Accurate land use/land cover (LULC) classification and the detection of seasonal dynamics are crucial for effective environmental monitoring and resource management. To improve the precision and temporal resolution of regional LULC classification products, this study combined the Otsu threshold method and Random Forest algorithm to generate a 10 m-resolution land cover classification map for Wensu County based on Sentinel-2 imagery, with a particular focus on orchard categories, and investigated the seasonal dynamics of LULC between winter and summer. The results show that the overall accuracy (OA) of the water and snow/ice models was 85.50%, with a Kappa coefficient of 0.8088; for the vegetation model, the OA was 93.77%, with a Kappa coefficient of 0.8755. Feature importance analysis indicated that terrain features were key factors in improving classification performance. Seasonal dynamics analysis showed that the snow/ice coverage area in winter increased by 6379.18 square kilometers compared to that in summer, with 5252.85 km² of bare land and 910.66 km² of grassland being covered by snow/ice. Meteorological data analysis revealed that land cover changes caused by winter snowfall were primarily concentrated in areas where temperatures exceeded −8 °C, while land cover changes were smaller in areas with either low or high precipitation. These findings provide valuable data support for regional resource management and agricultural development.

The advancement of urbanization has led to a decline in the ecological function and environmental quality of cities, seriously reducing the services and sustainable development capacity of urban ecosystems. The construction of the National Forest Urban Agglomeration of China is conducive to alleviating the ecological and environmental problems brought about by rapid urbanization and promoting sustainable urban development. A time series analysis of ecological network changes can quickly and effectively explore the development and changes of ecological spatial patterns over time. Identifying ecological protection and restoration areas in urban agglomerations is an important way to promote ecosystem restoration and optimize ecological networks. This paper takes the Pearl River Delta forest urban agglomeration as the research area, uses multi-source remote sensing data from 2000 to 2020 (every 5 years), identifies ecological sources based on the morphological spatial pattern analysis (MSPA) method, generates ecological corridors based on the minimum cumulative resistance (MCR) model, constructs a time series ecological network pattern in the Pearl River Delta region, and analyzes the evolution process of the ecological network pattern over time. The results indicate that over time, the core green area in the ecological network pattern of the Pearl River Delta first decreased and then increased, and the complexity of ecological corridors first decreased and then increased. The main reason is that the urbanization process in the early 21st century led to severe ecological fragmentation. Under the promotion of the national forest urban agglomeration construction, the ecological network pattern of the Pearl River Delta was restored in 2015 and 2020. The time series analysis of the ecological network pattern in the Pearl River Delta region of this research confirms the effectiveness of the construction of forest urban agglomerations, providing a scientific reference for the identification of ecological networks and optimization of spatial patterns in forest urban agglomerations.

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms.