The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future.

Dramatic changes of forests have strong influence on regional and global carbon cycles, biodiversity, and ecosystem services. Understanding dynamics of forests from local to global scale is crucial for policymaking and sustainable development. In this study, we developed an updating and object-based image analysis method to map forests in Northeast China using Landsat images from 1990 to 2015. The spatio–temporal patterns of forests were quantified based on resultant maps and geospatial analysis. Results showed that the percentage of forested area occupying the entire northeast China was more than 40%, about 94% of initial forest cover remained unchanged (49.37 × 10⁴ km²) over the course of 25 years. A small net forest loss (1051 km²) was observed during 1990–2015. High forest gain (10,315 km²) and forest loss (9923 km²) both occurred from 2010 to 2015. At the provincial level, Heilongjiang demonstrated the highest rate of deforestation, with a net loss of 1802 km² (0.89%). Forest changes along elevation, slope, and distance from settlements and roads were also investigated. Over 90% of forest changes occurred in plains and low mountain areas within the elevation of 200–1000 m and slope under 15°. The most dramatic forest changes can be found within the distance of 2000 m from settlements and roads. The reclamation of sloping land, construction of settlements and roads, and possible smallholder clearing contributed more to forest loss, while ecological projects and related government policies play an important role on afforestation and reforestation. These results can provide useful spatial information for further research on the driving forces and consequences of forest changes, which have critical implications for scientific conservation and management of forests.

Cultivated land multifunctionality plays an important role in balancing food security, rural development, and ecological sustainability. However, existing studies often remain fragmented, with limited integration of long-term functional evolution, interfunctional relationships, coordination, and governance-oriented zoning. Using multi-source data from 2000 to 2024, this study examined the spatiotemporal evolution, synergy–trade-off relationships, coupling coordination, spatial associations, and functional zoning of cultivated land production, social, ecological, and landscape functions in Northeast China. The results show pronounced temporal fluctuations and spatial differentiation. Landscape function maintained the highest overall level, with a mean of approximately 0.82, while ecological function remained relatively high and stable. Production function fluctuated over time, whereas social function increased markedly in 2020 before declining slightly in 2024. Synergies dominated among production, ecological, and landscape functions, with correlation coefficients ranging from 0.54 to 0.97, while social function remained weakly associated with the other functions (r < 0.22). The coupling coordination degree ranged from 0.639 to 0.665, indicating a moderate but relatively stable coordination level with clear regional differences. Functional zoning identified single-function-dominated, dual-function composite, multifunctional composite, and characteristic agricultural zones, accounting for 53.44%, 21.30%, 8.94%, and 16.33% of cultivated land, respectively. These findings provide a basis for shifting cultivated land management from uniform regulation toward differentiated governance that better coordinates agricultural production, ecological protection, and rural development.