基于PSO-PNN模型的喀什噶尔绿洲耕地盐渍化分析

doi:10.12118/j.issn.1000-6060.2022.018

Abstract

Abstract:

Salinization is one of the main causes of low yields in oasis agriculture and a major constraint and barrier to agricultural and sustainable development. In order to improve the productivity of saline-cultivated land and to promote the sustainable development of oasis agriculture, and taking the cultivated land of the Kashgar Oasis in Xinjiang, China, as the research object, this study used Landsat 8 OLI remote sensing image data to extract 20 remote sensing indices. It also calculated the reclamation age of cultivated land in the study area based on land use data and downscaled the vegetation net primary productivity (NPP) data simulated by the vegetation photosynthesis model using a linear-fitting method. In this analysis, soil sampling and measured data were used to obtain the relevant remote sensing characteristic variables, the probabilistic neural network (PNN) model of particle swarm optimization (PSO) optimization was used to classify the degree of salinization, and, finally, the distribution of the salinization level of cultivated land in the study area was obtained and then superimposed onto the cultivated land reclamation age and NPP in the study area. The following conclusions were reached: (1) In this paper, five remote sensing parameters: enhanced vegetation index (EVI), salinity index 2 (SI₂), humidity index (WI), MSAVI-WI-SI characteristic space (MWSI), and band 6 (B6, 2.11-2.29 μm), were selected to invert the degree of salinization using the PSO-PNN model, and this method was found to be effective for salinization inversion. (2) The greater the reclamation years of cultivated land, the lower the degree of salinization in the area. The newly reclaimed cultivated land is primarily located in the eastern part of the study area. The newly reclaimed cultivated land in the western part of the study area is more sparse and is mostly comprised of oasis agricultural areas with a land age of more than 45 years. (3) Salinization of cultivated land has significantly reduced its productivity. Most of the areas with a higher NPP of cultivated land are located in the west, and most of the lower areas are in the east, which is nearly the inverse of the hierarchical distribution of salinization degrees.

Key words: remote sensing (RS), geographic information system (GIS), salinization inversion, particle swarm optimization, probabilistic neural network

XIE Conghui,WU Shixin,LIN Juan,ZHUANG Qingwei,ZHANG Zihui,HOU Guanyu,LUO Geping. Analysis of cultivated land salinization in Kashgar Oasis based on PSO-PNN model[J].Arid Land Geography, 2022, 45(5): 1547-1558.

Figures/Tables 12

Fig. 1

Fig. 2

Tab. 1

Characteristic variable of remote sensing index"

类别	名称	公式
植被指数	归一化植被指数（NDVI）	$(N I R - R) / (N I R + R)$
	增强型植被指数（EVI）	$2.5 × (N I R - R) (N I R - 6 × R - 7.5 × B + 1)$
	修改型土壤调节植被指数（MSAVI）	$2 × N I R + 1 - 2 × N I R + 1 2 - 8 × N I R - R ÷ 2$
盐分指数	盐分指数（SI）	$B × R$
	归一化盐分指数（NDSI）	$R - N I R R + N I R$
	盐分指数2（SI₂）	$G 2 + R 2$
	盐分指数3（SI₃）	$G 2 + R 2 + N I R 2$
K-T变换因子	湿度指数（WI）	$0.1511 × B + 0.1972 × G + 0.3283 × R + 0.3047 × N I R - 0.7117 × S W I R 1 - 0.4559 × S W I R 2$
K-T变换因子	绿度指数（GVI）	$- 0.16 × B - 0.28 × G - 0.49 × R + 0.79 × N I R + 0.0002 × S W I R 1 - 0.14 × S W I R 2$
下垫面反射因子	地表反照率（Albedo）	$0.356 × B + 0.130 × R + 0.373 × N I R + 0.085 × S W I R 1 + 0.072 × S W I R 2 - 0.0018$
下垫面反射因子	$F e 2 O 3$ 质量分数 $（ I F e 2 O 3 ）$	$R B$
多维特征空间	MSAVI-WI特征空间（MWI）	$(M S A V I - 1) 2 + (W I - 1) 2$
	NDVI-SI特征空间（SDI）	$(N D V I - 1) 2 + S I 2$
	MSAVI-WI-SI特征空间（MWSI）	$(M S A V I - 1) 2 + (W I - 1) 2 + S I 2$
波段反射率因子	波段2~7（B2、B3、B4、B5、B6、B7）	$B 、 G 、 R 、 N I R 、 S W I R 1 、 S W I R 2$

Tab. 1

Fig. 3

Fig. 4

Tab. 2

Fig. 5

Tab. 3

Tab. 4

Fig. 6

Tab. 5

Fig. 7

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盐渍化程度等级	总盐/mg·g^-1
第一级	0.00~3.05
第二级	3.05~7.42
第三级	7.42~11.30
第四级	11.30~12.60
第五级	12.60~13.90
第六级	13.90~17.60

市县	盐渍化等级占比/%								总面积/km²
市县	第一级	第二级	第三级	第一至第三级	第四级	第五级	第六级	第四至第六级	总面积/km²
阿克陶县	25.01	0.14	46.38	71.53	21.41	1.67	5.39	28.47	463.77
疏附县	25.05	0.05	42.68	67.78	20.20	1.27	10.76	32.22	1030.86
喀什市	22.25	0.00	43.28	65.53	18.07	0.75	15.65	34.47	215.64
疏勒县	15.40	0.07	43.74	59.21	33.44	0.67	6.69	40.79	1292.31
英吉沙县	15.58	0.05	37.43	53.05	27.81	0.86	18.28	46.95	752.85
阿图什市	20.41	0.00	30.65	51.06	26.89	0.66	21.39	48.94	423.36
岳普湖县	10.92	0.11	33.13	44.16	43.42	0.42	12.00	55.84	985.23
伽师县	6.95	0.07	32.46	39.48	48.53	0.40	11.60	60.52	2162.34
总计	14.78	0.07	37.58	52.43	35.20	0.72	11.65	47.57	7326.36

耕地开垦年限/a	盐渍化等级占比/%						总面积/km²
耕地开垦年限/a	第一级	第二级	第三级	第四级	第五级	第六级	总面积/km²
0~10	1.27	0.08	13.24	57.28	1.09	27.03	2015.73
11~20	3.98	0.04	25.50	53.93	1.09	15.45	486.27
21~30	9.58	0.11	37.60	42.82	1.29	8.61	167.31
31~45	12.17	0.05	43.83	34.29	0.83	8.83	390.33
>45	22.83	0.07	49.89	22.42	0.47	4.32	4266.72
总计	14.78	0.07	37.58	35.20	0.72	11.65	7326.36

盐渍化等级	面积/km²	NPP/g·m^-2·a^-1
第一级	1063.80	719.33
第二级	4.86	630.39
第一至第二级	1068.66	718.93
第三级	2695.77	696.29
第四级	2515.14	651.98
第三至第四级	5210.91	674.90
第五级	50.13	636.22
第六级	808.47	639.25
第五至第六级	858.60	639.08

Analysis of cultivated land salinization in Kashgar Oasis based on PSO-PNN model

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