Accurate estimation of forest above-ground biomass (AGB) is essential for quantifying forest carbon stocks and supporting regional carbon accounting. However, regional AGB mapping requires the integration of field observations with satellite data, and the associated scale transformation often causes the loss of spatial detail and reduced estimation consistency. To address this issue, this study proposes a forest canopy height-constrained area-to-area regression kriging (CCAM) method for upscaling UAV-derived AGB and generating a high-precision wall-to-wall AGB map for artificial forests in the sandy lands of northwest Liaoning Province, China. The framework integrates RFE-SVM-based feature selection, XGBoost-based UAV-AGB modeling, and CHM-constrained residual correction within a Regression-then-Kriging (R-K) strategy, while also evaluating the effects of moving-window size, scale transition, and the order of regression and kriging on upscaling performance. The results showed that the reconstructed UAV-AGB model achieved the highest accuracy, with R² = 0.91 and rRMSE = 0.12, providing a reliable 0.1 m AGB baseline for subsequent upscaling. Among the tested moving-window sizes, the $7\times 7$ window was identified as optimal. Under this setting, CCAM achieved R² = 0.81 and rRMSE = 0.08, substantially outperforming direct GF-2-based estimation (R² = 0.49, rRMSE = 0.24). The final 2 m regional AGB map further attained a validation accuracy of R² = 0.79 and rRMSE = 0.18. These results demonstrate that CCAM can effectively preserve fine-scale UAV-derived biomass information during scale transformation and provide a reliable pathway for linking UAV and satellite observations in regional forest AGB mapping.

Coastal zones provide favorable geographical conditions for aquaculture. Accurate information on land and marine aquaculture (LMA) activities is essential for managing their spatial distribution and estimating blue-food production. However, complex coastal landscapes and the heterogeneity of aquaculture facilities make it difficult to extract LMA areas accurately and completely at large scales. To address this challenge, we developed an adaptive multi-level scale segmentation (AMLSS) method for the precise extraction of LMA areas and modified a radar dual-polarization water index to reduce interference from the seawater background. We then constructed a class-specific hierarchical decision-tree model using multi-source features to map LMA areas at the national scale. AMLSS increased extraction accuracy by 19.7% for pond aquaculture and 22.9% for marine aquaculture compared with single-scale segmentation and was particularly effective in northern China. In 2022, landward pond and marine cage/raft aquaculture within China’s coastal zone covered approximately 7237 km² and 1152 km², respectively; the resulting map achieved an overall accuracy of 90.8%. This study provides a methodological basis for the long-term monitoring and spatially explicit mapping of LMA areas.