Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information and multi-temporal phenological characteristics to accurately quantify diversity in large, heterogeneous forest areas are still lacking. In this study, combined with regression models, three different diversity indices, namely Simpson (λ), Shannon (H′), and Pielou (J′), were applied to characterize forest tree species diversity by using GEDI LiDAR data and Sentinel-2 imagery in temperate natural forest, northeast China. We used Mean Decrease Gini (MDG) and Boosted Regression Tree (BRT) to assess the importance of certain variables including monthly spectral bands, vegetation indices, foliage height diversity (FHD), and plant area index (PAI) of growing season and non-growing seasons (68 variables in total). We produced 12 forest diversity maps on three different diversity indices using four regression algorithms: Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Lasso Regression (LR). Our study concluded that the most important variables are FHD, NDVI, NDWI, EVI, short-wave infrared (SWIR) and red-edge (RE) bands, especially in the growing season (May and June). In terms of algorithms, the estimation accuracies of the RF (averaged R² = 0.79) and SVM (averaged R² = 0.76) models outperformed the other models (R² of KNN and LR are 0.68 and 0.57, respectively). The study demonstrates the accuracy of GEDI LiDAR data and multi-temporal Sentinel-2 images in estimating forest diversity over large areas, advancing the capacity to monitor and manage forest ecosystems.

Coastal wetlands are critical for global biodiversity and ecological stability, yet the invasive Spartina alterniflora (S. alterniflora) poses severe threats to these ecosystems. This study evaluates the effectiveness of management efforts targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The U-Net model, coupled with the Relief-F algorithm, achieved a superior extraction accuracy (Kappa > 0.9 and overall accuracy of 93%) compared to traditional machine learning methods. From 2019 to 2021, S. alterniflora expanded rapidly, increasing from 4055.06 hm² to 6105.50 hm², primarily in tidal flats and water bodies. A clearing project reduced its extent to 5063.62 hm² by 2022, and by 2023, only 0.55 hm² remained. These results underscore the effectiveness of Shandong’s management policies but highlight the risk of regrowth due to the species’ resilience. Continuous monitoring and maintenance are essential to prevent its resurgence and ensure wetland restoration. This study offers critical insights into dynamic vegetation monitoring and informs conservation strategies for wetland health.

Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km² within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km² to 3412 km², while the vegetation area decreased from 745 km² to 687 km². These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation.

Microplastic (MP) pollution is a rapidly spreading global problem, threatening the use and sustainability of freshwater resources. MPs in water can act as both a source and sink of dissolved organic compounds. This review summarizes the current knowledge of interactions between MPs and dissolved organic compounds, including the adsorption and release of dissolved organic compounds by MPs and the impacts of MPs on the source and sink of natural dissolved organic matter (DOM) in aquatic ecosystems. The key mechanisms for the adsorption of dissolved organic compounds on MPs are hydrophobic interactions, van der Waals forces, and π–π interactions. Particle size, morphological characteristics, density, and environmental factors (pH, ionic strength, and UV radiation) have a great influence on the adsorption of dissolved organic compounds on MPs. Although research on the interactions between dissolved organic compounds and MPs has progressed rapidly, to date, research on the impacts of increasing amounts of MPs on natural DOM cycles (production, transformation, and fate) in aquatic ecosystems has been very limited. Knowledge gaps and future research directions are outlined at the end of this review.

The northward expansion of Spartina alterniflora (S. alterniflora) poses a profound ecological threat to coastal ecosystems and biodiversity along China’s coastline. This invasive species exhibits strong adaptability to colder climates, facilitating its potential spread into northern regions and underscoring the urgent need for a nuanced understanding of its spatial distribution and invasion risks to inform evidence-based ecosystem management strategies. This study employed multi-temporal Sentinel-1/2 imagery (2016–2022) to map and predict the spread of S. alterniflora in Bohai Bay. An object-based random forest classification achieved an overall accuracy above 92% (κ = 0.978). Over the six-year period, the S. alterniflora distribution decreased from 46.60 km² in 2016 to 12.56 km² in 2022, reflecting an annual reduction of approximately 5.67 km². This decline primarily resulted from targeted eradication efforts, including physical removal, chemical treatments, and biological competition strategies. Despite this local reduction, MaxEnt modeling suggests that climate trends and habitat suitability continue to support potential northward expansion, particularly in high-risk areas such as the Binhai New District, the Shandong Yellow River Delta, and the Laizhou Bay tributary estuary. Key environmental drivers of S. alterniflora distribution include the maximum temperature of the warmest month, mean temperature of the wettest quarter, isothermality, sea surface temperature, mean temperature of the warmest quarter, and soil type. High-risk invasion zones, covering about 95.65 km². These findings illuminate the spatial dynamics of S. alterniflora and offer scientific guidance for evidence-based restoration and management strategies, ensuring the protection of coastal ecosystems and fostering sustainable development.

In terms of evident climate change and human activities, investigating changes in lakes and reservoirs is critical for sustainable protection of water resources and ecosystem management over the Nenjiang watershed (NJW), an eco-sensitive semi-arid region and the third-largest inland waterbody cluster in China. In this study, we established a multi-temporal dataset documenting lake and reservoir (area ≥ 1 km²) changes in this region using an object-oriented image classification method and Landsat series images from 1980 to 2015. Using the structural equation model (SEM), we analyzed the diverse impacts of climatic and anthropogenic variables on lake changes. Results indicated that lakes experienced significant changes with fluctuations over the past 35 years including obvious declines in the total area (by 42%) and number (by 51%) from 1980 to 2010 and a slight increase in the total lake area and number from 2010 to 2015. More than 235 lakes in the size class of 1–10 km² decreased to small lakes (area < 1 km²), while 59 lakes covering 243.75 km² disappeared. Total reservoir area and number had continuous increases during the investigated 35 years, with an areal expansion of 54.9% from 919 km² to 1422 km², and a number increase by 65.3% from 78 to 129. The SEM revealed that the lake area in the NJW had a significant correlation with the mean annual precipitation (MAP), suggesting that the MAP decline clarified most of the lake shrinkage in the NJW. Furthermore, agricultural consumption of water had potential impacts on lake changes, suggested by the significant relationship between cropland area and lake area.