Application of PL High Resolution Remote Sensing Image in Forest Fire Assessment

被引：0
|
作者：
Hu L. [1 ]
Wang L. [1 ]
Li H. [1 ]
Ding Y. [1 ]
Wei C. [1 ]
Li H. [1 ]
Zhao F. [2 ]
机构：
[1] Daxing'anling Institute of Agricultural and Forestry Heilongjiang Nenjiang National Positioning Observatory and Research Station of Forest Ecosystem, Daxing'anling National Permanent Scientific Research Base of Forested Wetlands Ecosystem, Jiagedaqi
[2] Key Laboratory of Forest Protection of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, CAF, Beijing
来源：
Linye Kexue/Scientia Silvae Sinicae | 2022年 / 58卷 / 03期
关键词：
Burned area; Fire severity; NDVI; PL images;
D O I：
10.11707/j.1001-7488.20220312
中图分类号：
学科分类号：
摘要：
【Objective】This paper aims to study the feasibility of forest fire image information extraction and data analysis by only using one high-resolution remote sensing image (Planet Labs, PL), so as to provide reliable remote sensing data source and extraction methods for forest fire assessment. 【Method】 In this study, the burned area of the "5•2" big forest fire in Bilahe in 2017 was used as the study area, three phases of PL images including before the fire, the year after the fire and one year after the fire were used as the data source, and the burned area was extracted by using ROIs. The variation characteristics of NDVI before and after fire interference were analyzed. Combined with the ground survey data, the difference normalized vegetation index (dNDVI) was used to divide the fire severity level, and the threshold was verified based on the classification standard of Luo Dekun fire damage levels. The damage status of vegetation in the burned area was evaluated to obtain the spatial distribution pattern of the degree of fire. 【Result】 Fire disturbance led to a sharp decrease in NDVI values, and the NDVI value slightly increased one year after fire, indicating that the ability of vegetation restoration was very limited. The 3 m high spatial resolution of PL Remote Sensing Image made its RGB image highly saturated and every land class clear, and directly classified the land cover types. The training sample separation was above 1.91, the land cover types could be divided into 4 types: forest, herbaceous swamp, road and river. The classification accuracy was 98.05% and the Kappa_Coefficient value was 0.95. The fire severity was classified into 4 levels: unburned area, light burned area, moderate burned area and severe burned area. The overall classification accuracy was 91.55%, and the Kappa_Coefficient was 0.91. The total study area in Bilahe was 10 711.18 hm2, and the burned area was 10 130.31 hm2, accounting for 94.58% of the total fire area. Most burned area was light burned one with 5 700.78 hm2, accounting for 53.22% of the total fire area., The moderate burned area was secondary, with an area of 3 035.12 hm2, accounting for 28.34%. The damaged forest area affected by moderate burned was the largest, up to 6 167.48 hm2, accounting for 60.88% of the total burned area. The secondary damaged forest area was in the light burned region, reached to 1 846.93 hm2, accounting for 29.95%. The damaged forest area in the severe burned swamp region was the least, accounting for only 22.60%. The damage of herbaceous swamp was less, with a burned area of 3 962.86 hm2, of which 97.25% of burned swamp area was concentrated in the light burned region. 【Conclusion】 Compared with the traditional research methods, the research method used in this paper can achieve more accurate results in the analysis of forest fire in Bilahe, with higher classification accuracy, more reliable results and more efficient data processing. At the same time, PL remote sensing has ultra-high frequency time resolution, which covers the whole world once a day, and satisfies the requirements of covering different research areas to a great extent. © 2022, Editorial Department of Scientia Silvae Sinicae. All right reserved.
引用
收藏
页码：107 / 116
页数：9
相关论文
共 27 条
  • [1] Ding H Y, Wu Y., Fire information detection of the bilahe forest based on moderate resolution imaging spectroradiometer data, Journal of University of Jinan (Science and Technology), 34, 3, pp. 231-246, (2020)
  • [2] Fu J X, Cao G C, Guo W J., Changes of growing season NDVI at different elevations, slopes, slope aspects and its relationship with meteorological factors in the southern slope of the Qilian Mountains, China from 1998 to 2017, Chinese Journal of Applied Ecology, 31, 4, pp. 1203-1212, (2020)
  • [3] Gao S Z, Zhou W C, Fan J R, Et al., Study on extraction method of vegetation index in ecological change monitoring after forest fire, Remote Sensing Information, 1, pp. 15-19, (1997)
  • [4] Gao T S., Network security situation prediction method based on spatio-temporal dimension analysis, Network Security Technology and Application, 3, pp. 20-21, (2018)
  • [5] Li M Z, Kang X R, Fan W Y., Burned area extraction in Huzhong forests based on remote sensing and the spatial analysis of the burned severity, Scientia Silvae Sinicae, 53, 3, pp. 163-174, (2017)
  • [6] Li W H., Discussion on site type distribution characteristics of bila River Forestry Bureau, Inner Mongolia Forestry Investigation and Design, 33, 3, pp. 48-50, (2010)
  • [7] Luo D K., Discussion on the regeneration mode of burned land in Daxing'an Mountains, Forestry Science and Technology, 53, 1, pp. 10-12, (1987)
  • [8] Liu S C, Chen X Z, Qin X L, Et al., Remote sensing assessment of forest fire damage degree in Bilahe Forest Farm, Inner Mongolia, Forest Resources Management, 53, 1, pp. 90-96, (2018)
  • [9] Ma L, Luo J S, Wang Y K., Research on forest fire monitoring based on multi-source remote sensing data, Geomatics and Spatial Information Technology, 44, 5, pp. 141-145, (2021)
  • [10] Miao Q L, Tian X R, Zhao F J., NDVI recovery process for post-fire vegetation in Daxing'anling, Scientia Silvae Sinicae, 51, 2, pp. 90-98, (2015)
123