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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (1) : 43-50     DOI: 10.6046/gtzyyg.2020.01.07
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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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.

Keywords LiDAR      forest parameters inversion      FCCS      forest fire potential mapping     
:  K909  
Corresponding Authors: Yong PANG     E-mail:
Issue Date: 14 March 2020
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Zhenyu MA
Bowei CHEN
Shengxi LIAO
Xianlin QIN
Huaiqing ZHANG
Cite this article:   
Zhenyu MA,Bowei CHEN,Yong PANG, et al. Forest fire potential forecast based on FCCS model[J]. Remote Sensing for Land & Resources, 2020, 32(1): 43-50.
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Fig.1  Geographic location of the study area
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  Sensor parameters of LiCHy system
Fig.2  Distribution of the LiDAR data in the study area
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  Information of all field plots
Fig.3  Scheme of this study
Fig.4  CHM of the study area
Fig.5  Examples of the segment results in the study area
Fig.6  Diagram of FCCS outputs
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
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  Summary statistics of multiple linear regressions of forest parameters with LiDAR metrics
Fig.7  Accuracy of forest parameters estimation for broadleaf and conifer plots
Fig.8  Forest fire potential mapping based on FCCS model
Fig.9  Subset of combined fire risk potential in the central of Puer City
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