The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) are fused by the CNN-based method to obtain the fused image with high spatial-spectral resolution. Secondly, considering the sequentiality of spatial distribution and spectral response, the spatial-spectral vision transformer (SSViT) is designed to extract sequential relationships from the fused images. After that, an external attention module is utilized for feature integration, and then the pixel-wise prediction is achieved for land-cover mapping. Finally, land-cover mapping and benthos data at the sites are analyzed consistently to reveal the distribution rule of benthos. Experiments on ZiYuan1-02D data of the Yellow River estuary wetland are conducted to demonstrate the effectiveness of the proposed framework compared with several related methods.

Jiangsu coastal wetland has the largest area of the invasive plant, Spartina alterniflora (S. alterniflora), in China. S. alterniflora has been present in the wetland for nearly 40 years and poses a substantial threat to the safety of coastal wetland ecosystems. There is an urgent need to control the distribution of S. alterniflora. The biological characteristics of the invasion process of S. alterniflora contribute to its multi-scale distribution. However, the current classification methods do not deal successfully with multi-scale problems, and it is also difficult to perform high-precision land cover classification on multi-temporal remote sensing images. In this study, based on Landsat data from 1990 to 2020, a new deep learning multi-scale residual convolutional neural network (MRCNN) model was developed to identify S. alterniflora. In this method, features at different scales are extracted and concatenated to obtain multi-scale information, and residual connections are introduced to ensure gradient propagation. A multi-year data unified training method was adopted to improve the temporal scalability of the MRCNN. The MRCNN model was able to identify the annual S. alterniflora distribution more accurately, overcame the disadvantage that traditional CNNs can only extract feature information at a single scale, and offered significant advantages in spatial characterization. A thematic map of S. alterniflora distribution was obtained. Since it was introduced in 1982, the distribution of S. alterniflora has expanded to approximately 17,400 ha. In Jiangsu, the expansion process of S. alterniflora over time was divided into three stages: the growth period (1982–1994), the outbreak period (1995–2004), and the plateau period (2005–2020). The spatial expansion direction was mainly parallel and perpendicular to the coastline. The hydrodynamic conditions and tidal flat environment on the coast of Jiangsu Province are suitable for the growth of S. alterniflora. Reclamation of tidal flats is the main factor affecting the expansion of S. alterniflora.

Spartina alterniflora (S. alterniflora) rapidly invade new habitats by seed jumping propagation, and the early invasion process is characterized by discrete large patches and diffuse distribution small-scale patches. Monitoring these early-invasion-stage patches is very important for effectively stopping the S. alterniflora invasion trends. In this study, a long time series of high spatial resolution (0.5 and 1 m) satellite remote sensing images from 2010 to 2020 were used to monitor the early invasion period of S. alterniflora . Using remote sensing technology, the early invasion behavior of S. alterniflora at patch scale was monitored and analyzed in the Yellow River Delta with the object-oriented image segmentation and support vector machine classification methods. The results indicate that the discrete patches were incorporated into bigger patches or gathering regions within a few years: 56.13% of the independent large patches lasted only for 1 year, and only 0.25% of which can last for 10 years. Second, we newly found out that the newborn S. alterniflora seedlings are uniformly distributed between discrete large patches and then quickly aggregate into a large gathering area within the next 2 years. Third, for the area that was undergoing invasion, the mean S. alterniflora coverage rate was stable at approximately 50%. Fourth, dominant factors that determined the invasion trend of S. alterniflora are the elevation of the intertidal zone, the tidal channel distribution and the topographic changes caused by S. alterniflora invasion.

Climate change has profoundly affected global ecological security. The most vulnerable region on Earth is the high-latitude Arctic. Identifying the changes in vegetation coverage and glaciers in high-latitude Arctic coastal regions is important for understanding the process and impact of global climate change. Ny-Ålesund, the northern-most human settlement, is typical of these coastal regions and was used as a study site. Vegetation and glacier changes over the past 35 years were studied using time series remote sensing data from Landsat 5/7/8 acquired in 1985, 1989, 2000, 2011, 2015 and 2019. Site survey data in 2019, a digital elevation model from 2009 and meteorological data observed from 1985 to 2019 were also used. The vegetation in the Ny-Ålesund coastal zone showed a trend of declining and then increasing, with a breaking point in 2000. However, the area of vegetation with coverage greater than 30% increased over the whole study period, and the wetland moss area also increased, which may be caused by the accelerated melting of glaciers. Human activities were responsible for the decline in vegetation cover around Ny-Ålesund owing to the construction of the town and airport. Even in areas with vegetation coverage of only 13%, there were at least five species of high-latitude plants. The melting rate of five major glaciers in the study area accelerated, and approximately 82% of the reduction in glacier area occurred after 2000. The elevation of the lowest boundary of the five glaciers increased by 50–70 m. The increase in precipitation and the average annual temperature after 2000 explains the changes in both vegetation coverage and glaciers in the study period.

Benzophenone-3 (BP-3), an organic UV filter extensively applied in sunscreens, cosmetics and daily plastic products, is classified as a typical emerging endocrine-disrupting micropollutant. This compound is highly susceptible to bioaccumulation in aquatic organisms, triggers coral bleaching, and incurs oxidative damage to algae, fish and invertebrates. Conventional wastewater treatment processes cannot efficiently eliminate BP-3 from aqueous media, thereby imposing severe ecological risks on freshwater and marine ecosystems. In this study, iron-impregnated biochar (Fe-BC) was synthesized via an impregnation–pyrolysis route using powder of cultivated Chlorella pyrenoidosa (green microalga) as raw feedstock. Batch adsorption experiments revealed that iron impregnation remarkably enhanced the removal efficiency of BP-3. The maximum Langmuir saturated adsorption capacity of Fe-BC reached 91.7 mg/g, considerably exceeding the value of 51.5 mg/g for pristine biochar. Kinetic data exhibited favorable fitting with the pseudo-first-order kinetic model, demonstrating that Fe-BC rapidly captures BP-3 and achieves adsorption equilibrium within 120 min. Solution pH exerted a prominent influence on adsorption performance: the material maintained high BP-3 adsorption capacity at pH 7–10, whereas adsorption capacity declined drastically under strongly acidic (pH < 5) and extreme alkaline conditions (pH > 10.5). Fourier-transform infrared spectroscopy (FTIR) validated the successful loading of iron species onto the biochar surface, as well as the binding of BP-3 onto Fe-BC. Combined with pH-controlled experimental results, hydrogen bonding, hydrophobic interactions, and pore-filling effects are inferred as the dominant adsorption mechanisms for BP-3 removal. Furthermore, Fe-BC retained favorable BP-3 removal performance in simulated seawater matrices, endowing it with preliminary potential for wastewater treatment in coastal zones and tourist scenic areas. This work offers basic laboratory insights into BP-3 adsorption, while further verification concerning environmental low-concentration conditions, authentic water matrices, material reusability and stability is essential for its practical application.