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Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information. [ABSTRACT FROM AUTHOR]

In grassland open-pit mining areas, net primary productivity (NPP) is mainly affected by climate conditions and human activities. The identification and assessment of the influence of human activities on NPP is important for mining production and the implementation of ecological restoration. In this study, we explored the influence of human activities on the NPP in the Shengli mining area in Inner Mongolia, China by using the Carnegie–Ames–Stanford Approach (CASA) model and the Chikugo model, in which a calibration method was applied. An analysis of four representative years showed that the proportion of NPP induced by human activities reached 56.2%, that the percentage of pixels with an inhibitory effect on NPP was 99% in 2011 with the highest intensity of mining activity, and that these two values decreased to 11.9% and 69% in 2020, respectively, with the steady implementation of ecological restoration. Moreover, from the analysis of global and local spatial correlation, mining activities and ecological restoration aggravated and weakened the aggregation of NPP induced by human activities, respectively. [ABSTRACT FROM AUTHOR]