Research Progress of Remote Sensing Monitoring of Soil Salinization

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.

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Research progress on remote sensing monitoring of phytoplankton in eutrophic water

Journal of Lake Sciences ◽

10.18307/2021.0502 ◽

2021 ◽

Vol 33 (5) ◽

pp. 1299-1314

Author(s):

Lai Lai ◽

◽

Zhang Yuchao ◽

Jing Yuanyuan ◽

Liu Zhaomin

Keyword(s):

Remote Sensing ◽

Research Progress ◽

Remote Sensing Monitoring ◽

Eutrophic Water

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

Sustainability ◽

10.3390/su14020741 ◽

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.

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Remote Sensing Monitoring of Soil Salinization Based on SI-Brightness Feature Space and Drivers Analysis: A Case Study of Surface Mining Areas in Semi-arid Steppe

IEEE Access ◽

10.1109/access.2021.3101873 ◽

2021 ◽

pp. 1-1

Author(s):

Zhenhua Wu ◽

Qingwu Yan ◽

Shutao Zhang ◽

Shaogang Lei ◽

Qingqing Lu ◽

...

Keyword(s):

Remote Sensing ◽

Surface Mining ◽

Feature Space ◽

Soil Salinization ◽

Remote Sensing Monitoring ◽

Mining Areas ◽

Arid Steppe ◽

Semi Arid

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Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17061846 ◽

2020 ◽

Vol 17 (6) ◽

pp. 1846 ◽

Cited By ~ 4

Author(s):

Wen Song ◽

Wei Song ◽

Haihong Gu ◽

Fuping Li

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Pollution Monitoring ◽

Atmospheric Environment ◽

Research Progress ◽

Ecological Environment ◽

Extensive Literature ◽

Remote Sensing Monitoring ◽

Mining Areas

Based on the results of an extensive literature research, we summarize the research progress of remote sensing monitoring in terms of identifying mining area boundaries and monitoring land use or land cover changes of mining areas. We also analyze the application of remote sensing in monitoring the biodiversity, landscape structure, vegetation change, soil environment, surface runoff conditions, and the atmospheric environment in mining areas and predict the prospects of remote sensing in monitoring the ecological environment in mining areas. Based on the results, the accurate classification of land use or land cover and the accurate extraction of environmental factors are the basis for remote sensing monitoring of the ecological environment in mining areas. In terms of the extraction of ecological factors, vegetation extraction is relatively advanced in contrast to the extraction of animal and microbial data. For the monitoring of environmental conditions of mining areas, sophisticated methods are available to identify pollution levels of vegetation and to accurately monitor soil quality. However, the methods for water and air pollution monitoring in mining areas still need to be improved. These limitations considerably impede the application of remote sensing monitoring in mining areas. The solving of these problems depends on the progress of multi-source remote sensing data and stereoscopic monitoring techniques.

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