基于遥感影像的新疆渭干河—库车河三角洲土壤水盐与植被覆盖度的关系

doi:10.13866/j.azr.2024.07.05

Abstract

Abstract:

Based on the soil samples and remote sensing image data, this paper takes the Ugan-Kuqa River Delta Oasis, a typical arid region in Xinjiang, as the study area. FVC was calculated utilizing the pixel dichotomous model. Inverse distance weight interpolation and statistical analysis methods such as correlation and mediating effect analyses were used to study the impact of soil water content and salinity at depths of 0-50 cm on the spatial changes of FVC. This paper can provide a theoretical basis for optimizing resource allocation and formulating ecological restoration measures in arid areas. The results were: (1) FVC showed a west-east decreasing trend in space, directly proportional to the water content and inversely with the salinity. (2) The water content of each soil layer gradually enhanced from top to bottom, and the salinity revealed an inverse trend, with≥moderate variability in water content and salinity at different depths. (3) The soil water content at the 10-20 cm layer alone exhibited a mediating effect between FVC and the water content of the 0-10 cm surface layer. The salinity in the deeper layers of 10-20, 10-30, and 30-50 cm exhibits a mediating effect between FVC and salinity in the 0-10 cm layer. In other words, the salinity in deeper soil layers influenced FVC by affecting the salinity of the surface soil. (4) The salinity in the 0-10 cm layer most significantly impacted the FVC, influencing>47.90% of the spatial variability of FVC. Additionally, the impact of salinity at various soil depths on FVC surpasses that of water content.

Key words: Ugan-Kuqa River Delta Oasis, soil water content, soil salinity, fractional vegetation cover, correlation analysis

ZHENG Liuna, JIANG Hongnan, SUN Mengting. Correlations between soil water content, salinity, and fractional vegetation cover in the Ugan-Kuqa River Delta Oasis of Xinjiang ascertained based on remote sensing images[J].Arid Zone Research, 2024, 41(7): 1131-1139.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 1

Tab. 2

Tab. 3

Tab. 4

Tab. 5

References 20

[1]	Liu Y W, Shen X J, Zhang J Q, et al. Temporal and spatial variation in vegetation coverage and its response to climatic change in marshes of Sanjiang Plain, China[J]. Atmosphere, 2022, 13(12): 2077.
[2]	Li M L, Qin Y B, Zhang T B, et al. Climate change and anthropogenic activity Co-Driven vegetation coverage increase in the Three-North Shelter Forest Region of China[J]. Remote Sensing, 2023, 15(6): 1509.
[3]	Xu Y M, Zhuang Q L. The importance of interactions between snow, permafrost and vegetation dynamics in affecting terrestrial carbon balance in circumpolar regions[J]. Environmental Research Letters, 2023, 18(4): 044007.
[4]	Trautmann T, Koirala S, Carvalhais N, et al. The importance of vegetation in understanding terrestrial water storage variations[J]. Hydrology and Earth System Sciences, 2022, 26(4): 1089-1109.
[5]	高瑜莲, 柳锦宝, 柳维扬, 等. 近14 a新疆南疆绿洲地区地表蒸散与干旱的时空变化特征研究[J]. 干旱区地理, 2019, 42(4): 830-837.
	[Gao Yulian, Liu Jinbao, Liu Weiyang, et al. Spatio-temporal variation characteristics of surface evapotranspiration and drought in the oasis area in recent 14 years, the southern Xinjiang[J]. Arid Land Geography, 2019, 42(4): 830-837.]
[6]	Xin Z M, Feng W, Zhan H B, et al. Atmospheric vapor impact on desert vegetation and desert ecohydrological system[J]. Plants, 2023, 12(2): 223.
[7]	Huang K D, Xu C, Qian Z Z, et al. Effects of pruning on vegetation growth and soil properties in poplar plantations[J]. Forests, 2023, 14(3): 501.
[8]	Zhao W J, Zhou C, Zhou C Q, et al. Soil salinity inversion model of oasis in arid area based on UAV multispectral remote sensing[J]. Remote Sensing, 2022, 14(8): 1804.
[9]	Chen Y W, Du Y Y, Yin H Y, et al. Radar remote sensing-based inversion model of soil salt content at different depths under vegetation[J]. PeerJ, 2022, 10: e13306.
[10]	康满萍, 赵成章, 白雪, 等. 苏干湖湿地植被覆盖度时空变化格局[J]. 生态学报, 2020, 40(9): 2975-2984.
	[Kang Manping, Zhao Chengzhang, Bai Xue, et al. The temporal and spatial variation pattern of vegetation coverage in Suganhu wetland[J]. Acta Ecologica Sinica, 2020, 40(9): 2975-2984.]
[11]	Tang K S, Wulan T Y, Wu Z F, et al. Correlation between vegetation coverage and thickness of chestnut soil layer in typical grassland based on multisource satellite remote sensing[J]. Mobile Information Systems, 2022, 2022: 1116781.
[12]	Wang F, Chen Y N, Li Z, et al. Assessment of the irrigation water requirement and water supply risk in the Tarim river Basin, Northwest China[J]. Sustainability, 2019, 11(18): 4941.
[13]	江红南. 新疆不同区域土壤盐渍化光学遥感定量监测研究[D]. 武汉: 武汉大学, 2018.
	[Jiang Hongnan. Quantitative Monitoring on Soil Salinization Utilizing Optical Remote Sensing in Different Regions of Xinjiang, China[D]. Wuhan: Wuhan University, 2018.]
[14]	何宝忠, 丁建丽, 刘博华, 等. 渭库绿洲土壤盐渍化时空变化特征[J]. 林业科学, 2019, 55(9): 185-196.
	[He Baozhong, Ding Jianli, Liu Bohua, et al. Spatiotemporal variation of soil salinization in Weigan-Kuqa River Delta Oasis[J]. Scientia Silvae Sinicae, 2019, 55(9): 185-196.]
[15]	Guo B, Han F, Jiang L. An improved dimidiated pixel model for vegetation fraction in the Yarlung Zangbo River Basin of Qinghai-Tibet Plateau[J]. Journal of the Indian Society of Remote Sensing, 2018, 46(2): 219-231.
[16]	Yan K, Gao S, Chi H, et al. Evaluation of the vegetation-index-based dimidiate pixel model for fractional vegetation cover estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-14.
[17]	李苗苗. 植被覆盖度的遥感估算方法研究[D]. 北京: 中国科学院研究生院(遥感应用研究所), 2003.
	[Li Miaomiao. The Method of Vegetation Fraction Estimation by Remote Sensing[D]. Beijing: Graduate University of Chinese Academy of Sciences, 2003.]
[18]	边慧芹. 渭干河-库车河三角洲绿洲植被覆盖度与土壤盐渍化响应关系研究[D]. 乌鲁木齐: 新疆师范大学, 2020.
	[Bian Huiqin. Study on the Relationship Between Vegetation Coverage and Soil Salinization Response in Weigan-Kuche River Delta Oasis in Xinjiang, China[D]. Urumqi: Xinjiang Normal University, 2020.]
[19]	温忠麟, 叶宝娟. 中介效应分析: 方法和模型发展[J]. 心理科学进展, 2014, 22(5): 731-745. doi: 10.3724/SP.J.1042.2014.00731
	[Wen Zhonglin, Ye Baojuan. Analyses of mediating effects: The development of methods and models[J]. Advances in Psychological Science, 2014, 22(5): 731-745.] doi: 10.3724/SP.J.1042.2014.00731
[20]	Howard J H, Baldwin R F, Brown B L. Exploratory analysis for complex-life-cycle amphibians: Revealing complex forest-reproductive effort relationships using redundancy analysis[J]. Forest Ecology and Management, 2012, 270: 175-182.

年份	不同土层	土壤含水量/%				变异系数/%	土壤含盐量/（g·kg^-1）				变异系数/%
年份	不同土层	最小值	最大值	均值	标准差	变异系数/%	最小值	最大值	均值	标准差	变异系数/%
2011年	0~10 cm	0.05	37.62	7.26	8.01	110.33	0.10	224.4	51.65	54.45	105.42
	10~30 cm	1.02	47.90	15.68	8.65	55.17	0.10	27.90	5.90	5.26	89.15
	30~50 cm	1.55	30.08	16.05	7.75	48.29	0.10	25.70	4.31	4.36	101.16
2021年	0~10 cm	0.61	23.99	10.73	6.51	60.68	0.10	69.70	21.56	21.25	98.55
2021年	10~20 cm	6.23	30.15	16.70	5.84	35.00	0.10	23.40	5.86	5.02	85.67

变量	2011年FVC	2021年FVC
0~10 cm土壤含水量	0.394^**	0.488^**
0~10 cm土壤含盐量	-0.587^**	-0.584^**

年份	影响因素	对数		二次多项式回归模型		三次多项式回归模型
年份	影响因素	R²	P值	R²	P值	R²	P值
2011年	0~10 cm土壤含水量	0.074	0.055	0.180	0.009^**	0.254	0.003^**
2011年	0~10 cm土壤含盐量	0.543	0.000^**	0.457	0.000^**	0.519	0.000^**
2021年	0~10 cm土壤含水量	0.160	0.000^**	0.255	0.000^**	0.255	0.000^**
2021年	0~10 cm土壤含盐量	0.479	0.000^**	0.401	0.000^**	0.439	0.000^**

2011年			2021年
控制变量	实验变量	偏相关系数	控制变量	实验变量	偏相关系数
0~10 cm土壤含水量	r_{10~30 cm土壤含水量、FVC 0~10 cm土壤含水量}	-0.025	0~10 cm土壤含水量	r_{10~20 cm土壤含水量、FVC 0~10 cm土壤含水量}	0.119
0~10 cm土壤含水量	r_{30~50 cm土壤含水量、FVC 0~10 cm土壤含水量}	-0.210	0~10 cm土壤含盐量	r_{10~20 cm土壤含盐量、FVC 0~10 cm土壤含盐量}	-0.282^*
0~10 cm土壤含盐量	r_{10~30 cm土壤含盐量、FVC 0~10 cm土壤含盐量}	-0.288^*
0~10 cm土壤含盐量	r_{30~50 cm土壤含盐量、FVC 0~10 cm土壤含盐量}	-0.205

2011年				2021年
检验步骤	标准化方程	自变量	P值	检验步骤	标准化方程	自变量	P值
第一步	FVC=0.135W₃₀	W₃₀	0.349	第一步	FVC=0.403W₂₀	W₂₀	0.000^**
第二步	W₁₀=0.397W₃₀	W₃₀	0.004^**	第二步	W₁₀=0.665W₂₀	W₂₀	0.000^**
第三步	FVC=-0.025W₃₀+0.405W₁₀	W₃₀	0.863	第三步	FVC=0.140W₂₀+0.396W₁₀	W₂₀	0.301
第三步		W₁₀	0.008^**			W₁₀	0.004^**
第一步	FVC=0.019W₅₀	W₅₀	0.896	第一步	FVC=-0.561S₂₀	S₂₀	0.000^**
第二步	W₁₀=0.478W₅₀	W₅₀	0.00^**	第二步	S₁₀=0.671S₂₀	S₂₀	0.000^**
第三步	FVC=-0.220W₅₀+0.499W₁₀	W₅₀	0.148	第三步	FVC=-0.308S₂₀-0.377S₁₀	S₂₀	0.014^*
第三步		W₁₀	0.002^**			S₁₀	0.003^**
第一步	FVC=-0.571S₃₀	S₃₀	0.000^**
第二步	S₁₀=0.682S₃₀	S₃₀	0.000^**
第三步	FVC=-0.319S₃₀-0.370S₁₀	S₃₀	0.045^*
第三步		S₁₀	0.021^*
第一步	FVC=-0.501S₅₀	S₅₀	0.000^**
第二步	S₁₀=0.624S₅₀	S₅₀	0.000^**
第三步	FVC=-0.210S₅₀-0.468S₁₀	S₅₀	0.163
		S₁₀	0.003^**

Correlations between soil water content, salinity, and fractional vegetation cover in the Ugan-Kuqa River Delta Oasis of Xinjiang ascertained based on remote sensing images

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 20

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZHANG Hongwei, BIE Qiang, SHI Ying, SU Xiaojie, LI Xinzhang. Characteristics of vegetation cover changes in the upper reaches of the Yellow River Basin and the influencing factors [J]. Arid Zone Research, 2024, 41(8): 1385-1394.
[2]	LONG Weiyi, SHI Jianfei, LI Shuangyuan, SUN Jinjin, WANG Yugang. Evaluation of multimodel inversion effects on soil salinity in oasis basin [J]. Arid Zone Research, 2024, 41(7): 1120-1130.
[3]	PEI Zhilin, CAO Xiaojuan, WANG Dong, LI Di, WANG Xin, BAI Aiyuan. Spatiotemporal variation in vegetation coverage in Inner Mongolia and its response to human activities [J]. Arid Zone Research, 2024, 41(4): 629-638.
[4]	SHAO Jie, YANG Xinjie, CHEN Xiqing, TENG Chao, YI Jinjun, DONG Meiling, ZHANG Zechen, CAO Jun, ZHU Ning, XIAO Deng, SUN Siyuan, LYU Fei. Hydrochemical characteristics and control factors of surface water in the Yigong Lake Basin, Tibet [J]. Arid Zone Research, 2024, 41(2): 250-260.
[5]	ZHAO Yuqi, WEI Tianxing. Changes in vegetation cover and influencing factors in typical counties of the Loess Plateau from 1990 to 2020 [J]. Arid Zone Research, 2024, 41(1): 147-156.
[6]	SHI Jianzhou, LIU Xiande, TIAN Qing, YU Pengtao, WANG Yanhui. Rainfall response of soil water content on a slope of Larix principis-rupprechtii plantation in the semi-arid Liupan Mountains [J]. Arid Zone Research, 2023, 40(4): 594-604.
[7]	SUN Guanfang, GAO Zhaoliang, ZHU Yan, YANG Jinzhong, QU Zhongyi. Spatio-temporal patterns of soil salinity in Hetao Irrigation District based on spatio-temporal Kriging [J]. Arid Zone Research, 2023, 40(2): 182-193.
[8]	YANG Hang, HOU Jingwei, MA Caihong, YANG Chen, WANG Yanjuan. Spatio-temporal differentiation of the composite ecosystem resilience in the ecologically fragile area in the upper reaches of the Yellow River: A case study in Ningxia [J]. Arid Zone Research, 2023, 40(2): 303-312.
[9]	HUI Rong, TAN Huijuan, HUANG Lei, LI Xinrong. Characteristics of nutrient and enzyme activity in salt-affected soils of the Qaidam Basin [J]. Arid Zone Research, 2023, 40(11): 1776-1784.
[10]	NIU Zilu, WANG Lei, QI Tuoye, ZHANG Yijing, SHEN Jianxiang, YANG Zhuqing, WANG Entian, JIANG Shuting. Soil salinization characteristics in irrigation region of Yellow River of Hongsipu, Ningxia [J]. Arid Zone Research, 2023, 40(11): 1785-1796.
[11]	LIU Xin, HAO Yuanyuan, HUA Limin. Spatial differentiation characteristics of soil salinity in Minqin Basin, downstream of Shiyang River, China [J]. Arid Zone Research, 2023, 40(10): 1615-1624.
[12]	YANG Hui,ZHANG Ze,ZHANG Lan,YAN Xingfu. Responses of seed germination of Caragana korshinskii to different temperatures and soil water content [J]. Arid Zone Research, 2022, 39(6): 1875-1884.
[13]	LI Zehou,LI Ruixi,ZHANG Shubin,WANG Chongbin,ZHENG Mingming,DONG Yeqing,WU Xue. Responses of leaf structural and chemical trait of Tamarix ramosissima to soil water changes [J]. Arid Zone Research, 2022, 39(5): 1486-1495.
[14]	YAO Jia,CHEN Qihui,LI Qiongfang,CUI Gang,ZHANG Liangjing. Spatial and temporal variability of evapotranspiration and influencing factors in the Ili River-Balkhash Lake Basin [J]. Arid Zone Research, 2022, 39(5): 1564-1575.
[15]	LIU Lei,CHEN Junfeng,LYU Pengpeng,ZHAO Dexing,DU Qi. Variation in soil salinity and ions in a shallow groundwater area under freeze-thaw [J]. Arid Zone Research, 2022, 39(2): 448-455.