PL高分辨率遥感影像在森林火灾评估上的应用

doi:10.11707/j.1001-7488.20220312

Abstract

Abstract:

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.

Key words: PL images, burned area, NDVI, fire severity

CLC Number:

S762

Linlin Hu,Lizhong Wang,Hua Li,Yongquan Ding,Changlei Wei,Huiren Li,Fengjun Zhao. Application of PL High Resolution Remote Sensing Image in Forest Fire Assessment[J]. Scientia Silvae Sinicae, 2022, 58(3): 107-116.

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NDVI时序 Time series of NDVI	最小值 Min	最大值 Max	平均值 Mean	标准差 SD
20160706	-0.04	0.79	0.60	0.08
20170702	-0.59	0.40	0.03	0.16
20180722	-0.62	0.44	0.17	0.11

土地覆盖类型 Land cover types		Jeffries Matusita	Transformed Divergence
草本沼泽Herbaceous swamp	森林Forest	1.94	1.95
草本沼泽Herbaceous swamp	道路Road	1.91	1.99
水体River	道路/Road	2.00	2.00
草本沼泽Herbaceous swamp	水体/River	1.91	1.92
森林Forest	水体/River	1.93	1.96
森林Forest	道路/Road	1.98	2.00

火烧等级 Fire severity levels	编号 Code	dNDVI阈值 Threshold of dNDVI
未火烧Unburned	0	< 0.34
轻度Light	1	0.34~0.56
中度Moderate	2	0.57~0.74
重度Severe	3	>0.75

火烧等级 Fire severity levels	经度 Longitude	纬度 Latitude	乔木总株数 Total number of trees	火烧死亡株数 Number of fire dead trees	自然死亡株数 Humber of natural death	火烧死亡株数百分比 Percentage of fire dead trees(%)
未火烧区 Unburned	123°0′47.688″E	49°29′1.248″N	881	0	11	0
	122°59′42.776″E	49°28′59.812″N	1 020	0	15	0
	123°7′49.213″E	49°37′38.485″N	955	0	22	0
	123°10′11.578″E	49°32′37.639″N	758	0	13	0
	123°5′18.627″E	49°31′3.408″N	1 203	0	28	0
轻度火烧 Light	123°1′3.202″E	49°29′17.956″N	682	40	20	5.87
	123°0′2.153″E	49°29′8.703″N	756	50	15	6.61
	123°2′8.908″E	49°29′59.35″N	623	48	12	7.70
	123°7′44.742″E	49°31′34.168″N	662	126	19	19.03
	123°10′13.566″E	49°32′37.81″N	589	157	25	26.66
中度火烧 Moderate	123°1′8.791″E	49°29′22.775″N	650	339	15	43.18
	122°59′57.373″E	49°29′2.844″N	785	369	12	47.00
	123°2′9.387″E	49°30′9.355″N	693	378	10	54.55
	123°8′1.291″E	49°31′20.445″N	588	324	17	55.10
	123°10′18.627″E	49°32′37.022″N	569	359	19	63.09
重度火烧 Severe	123°0′54.91″E	49°29′12.885″N	346	320	6	92.49
	122°59′48.584″E	49°29′1.662″N	420	352	5	83.81
	123°2′9.319″E	49°30′3.53″N	398	330	6	82.91
	123°8′11.416″E	49°31′21.319″N	360	299	2	83.05
	123°10′16.53″E	49°32′39.009″N	398	289	2	72.61

各植被类型面积及占比 Area and proportion of each vegetation type	轻度火烧区 Light burned area	中度火烧区 Moderate burned area	重度火烧区 Severe burned area
森林面积Area of forest/hm²	1 846.93	2 926.56	1 393.98
草本沼泽面积Area of herbaceous swamp/hm²	3 853.84	108.56	0.45
森林占比Proportion of forest(%)	22.60	29.95	47.45
草本沼泽占比Proportion of herbaceous swamp(%)	2.74	0.01	97.25

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

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