This study examined the relationship between the leaf reflectance of different seasons and the concentration of heavy metal elements in leaves, such as Co, Cu, Mo, and Ni in a post-mining area. The reflectance spectra and leaf samples of three typical plants were measured and collected in a whole growth cycle (June, July, August, and September). The Red Edge Position (REP), Readjustment Normalized Difference Vegetation Index (RE-NDVI), and Photochemical Reflectance Index (PRI) were extracted and used to explore its relation with the heavy metals concentrations in leaves between different seasons. The results show that all three Vegetation Indices (VIs) were insensitive indicators for monitoring the metal effects of vegetation in different seasons, which showed similar trends. Based on this, the Continuum Removal Indices (CRIs) were proposed from the continuum removed approach and extended for detecting the effects of heavy metal pollution over a full growth cycle. The relationship between the metal concentrations and CRIs of different plants was respectively analyzed by Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR). It is found that a significant correlation exists between the band depth and the concentration of Cu and Ni based on the White birch data sets using the PLSR, resulting in a small deviation from the established relationships. Compared with VIs, the approach of coupling CRIs and multiple regressions was effective for improving the estimation accuracy. The presented study provides a detection model of leaf heavy metals that can be adapted to different growing cycles, even an arbitrary growing cycle.

Sentinel-2 mission has been shown to have promising applications in coral reef remote sensing because of its superior properties. It has a 5-day revisit time, spatial resolution of 10 m, free data, etc. In this study, Sentinel-2 imagery was investigated for bleaching detection through simulations and a case study over the Lizard Island, Australia. The spectral and image simulations based on the semianalytical (SA) model and the sensor spectral response function, respectively, confirmed that coral bleaching cannot be detected only using one image, and the change analysis was proposed for detection because there will be a featured change signal for bleached corals. Band 2 of Sentinel-2 is superior to its other bands for the overall consideration of signal attenuation and spatial resolution. However, the detection capability of Sentinel-2 is still limited by the water depth. With rapid signal attenuation due to the water absorption effect, the applicable water depth for bleaching detection was recommended to be less than 10 m. The change analysis was conducted using two methods: one radiometric normalization with pseudo invariant features (PIFs) and the other with multi-temporal depth invariant indices (DII). The former performed better than the latter in terms of classification. The bleached corals maps obtained using the PIFs and DII approaches had an overall accuracy of 88.9 and 57.1%, respectively. Compared with the change analysis based on two dated images, the use of a third image that recorded the spectral signals of recovered corals or corals overgrown by algae after bleaching significantly improved the detection accuracy. All the preliminary results of this article will aid in the future studies on coral bleaching detection based on remote sensing.

Salt marshes are one of the three blue carbon ecosystems recognized by the Intergovernmental Panel on Climate Change (IPCC). However, coastal salt marshes in China are facing the risk of degradation. To reveal the status of the salt marsh wetland ecosystem in Liaohe Estuary, an Ecopath model composed of 14 functional groups was constructed based on the 2019 ecological survey data. A comprehensive analysis of the system’s food web structure, energy flow processes, and overall ecosystem characteristics was conducted. The results show that the energy flow in the Liaohe Estuary salt marsh wetland ecosystem is mainly distributed in three integrated trophic levels. The utilization rates of trophic levels II and III are low, easily causing blockages in the lower trophic levels of the ecosystem’s energy flow. The total system throughput of the Liaohe Estuary salt marsh wetland ecosystem is 49,099.039 t·km²·a⁻¹;. The system connectivity index and the system omnivory index are 0.207 and 0.109, respectively. Compared with other wetland systems, the ecosystem has a larger scale, but the overall ecosystem characteristic index reveals lower stability and complexity of the Liaohe Estuary salt marsh wetland system.

Coastal erosion disaster is one of the main marine geological disasters. Hazard degree of coastal erosion means that coastal erosion range may be occurred in a future period according to the occurrence mechanism of coastal erosion and its damage characteristic. To effectively cope with the coastal erosion disasters prevention and emergency management needs, the spatial distribution of coastal erosion were obtained, and a shoreline positions prediction model were established by using DSAS, GIS platform, and validated the prediction results by using fields' measure shoreline data at Penglai sandy coast. The results show that (1) there were varying degrees of erosion in the monitoring shore between 2006 and 2015. (2) There are a better coincident between the prediction model results and the fields' measurement results. (3) The spatial shoreline positions of the monitoring shore by the prediction model in 11/13/2016 and 11/13/2020 were obtained. The beach width of the monitoring shore is gradually decreasing, which affects the coastal tourism's use of the beach. These results would be provided a technical support for carrying out the coastal erosion disasters comprehensive management and rational planning of industrial layout along the coast.

Accurate mapping of offshore aquaculture remains challenging in complex coastal environments due to heterogeneous backgrounds, variable sea states, blurred pond boundaries, adhesion among densely distributed cages, and the weak texture of floating rafts. To address these limitations, this study proposes a bi-temporal multimodal cross-fusion network (BMCF-Net) for fine-scale offshore aquaculture segmentation from Sentinel-1/2 imagery. The framework jointly exploits bi-temporal observations acquired during non-ice and sea-ice periods and integrates them through a bi-temporal fusion module to enhance target–background separability and suppress environmental noise. In addition, a feature correction module and a multi-head feature fusion module are introduced to strengthen cross-modal alignment between SAR structural information and optical spectral–textural cues, thereby improving the separation of dense aquaculture units and the detection of weak-texture targets. Experiments conducted on a multimodal dataset from the Liaoning coastal zone show that BMCF-Net achieves F1-scores of 93.15%, 93.90%, and 89.04% for aquaculture ponds, cages, and floating rafts, respectively, outperforming state-of-the-art segmentation models such as FTransUNet. The proposed model was further applied to produce a high-resolution aquaculture distribution map for Liaoning Province in 2023 and to analyze area dynamics over the past six years. The results demonstrate the potential of BMCF-Net for large-scale offshore aquaculture monitoring and coastal resource management.