Solar energy, as an environmentally sustainable power source, is gaining increasing popularity worldwide, driving a surge in the number of solar photovoltaic (PV) plants. China, which has a prominent role in this domain, requires continuous updates to its PV plant data for spatiotemporal analyses. However, there remains an absence of a comprehensive and timely dataset of PV plants across China, leaving PV installation dates and other crucial attributes for comprehensive analyses underexplored. This study leverages Sentinel-2 data as a primary source to propose an optimized deep learning approach for PV plant extraction in China. Statistical analyses of PV plant attributes, including its installation date, size, site slope, and site land cover, were implemented from multiple data sources. Comprehensive analyses were conducted to unravel their spatiotemporal development patterns in the 21st century. The results indicate that as of 2023, China boasts 4347 PV plants, collectively spanning 4146 km², which are predominantly concentrated in Northwest and North China. 2016 and 2017 marked substantial growth in China’s PV plants, while other years exhibited stability. These plants exhibit the distinct spatial characteristics of installing smaller PV plants on flat terrain covered by vegetation or barren land. Over time, a notable trend in the installation of China’s PV plants has been the increasing preference for establishing larger ones in smooth terrain, with a focus on preventing damage to natural resources. The results reveal China’s optimization of PV plant site selection and construction strategies, aligning with global environmental goals and sustainable energy practices.

While it is crucial to monitor the spatio-temporal dynamics of forests at the sub-pixel scale, most available nonlinear methods are used to predict forest cover fraction maps only at the acquisition time of the training samples and are, thus, unable to estimate time-series forest cover fraction beyond the acquisition time. Based on MODIS NDVI, VCF and Landsat tree canopy height data, we developed a spatio-temporal regression (STR) method to estimate annual forest cover fraction maps during 2000-2020 on China's Han River Basin. Results obtained by the proposed STR method achieved significantly higher accuracy (R ² = 0.897, RMSE = 0.1364, MAE = 0.077) than that obtained by a traditional non-linear regression method. Moreover, the STR exhibits increased accuracy when using training samples from both 2000 and 2020 compared to those using training samples solely from either 2000 or 2020. We also introduced Landsat tree cover maps in 2005, 2010 and 2015 as reference data to verify the effectiveness of the STR method. The STR method was employed to produce annual forest cover fraction maps from 2000 to 2020. The outcomes revealed a distinct overall recovery trend of forest cover at both the pixel and sub-pixel scales in China's Han River Basin during 2000-2020, notably around the vicinity of the Danjiangkou reservoir area. In general, the STR method proposed in this research is superior in accurately tracking time-series high-intensity and low-intensity forest cover fraction changes in a complex forest ecosystem, which is composed of various forest types in a subtropical monsoon climate.

Tropical evergreen forests represent the richest biodiversity in terrestrial ecosystems, and the fine spatial-temporal resolution mapping of these forests is essential for the study and conservation of this vital natural resource. The current methods for mapping tropical evergreen forests frequently exhibit coarse spatial resolution and lengthy production cycles. This can be attributed to the inherent challenges associated with monitoring diverse surface changes and the persistence of cloudy, rainy conditions in the tropics. We propose a novel approach to automatically map annual 10 m tropical evergreen forest covers from 2017 to 2023 with the Sentinel-2 Dynamic World dataset in the biodiversity-rich and conservation-sensitive Central African Republic (CAR). The Copernicus Global Land Cover Layers (CGLC) and Global Forest Change (GFC) products were used first to track stable evergreen forest samples. Then, initial evergreen forest cover maps were generated by determining the threshold of evergreen forest cover for each of the yearly median forest cover probability maps. From 2017 to 2023, the annual modified 10 m tropical evergreen forest cover maps were finally produced from the initial evergreen forest cover maps and NEFI (Non-Evergreen Forest Index) images with the estimated thresholds. The results produced by the proposed method achieved an overall accuracy of >94.10% and a Cohen’s Kappa of >87.63% across all years (F1-Score > 94.05%), which represents a significant improvement over the performance of previous methods, including the CGLC evergreen forest cover maps and yearly median forest cover probability maps based on Sentinel-2 Dynamic World. Our findings demonstrate that the proposed method provides detailed spatial characteristics of evergreen forests and time-series change in the Central African Republic, with substantial consistency across all years.

Tropical evergreen forests, among the most diverse and complex forest ecosystem, host an extraordinary variety of plants and animal species, underscoring the importance of monitoring their spatio-temporal dynamics. Despite substantial advances in tracking tropical forest disturbances with Landsat images, understanding of post-disturbances recovery, particularly small-scale disturbances, remains limited. Here, by enhancing the subtle signal caused by small-scale disturbances, we propose a method (i.e., framework) to simultaneously monitor tropical evergreen forest disturbances and post-disturbance recovery using annual maximal Landsat canopy openings. A baseline evergreen forest cover map was automatically generated by integrating median Normalized Difference Fraction Index (NDFI) and maximal self-referenced NDFI (rNDFI) images. Utilizing the baseline map and long-term spatio-temporally filtered annual maximal rNDFI images, three additional thematic maps were produced: first forest disturbance year, inter-annual forest disturbance frequency, and post-disturbance forest recovery. For the study areas of Brazil Mato Grosso, the Democratic Republic of the Congo Kasaï and Indonesia Kalimantan Tengah, the results demonstrated that the proposed framework not only detected more forest disturbance events, particularly enhancing the small-scale disturbances, but also predicted forest disturbances more accurately than the Global Forest Change (GFC) forest loss and Joint Research Centre (JRC) Tropical Moist Forest (TMF) deforestation and degradation products. The generated inter-annual forest disturbance frequency and post-disturbance forest recovery maps exhibit high accuracies, with the R², MAE and RMSE in the ranges 0.82-0.95, 0.47-1.15 and 1.33-2.88, respectively, which are more accurate than the benchmark results extracted from JRC TMF product. Our results are more sensitive to the inter-annual canopy opening and recovery of small-scale disturbances due to smallholder clearing and selective logging than JRC TMF. This research offers an effective framework for addressing the existing knowledge gap on post-change dynamics after forest disturbances and the net carbon change of tropical evergreen forests.