Underground mining activity has existed for more than 100 years in Nansi lake. Coal mining not only plays a supporting role in local social and economic development but also has a significant impact on the ecological environment in the region. Landsat series remote sensing data (1988~2019) are used to research the impact of coal mining on the ecological environment in Nansi lake. Then Support Vector Machine (SVM) classifier is applied to extract the water area of the upstream lake from 1988 to 2019, and ecological environment and spatiotemporal variation characteristics are analyzed by Remote Sensing Ecology Index (RSEI). The results illustrate that the water area change is associated with annual precipitation. Compared with 2009, the ecological quality of the lake is worse in 2019, and then the reason for this change is due to large-scale underground mining. Therefore, the coal mines from the natural reserve may be closed or limited to the mining boundary for protecting the lake's ecological environment.

Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R2 value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands.

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Underground mining activity has existed for more than 100 years in Nansi lake. Coal mining not only plays a supporting role in local social and economic development but also has a significant impact on the ecological environment in the region. Landsat series remote sensing data (1988~2019) are used to research the impact of coal mining on the ecological environment in Nansi lake. Then Support Vector Machine (SVM) classifier is applied to extract the water area of the upstream lake from 1988 to 2019, and ecological environment and spatiotemporal variation characteristics are analyzed by Remote Sensing Ecology Index (RSEI). The results illustrate that the water area change is associated with annual precipitation. Compared with 2009, the ecological quality of the lake is worse in 2019, and then the reason for this change is due to large-scale underground mining. Therefore, the coal mines from the natural reserve may be closed or limited to the mining boundary for protecting the lake's ecological environment.

This paper discusses knowledge discovery and feature extraction on the basis of traditional classification method of the remote sensing picture. Multi-level classification method is used in monitoring of mining area land cover. This method is superior to the classifying device of single routine. In this paper, Kailuan mining area land cover change is obtained by using multi-level classification method dealing with Kailuan mining area remote sensing images.

After the mining subsidence area reaches the stable state of surface movement, due to the continuous existence of the mutual extrusion and activation state between rock strata, the movement of particles, rock creep, and soil compression will affect the surface movement in the long-term stability, thereby threatening the safety and stability of the above ground buildings. In order to study the deformation law of rock strata movement and surface displacement after the mining subsidence area is stabilized, establish a ground deformation model caused by mining, explore the characteristics of ground movement and deformation under the action of building loads, design a similar material simulation experiment, record the ground movement changes within 378 days after the mining is completed, after the settlement is stabilized, apply equivalent loads of 10–30 floors to the model at different locations, and study various surface deformation data, Analyze the change rule. In order to quantitatively analyze the impact of residual deformation of goaf on buildings under building load, the D5 gate area of Tangshan World Horticultural Exposition in mining subsidence area is taken as an example to calculate the ground settlement value and other deformation data using probability analysis method, and the Kelvin model in rock mechanics is introduced in terms of the duration of residual deformation. The calculation results are close to the actual measured values, and the impact of residual deformation on the proposed building is analyzed.

In this paper, in view of the scope of coal mining subsidence, the magnitude of subsidence deformation and the impact on buildings (structures), InSAR technology is selected for monitoring, time series analysis method combined with D-InSAR method for processing, and Sentinel- 1 Satellite IW data and RADARSAT-2 satellite XF data have been monitored for four years in the mining-affected area and its surrounding areas. The results show that there are obvious subsidence ranges on the surface of the current mining area and the unstable settlement area. The Shen District has been in a stable state in recent years, and the time sequence analysis for more than 3 years has verified the high accuracy and reliability of the monitoring results. The analysis of the monitoring results shows that the InSAR technology can be used for mining subsidence monitoring, especially when determining the subsidence boundary and whether it is stable or not, it has its technical superiority.