Suburban open-pit mining concentration areas are both the frontline of urban expansion and the main battlefield in mineral resource development. These dual forces have resulted in significant land use/cover changes (LUCC), which play a crucial role in determining the ecological environment quality (EEQ). However, research examining how LUCC affects EEQ under the coupled impact of these two development events is currently lacking. In this study, the response of EEQ to LUCC was evaluated using Landsat images from 2000, 2010, and 2020 for the southern suburban open-pit mining concentration area in Jinan City. A relative contribution index was used to address the ecological and environmental effects of non-dominant land use/cover types, and the impact of LUCC on EEQ and its spatial spillover effects were revealed by also carrying out a buffer zone analysis. The findings of this study indicate that: (1) the dominant land use/cover types that influence the EEQ spatial pattern are farmland, grassland, and construction land. Among them, the area of farmland was the largest, with more than 1800 $\mathrm{k}{\mathrm{m}}^2$. Changes in non-dominant land use/cover types to mining land and mine rehabilitation made the most significant relative contribution to the changes in EEQ, i.e., 0.0735 and 0.0184, respectively. (2) The transformation of farmland into construction land and mining land and woodland into mining land was shown to exacerbate the deterioration of the EEQ in the study area, with a deterioration area of 1367.54 $\mathrm{k}{\mathrm{m}}^2$ and spatial spillovers of up to 1000 m. (3) Returning farmland to woodland and grassland, as well as returning mine rehabilitation, were found to be the main factors contributing to the improvement of EEQ in the study area, with an improvement area of 1335.67 $\mathrm{k}{\mathrm{m}}^2$ and spatial spillover extending from 500 to 800 m. (4) Nevertheless, uneven changes in land use/cover continue to aggravate the agglomerative effect of EEQ deterioration. Further refinement and enhancement of the methods and standards of ecological governance are urgently needed to counterbalance the uneven spatial spillover effects between ecological degradation and improvement. This study provides a scientific reference for the promotion of ecological protection and sustainable development in mining cities.

The continuous acquisition of spatial distribution information for offshore hydrocarbon exploitation (OHE) targets is crucial for the research of marine carbon emission activities. The methodological framework based on time-series night light remote sensing images with a feature increment strategy coupled with machine learning models has become one of the most novel techniques for OHE target extraction in recent years. Its performance is mainly influenced by machine learning models, target features, and regional differences. However, there is still a lack of internal comparative studies on the different influencing factors in this framework. Therefore, based on this framework, we selected four different typical experimental regions within the hydrocarbon basins in the South China Sea to validate the extraction performance of six machine learning models (the classification and regression tree (CART), random forest (RF), artificial neural networks (ANN), support vector machine (SVM), Mahalanobis distance (MaD), and maximum likelihood classification (MLC)) using time-series VIIRS night light remote sensing images. On this basis, the influence of the regional differences and the importance of the multi-features were evaluated and analyzed. The results showed that (1) the RF model performed the best, with an average accuracy of 90.74%, which was much higher than the ANN, CART, SVM, MLC, and MaD. (2) The OHE targets with a lower light radiant intensity as well as a closer spatial location were the main subjects of the omission extraction, while the incorrect extractions were mostly caused by the intensive ship activities. (3) The coefficient of variation was the most important feature that affected the accuracy of the OHE target extraction, with a contribution rate of 26%. This was different from the commonly believed frequency feature in the existing research. In the context of global warming, this study can provide a valuable information reference for studies on OHE target extraction, carbon emission activity monitoring, and carbon emission dynamic assessment.