scholarly journals Remote Sensing Monitoring and Driving Force Analysis of Salinized Soil in Grassland Mining Area

2022 ◽  
Vol 14 (2) ◽  
pp. 741
Author(s):  
Zhenhua Wu ◽  
Mingliang Che ◽  
Shutao Zhang ◽  
Linghua Duo ◽  
Shaogang Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Salt Content ◽  
Feature Space ◽  
Mining Area ◽  
Soil Salinization ◽  
Driving Factors ◽  
Remote Sensing Monitoring ◽  
Saline Land ◽  
Semi Arid ◽  
Salinized Soil

To deal with the problem of soil salinization that exists widely in semi-arid grassland, the Shengli Coalfield in Xilinhot City was selected as the study area. Six periods of Landsat remote sensing data in 2002, 2005, 2008, 2011, 2014, and 2017 were used to extract the salinity index (SI) and surface albedo to construct the SI-Albedo feature space. The salinization monitoring index (SMI) was used to calculate and classify the soil salinization grades in the study area. The soil salinization status and its dynamic changes were monitored and analyzed. Combined with the logistic regression model, the roles of human and natural factors in the development of soil salinization were determined. The results were as follows: (1) The SMI index constructed using the SI-Albedo feature space is simple and easy to calculate, which is conducive to remote sensing monitoring of salinized soil. R2 of the SMI and soil salt content in the 2017 data from the study area is 0.7313, which achieves good results in the quantitative analysis and monitoring of soil salinization in the Xilinhot Shengli Coalfield. (2) The study area is a grassland landscape. However, grassland landscapes are decreasing year by year, and town landscapes, mining landscapes, and road landscapes are greatly increased. The areas of soil salinization reversion in the Shengli mining area from 2002–2005, 2005–2008, 2008–2011, 2011–2014, 2014–2017, and 2002–2017 were 65.64 km2, 1.03 km2, 18.44 km2, 0.9 km2, 7.52 km2, and 62.33 km2, respectively. The overall trend of soil salinization in the study area was reversed from 2002 to 2017. (3) The driving factors of salinized land from 2002 to 2008 are as follows: the distance to the nearest town landscape > the distance to the nearest mining landscape > the distance to the nearest road landscape. The driving factors of salinized land from 2008 to 2017 are as follows: the distance to nearest mining landscape > the distance to the nearest water landscape > the distance to nearest town landscape > altitude > aspect. Coal exploitation and town expansion have occupied a large amount of saline land, and petroleum exploitation and abandoned railway test sites have intensified the development of saline land. This study provides a reference for the treatment and protection of soil salinization in semi-arid grassland mining areas.

scholarly journals Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia

2020 ◽  
Vol 6 (4) ◽  
pp. 2487-2493 ◽  
Author(s):  
Hazem T. Abd El-Hamid ◽  
Guan Hong
Keyword(s):  
Remote Sensing ◽  
Spectral Data ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Salt Content ◽  
Soil Salinization ◽  
Northern Region ◽  
Semiarid Region ◽  
First Order ◽  
Remote Sensing Monitoring

Abstract Soil salinization affects negatively on agricultural productivity in the semiarid region of Ningxia. In this study, the performance of inversion model to determine soil salinization was assessed using some analysis and reflectance of wavelength. About 42 vegetation samples and 42 soil samples were collected for model extraction. Hyper-spectral data processing method was used to analyze spectral characteristics of different levels of salinization area vegetation. Spectral data were transformed in 16 different approaches, including root mean squares, logarithm, inversion logarithm, and first-order differentiation. After the transformation, the obtained soil and vegetation characteristics spectra correlate well with soil salt content, built soil index, and many vegetation indices. Nonlinear regression was employed to establish soil salinization remote sensing monitoring model. By comparing various spectral transformations, the first-order differential of soil spectral was the most sensitive to soil salinization degrees. The model of the current research was based on salinity index (SI) and improved soil-adjusted vegetation index (MSAVI). The correlation between simulated values and measured values was 0.758. Therefore, remote sensing monitoring derived from MSAVI–SI can greatly improve the dynamic and periodical monitoring of soil salinity in the study area.

scholarly journals Spatiotemporal Changes of Soil Salinization in the Yellow River Delta of China from 2015 to 2019

2021 ◽  
Vol 13 (2) ◽  
pp. 822
Author(s):  
Lingling Bian ◽  
Juanle Wang ◽  
Jing Liu ◽  
Baomin Han
Keyword(s):  
Vegetation Index ◽  
Yellow River ◽  
Salt Content ◽  
Feature Space ◽  
Yellow River Delta ◽  
Soil Salinization ◽  
River Delta ◽  
The Yellow River ◽  
The Yellow River Delta ◽  
Soil Salt Content

Soil salinization poses a significant challenge for achieving sustainable utilization of land resources, especially in coastal, arid, and semi-arid areas. Timely monitoring of soil salt content and its spatial distribution is conducive to secure efficient agricultural development in these regions. In this study, to address the persistent problem of soil salinization in the Yellow River Delta in China, the feature space method was used to construct multiple feature spaces of surface albedo (Albedo)–modified soil-adjusted vegetation index (MSAVI), salinity index (SI)–Albedo, and SI–normalized difference vegetation index (NDVI), and an optimal inversion model of soil salinity was developed. Based on Landsat 8 Operational Land Imager (OLI) image data and simultaneous field-measured sampling data, an optimal model from 2015 to 2019 was used to obtain the soil salt content in the region at a 30 m resolution. The results show that the proportion of soil salinization in 2015 and 2019 was approximately 76% and 70%, respectively, and overall soil salinization showed a downward trend. The salinization-mitigated areas are primarily distributed in the southwest of the Yellow River Delta, and the aggravated areas are distributed in the northeast and southeast. In general, the spatial variation characteristics show an increasing trend from the southwest to the eastern coastal areas, corresponding to the formation mechanism of salt accumulation in the region. Further, corresponding sustainable development countermeasures and suggestions were proposed for different salinity levels. Meanwhile, this study revealed that the SI–Albedo feature space model is the most suitable for inversion of salinization in coastal areas.

Sign in / Sign up

Export Citation Format

Share Document