Retrieval of Dust Retention Distribution in Beijing Urban Green Space Based on Spectral Characteristics

Ge WANG; Qiang YU; Di Yang; Teng NIU; Qian-qian LONG; [in Chinese]

doi:10.3964/j.issn.1000-0593(2022)08-2572-07

Spectroscopy and Spectral Analysis, Volume. 42, Issue 8, 2572(2022)

Retrieval of Dust Retention Distribution in Beijing Urban Green Space Based on Spectral Characteristics

Ge WANG1,*... Qiang YU1,1; *;, Di Yang2,2;, Teng NIU1,1;, Qian-qian LONG1,1; and [in Chinese]2,2; |Show fewer author(s)

Author Affiliations

¹1. Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China

²2. Geographic Information Center, University of Wyoming, Laramie 82070, USA

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Figures & Tables(12)

Fig. 1. Distribution of sampling points

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Fig. 2. Indoor dust retention capacity and spectral determination process of leaves (a) and principle of spectral determination of leaves (b)

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Fig. 3. Land covers and urban green spaces of beijing in 2020

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Fig. 4. Effect of dust retention on spectral characteristics (a) and spectral curve (b) after first derivative over treatment before and after dust removal

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Fig. 5. Relationship between dust retention and spectral reflectance ratio (before and after dust removal)

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Fig. 6. Inversion model based on EVI index

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Fig. 7. Dust retention distribution of green space in Beijing (a) and dust retention inversion verification (b)

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Fig. 8. Distribution of atmospheric dust pollution in Beijing and spatial autocorrelation analysis

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Table 1. vegetation index formula

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Table 1. vegetation index formula

植被指数	计算公式	植被指数比 (滞尘/除尘)
NDVI	NDVI= $\frac{NIR - R}{NIR + R}$	R_NDVI= $\frac{NDV I_{dust}}{NDV I_{clean}}$
RVI	RVI= $\frac{NIR}{R}$	R_RVI= $\frac{RV I_{dust}}{RV I_{clean}}$
DVI	DVI=NIR-R	R_DVI= $\frac{DV I_{dust}}{DV I_{clean}}$
PVI	PVI= $\frac{NIR - 10.489 R - 6.660 4)}{\sqrt[]{(1 + 10 . 489^{2}} (10.547)}$	R_PVI= $\frac{PV I_{dust}}{PV I_{clean}}$
EVI	EVI= $\frac{2.5 (NIR - R)}{NIR + 6 R - 7.5 B + 1}$	R_EVI= $\frac{EV I_{dust}}{EV I_{clean}}$
GNDVI	GNDVI= $\frac{NIR - G}{NIR + G}$	R_GNDVI= $\frac{GNDV I_{dust}}{GNDV I_{clean}}$
RDVI	RDVI= $\frac{NIR - R}{\sqrt[]{NIR + R}}$	R_RDVI= $\frac{RDV I_{dust}}{RDV I_{clean}}$
SAVI	SAVI= $\frac{NIR - R}{NIR + R + L}$ (1+L), L=0.5	R_SAVI= $\frac{SAV I_{dust}}{SAV I_{clean}}$
OSAVI	OSAVI= $\frac{NIR - R}{NIR + R + 0.16}$	R_OSAVI= $\frac{OSAV I_{dust}}{OSAV I_{clean}}$
NLI	NLI= $\frac{{(NIR)}^{2} - R}{{(NIR)}^{2} + R}$	R_NLI= $\frac{NL I_{dust}}{NL I_{clean}}$

Table 2. Effect of dust retention on blue, yellow and red edges of spectrum

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Table 2. Effect of dust retention on blue, yellow and red edges of spectrum

参数类型	含义	除尘前	除尘后
蓝边幅值	红边(490~530 nm)内一阶导数最大值	0.002	0.003
黄边幅值	黄边(560~640 nm)内一阶导数最大值	-0.001	-0.002
红边幅值	红边(680~760 nm)内一阶导数最大值	0.012	0.016
蓝边面积	蓝边内一阶微分的总和	6.725	7.704
黄边面积	黄边内一阶微分的总和	14.904	15.278
红边面积	红边内一阶微分的总和	37.039	43.282

Table 3. Relationship between vegetation index ratio and dust retention of five satellites

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Table 3. Relationship between vegetation index ratio and dust retention of five satellites

	Sentinel-2	Landsat8	MODIS	ZY₃	Spot6
NDVI	-0.412	-0.375	-0.362	-0.339	-0.372
RVI	-0.398	-0.348	-0.341	-0.331	-0.328
DVI	-0.768^**	-0.689	-0.679	-0.681	-0.683
PVI	0.401	0.449	0.473	0.451	0.475
EVI	-0.809^**	-0.745^**	-0.719^**	-0.718^**	-0.748^**
GNDVI	-0.379	-0.288	-0.268	-0.288	-0.292
RDVI	-0.351	-0.521	-0.273	-0.502	-0.508
SAVI	-0.512	-0.522	-0.518	-0.481	-0.471
OSAVI	-0.458	-0.401	-0.405	-0.395	-0.389
NLI	-0.561	-0.409	-0.507	-0.498	-0.495

Table 4. Dust retention inversion model
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Table 4. Dust retention inversion model
模型类型反演模型 R² RMSE
线性 y=-22.74x+25.74 0.705 1.691
二次 y=49.52x²-102.3x+57.1 0.751 1.298

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Ge WANG, Qiang YU, Di Yang, Teng NIU, Qian-qian LONG, [in Chinese]. Retrieval of Dust Retention Distribution in Beijing Urban Green Space Based on Spectral Characteristics[J]. Spectroscopy and Spectral Analysis, 2022, 42(8): 2572

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