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国土资源遥感  2020, Vol. 32 Issue (1): 43-50    DOI: 10.6046/gtzyyg.2020.01.07
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基于林火特征分类模型的森林火情等级制图
马振宇1,陈博伟1,庞勇1(),廖声熙2,覃先林1,张怀清1
1. 中国林业科学研究院资源信息研究所,北京 100091
2. 中国林业科学研究院资源昆虫研究所,昆明 650216
Forest fire potential forecast based on FCCS model
Zhenyu MA1,Bowei CHEN1,Yong PANG1(),Shengxi LIAO2,Xianlin QIN1,Huaiqing ZHANG1
1. Research Institute of Forest Resource Information Techniques, Chinese Academic of Forestry, Beijing 100091, China
2. Research Institute of Resources Insects, Chinese Academic of Forestry, Kunming 650216, China
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摘要 

森林中可燃物的分布状况是影响林火产生、扩散的重要因素之一,本研究的目的是结合森林资源调查数据、激光雷达(light laser detection and ranging,LiDAR)点云数据、地形和气象因子共同驱动的可燃物特征分类系统(fuel characteristic classification system,FCCS)模型来实现森林火险等级预测。以云南省普洱市为研究区,首先,利用机载LiDAR数据生产的树冠高度模型进行面向对象分割,与森林资源二类清查数据叠加分析确定分割单元,并根据可燃物的可燃性将研究区内的可燃物分为针叶林、阔叶林、竹林和灌木林等4种类型,在此基础上采用分层随机抽样形成验证数据集; 然后,提取LiDAR变量因子,采用多元逐步回归法反演不同可燃物的森林参数; 最后,将森林参数连同气象和地形因子作为FCCS模型的输入,完成各个分割单元的火情等级评价,实现该地区潜在火行为、树冠火、有效可燃物和综合火灾险情的制图。研究结果表明,研究区有效可燃物火险等级比较低,符合研究区的实际情况; 森林垂直结构与森林火险等级关系密切,森林参数的准确估测对整个可燃物的制图具有非常重要的作用。

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马振宇
陈博伟
庞勇
廖声熙
覃先林
张怀清
关键词 激光雷达森林参数反演林火特征分类模型火险制图    
Abstract

The distribution of combustibles in forest is one of the important factors that affect the occurrence and spread of forest fires. The purpose of this study is to combine the traditional forest survey data with point cloud data from light laser detection and ranging(LiDAR), slope and meteorological factors so as to evaluate forest fire potentials with fuel characteristic classification system(FCCS). Pu’er City of Yunnan Province was selected as the research area in this paper. An object-oriented based segmentation was performed based on the crown height model(CHM) which was produced by the airborne LiDAR data, and the overlay analysis of the provincial level inventory data of forest resources of the research area was used to determine the division unit and vegetation type according to the flammability of vegetation, which was divided into coniferous forest, broad-leaved forest, shrub and bamboo forest. On such a basis, stratified random sampling was used to form the validation dataset. Then the authors extracted the LiDAR variables and applied the multivariate stepwise regression method to analyzing the extracted variables with the reference data set to obtain the forest parameters of different vegetation types. In the end, the forest parameters together with the meteorological factors were used as inputs to the forest fire classification model (FCCS), and the fire potential of each segmentation unit was calculated by the model. Finally, the authors compiled maps of potential fire behavior, crown fire, effective combustibles and comprehensive fire hazard result. The results showed that the overall fire potential level of combustible materials in the research area is relatively low, which is consistent with the actual situation in the study area; the vertical structure of the forest is closely related to the forest fire risk potentials. Accurate estimation of forest parameters plays a very important role in the mapping of combustibles.

Key wordsLiDAR    forest parameters inversion    FCCS    forest fire potential mapping
收稿日期: 2018-01-29      出版日期: 2020-03-14
ZTFLH:  K909  
基金资助:国家自然科学基金项目“基于高分辨率遥感数据的森林生物多样性监测”(编号: 31570546);中央级公益性科研院所基本科研业务费专项资金项目“机载光学全谱段数据处理及林火预警技术研究”(编号: CAFYBB2018SZ009)
通讯作者: 庞勇     E-mail: pangy@ifrit.ac.cn
作者简介: 马振宇(1993-),男,硕士研究生,主要从事激光雷达森林参数反演及遥感林业应用方面的研究。Email: mazhenyu22@163.com。
引用本文:   
马振宇,陈博伟,庞勇,廖声熙,覃先林,张怀清. 基于林火特征分类模型的森林火情等级制图[J]. 国土资源遥感, 2020, 32(1): 43-50.
Zhenyu MA,Bowei CHEN,Yong PANG,Shengxi LIAO,Xianlin QIN,Huaiqing ZHANG. Forest fire potential forecast based on FCCS model. Remote Sensing for Land & Resources, 2020, 32(1): 43-50.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.01.07      或      http://www.gtzyyg.com/CN/Y2020/V32/I1/43
Fig.1  研究区地理位置
LiDAR测量系统: Riegl LMS-Q680i
波长/nm 1 550 激光脉冲长度/ns 3
最大频率/kHz 400 扫描角/(°) ±30
最大扫描速度/(lines/s) 200 采样间隔/ns 1
垂直精度/m 0.15 激光发散角/mrad 0.5
CCD相机: DigiCAM-60
像元分辨率 8 964×6 716 像元尺寸/μm 6
成像传感器尺寸/(mm×mm) 40.30×53.78 位深度/bits 16
视场角/(°) 56.2 焦距/mm 50
高光谱相机: AISA Eagle II
光谱范围/nm 400~1 000 像元数/个 1 024
焦距/mm 18.1 光谱分辨率/nm 3.3
总视场角/(°) 37.7 瞬时视场角/(°) 0.037
波段数/个 488 帧率/(帧/s) 160
Tab.1  LiCHy机载传感器主要参数
Fig.2  研究区LiDAR数据覆盖范围
样地类型 林龄 样地号 样地规格/(m×m) 树种 平均胸径/cm 平均树高/m 最大树高/m 样木总数
针叶林 幼龄林 CY-a2 15×15 思茅松∶阔叶=4∶6 7.90 6.10 7.6 49
CY-b1 30×30 思茅松纯林 12.45 11.63 13.2 71
CY-c6 10×10 思茅松纯林 6.79 5.55 7.8 45
中龄林 CP-a3 10×10 思茅松∶阔叶=1∶1 10.27 6.82 16.2 35
CP-b5 15×15 思茅松∶阔叶=2∶8 11.16 9.00 18.8 42
CP-c5 15×15 思茅松∶阔叶=2∶8 11.95 9.81 21.5 42
成熟林 CM-a2 15×15 阔叶林 12.67 9.93 15.0 43
CM-c7 30×30 思茅松∶阔叶=2∶8 13.24 9.50 24.5 84
CM-c4 20×20 思茅松∶阔叶=3∶7 14.06 10.12 17.5 41
阔叶林 幼龄林 BY-b3 15×15 14.12 9.61 13.8 18
BY-a9 15×15 7.07 6.01 8.6 57
BY-c3 15×15 8.88 8.19 13.5 56
中龄林 BP-a6 30×30 10.52 10.33 19.8 58
BP-b2 20×20 10.85 10.35 21.0 83
BP-c8 20×20 13.19 12.44 18.1 56
成熟林 BM-c5 30×30 16.80 9.12 15.2 22
BM-a5 30×30 10.56 9.87 20.4 233
BM-b3 15×15 14.34 12.30 24.2 104
竹林 竹林 B1 15×15 龙竹 10.70 16.20 21.5 101
B2 10×10 龙竹 7.00 8.60 12.5 79
B3 10×10 龙竹 4.70 11.40 15.0 53
灌木林 灌木林 S-c2 15×15 咖啡 2.25 2.5
S-a1 15×15 茶叶 1.00 1.1
S-b1 15×15 咖啡 1.95 2.2
Tab.2  地面调查数据汇总
Fig.3  技术路线
Fig.4  研究区CHM
Fig.5  研究区局部地区分割结果
Fig.6  FCCS模型输出示意图
植被类型 森林参数 LiDAR变量 截距项 模型系数 相关系数R2
阔叶林 样地内最大树高 大于均值高度的点云数占全部点云数的百分比 0.356 0.81
样地内林木株数 海拔高度 192.016 0.88
郁闭度 高度绝对偏差中位数的众数 0.843 0.004 0.95
针叶林 样地内最大树高 99%高度分位数 0.931 0.98
样地内林木株数 总回波的点云数 0.067 0.97
郁闭度 大于最低高度的第一次回波的点云数 0.883 -0.000 003 0.55
竹林 样地内生物量 点云强度的变异系数 -18.936 273.384 0.99
样地内最大树高 高度最大值 0.806 0.99
盖度 大于最低高度的第四次回波的点云数高度绝对偏差中位数的众数 0.843 106 0.000 36
0.003 949
0.81
灌木林 样地内最大树高 大于0.5 m的点云数占全部点云数的百分比 0.010 0.99
盖度 大于均值高度的点云数占全部点云数的百分比 0.048 0.98
Tab.3  LiDAR变量多元逐步回归估测森林参数统计量一览表
Fig.7  阔叶林和针叶林样地森林参数反演建模精度
Fig.8  基于FCCS模型的火险等级制图
Fig.9  普洱市中部典型区域综合火灾险情
[1] Chirici G, Scotti R, Montaghi A , et al. Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2013,25(1):87-97.
[2] San-Miguel-Ayanz J, Rodrigues M, Oliveira S S D , et al. Land cover change and fire regime in the European Mediterranean region[M] //Moreira F,Arianoutsou M,Corona P,et al.Post-Fire Management and Restoration of Southern European Forests.Springer, 2012: 21-43.
[3] Schmidt K M, Menakis J P, Hardy C C , et al. Development of Coarse-Scale Spatial Data for Wildland Fire and Fuel Management[R]. Ogden:USDA Forest Service, Rocky Mountain Research Station, 2002,87.
[4] Chuvieco E. Wildland Fire Danger Estimation and Mapping:The Role of Remote Sensing Data[M]. World Scientific Publishing Company, 2003.
[5] Arroyo L A, Pascual C, Manzanera J A . Fire models and methods to map fuel types:The role of remote sensing[J]. Forest Ecology and Management, 2008,256(6):1239-1252.
[6] Moreira F, Viedma O, Arianoutsou M , et al. Landscape-wildfire interactions in southern Europe:Implications for landscape management[J]. Journal of Environmental Management, 2011,92(10):2389-2402.
[7] Andersen H E, Mcgaughey R J, Reutebuch S E . Estimating forest canopy fuel parameters using LIDAR data[J]. Remote Sensing of Environment, 2005,94(4):441-449.
[8] Kramer H A, Collins B M, Kelly M , et al. Quantifying ladder fuels:A new approach using LiDAR[J]. Forests, 2014,5(6):1432-1453.
[9] Sandberg D V, Riccardi C L, Schaaf M D . Fire potential rating for wildland fuelbeds using the fuel characteristic classification system[J]. Canadian Journal of Forest Research, 2007,37(12):2456-2463.
[10] Mallinis G, Galidaki G, Gitas I . A comparative analysis of EO-1 Hyperion,Quickbird and Landsat TM imagery for fuel type mapping of a typical mediterranean landscape[J]. Remote Sensing, 2014,6(2):1684-1704.
[11] Thomas V, Treitz P, Mccaughey J H , et al. Mapping stand-level forest biophysical variables for a mixedwood boreal forest using LiDAR:An examination of scanning density[J]. Canadian Journal of Forest Research, 2006,36(1):34-47.
[12] 庞勇, 李增元, 陈尔学 , 等. 激光雷达技术及其在林业上的应用[J]. 林业科学, 2005,41(3):129-136.
Pang Y, Li Z Y, Chen E X , et al. LiDAR remote sensing technology and its application in forestry[J]. Scientia Silvae Sinicae, 2005,41(3):129-136.
[13] Asner G P, Mascaro J, Muller-Landau H C , et al. A universal airborne LiDAR approach for tropical forest carbon mapping[J]. Oecologia, 2012,168(4):1147-1160.
[14] Rothermel R C . A Mathematical Model for Predicting Fire Spread in Wildland Fuels[R]. Ogden:USDA Forest Service,Intermountain Forest and Range Experiment Station, 1972,115.
[15] Albini F A . Estimating Wildfire Behavior and Effects[R]. Ogden:USDA Forest Service,Intermountain Forest and Range Experiment Station, 1976,030.
[16] Anderson H E . Aids to Determining Fuel Models for Estimating Fire Behavior[R]. Ogden:USDA Forest Service, Intermountain Forest and Range Experiment Station, 1982,122.
[17] Ottmar R D, Sandberg D V, Riccardi C L , et al. An overview of the fuel characteristic classification system:Quantifying,classifying,and creating fuelbeds for resource planning[J]. Revue Canadienne De Recherche Forestière, 2007,37(37):2383-2393.
[18] Pettinari M L, Chuvieco E . Generation of a global fuel dataset using the fuel characteristic classification system[J]. Biogeosciences, 2016,12(20):2061-2076.
[19] Fang J Y, Song Y C, Liu H Y , et al. Vegetation climate relationship and its application in the division of vegetation zone in China[J]. Acta Botanica Sinica, 2002,44(9):1105-1122.
[20] Pang Y, Li Z Y, Ju H B , et al. LiCHy:The CAF’s LiDAR,CCD and hyperspectral integrated airborne observation system[J]. Remote Sensing, 2016,8(5):398.
[21] McGaughey R J . FUSION/LDV:Software for LiDAR Data Analysis and Visualization[R]. Seattle:USDA,Forest Service, Pacific Northwest Research Station, 2016.
[22] Scott J H, Burgan R E . Standard Fire Behavior Fuel Models:A Comprehensive Set for Use with Rothermel’s Surface Fire Spread Model[R]. Ogden:USDA,Forest Service, Rocky Mountain Research Station, 2005,153.
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