The essence of the 2030 Agenda for Sustainable Development adopted by the United Nations is described in 17 Sustainable Development Goals (SDGs). SDG 15 focuses on Life on Land, in other words, terrestrial biodiversity and ecosystems, as well as their services. Land degradation is a severe anthropic and natural phenomenon that is affecting land use/cover globally; therefore, a dedicated target of the SDG 15 (the indicator 15.3.1) was proposed. The identification of the areas where land degradation has occurred and the analysis of its drivers allow for the design of solutions to prevent further degradation in the studied areas. We followed the methodology proposed by the United Nations Convention to Combat Desertification (UNCCD) to study the land degradation in the Honghe Prefecture in southwest China between 2005 and 2015. Through spatial analysis, we found that the degraded areas were consistent with the areas of active human activities (such as urban centers), while the impact of natural factors (such as disasters) on land degradation existed in small areas at high altitudes. Land degradation was affected primarily by the loss of land productivity and secondly by land cover changes caused by the growth of artificial areas. Changes in the soil organic carbon were not significant. We concluded that human activity was the main driver of land degradation in Honghe Prefecture. Decision makers should work to find a balance between economic development and environmental protection to restore degraded land and strive to achieve a land degradation-neutral prefecture to defend all ecosystem services.

Sea ice concentration (SIC) is an important dimension for characterising the geographical features of the pan-Arctic region. Trends in SIC bring new opportunities for human activities in the Arctic region. In this paper, we propose a deep learning technology-based sea ice concentration prediction model, SICFormer, which can realise end-to-end daily sea ice concentration prediction. Specifically, the model uses a 3D-Swin Transformer as an encoder and designs a decoder to reconstruct the predicted image based on PixelShuffle. This is a new model architecture that we have proposed. Single-day SIC data from the National Snow and Ice Data Center (NSIDC) for the years 2006 to 2022 are utilised. The results of 8-day short-term prediction experiments show that the average Mean Absolute Error (MAE) of the SICFormer model on the test set over the 5 years is 1.89%, the Root Mean Squared Error (RMSE) is 5.99%, the Mean Absolute Percentage Error (MAPE) is 4.32%, and the Nash–Sutcliffe Efficiency (NSE) is 0.98. Furthermore, the current popular deep learning models for spatio-temporal prediction are employed as a point of comparison given their proven efficacy on numerous public datasets. The comparison experiments show that the SICFormer model achieves the best overall performance.

In the context of global warming, the accurate prediction of Arctic Sea Ice Concentration (SIC) is crucial for the development of Arctic shipping routes. We have therefore constructed a lightweight, non-recursive spatio-temporal prediction model, the Spatio-Temporal Decomposition Network (STDNet), to predict the daily SIC in the Arctic. The model is based on the Seasonal and Trend decomposition using Loess (STL) decomposition idea to decompose the model into trend and seasonal components. In addition, we have designed the Global Sparse Attention Module (GSAM) to help the model extract global information. STDNet not only extracts seasonal signals and trend information with periodical correspondence from the data but also obtains the spatio-temporal dependence features in the data. The experimental methodology involves predicting the next 10 days based on the first 10 days of data. The prediction results provided the following metrics for the 10-day forecast of STDNet: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination of 1.988%, 3.541%, 5.843%, and 0.979, respectively. The average Binary Accuracy (BACC) at the beginning of September for the period 2018–2022 reached 93.85%. The proposed STDNet model outperforms and is lighter than existing deep-learning-based SIC prediction models.

Regional image coverage retrieval is a critical problem in large-scale remote sensing data processing. However, traditional vector topology-based methods suffer from rapidly increasing computational costs when handling massive and highly overlapping datasets. This paper proposes a coverage retrieval approach based on the Discrete Global Grid System (DGGS), which transforms geometric topological operations into grid-encoding set operations, thereby reconstructing the coverage computation process under a discrete spatial indexing framework. A heuristic greedy strategy is integrated to achieve efficient coverage selection. Experimental results demonstrate that the proposed DGGS-based method achieves speedups ranging from approximately 1.5× to 11×, depending on dataset scale and coverage. Grid-level analysis indicates that level 7 grids generally provide a favorable balance between spatial accuracy and computational efficiency for near-complete coverage retrieval, whereas level 6 grids offer a more computationally efficient alternative for rapid coverage estimation and sparse-coverage scenarios, with only marginal accuracy loss. Furthermore, the method exhibits near-linear scalability with increasing data volume and maintains stable performance under incomplete coverage scenarios. The results confirm that DGGS-based discrete modeling significantly reduces computational complexity while preserving spatial reliability, providing an efficient and scalable solution for PB-scale remote sensing data processing.