The freeze‐thaw (FT) erosion is a type of soil erosion like water erosion and wind erosion. Limited by many factors, the grading evaluation of soil FT erosion quantities is not well studied. Based on the comprehensive analysis of the evaluation indices of soil FT erosion, we for the first time utilized the sensitivity of microwave remote sensing technology to soil moisture for identification of FT state. We established an estimation model suitable to evaluate the soil FT erosion quantity in Silingco watershed wetland of Northern Tibet using weighted summation method of six impact factors including the annual FT cycle days, average diurnal FT phase‐changed water content, average annual precipitation, slope, aspect, and vegetation coverage. Finally, with the support of GIS, we classified soil FT erosion quantity in Silingco watershed wetland. The results showed that soil FT erosion are distributed in broad areas of Silingco watershed wetland. Different soil FT erosions with different intensities have evidently different spatial and geographical distributions.

Vegetation is essential for maintaining urban ecosystems, climate regulation, and resident health. To explore the variations in city-level vegetation greening (VG) and its relationship to urban expansion, VG in 439 Chinese cities was extracted using the Theil–Sen and Mann–Kendall algorithms based on Moderate Resolution Imaging Spectroradiometer EVI (enhanced vegetation index) data from 2001 to 2020. The spatial variations in VG and its patterns, as well as its relationship with urban expansion, were then analyzed. The following results were obtained: (1) cities with larger greening areas were primarily located in the central and eastern provinces of China, followed by the southeastern, southwestern, and western provinces. The 48 cities with the largest greening areas accounted for 60.47% of the total greening area. (2) VG patches in northern China exhibited better integrity. (3) The centralization trend of VG was evident; the location of VG patterns was influenced by the form of urban expansion. (4) The intensity of artificial impervious area expansion had a weak negative correlation with the VG. Therefore, we must enhance vegetation in new urban areas to improve the spatial balance of VG. The present results of this study can provide a foundation for developing effective policies for the construction and management of urban greenery projects.

Stipa purpurea is the representative type of alpine grassland in Tibet and the surviving and development material for herdsmen. This paper takes Shenzha County as the research area. Based on the analysis of typical hyperspectral variables sensitive to chlorophyll content of Stipa purpurea, 10 spectral variables with significant correlation with chlorophyll were extracted. The estimation model of chlorophyll was established. The photosynthetic pigment contents in the Shenzha area were calculated by using HJ-1A remote sensing images. The results show that there are significant correlations between chlorophyll content and spectral variables; in particular, the coefficient of Chlb in Stipa purpurea with RVI is the largest (0.728); 10 variables are correlated with chlorophyll, and the order of correlation is Chlb > Chla > Chls; for the estimation of Chla, the EVI is the best variable. RVI, NDVI, and VI2 are suitable for Chlb; RVI and NDVI are also suitable for the estimation of Chls; the mean estimated content of Chla in Stipa bungeana is about 4.88 times that of Chlb, while Cars is slightly more than Chlb; the distribution of Chla is opposite to Chlb and Chls content in water area.

Identifying flow patterns from massive trajectories of car tourists is considered a promising way to improve the management of tourism traffic. Previous researches have mainly focused on tourist movements at the macro-scale, such as inbound, domestic, and urban tourism using flow maps. Compared with modeling the flow patterns of tourists at the macro-scale, modeling tourist flow at the microscale is more complicated. This paper takes Dapeng Island located in Shenzhen as the study area and uses the car recognition devices to collect traffic flow. Firstly, car tourists are separated from the mixed traffic flow after analyzing the spatial-temporal characteristics of tourists and residents. Next, daily graphs of tourist movements between road segments and tourist attractions are constructed. Finally, a frequent subgraph mining algorithm is used to extract the flow patterns of car tourists. The experimental results show that (1) car tourists have obvious preferences in the selection of trip time and tourist attractions; (2) the intercity tourists tend to take multidestination trips rather than a single destination trip in the same type of attractions; (3) car tourists are inclined to park their cars in an easy-to-access place, even if the attractions visited are changed. The main contribution of this paper is to present a new method for discovering the flow patterns of car tourists hidden in massive amounts of license plate data.

This paper examines the multi-scale super-resolution challenge of digital elevation models in remote sensing. A dual-domain multi-scale attention fusion network is proposed, which reconstructs digital elevation image details step-by-step using cascading sub-networks. This model incorporates components like the wavelet guidance and separation module, multi-scale attention fusion blocks, dilated convolutional inception module, and edge enhancement module to improve feature extraction and fusion capabilities. A new loss function is designed to enhance the model’s robustness and stability. Experiments indicate that the proposed model outperforms 15 benchmark models in PSNR, RMSE, MAE, $RMS{E}_{slope}$, and $RMS{E}_{aspect}$ metrics. In HMA data, The proposed model’s PSNR increases by 0.89 dB (~1.81%), and RMSE decreases by 1.22 m (~8.6%) compared to a state-of-the-art model. Compared to EDEM, which has the best elevation index, $RMS{E}_{slope}$ decreases by 0.79$°$ (~16%). Additionally, the effectiveness and contribution of each DDMAFN component were verified through ablation experiments. Finally, on the SRTM dataset, The proposed model demonstrates superior performance even with interpolated degradation.