Land

Land use/cover (LULC) type change is one of the important causes of global change and the imbalance of the carbon cycle. Investigating the temporal variation in regional soil organic carbon storage (Rsoc) driven by LULC change is of great significance for scientifically guiding sustainable regional land management and facilitating the realization of China’s “dual carbon” objectives. Focusing on the main stream of the Tarim River, based on the LULC data of 1990, 2000, 2010, and 2020, combined with the data of soil organic carbon (SOC) content and soil bulk density, the temporal variation in the LULC and its impact on the Rsoc in the main stream of the Tarim River were analyzed. The results indicate that the LULC exhibited a pattern of “slow change—sharp change—slow change” across the three periods 1990–2000, 2000–2010, and 2010–2020. Grassland (GL) area consistently declined, while other types of LULC fluctuated during the period 1990–2020. The type and area of LULC conversion varied across the three periods: 1990–2000, 2000–2010, and 2010–2020. The area of the GL and bare land (BL) conversion was greater than that of conversion between other LULC in all three periods. The total amount of soil organic carbon (Tsoc) associated with different LULC types in the main stream of the Tarim River varied in 1990, 2000, 2010, and 2020, with the GL contributing the highest SOC levels, and conversion from the BL to GL had the largest increase in the Tsoc for the BL among the three periods, which was 0.20 × 10¹⁰~0.31 × 10¹⁰ kg, 0.97 × 10¹⁰~1.48 × 10¹⁰ kg, and 0.04 × 10¹⁰~0.06 × 10¹⁰ kg during1990–2000, 2000–2010, and 2010–2020 periods, respectively. Overall, the Rsoc in the 0–100 cm soil layer decreased from 2.18 × 10¹⁰ to 2.18 × 10¹⁰ kg during the period 1990–2020 in the main stream of the Tarim River.

Earth Observation increasingly uses machine learning to evaluate and monitor the environment. However, the potential of deep learning for studying wilderness is an under-explored frontier. This study aims to give insights into using different architectures (ResNet18, ResNet50, U-Net, DeepLabV3, and FCN), batch sizes (small, medium, and large), and spectral setups (RGB, RGB+NIR, full spectrum) for the classification and semantic segmentation of Sentinel-2 images. The focus is on optimising performance over accuracy using limited computational resources and pre-trained networks widely from the AI community. Experiments are performed on the AnthroProtect dataset, which was developed explicitly for this purpose. Results show that when computation resources are a concern, ResNet18 with 64 or 256 batch size is an optimal configuration for image classification. The U-Net is a sub-optimal solution for semantic segmentation, but our experiments did not identify a clear optimality for the batch size. Finally, different spectral setups highlight no significant impact on the data processing, thus raising critical thinking on the usefulness of neural networks in Earth Observation that are pre-trained with generic data like ImageNet, which is widely used in the AI community.

Post-harvest forest residue management and liming practices can significantly affect soil quality. This study evaluated the impacts of burnt pine harvest residues and lime application methods (surface-applied vs. incorporated) on the chemical and physical properties of a Dystric Cambisol in Southern Brazil. Soil samples were collected at two depths (0–10 cm and 10–20 cm) and analyzed for pH, exchangeable acidity, organic carbon, cation exchange capacity, macroporosity, microporosity, and bulk density. The results showed that changes were more pronounced in the 0–10 cm layer and mainly affected chemical attributes. Incorporated lime increased pH from 4.7 to 5.1, increased base saturation from 17% to 36%, and reduced Al saturation from 45% to 13% in the 0–10 cm layer. Burnt residues alone did not significantly alter soil properties, whereas lime incorporation led to improved chemical conditions and enhanced soil structure, especially in the surface layer. The treatments that maintained pine residues on the surface favored biological processes in the topsoil, while the burning of these residues had variable impacts on soil structure and nutrient availability. These findings highlight the importance of incorporating lime to optimize soil rehabilitation following pine harvesting in subtropical forest systems.