Net ecosystem productivity (NEP) plays a vital role in quantifying the carbon exchange between the atmosphere and terrestrial ecosystems. Understanding the effects of dominant driving forces and their respective contribution rates on NEP can aid in the effective management of terrestrial carbon sinks, especially in rapidly urbanizing coastal areas where climate change (CC) and human activities (HA) occur frequently. Combining MODIS NPP products and meteorological data from 2000 to 2020, this paper established a Modis NPP-Soil heterotrophic respiration (R_h) model to estimate the magnitude of NEP in China’s coastal zone (CCZ). Hotspot analysis, variation trend, partial correlation, and residual analysis were applied to explore the spatiotemporal patterns of NEP and the contributions of CC and HA to the dynamics of NEP. We also explored the changes in NEP in different land use types. It was found that there is a clear north–south difference in the spatial pattern of NEP in CCZ, with Zhejiang Province serving as the main watershed for this difference. In addition, NEP in most regions showed an improvement trend, especially in the Beijing–Tianjin–Hebei region and Shandong Province, but the pixel values of NEP here were generally not as high as that in most southern provinces. According to the types of driving forces, the improvement of NEP in these regions primarily results from the synergistic effects of CC and HA. NEP changes in provinces south of Zhejiang are mainly dominated by single-factor-driven degradation. The area where HA contributes to the increase in NEP is much larger than that of CC. From the perspective of land use types, forests and farmland are the dominant contributors to the magnitude of NEP in CCZ.

Pre-trained foundation models, trained on large-scale datasets, have demonstrated significant success in a variety of downstream vision tasks. Parameter-efficient fine-tuning (PEFT) methods aim to adapt these foundation models to new domains by updating only a small subset of parameters, thereby reducing computational overhead. However, the effectiveness of these PEFT methods, especially in the context of forestry remote sensing—specifically for individual tree detection—remains largely unexplored. In this work, we present a simple and efficient PEFT approach designed to transfer pre-trained transformer models to the specific tasks of tree crown detection and species classification in unmanned aerial vehicle (UAV) imagery. To address the challenge of mitigating the influence of irrelevant ground targets in UAV imagery, we propose an Adaptive Salient Channel Selection (ASCS) method, which can be simply integrated into each transformer block during fine-tuning. In the proposed ASCS, task-specific channels are adaptively selected based on class-wise importance scores, where the channels most relevant to the target class are highlighted. In addition, a simple bias term is introduced to facilitate the learning of task-specific knowledge, enhancing the adaptation of the pre-trained model to the target tasks. The experimental results demonstrate that the proposed ASCS fine-tuning method, which utilizes a small number of task-specific learnable parameters, significantly outperforms the latest YOLO detection framework and surpasses the state-of-the-art PEFT method in tree detection and classification tasks. These findings demonstrate that the proposed ASCS is an effective PEFT method, capable of adapting the pre-trained model’s capabilities for tree crown detection and species classification using UAV imagery.

In light of international climate agreements and the Sustainable Development Goals (SDGs), there is a growing need to enhance the understanding of the linkages among land use/cover change (LUCC) and its carbon effects (CEs), as well as human well-being (HW). While existing studies have primarily focused on the impacts of LUCC on CEs or ecosystem services, there remains a gap in systematically elucidating the complex relationships among LUCC, CEs, and HW. This paper presents a comprehensive review of the nexus between land-based CEs and HW, examining: (1) the correlation between LUCC and CEs, encompassing methodologies for investigating LUCC CEs; (2) the association between CEs and HW, introducing the concept of “low-carbon human well-being” and evaluation framework; and (3) the proposed framework of “LUCC-CEs-HW,” which delves into the intricate connections among three elements. The study identifies research gaps and outlines potential future directions, including assessments of LUCC CEs and low-carbon HW, exploration of the “LUCC-CEs-HW” nexus, and the development of standardized measurement approaches. Key opportunities for further investigation include establishing a unified evaluation index system and developing scalable methods. This paper elucidates the relationships among LUCC, CEs, and HW, offering insights for future works.

As a natural event that poses a serious threat to human life, property, and the natural ecology, the effective identification, assessment, and early prevention of geological hazards are crucial. Hunan Province in China is a region with a high incidence of geological hazards, exhibiting complex chain-generated characteristics due to the influence of terraced topography, heavy rainfall, and human activities. Existing landslide monitoring methods have insufficient ability to capture weak deformation at small spatial scales, making it challenging to identify landslide disaster precursors in this region effectively. This paper proposes a multi-resolution SAR collaborative monitoring method using SBAS-InSAR technology for wide-area screening, followed by a joint PS/DS-InSAR processing framework to identify weak deformation signals at small spatial scales. Using 2441 registered geohazard sites in the work area as the background dataset, wide-area InSAR monitoring and remote-sensing interpretation delineated 180 suspected geohazard target areas. Field investigation confirmed 83 of the 180 candidate target zones as active hidden-danger points, corresponding to a field-confirmed rate of 46.11% among the interpreted candidates.