The transnational area of Changbai Mountain (TACM) is crucial to sustainable development in Northeast Asia owing to its abundant forest, which helps in maintaining biodiversity and multiple ecosystem services. However, the spatiotemporal patterns of forest in the TACM have been poorly understood across the whole region. The objectives of this study were to quantify the spatiotemporal patterns of forest in the TACM from 1977 to 2015, investigate the causes of forest dynamics, and assess the impacts of forest dynamics on habitat quality. To do this, we first extracted the forest in the TACM from Landsat images acquired in 1977, 1988, 1999, 2007, and 2015 using visual interpretation. Then, we analyzed the spatiotemporal patterns of forest in the TACM from 1977 to 2015 using landscape metrics and compared the dynamics of forest between the area in China and the area in the Democratic People’s Republic of Korea (DPRK). After that, we investigated the driving forces of forest dynamics and the impacts of forest dynamics on habitat quality. We found that the TACM experienced a noticeable forest decrease—from 1.57 million ha in 1977 to 1.48 million ha in 2015, a decline of 5.78%—and underwent a forest fragmentation process. In particular, the sub-region in the DPRK had a much larger decrease (17.75%) than the sub-region in China (2.86%). We found that timber harvesting, urban expansion, agricultural reclamation, and typhoon disasters were the main driving forces behind forest decreases in the TACM. Specifically, agricultural reclamation was the most important factor in the DPRK sub-region, while urban expansion was the dominant factor in the Chinese sub-region. Furthermore, such forest loss and fragmentation has resulted in declines in habitat quality across both sides of the TACM. Thus, we suggest that more effective forest management with cooperation between China and DPRK is needed to maintain and improve forest coverage in the TACM.

The massive increase in the amount of greenhouse gases in the atmosphere, especially carbon dioxide (CO₂), has had a significant impact on the global climate. Research has revealed that lakes play an important role in the global carbon cycle and that they can shift between the roles of carbon sources and sinks. This study used Landsat satellite images to analyze the spatiotemporal characteristics and factors influencing the CO₂ changes in Chagan Lake in China. We conducted six water sampling campaigns at Chagan Lake in 2020–2021 and determined the partial pressure of carbon dioxide (pCO₂) from 110 water samples. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 75 matched pairs. A regression analysis indicated strong associations between pCO₂ and both the band difference model of the near-infrared band and green band (Band 5-Band 3, R² = 0.83, RMSE = 27.55 μatm) and the exponential model [((exp(b3) − exp(b5))²/(exp(b3) + exp(b5))², R² = 0.82, RMSE = 27.99 μatm]. A comparison between the performances of a linear regression model and a machine learning model found that the XGBoost model had the highest fitting accuracy (R² = 0.94, RMSE = 16.86 μatm). We used Landsat/OLI images acquired mainly in 2021 to map pCO₂ in Chagan Lake during the ice-free period. The pCO₂ in the surface water of Chagan Lake showed considerable spatiotemporal variability within a range of 0–200 μatm. pCO₂ also showed significant seasonal variations, with the lowest and highest mean values in autumn (30–50 μatm) and summer (120–150 μatm), respectively. Spatially, the pCO₂ values in the southeast of Chagan Lake were higher than those in the northwest. The CO₂ fluxes were calculated based on the pCO₂ and ranged from −3.69 to −2.42 mmol/m²/d, indicating that Chagan Lake was absorbing CO₂ (i.e., it was a weak carbon sink). Temperature, chlorophyll a, total suspended matter, and turbidity were found to have reinforcing effects on the overall trend of pCO₂, while the Secchi disk depth was negatively correlated with pCO₂. The results of this study provide valuable insights for assessing the role of lakes in the carbon cycle in the context of climate change.