The coupling coordination relationship between regional highway transportation and new urbanization is an increasingly important issue in the field of regional sustainable development. This paper aims to establish an index system and develop an integrated method to measure the coupling coordination between regional highway transportation and new urbanization. The evaluation index system comprised two hierarchies, six aspects and 19 indexes, which were weighted by the entropy weight method (EWM). Furthermore, a coupling degree model (CDM) and a coupling coordination degree model (CCDM) were established based on the statistical data from 2011 to 2017 in Heilongjiang Province, China. The ArcGIS spatial analysis method was utilized to analyze the spatial–temporal coupling evolution characteristics of the two subsystems. The findings revealed that the development level of the two subsystems showed an upward tendency. The coupling degree between the two subsystems was leaping from antagonistic stage to breaking-in period. The coupling coordination degree between the two subsystems was the transition stage from imbalance to coordination. Specifically, the spatial differentiation characteristics of the coupling coordination development between the two subsystems were prominent in Heilongjiang Province, China. The results showed that the approach combining CCDM with ArcGIS spatial analysis can be applied as an effective method to explore the spatial–temporal evolution of coupling coordination development.

The accuracy of forest canopy height estimation is crucial for forest resource management and ecosystem carbon sequestration. However, existing approaches often face limitations in effectively integrating multisource remote sensing data, feature representation, and model learning strategies. To enhance the prediction performance of the model in complex terrain and multisource data environments, this study comprehensively used ICESat-2/ATLAS photon point clouds, Sentinel-2/MSI multispectral imagery, and SRTM-DEM to construct a remote sensing-driven multisource feature system, which eliminated redundant interference using permutation feature importance analysis. Additionally, a self-attention (SA) mechanism was introduced to strengthen high-dimensional feature representation. Three heterogeneous models, incorporating deep neural network (DNN), extreme gradient boosting (XGBoost), and residual network (ResNet), were independently applied for forest canopy height estimation and were further used as base learners, with a random forest as the meta-learner, and an SA-Blending heterogeneous ensemble model that combines a blending technique with an SA mechanism was proposed to enhance the accuracy of forest canopy height estimation. To evaluate the SA optimization strategy and the role of multisource fusion, this study used the original features, SA-optimized features, and multisource fusion features (i.e., the concatenation and fusion of original features and self-attention mechanism features) as inputs to comprehensively compare the performance of each single model and the integrated model. The results show that: (1) The self-attention mechanism significantly improves the prediction performance of heterogeneous models. Compared with original features inputs, the R² of DNN (SA-Only) and XGBoost (SA-Only) increased to 0.706 and 0.708, respectively, and the RMSE decreased to 1.691 m and 1.613 m. Although the R² for ResNet (SA-Only) decreased slightly to 0.699 and the RMSE increased to 1.712 m, the overall impact was not significant. (2) Under the condition of multisource fusion feature input, DNN+SA, XGBoost+SA, and ResNet+SA all demonstrated higher fitting accuracy and stability, verifying the enhancing effect of the SA mechanism on the association expression of multisource information. (3) The SA-Blending model achieved the best overall performance, with R² of 0.766 and RMSE of 1.510 m. It outperformed individual models and the SA-optimized model in terms of overall accuracy, stability, and robustness. The results can provide technical support for high-precision forest canopy height mapping and are of great significance for ecological monitoring applications.

The nonlinear associations and scale dependence of yellow–green–gray spatial patterns with atmospheric CO₂ and vegetation productivity remain insufficiently understood in cold-region cities characterized by mixed cropland–forest landscapes. Taking Harbin, China, as a case study, we integrated morphological spatial pattern analysis (MSPA), landscape metrics, and three-dimensional structural indicators with multiscale geographically weighted regression (MGWR), seven machine learning algorithms, and interpretable machine learning methods. Spatial block cross-validation was used to evaluate predictive performance at 3 km and 9 km grid scales. The results showed that (1) column-averaged atmospheric CO₂ (XCO₂) and vegetation net primary productivity (NPP) exhibited significant spatial clustering, with high-XCO₂ areas generally corresponding to low-NPP areas; (2) cropland coverage (YC) ranked first in relative SHAP importance across all four scale-response combinations and was generally associated with higher predicted XCO₂ and lower predicted NPP, whereas forest coverage (GC) showed the opposite patterns; (3) nonlinear predictive responses and non-additive interactions differed between the two response variables, with building-density-related combinations more prominent for XCO₂ and cropland–forest combinations more prominent for NPP; and (4) the mean spatial cross-validation R² values of random forest models were 0.524 and 0.660 for XCO₂ and NPP at 3 km, respectively, compared with 0.597 and 0.630 at 9 km. These findings highlight the predictive relevance of cropland composition and landscape interactions while demonstrating the scale dependence of their associations with XCO₂ and NPP. The results provide spatial planning insights for cold-region cities with mixed agricultural and forest landscapes.