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

干旱区地理 ›› 2022, Vol. 45 ›› Issue (5): 1547-1558.doi: 10.12118/j.issn.1000-6060.2022.018

基于PSO-PNN模型的喀什噶尔绿洲耕地盐渍化分析

谢聪慧^1,²(),吴世新¹(),林娟³,庄庆威⁴,张子慧^1,²,侯冠宇^1,²,罗格平¹

1.中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室,新疆乌鲁木齐 830011
2.中国科学院大学,北京 100049
3.新疆维吾尔自治区自然资源规划研究院,新疆乌鲁木齐 830011
4.武汉大学测绘遥感信息工程国家重点实验室,湖北武汉 430079

收稿日期:2022-01-11 修回日期:2022-02-11 出版日期:2022-09-25 发布日期:2022-10-20
通讯作者: 吴世新
作者简介:谢聪慧（1997-）,女,硕士研究生,主要从事遥感与地理信息系统应用研究. E-mail: xieconghui19@mails.ucas.ac.cn
基金资助:
中国科学院战略性先导科技专项（A类）项目(XDA23100201);国家科技基础资源调查专项课题(2017FY101004)

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

XIE Conghui^1,²(),WU Shixin¹(),LIN Juan³,ZHUANG Qingwei⁴,ZHANG Zihui^1,²,HOU Guanyu^1,²,LUO Geping¹

1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Xinjiang Uygur Autonomous Region Institute of Natural Resources Planning, Urumqi 830011, Xinjiang, China
4. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China

Received:2022-01-11 Revised:2022-02-11 Online:2022-09-25 Published:2022-10-20
Contact: Shixin WU

摘要/Abstract

摘要：

盐渍化是构成绿洲农业低产的主要原因之一,也是农业开发和可持续发展的重要限制因素。为提高盐渍化耕地生产力,促进绿洲农业的可持续发展,以喀什噶尔绿洲耕地为研究对象,利用Landsat 8 OLI遥感影像数据提取遥感指数20个,利用土地利用数据计算研究区耕地开垦年限,用线性拟合的方法将用植被光合作用模型（VPM）模拟的植被净初级生产力（NPP）数据进行降尺度,将遥感指数同土壤采样及实测数据进行相关分析,得到优选的遥感特征变量,再用粒子群优化算法（PSO）优化的概率神经网络（PNN）模型进行盐渍化程度分类,得到研究区耕地盐渍化等级分布情况,后与研究区耕地开垦年限和NPP进行叠加分析。结果表明：（1）选取增强型植被指数（EVI）、盐分指数2（SI₂）、湿度指数（WI）、MSAVI-WI-SI特征空间（MWSI）、波段6（B6,2.11~2.29 μm）5个遥感参量通过PSO-PNN模型进行盐渍化程度反演准确率约为80%。（2）耕地开垦年限越大的区域盐渍化程度越低。新开垦的耕地主要分布在研究区东部,而研究区西部大都为开垦年限在45 a以上的老绿洲农业区。（3）耕地盐渍化严重降低了耕地农作物生产力。研究区耕地NPP较高的区域大都分布在西部,较低的区域大都分布在东部,与盐渍化程度等级分布大致相反。上述研究方法与结果可为后续使用遥感参量进行盐渍化反演的研究提供参考,对干旱半干旱区的盐渍化耕地改良具有一定的参考意义。

关键词: 遥感（RS）, 地理信息系统（GIS）, 盐渍化反演, 粒子群优化算法, 概率神经网络

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

谢聪慧,吴世新,林娟,庄庆威,张子慧,侯冠宇,罗格平. 基于PSO-PNN模型的喀什噶尔绿洲耕地盐渍化分析[J]. 干旱区地理, 2022, 45(5): 1547-1558.

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.

图/表 12

图1

图2

表1

遥感指数特征变量"

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）	$B 、 G 、 R 、 N I R 、 S W I R 1 、 S W I R 2$

表1

图3

图4

表2

图5

表3

表4

图6

表5

图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

基于PSO-PNN模型的喀什噶尔绿洲耕地盐渍化分析

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

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