The study analyzes the survey data on the surface sediments in 355 stations in Xiamen Sea area during 2004‐2006, 2007‐2009, 2010‐2013, and 2014‐2016. The result finds that contents of TOC, sulfide, and oils in the surface sediments were generally low but showed significant spatial differences (p < 0.05) in most cases, with TOC in the West Sea area (WS) staying significantly higher than other sea areas. Xiamen Sea area suffered universal heavy metal pollution mainly from Cu and Zn for years in the WS, the Jiulong River Estuary (JE), and the East Sea area (ES), and the heavy metal pollution index (HPI) exhibited significant spatial differences across the four periods (p < 0.05). With the source of heavy metals in the WS being mainly related to human activities, Cu and Zn were related to the development of port shipping and Pb to aquaculture. The heavy metal pollution in JE was mainly related to the input of rivers emptying into the sea. The high Cu content in the ES was related to the direct discharge of massive domestic sewage into the sea at the time. From 2004 to 2016, the potential ecological risks of heavy metals in Xiamen Sea area stayed low.

Xiamen Bay (XMB), a representative semi-enclosed bay, demonstrates hydrodynamic conditions and water exchange characteristics that are significantly influenced by alterations in the coastline. The three-dimensional hydrodynamic model and remote sensing interpretation techniques were utilized to examine coastline changes and evaluated the spatio-temporal variations in water residence time in XMB from 1955 to 2021. The results indicate that the coastline of the XMB has been considerably modified by extensive reclamation activities. The total reclaimed area reached up to 188.08 km² during the period of 1955–2021, resulting in a 17.8% reduction in the total bay area. The average residence time increased from 13.28 days in 1955 to 16.94 days in 2003 and then decreased to 16.12 days because of ecological restoration initiatives. Spatially, water residence time increased from the outer sea towards the inner bay, with the high value observed in the northwest part of XMB while the low value was observed in the southeastern region. Among the various sub-regions, Tong’an Bay experienced the most significant change in residence time, followed by the West Sea. Conversely, the Dadeng Waters and Jiulong River Estuary showed relatively minor increases in residence time. The primary factors influencing variations in water residence time are large-scale reclamation projects and ecological restoration measures. These findings provide a significant scientific foundation and technical support for the integrated management of the coastal zone and ecological restoration construction in XMB.

Accurately mapping agricultural land use in fragmented hilly and mountainous areas is crucial for resource management but is severely challenged by spatial heterogeneity. While high-resolution (HR) images excel at delineating fine parcel boundaries, their limited spectral and temporal information often leads to spectral confusion among diverse agricultural types. To address this limitation, this study proposes a novel spatiotemporal feature-driven geographical zoning method integrating vegetation phenology, topography, and human activity. This zoning strategy decouples the complex global classification task into relatively simple local problems, providing explicit geoscientific constraints for subsequent classification. The proposed method was validated by classifying plain open-field croplands, sloping croplands, terraces, and greenhouses in the hilly and mountainous areas of Beijing using 2 m resolution satellite images. Compared to traditional global classification methods, the proposed zoning-based method increased the overall accuracy from 84.81% to 90.81%, the Kappa coefficient from 0.74 to 0.85, and the Intersection over Union (IoU) from 77.85% to 90.85%. The advantages of geographic zoning were particularly evident in mitigating spatial heterogeneity and enhancing boundary precision. These findings indicate that integrating dynamic geographical zoning as a priori knowledge successfully bridges the gap between HR spatial details and environmental contexts, offering a robust solution for mapping fragmented agricultural landscapes.