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Featured Application: This study proposes a new method for assessing the spatial and temporal changes in the ecological quality in resource cities. The rapid and effective assessment of ecological quality changes in resource cities was realized. This study provides valuable data support and a policy reference for the ecological protection and sustainable development of resource cities. In coal-dependent urban economies, the dichotomy between resource exploitation and ecological conservation presents a pronounced challenge. Traditional remote sensing ecological assessments often overlook the interplay between mining activities and urban environmental dynamics. To address this gap, researchers developed an innovative Resource-Based City Ecological Index (RCEI), anchored in a Pressure–State–Response (PSR) framework and synthesized from six discrete ecological indicators. Utilizing geodetic remote sensing data, the RCEI facilitated a comprehensive spatiotemporal analysis of Jincheng City's ecological quality from 1990 to 2022. The findings corroborated the RCEI's efficacy in providing a nuanced portrayal of the ecological state within mining regions. (1) Jincheng City's ecological quality predominantly sustained a mudhopper-tier status, exhibiting an overarching trend of amelioration throughout the study period. (2) Disparities in ecological landscape quality were pronounced at the county level, with Moran's Index exceeding 0.9, signifying a clustered ecological quality pattern. High–high (H–H) zones were prevalent in areas of elevated altitude and dense vegetation, whereas low–low (L–L) zones were prevalent in urban and mining sectors. (3) Further, a buffer zone analysis of two coal mines, differing in their mining chronology, geographical positioning, and operational status, elucidated the ecological impact exerted over a 32-year trajectory. These insights furnish a robust scientific and technical foundation for resource-centric cities to fortify ecological safeguarding and to advance sustainable development stratagems. [ABSTRACT FROM AUTHOR]

The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model's performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method. [ABSTRACT FROM AUTHOR]

The study of Land Use and Landscape Patterns (LULPs) changes and their ecological quality effects in Haikou city under the background of the Hainan Free Trade Port Plan (HFTPP) helps to promote coordinated development between cities and the environment. To date, most research on ecological quality has focused on areas with extremely fragile ecology and/or is related to LULP analysis. There are few studies in the literature focusing on the impact of high-intensity human activities caused by relevant policies on urban LULPs. The purpose of this research was to design a framework that monitors urban ecological security by considering the effect of the developing free trade port. The proposed framework was constructed by integrating multi-temporal Sentinel-2 remote sensing images, night light remote sensing data, digital elevation model (DEM) data, and spectral index features such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), bare soil index (BSI), and normalized intertidal mangrove index (NIMI), as well as analytical approaches such as the land use transfer matrix, land use dynamic degree, land use degree and transfer matrix, land use gravity center measurement, and landscape pattern index. The framework takes advantage of the Google Earth Engine (GEE) cloud platform and was applied to a highly developed Haikou city, the capital of Hainan province. Maps of brightness (SBI), greenness (GVI), and humidity (WET) were created annually from 2016 to 2021, enabling detailed ecological environment quality evaluation and analysis. The advantages of this study are (1) reliable land cover results obtained automatically and quickly; (2) the strong objectivity of the quantitative research on landscape patterns and land use; and (3) deep integration with free trade port policies. Through the research on the ecological quality problems caused by the change in LULP in the study area, the research results show that, from 2016 to 2021, the spatial distribution of land use and landscape pattern in Haikou city had been constantly changing; the area of construction land has decreased, with most of it having been converted into forest land and farmland; the gravity center of the building land has moved to the northwest; the degree of landscape fragmentation has decreased and the heterogeneity of landscape distribution has increased; the free trade port policies have promoted Haikou's economic development and ecological civilization construction; and finally, Haikou's ecological environmental quality has improved significantly. [ABSTRACT FROM AUTHOR]