Deep learning algorithms offer an effective solution to the inefficiencies and poor results of traditional methods for building a footprint extraction from high-resolution remote sensing imagery. However, the heterogeneous shapes and sizes of buildings render local extraction vulnerable to the influence of intricate backgrounds or scenes, culminating in intra-class inconsistency and inaccurate segmentation outcomes. Moreover, the methods for extracting buildings from very high-resolution (VHR) images at present often lose spatial texture information during down-sampling, leading to problems, such as blurry image boundaries or object sticking. To solve these problems, we propose the multi-scale boundary-refined HRNet (MBR-HRNet) model, which preserves detailed boundary features for accurate building segmentation. The boundary refinement module (BRM) enhances the accuracy of small buildings and boundary extraction in the building segmentation network by integrating edge information learning into a separate branch. Additionally, the multi-scale context fusion module integrates feature information of different scales, enhancing the accuracy of the final predicted image. Experiments on WHU and Massachusetts building datasets have shown that MBR-HRNet outperforms other advanced semantic segmentation models, achieving the highest intersection over union results of 91.31% and 70.97%, respectively.

The quality of the ecological environment determines human well-being, and the degree of ecological environment quality has a significant impact on regional sustainable development. Currently, the assessment content of ecological environment quality in Luoyang is relatively single-indicator-based and is insufficient to comprehensively reflect the changes in the ecological environment quality of Luoyang city. Therefore, the study aims to use the Remote Sensing Ecological Index (RSEI), a comprehensive evaluation model, with Landsat remote sensing images and statistical yearbooks as the data sources, to evaluate the spatiotemporal dynamic changes in the ecological environment quality of Luoyang city from 2002 to 2022 through trend analysis and mutation testing; the study employs geographical detectors to analyze the driving factors about the changes in ecological environment quality. The study found that: (1) the average RSEI value in Luoyang city has increased by 0.102 in the past 20 years, indicating an overall improvement in the ecological environment quality of Luoyang city. (2) The northern region of the study area has lower RSEI values, while the southern region has better ecological environment quality, which corresponds to the fact that the northern part of Luoyang city has intensive human activities, while the southern part is characterized by higher vegetation coverage in mountainous areas. (3) The proportion of areas with medium and above ecological environment quality grades have increased from 47.2% to 67.5%, indicating a positive trend in future ecological environment quality changes. (4) The population change was the strongest single factor influencing the ecological environment quality change in Luoyang city. The interaction between temperature and GDP was relatively the strongest. The current ecological environment status in the study area is the result of the combined effects of natural and anthropogenic factors. The research conclusions contribute to improving regional ecological environment quality and are of great significance for the regional ecological environment planning and the achievement of sustainable development goals.

Dianchi Lake is the sixth largest freshwater lake inland in China. The change of water area is an obvious reflection of the impact of wetland ecosystem on climate and social economic development. This study, Dianchi Lake is taken as the research object, based on GEE（Google Earth Engine） cloud platform, Landsat remote sensing images are selected to comprehensively improve the Modified Normalized Difference water index. MNDWI), the otsu binary algorithm was used to extract the Dianchi Lake water area,and the area variation trend of Dianchi Lake water area from to was discussed. Landsat Level Collection Tier data were used to study the changes of Dianchi Lake water area in different seasons in nine years, and the time series of Dianchi Lake water area and season were reconstructed. The results show that the overall trend area of Dianchi Lake has little change in different years from to.In most years, the seasonal area changes obviously. Three of images were selected to study the seasonal OA index and Kappa coefficient, and the OA index was above %, indicating high accuracy. According to the investigation, the spatial and temporal variation of Dianchi Lake water area is related to many factors :（1） rainfall. Influenced by the differentiation of dry season and rainy season（rainy season: April to September, dry season: October to the next March）, the water body of Dianchi Lake shows obvious seasonal variation.（2） Average temperature. In summer, the sunshine is sufficient and the sunshine duration is long. The sunshine duration is negatively correlated with the water area, and the water evaporation is large, which forms a sharp contrast with the stability in winter.（3） The impact of human activities. Since, Kunming city has launched the "-year Action Plan for Dianchi Lake Governance", and the area of Dianchi Lake has recovered significantly. However, with the human activities, the area of Kunming city is constantly changing, and the water area is constantly fluctuating.

人类活动不可避免地会对区域生态系统造成一定程度影响.客观评价生态环境质量,是有效控制改善生态环境质量,实现可持续发展的前提.生态环境质量变化是多因素共同作用结果,相比于其他研究方法,利用遥感数据评估生态环境的遥感生态指数(RSEI)可以快速、全面、高效地监测生态环境状态.太行山脉是华北平原与黄土高原的分界线,受到自然环境变化以及人类活动影响,植被一度大幅减少,水土流失严重.本研究以2001-2021年Landsat遥感影像为数据源,采用RSEI为计算指标,结合Mann-Kendall趋势分析与Moran's I指数,基于GEE云平台开展太行山生态环境质量的时空格局以及变化趋势研究.研究结果表明:(1)2001-2021年RSEI均值为0.519,时间上呈现先下降后增加趋势,空间上呈中间高四周低的分布特征;(2)太行山地区生态环境质量有明显的空间自相关性,生态环境质量高-高聚类多集中于山区的林地、草地,而低-低聚类多集中于平原的人造地表以及耕地;(3)研究区域生态环境质量改善地区多位于西部,而东部地区存在持续性退化区域.整体上,未来生态变化趋势主要以改善提升为主,但仍有21.49％的区域存在退化趋势.本研究可为区域生态环境动态监测治理与可持续发展提供科学参考.

Research on the spatiotemporal changes in land use/cover (LUC) and carbon storage (CS) in the region of the Taihang Mountains in various developmental scenarios can provide significant guidance for optimizing the structure of LUC and formulating ecologically friendly economic development policies. We employed the PLUS and InVEST models to study change in LUC and CS in the Taihang Mountains from 1990 to 2020. Based on these results, we established three distinct development scenarios: a business-as-usual development scenario, a cropland protection scenario, and an ecological conservation scenario. Based on these three developmental scenarios, we simulated the spatiotemporal changes in LUC and CS in the Taihang Mountains in 2035. The results indicate that: (1) from 1990 to 2020, the CS in the Taihang Mountains increased from 1575.91 Tg to 1598.57 Tg, with a growth rate of approximately 1.44%. The primary source of this growth is attributed to the expansion of forests. (2) In the business-as-usual development scenario, the growth rate of CS in the Taihang Mountains was approximately 0.45%, indicating a slowdown in the trend. This suggests that economic development has the consequences of aggravating human–land conflicts, leading to a deceleration in the growth of CS. (3) In the cropland protection scenario, the increase in the CS in the Taihang Mountains was similar to the CS increase in the business-as-usual development scenario. However, the expansion of cropland dominated by impermeable surfaces, which indicates economic development, was considerably constrained in this scenario. (4) In the ecological conservation scenario, the increase in carbon storage in the Taihang Mountains was 1.16%, which is the fastest among all three scenarios. At the same time, there was a certain degree of development of impermeable surfaces, achieving a balance between economic development and ecological conservation.