Remote Sensing

Accurate estimation of forest aboveground carbon (AGC) is crucial for understanding the carbon cycle and formulating climate policies, yet it remains challenging in complex mountainous regions. This study used machine learning framework to estimate the spatiotemporal dynamics of AGC in the Three Parallel Rivers region of China from 2003 to 2024. By integrating China’s National Forest Continuous Inventory (NFCI) data with multispectral satellite imagery, we employed a two-stage feature selection strategy to identify key predictor variables. Among three ensemble algorithms tested, the Random Forest model achieved the optimal performance (R² = 0.74). The results indicated a net increase of 67.05 Tg in total AGC over the two decades, with a spatial pattern characterized by higher densities in the west and north. Geographical Detector analysis revealed that the driving forces were synergistic, with the interaction between temperature and population density exhibiting the most prominent explanatory capacity. This study provides a high-resolution (30 m) benchmark for AGC in a global biodiversity hotspot and underscores the critical role of ecological protection policies in enhancing carbon sequestration, offering valuable insights for managing similar mountain ecosystems worldwide.

Resident Space Objects (RSOs) are a collection of both man-made and natural objects in near-Earth space. Given their large orbital velocities and rapidly increasing quantity, they pose a collision threat to space assets, necessitating better Space Situational Awareness (SSA). SSA begins with detecting these objects in the first place and can be accomplished by using space-based optical images, such as images from the Fast Auroral Imager (FAI) on the CASSIOPE satellite. However, these short-exposure images are low in resolution and contain various artifacts and noise, posing challenges to traditional source detection methods. Furthermore, the background stars and RSOs both move due to the satellite’s non-constant attitude, posing a challenge for tracking algorithms. Nevertheless, these images are a valuable source of SSA data, which can be used to develop algorithms to ultimately augment the capabilities of current SSA systems. Such augmentations include performing RSO detection as a simultaneous function on existing spacecraft or allowing dedicated SSA payloads to detect RSOs during slew maneuvers, where background stars will similarly move. This paper proposes a rules-based RSO tracking algorithm tailored for low-resolution, short-exposure, space-based imagery with non-constant spacecraft attitude, addressing the challenge of distinguishing RSOs from background stars that are also in motion. This method consists of a custom thresholding algorithm, along with the Iterative Closest Point (ICP) algorithm to correct the motion of the background stars, followed by a tracking algorithm to finally detect the RSOs within the imagery, returning their pixel positions. The algorithm was tested on an 878-image dataset, achieving 79% precision and 71% recall, while detecting 87% of all RSOs at least once. These results prove that the algorithm is a feasible method for detecting RSOs in non-constant-attitude imagery, providing a means to develop current SSA systems.

Remote sensing semantic segmentation is fundamental for fine-grained urban scene understanding, which in turn provides pixel-level semantic insights for urban development and environmental surveillance. However, existing hybrid segmentation architectures fail to incorporate intrinsic geometric and physical priors, inevitably leading to structural fragmentation, boundary ambiguity, and spatial misalignment of heterogeneous features. Therefore, we propose a Superpixel-Tokenized and Frequency-Modulated Hybrid CNN–Transformer network (SFCT-Net) for remote sensing semantic segmentation. The proposed network integrates superpixel tokens and high-frequency constraints to preserve structural integrity and boundary precision. First, our Superpixel-Tokenized Linear Position Attention (STLPA) module replaces rigid window tokens with semantic superpixels to ensure object integrity with linear computational complexity. Second, we construct a Frequency-Modulated Deformable Edge Refinement (FMDER) module that leverages high-frequency spectral priors to modulate deformable sampling, achieving robust boundary recovery. Finally, we develop the Spatial–Semantic Feature Coupling (SSFC) module, which employs a dual-branch strategy to correct spatial drift and align deep semantic features with shallow details. Experiments conducted on our self-built Taiyuan Satellite Remote Sensing Dataset (TSRSD) along with the ISPRS Vaihingen and Potsdam benchmark datasets demonstrate that our proposed SFCT-Net delivers state-of-the-art performance and efficiency by fusing superpixel and frequency priors for robust structural and boundary recovery.