Monitoring Larimichthys crocea aquaculture in a low-cost, efficient and flexible manner with remote sensing data is crucial for the optimal management and the sustainable development of aquaculture industry and aquaculture industry intelligent fisheries. An innovative automated framework, based on the Segment Anything Model (SAM) and multi-source high-resolution remote sensing image data, is proposed for high-precision aquaculture facility extraction and overcomes the problems of low efficiency and limited accuracy in traditional manual inspection methods. The research method includes systematic optimization of SAM segmentation parameters for different data sources and strict evaluation of model performance at multiple spatial resolutions. Additionally, the impact of different spectral band combinations on the segmentation effect is systematically analyzed. Experimental results demonstrate a significant correlation between resolution and accuracy, with UAV-derived imagery achieving exceptional segmentation accuracy (97.71%), followed by Jilin-1 (91.64%) and Sentinel-2 (72.93%) data. Notably, the NIR-Blue-Red band combination exhibited superior performance in delineating aquaculture infrastructure, suggesting its optimal utility for such applications. A robust and scalable solution for automatically extracting facilities is established, which offers significant insights for extending SAM’s capabilities to broader remote sensing applications within marine resource assessment domains.

The spatial distribution of oyster reefs is an important indicator for assessing environmental changes in nearshore fishery habitats. However, due to tidal fluctuations, images of oyster reef distribution acquired under low-light conditions such as early morning or evening often exhibit common issues such as bright spots and shadows. Thermal infrared (TIR) images, which are unaffected by external lighting conditions, can effectively address this problem. Aerial imaging of Liya Mountain, Haimen, Jiangsu Province, China, was conducted in this study. Based on unmanned aerial vehicles (UAVs) imagery acquired in 2025 using multispectral and TIR sensors, the total oyster reef area was estimated to be 6.61 ha. When compared with the oyster reef distribution derived from visible light aerial imagery collected in 2023 under favorable environmental conditions, this represents a decrease of 0.36 ha (5.4%), with the largest individual reef measuring 3388.17 m². To demonstrate the improvement in extraction accuracy achieved by integrating TIR data with multispectral imagery, the research team compared the extraction accuracy for oyster reefs of different sizes: a 1.91% improvement was observed for small reefs, a 9.02% improvement for middle reefs, and an 18.98% improvement for large reefs. Experimentally, the emissivity of oyster reefs was determined to be 0.982 ± 0.002 using an isothermal method in the laboratory. The emissivity derived from in situ measurements showed similar values, supporting the reliability of the laboratory result and providing a crucial parameter for the inversion of reef surface temperature. Experimental results demonstrate that the TIR band can effectively enhance the spatial accuracy of oyster reef measurements under low-light conditions.

Yield prediction in longline aquaculture is essential for evaluating environmental impacts, facilitating risk assessment, and promoting sustainable management in fisheries. However, since cultured organisms in longline aquaculture are submerged and cannot be directly observed, existing yield prediction approaches are mostly based on indirect environmental proxies, which often lead to unsatisfactory accuracy. The Shadow Geometry Inversion for Aquaculture (SGIA) method enables direct and accurate yield estimation in longline aquaculture by utilizing the submergence state of buoys to infer load, which is determined by the weight of the cultured organisms and estimated by shadow lengths combined with solar altitude angles and buoy physical parameters in high-resolution unmanned aerial vehicle (UAV) imagery. Experiments have been conducted in a water body located in Shanghai and Fuding to validate the effectiveness of the SGIA method. The best results were achieved under solar altitudes of 10–25° and calm water conditions. Under these conditions, the SGIA-predicted yields closely matched the measured loads in the Shanghai controlled experiment (R² = 0.985, MAPE = 9.19%). In the Fuding field application, the model effectively captured spatial variations in buoy loads across the farming area, demonstrating its practicality and scalability for large-scale yield mapping in real aquaculture environments.

Monitoring the spatial distribution of oyster reefs in an accurate, efficient, and flexible manner is crucial for assessing changes in nearshore fishery habitat environments. However, traditional optical remote sensing methods are often affected by illumination variation, tidal fluctuation, and spectral confusion in complex intertidal environments. An automated extraction framework based on the SAM (Segment Anything Model) using UAV (Unmanned Aerial Vehicle) DOM (Digital Orthophoto Map) imagery is proposed to achieve high-precision oyster reef identification and area estimation. Multi-resolution UAV imagery was processed, and key SAM parameters were systematically optimized under different illumination conditions. The results show that spatial resolution significantly influences segmentation accuracy, and appropriate resolution selection improves both stability and reliability. Based on UAV data acquired in 2025, the total oyster reef area in the study region was estimated to be 2.24 ha. After parameter optimization, segmentation accuracy improved from 93.22% to 97.61% in illuminated areas and from 93.30% to 96.67% in shaded areas. The proposed method demonstrates strong robustness under varying environmental conditions and effectively enhances boundary detection accuracy. A scalable and reliable approach is provided for coastal habitat monitoring and offers new insights into automated object extraction in complex remote sensing imagery.

Spatially explicit water-quality information is critical for precision management in pond aquaculture but point sampling alone cannot capture pond-to-pond heterogeneity in multi-unit farms. This single-date, single-farm study re-evaluated the potential of UAV hyperspectral imagery for water-quality screening in inland aquaculture ponds in Shanghai, China, using site-matched extraction from a 138-band orthomosaic (450–998 nm, Cubert S185) acquired during a single UAV survey on 24 August 2023 and matched with 23 GPS-registered sampling sites. Eight water-quality parameters were analyzed: chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), ammonium (${\mathrm{NH}}_4^{+}$ ), nitrite (${\mathrm{NO}}_2^{-}$), nephelometric turbidity unit (NTU), chlorophyll-a (Chla), and total suspended solids (TSS). Raw single-band correlations were modest ($\left|\mathrm{r}\right|$= 0.236–0.417), but two-band difference spectral indices (DSI), normalized spectral indices (NSI), and ratio spectral indices (RSI) substantially improved sensitivity, with $\left|\mathrm{r}\right|$ reaching 0.558–0.928. Quadratic inversion models were calibrated on the full dataset and assessed using three validation layers: two-fold cross-validation, nested leave-one-pond-out (LOPO) validation with within-fold predictor reselection, and extraction-window sensitivity tests. Bootstrap 95% confidence intervals for calibration (Cal) R² characterize small-sample uncertainty (n = 23). Three parameters satisfied all three defensibility criteria (Cal R² > 0.5, CV R² > 0.2, and LOPO R² > 0.2): ${\mathrm{NH}}_4^{+}$ (Cal R² = 0.836 [0.61, 0.94]; LOPO R² = 0.420), COD (0.607 [0.34, 0.82]; 0.328), and NTU (0.862 [0.77, 0.96]; 0.204). TP, TN, ${\mathrm{NO}}_2^{-}$, TSS, and Chla showed overfit behavior under nested holdout and were demoted to exploratory products. A TreeSHAP analysis confirmed that band-to-band contrast carried more explanatory power than raw reflectance magnitude. Extraction-sensitivity tests further demonstrated that positional uncertainty (±2-pixel offset: ΔCV $R^2$= 0.23–0.41) exceeded averaging-window sensitivity (3 × 3→10 × 10: ΔCV $R^2$ ≤ 0.11), identifying geolocation control as the dominant robustness constraint. This single-date, single-farm reanalysis suggests that UAV hyperspectral imagery may support exploratory pond-scale screening of ${\mathrm{NH}}_4^{+}$, COD, and NTU. However, robust quantitative inversion and broader transferability remain unverified and will require denser sampling, improved geolocation control, pond-edge masking, multi-site observations, and multi-temporal calibration.

Morphometric information provides quantitative descriptors of cetacean size and shape and may support future assessments of individual condition and population status when combined with appropriate biological calibration. However, conventional contact-based measurements are difficult to apply to free-ranging Yangtze finless porpoises (Neophocaena asiaeorientalis). UAV imagery offers a non-contact means of acquiring porpoise morphometric data, but automated workflows for converting UAV observations into reliable body-surface measurements remain limited. To address this gap, an oriented detection-guided SAM2 morphometric workflow, termed ODG-SAM2-Morph, was developed to automatically extract body-surface morphometric parameters from UAV imagery. The workflow integrates YOLO26-OBB for oriented target localization, SAM2 for prompt-guided body-surface segmentation, and differentiated morphometric extraction strategies for complete-body and partial-body samples. Guided by oriented detections, the small SAM2 model with the R-Box + 1FG prompt generated body-surface masks with mean IoU, mean Dice, Precision, and Recall values of 0.824, 0.901, 0.883, and 0.932, respectively. For complete-body samples, automatically extracted body-length and body-width parameters showed preliminary agreement with manual measurements. For 114 partial-body samples, the automatically extracted maximum visible body width agreed reasonably well with manual measurements, with a MAE of 3.32 cm and a MAPE of 11.93%. Continuous-sequence analysis further showed that the representativeness of maximum visible body width depended on trunk exposure, contour clarity, body posture, and inter-frame stability. By converting UAV observations into quantitative image-derived morphometric measurements, ODG-SAM2-Morph provides a practical basis for non-contact morphometric monitoring of Yangtze finless porpoises under natural survey conditions.