80 31.65 58.91 25.28 15.81
Tab.5  不同海拔森林受损面积

坡向
类别		 图像阈值法 阈值比值法
重度受灾 中度受灾 轻度受灾 重度受灾 中度受灾 轻度受灾

北面		 面积/104hm2 22.68 11.64 9.30 23.58 11.85 8.18
占比/% 52.00 26.68 21.32 54.07 27.17 18.76

东北		 面积/104hm2 23.85 11.73 9.64 24.38 12.38 8.46
占比/% 52.73 25.94 21.33 53.91 27.38 18.72

西北		 面积/104hm2 28.73 14.62 11.38 29.81 14.59 10.32
占比/% 52.49 26.71 20.80 54.47 26.67 18.86

南面		 面积/104hm2 26.02 12.74 10.80 26.29 13.84 9.44
占比/% 52.50 25.71 21.79 53.04 27.92 19.04

东南		 面积/104hm2 33.83 15.80 13.56 33.60 17.70 11.90
占比/% 53.54 25.00 21.46 53.17 28.00 18.83

西南		 面积/104hm2 23.86 11.94 9.66 24.18 12.73 8.55
占比/% 52.48 26.26 21.26 53.19 28.00 18.82
Tab.6  不同坡向森林受灾面积
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