<trans-title>Study on surface thermal environment differentiation effect in mining intensive area through developing remote sensing assessment model</trans-title>

Chun-Hua HOU; Fu-Ping LI; Bao-Jie HE; Hai-Hong GU; Wen SONG

doi:10.11972/j.issn.1001-9014.2020.05.015

Journal of Infrared and Millimeter Waves, Volume. 39, Issue 5, 635(2020)

Study on surface thermal environment differentiation effect in mining intensive area through developing remote sensing assessment model

Chun-Hua HOU¹, Fu-Ping LI^1,2,3、*, Bao-Jie HE⁴, Hai-Hong GU^1,2,3, and Wen SONG^1,5

Author Affiliations

¹College of Mining Engineering，North China University of Science and Technology，Tangshan，063210，China

²Hebei Key Laboratory of Mining Developmeng and Security Technology，Tangshan，063210，China

³Hebei Industrial Technology Institute of Mine Ecological Remediation，Tangshan063210，China

⁴Faculty of Built Environment，University of New South Wales，Sydney2052，Australia

⁵Key Laboratory of Land Surface Pattern and Simulation，Institute of Geographic Sciences and Natural Resources Research，CAS，Beijing100101，China

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Figures & Tables(20)

Fig. 1. Landsat satellite image of study area

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Fig. 2. Feature space of NDVI-DFI in 2000

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Fig. 3. RSIEI flow chart of calculation

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Fig. 4. Comparison on RTE of　Landsat data　and　MODIS　temperature　product

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Fig. 5. Land use classification of Qian’an City of 2018

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Fig. 6. Percentage of land occupied by various types of ground objects in 8 townships on 2018

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Fig. 7. Spatial distribution of LST in study area from 2000 to 2018

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Fig. 8. Spatial distribution patterns of LST and RSIEI in the study area on 2018

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Fig. 9. Spatial distribution patterns of LST and RSIEI in the A town on 2018.

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Fig. 10. Spatial distribution of RSIEI in study area from 2000 to 2018

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Fig. 11. RSIEI values of four areas of mining intensive development from 2000 to 2018

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Fig. 12. Single factor regression results based on RSIEI(x) and normalized LST(y) of 2018

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Fig. 13. Comparison of normalized RSIEI and normalized LST between four mining intensive towns and four non mining intensive towns in 2018

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Table 1. 遥感影像数据

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Table 1. 遥感影像数据

卫星	传感器类型	影像过境日期	影像过境时间	云量/%	轨道号	波段数/个
Landsat 5	TM	2000-9-6	AM 10：25	0.01%	122/32	7
Landsat 5	TM	2008-9-12	AM 10：32	0.01%	122/32	7
Landsat 8	OLI/TIRS	2018-9-8	AM 10：46	1.98%	122/32	9/2

Table 2. 大气校正参数
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Table 2. 大气校正参数
大气校正参数 2000-9-6 2008-9-12 2018-9-8
热红外波段大气透过率τ 0.92 0.80 0.83
大气向上辐射亮度L_u 0.55 1.51 1.27
大气向下辐射亮度L_d 0.96 2.51 2.14

Table 3. 2000~2018年矿业开发密集区各地物LST均值（°C）

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Table 3. 2000~2018年矿业开发密集区各地物LST均值（°C）

年份	区域	林地	耕地	工矿用地	居民地	水域
2000年	A	17.53	22.53	31.38	27.62	16.78
	B	18.62	22.23	32.26	26.56	15.25
	C	18.44	21.07	31.59	28.11	14.97
	D	19.03	22.01	31.21	27.74	15.36
2008年	A	26.20	28.11	37.52	29.80	22.53
	B	27.78	30.47	37.88	31.12	21.58
	C	27.10	30.97	36.87	32.19	20.22
	D	26.94	29.68	36.99	29.87	21.87
2018年	A	25.25	31.77	36.78	33.54	22.61
	B	26.32	32.01	37.21	33.33	22.02
	C	25.88	32.58	36.82	34.25	21.32
	D	25.78	30.15	36.32	32.89	20.20

Table 4. 2018年矿业开发密集区4个生态参数与LST回归分析

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Table 4. 2018年矿业开发密集区4个生态参数与LST回归分析

Town

f_PV - LST

NDMI - LST

NDBI - LST

BSI - LST

y = -7.54x + 35.09

R² = 0.45

y = -8.43x + 35.39

R²= 0.59

y = 8.25x + 26.88

R² = 0.55

y = 8.45x + 26.71

R² = 0.63

y = -9.02x + 35.31

R² = 0.61

y = -10.14x + 35.32

R² = 0.75

y = 10.00x + 25.05

R²= 0.74

y = 8.63x + 25.58

R² = 0.76

y = -7.63x + 35.00

R²= 0.46

y = -8.22x + 34.79

R² = 0.64

y = 8.48x + 26.36

R² = 0.59

y = 7.83x + 26.62

R² = 0.61

y = -8.42x + 34.70

R² = 0.69

y = -9.17x + 34.98

R² = 0.81

y = 9.16x + 25.64

R² = 0.81

y = 8.28x + 25.91

R²= 0.82

Table 5. 2000~2018年4个陆表生物物理指标主成分分析

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Table 5. 2000~2018年4个陆表生物物理指标主成分分析

年份

Year

指标

Indicator

第1主成分

PCA₁

第2主成分

PCA₂

第3主成分

PCA₃

第4主成分

PCA₄

2000

特征值贡献率

Percent of Eigenvalue(%)

96.49

3.20

0.31

0.00

2008

特征值贡献率

Percent of Eigenvalue(%)

92.50

7.04

0.47

0.00

2018

特征值贡献率

Percent of Eigenvalue(%)

92.50

6.36

1.13

0.00

Table 6. 2000~2018年4个陆表生物物理指标PCA1载荷统计值

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Table 6. 2000~2018年4个陆表生物物理指标PCA1载荷统计值

年份	区域	f_PV	NDMI	NDBI	BSI
2000	A	-0.4800	-0.5079	0.5042	0.5074
	B	-0.4392	-0.5115	0.5107	0.5336
	C	-0.4358	-0.5072	0.5091	0.5418
	D	-0.4072	-0.5102	0.5132	0.5572
2008	A	-0.4651	-0.5157	0.5138	0.5038
	B	-0.4440	-0.5122	0.5106	0.5290
	C	-0.3756	-0.5318	0.5314	0.5420
	D	-0.3183	-0.5462	0.5465	0.5492
2018	A	-0.4963	-0.5061	0.5015	0.4961
	B	-0.4624	-0.5112	0.5079	0.5166
	C	-0.4430	-0.5191	0.5182	0.5155
	D	-0.4448	-0.5077	0.5074	0.5356

Table 7. 2000~2018年RSIEI等级变化

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Table 7. 2000~2018年RSIEI等级变化

等级	RSIEI	区域	面积占比%			变化幅度
等级	RSIEI	区域	2000	2008	2018	2000—2008	2008—2018	2000—2018
差	0~0.2	A	18.86	17.08	20.30	-1.78	2.5	1.44
		B	5.93	20.75	13.31	7.91	-7.44	7.38
		C	3.36	6.46	5.19	3.1	-1.27	1.83
		D	1.33	3.05	3.63	1.72	0.59	2.30
较差	0.2~0.4	A	9.41	18.64	15.55	9.23	-3.09	6.14
		B	4.32	23.56	12.56	8.25	-11	8.24
		C	5.04	13.58	12.40	8.54	-1.18	7.36
		D	2.35	10.18	10.12	7.83	-0.06	7.77
中等	0.4~0.6	A	8.91	16.89	14.71	7.98	-2.18	5.8
		B	5.75	12.29	14.41	7.84	2.12	8.66
		C	6.80	18.91	15.71	12.11	-3.2	8.91
		D	4.53	18.17	14.01	13.64	-4.16	9.48
良好	0.6~0.8	A	8.48	13.51	12.70	5.03	-0.81	4.22
		B	6.81	1.98	14.43	6.82	12.45	7.62
		C	6.10	19.64	15.16	13.54	-4.48	9.06
		D	5.25	22.86	15.95	17.61	-6.92	10.70
优秀	0.8~1	A	54.34	33.88	36.74	-20.46	2.86	-17.6
		B	77.19	41.43	45.2 9	-30.82	3.86	-31.9
		C	78.70	41.41	51.54	-37.29	10.13	-27.16
		D	86.54	45.74	56.29	-40.8	10.55	-30.25

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Chun-Hua HOU, Fu-Ping LI, Bao-Jie HE, Hai-Hong GU, Wen SONG. Study on surface thermal environment differentiation effect in mining intensive area through developing remote sensing assessment model[J]. Journal of Infrared and Millimeter Waves, 2020, 39(5): 635

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