The Lake Chaohu Basin (LCB) represents a focal point for water pollution control in China, and its water quality status has drawn substantial attention. We conducted a holistic assessment of water quality using the water quality index (WQI) in the LCB based on monthly measurements of 13 water quality parameters at 16 stations during 2016–2020. We observed a general upward trend in the annual average WQI for Lake Chaohu, suggesting an improvement in water quality. A notable shift in the spatial pattern of water quality was observed. The annual average WQI for inflowing rivers also exhibited an overall upward trend. The WQIs of the lake and inflowing rivers, particularly in the western lake, exhibited a significant positive correlation, suggesting that the water quality of the inflowing rivers is a crucial factor influencing the water quality of Lake Chaohu. The crucial water quality parameters influencing the WQI of the LCB included ammonia nitrogen, total phosphorus, total nitrogen, dissolved oxygen, and permanganate index, which were used to construct a minimum WQI (WQImin). The WQImin showed outstanding performance in the LCB. The findings of this study could deepen the understanding of water quality patterns and the response of lake water quality to inflowing river water quality within lake-type basins.

The urban scaling theory (UST) strives for a universal taxonomy that depicts relationships among urban indicators (e.g. energy consumption, economic output) with city size. However, the lack of international agreement on city definitions and statistics complicates cross-country comparisons of urban scaling performance. Remote sensing provides a uniform standard for measuring cities around the world. To scrutinize the consistency of UST, we quantified changes in remotely sensed urban built-up areas (UBA) and nighttime lights (NTL) distributions from 11,581 cities in 61 countries spanning 2000-2020, representing urban physical elements and socioeconomic activities, respectively. We find that UBA is well described by UST in all analyzed countries, while NTL aligns with 98% of them. UST quantified by remote sensing shows greater robustness than country-dependent aggregate statistics. We also observed disparities of scaling exponents (beta) among countries, with UBA all being sublinear (beta < 1), and NTL ranging from 0.46 to 1.22 with a median of 0.94. Both UST and rank-size distributions of urban area and population show stronger scaling relationships for countries with larger networks of built environments, (Brazil, China, Germany, Japan, Russia, United States) suggesting that both size and evolution of urban systems impact the underlying scaling processes. Comparison of scaling properties of remotely sensed UBA and NTL captures complementary physical characteristics of built environments while minimizing the Modifiable Area Unit Problem introduced using spatially aggregated metrics within administrative units. Our findings highlight the consistency of urban growth patterns while confirming the systematic socioeconomic disparities among urban systems of varying size and growth trajectory.

China has entered an era of population decline, yet urbanization continues as rural-to-urban migration persists. This demographic transition has prompted a strategic shift in urban development from extensive spatial expansion toward quality-oriented, intensive growth models. However, evolving human–land supply–demand dynamics in cities historically characterized by population inflows remain insufficiently understood. This study focuses on Xiamen, a prototypical coastal migrant-receiving city, to investigate land use simulation under demographic transition. By integrating the cohort-component method with the Patch-generating Land Use Simulation (PLUS) model, we project Xiamen’s population under three scenarios by 2030: Stable Continuation (SCS), Natural Development (NDS), and National 2030 Population Planning (NPP), with projected increases of 5.56%, 6.76%, and 24.69%, respectively. Results show continued but decelerating population growth, with adequate labor supply and persistent demographic dividend. Notably, the NPP scenario reveals a negative correlation between population growth and construction land expansion. In NPP-High, prioritizing compact development and ecological conservation, population grows by 1.27 million while construction land decreases by 2.85% and forest land increases by 4.09%. This framework provides empirical evidence for compact urban development under the dual constraints of land-use efficiency and ecological protection.

Driven by the “dual-carbon” strategy, the development of zero- and low-carbon parks has become a crucial approach to resolving the conflict between urban expansion and ecological limits. Using urban functional zoning and land use data, this study estimates carbon emissions in Xiamen and examines their spatial distribution at the functional zone level, along with an assessment of carbon balance zoning. The results indicate that (1) Carbon sources far exceed sinks, with spatial concentrations in southern and northern areas, respectively. Commercial, transportation, and industrial zones are major emission sources. (2) A significant negative spatial correlation in carbon emissions exists among functional zones, manifesting as an alternating pattern of high- and low-carbon zones. (3) 72% of the zones have an ecological support coefficient below one, indicating severe carbon imbalance. (4) Xiamen can be categorized into four carbon balance functional zones, with carbon-source regulation zones accounting for 70%, core carbon-source zones accounting for 5%, and carbon-sink stressed zones accounting for 25%. No core carbon sink zones are identified. Based on these findings, targeted strategies are proposed: ecological restoration in northern Xiamen, carbon emission regulation in central areas, and source reduction in the south. These measures provide a scientific foundation for supporting Xiamen’s low-carbon transition and sustainable development.

This study focuses on the nine provinces along the Yellow River, and establishes a water resources system resilience evaluation framework consisting of 34 indicators based on a meteorology-hydrology-socioeconomy-ecology-engineering multidimensional system. By applying a TOPSIS for assessing water resources system resilience, that incorporates combination weighting approach based on game theory, this study investigates the spatiotemporal evolution of the water resources system resilience from 2009 to 2022.The resilience of water resources system in the provinces along the Yellow River exhibited an overall fluctuating upward trend. Since 2019, resilience levels generally increased, with the lowest values of 0.36 in 2009 and 2010, and the highest value of 0.59 in 2021. Some provinces, including Shanxi, Gansu, and Qinghai experienced significant fluctuations in resilience due to climate variability and the implementation of local policies, whereas regions such as Shaanxi and Shandong maintained relatively stable resilience levels. From 2009 to 2022, the resilience levels of water resources system in the provinces along the Yellow River were ranked in descending order: Sichuan > Henan > Shaanxi > Inner Mongolia > Qinghai > Shandong > Ningxia > Gansu > Shanxi. Sichuan and Henan achieved Level II (higher resilience), with the rest at Level III (moderate resilience).

The accelerated global urbanization process has positioned land use/land cover change modeling as a critical component of contemporary geographic science and urban planning research. Traditional approaches face substantial challenges when addressing urban system complexity, multiscale spatial interactions, and high-dimensional data associations, creating urgent demand for sophisticated analytical frameworks. This review comprehensively evaluates machine learning applications in land use prediction through systematic analysis of 74 publications spanning 2020–2024, establishing a taxonomic framework distinguishing traditional machine learning, deep learning, and hybrid methodologies. The review contributes a comprehensive methodological assessment identifying algorithmic evolution patterns and performance benchmarks across diverse geographic contexts. Traditional methods demonstrate sustained reliability, while deep learning architectures excel in complex pattern recognition. Most significantly, hybrid methodologies have emerged as the dominant paradigm through algorithmic complementarity, consistently outperforming single-algorithm implementations. However, contemporary applications face critical constraints including computational complexity, scalability limitations, and interpretability issues impeding practical adoption. This review advances the field by synthesizing fragmented knowledge into a coherent framework and identifying research trajectories toward integrated intelligent systems with explainable artificial intelligence.

Algal blooms pose a serious threat not only to the lake ecosystem of Lake Bosten but also by negatively impacting its rapidly developing fisheries and tourism industries. This study focuses on Lake Bosten as the research area and utilizes multi-source remote sensing imagery from Landsat TM/ETM+/OLI and Sentinel-2 MSI. The Adjusted Floating Algae Index (AFAI) was employed to extract algal blooms in Lake Bosten from 2004 to 2023, analyze their spatiotemporal evolution characteristics and driving factors, and construct a Long Short Term Memory (LSTM) network model to predict the spatial distribution of algal-bloom frequency. The stability of the model was assessed through temporal segmentation of historical data combined with temporal cross-validation. The results indicate that (1) during the study period, algal blooms in Lake Bosten were predominantly of low-risk level, with low-risk bloom coverage accounting for over 8% in both 2004 and 2005. The intensity of algal blooms in summer and autumn was significantly higher than in spring. The coverage of medium- and high-risk blooms reached 2.74% in the summer of 2004 and 3.03% in the autumn of 2005, while remaining below 1% in spring. (2) High-frequency algal bloom areas were mainly located in the western and northwestern parts of the lake, and the central region experienced significantly more frequent blooms during 2004–2013 compared to 2014–2023, particularly in spring and summer. (3) The LSTM model achieved an R2 of 0.86, indicating relatively stable performance. The prediction results suggest a continued low frequency of algal blooms in the future, reflecting certain achievements in sustainable water-resource management. (4) The interactions among meteorological factors exhibited significant influence on bloom formation, with the q values of temperature and precipitation interactions both exceeding 0.5, making them the most prominent meteorological driving factors. Monitoring of sewage discharge and analysis of agricultural and industrial expansion revealed that human activities have a more direct impact on the water quality of Lake Bosten. In addition, changes in lake area and water environment were mainly influenced by anthropogenic factors, ultimately making human activities the primary driving force behind the spatiotemporal variations of algal blooms. This study improved the timeliness of algal-bloom monitoring through the integration of multi-source remote sensing and successfully predicted the future spatial distribution of bloom frequency, providing a scientific basis and decision-making support for the sustainable management of water resources in Lake Bosten.

Habitat quality is an important basis for human well-being and the achievement of sustainable development. Based on land-use data for the Bosten Lake Basin in 2000, 2005, 2010, 2015, and 2022, the PLUS and InVEST models are applied in this study to predict and analyze land-use changes and explore the spatial and temporal evolution characteristics of the region’s habitat quality. Additionally, we use a geographic detector model to reveal the drivers of spatial variation in habitat quality. The results show that: (1) Land use in Bosten Lake Basin is dominated by grassland and bare land, with an area share of 93.21%. Habitat quality shows a trend of degradation followed by improvement, with a spatial pattern of high in the northwest and low in the southeast. (2) Habitat quality in 2030 increased from 2022 in all cases, with a mean of 0.354 for the natural development scenario, a maximum of 0.355 for the ecological development scenario, and a minimum of 0.353 for the economic development scenario. (3) The main drivers affecting habitat quality in the Bosten Lake watershed are DEM, mean annual precipitation (MAP), and GDP per capita. X1∩X4 (0.50) and X4∩X10 (0.51) are the interaction factors with the largest dominant effect in 2000, 2010 and 2020, respectively.

Urban open spaces (UOS) are crucial for urban life, offering benefits across individual and societal levels. However, the understanding of the systematic dynamic of UOS scaling with city size and its potential non-linear performance remains a limited clarity area. This study bridges this gap by integrating urban scaling laws with remote sensing data from 1990 to 2020, creating a framework to analyze UOS trends in China. Our findings reveal that UOS growth is sub-linear scaling with city size, exhibiting economies of scale with scaling exponents between 0.55 and 0.65 and suggesting potential shortages. The distribution structure of UOS across cities is becoming increasingly balanced, as indicated by the rising Zipf’s slope from 0.66 to 0.88. Southeastern coastal cities outperform, highlighting spatial variations and path dependency in UOS development. Additionally, using metrics of Scale-adjusted metropolitan indicator (SAMI) and the ratio of open space consumption to population growth rates (OCRPGR), we observe a trend towards more coordinated development between UOS and population, with a declining proportion of uncoordinated cities. Our long-term, large sample coverage study of UOS in China may offer positive significance for urban ecological planning and management in similar rapidly urbanizing countries, contributing to critical insights for quantifying and monitoring urban sustainable development.

The response of dryland vegetation to climate change is particularly sensitive in the context of global climate change. This paper analyzes the characteristics of spatial and temporal dynamics of vegetation cover in the Tarim River Basin, China, and its driving factors in order to investigate the response of vegetation growth to water storage changes in the basin. The Enhanced Vegetation Index (EVI), the GRACE gravity satellite, and meteorological data from 2002 to 2022 are used to decipher the characteristics of the response of water storage changes to vegetation changes, which is of great significance to the realization of regional ecological development and sustainable development. The results of the study show the following: (1) The vegetation in the Tarim River Basin has an overall increasing trend, which is mainly distributed in the Aksu Basin and the Weigangkuche River Basin and is spatially distributed in the form of a ring. (2) Vegetation distribution greatly improved during the 20-year study period, dominated by high-cover vegetation, with a change rate of 200.36%. Additionally, vegetation changes are centered on the watersheds and expand to the surrounding area, with a clear increase in vegetation in the Kumukuri Basin. Areas with a vegetation Hurst index of <0.5 account for 63.27% of the study area, and the areas with a continuous decrease were mainly located in the outer contour area of the Tarim River and Kumu Kuri Basins. (3) There are obvious spatial differences in the correlation between EVI and temperature and precipitation elements. The proportion of areas with positive correlation with temperature within the study area is 64.67%. EVI tends to be consistent with the direction of migration of the center of gravity of the population and GDP, and the areas with positive correlation between vegetation and terrestrial water reserves are mainly distributed in the northern slopes of the Kunlun Mountains, with an area proportion of about 50.513%. The Kumukuli Basin also shows significantly positive correlation.

The northern Tianshans region in the arid zone of northwestern China plays a key role in promoting high-quality development of the ecological environment. In recent years, ecological environmental protection in this region has encountered major challenges due to the dual impacts of human activities and natural changes. In order to accurately assess the current status of the ecological environment in the northern Tianshans, this study analyzed the spatial and temporal changes in land use and ecological and environmental effects using land use data from 2000 to 2020 and explored the current status of land use, land use dynamic process, and ecosystem service value (ESV) in the region. Two-factor spatial autocorrelation analysis revealed the spatio-temporal characteristics of the value changes over the 20-year period as well as their spatial heterogeneity. The results show that: (1) land use changes are dominated by increases in cultivated land, forest land, watershed, and wetland, and decreases in grassland, glacier snow, and bare land. Of these changes, the expansion of cultivated land area is the most significant, showing a total increase of 1136.13 × 103 hm2. (2) The ESV increased and then decreased, reaching the highest value in 2005 and the lowest in 2020. The value of individual ecosystem services is dominated by regulating services, accounting for about 67% of the total value. (3) The overall regional balance of ecological environment quality and the contribution rate of the conversion from bare land to other land types is as high as 82.7986%, constituting the main factor in regional ecological environment improvement. The spatial distribution pattern exhibits the characteristic of “high in the northeast and low in the southwest”. (4) There is a positive correlation between the ESV, the Normalized Difference Vegetation Index (NDVI), and the Anthropogenic Impact Composite Index, with the NDVI being the main cause of spatial heterogeneity in the ESV. The research results provide a scientific basis for ecological protection, land management, and policy formulation in the northern foothills of the Tianshan Mountains and are of great significance for promoting regional sustainable development.

Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.

Different types of ecosystems form a complex community of life. Hence, ecosystem protection and restoration should not focus solely on a single ecosystem. Ecosystem health assessments should consider the integrity and systematicity of interrelated ecosystems to inform rational environmental planning and management. In this study, the key characteristic indicators of major ecosystems (mountain, water, forest, and cropland) and ecosystem service capacity indicators in Anxi County, China, were selected to construct an integrated assessment system of ecosystem health that led to integrated ecosystem restoration pathways that addressed the county’s ecological problems. The results revealed that ecosystem health was higher in the western and lower in the eastern parts of the county. Throughout the county, “medium” and “poor” ecosystem health levels predominated, revealing that overall ecosystem sustainability was weak. Ecosystem restoration programmes should be tailored to each health level. Where there was “excellent” and “good” ecosystem health ratings, those healthy ecosystem functions should be strengthened and maintained. In the “medium” health areas, the control and prevention of ecological problems should be strengthened. “Poor” health areas require immediate integrated ecological restoration projects that ensure the connectivity and coordination of restoration tasks in fragile ecosystems. This then will enhance holistic ecosystem stability and sustainability.

总结了生态修复工程的现状、我国在推进生态修复上的举措,指出未来生态修复的着重点是要找到更科学且高效的生态修复途径,以及将本地人福祉有机整合到生态修复当中以全面提高生态系统服务效果的方法.阐述了目前常见的生态修复方式,对其各自通常的适用情况及作用特征进行分析,并在此基础上得出一种生态修复的理想模式.阐述了景感生态学的定义,分析了景感生态学、景感营造以及其在稳定、提高生态系统服务及其价值方面的作用机制;讨论了如何基于景感生态学和景感营造分步实现生态修复的理想模式,其重点在于营造景感生态系统,使生态系统服务得到充分发挥,为本地谋取更大福祉.山水林田湖草生态修复工程的决策者、规划者和实施者要在观念、策略、方法以及评价四个方面做出转变.

Research on spatiotemporal characteristics and influencing factors of industrial carbon emissions intensity is crucial to the efforts of reducing carbon emissions. This paper measures the industrial carbon emissions intensity (CI) by energy consumption in Guangdong from 2012 to 2020 and evaluates the regional differences of CI. In addition, we apply the extended STIRPAT (stochastic impacts by regression on population, affluence and technology) and GTWR (geographically and temporally weighted regression) models to reveal the influence of driving factors on CI from spatial–temporal perspectives, based on the economic panel data and night-time light (NTL) data of 21 cities in Guangdong. To show the robustness of the results, we introduce the ordinary least squares (OLS) model, geographically weighted regression (GWR) model and temporally weighted regression (TWR) model compared with the GTWR model and find that the GTWR model outperforms these models. The results are as follows: (1) CI shows an overall downward trend and presents a pattern of being low in the middle and being high on both sides in space. (2) The industrial carbon emission is mainly affected by six main factors: economic development level, population scale, energy intensity, urbanization level, industrial structure and energy consumption structure. Among them, energy intensity occupies a significant position and poses a positive impact on the CI of the industrial sector.

Quantifying the evolution of urban expansion is of vital importance for optimizing land use patterns and promoting sustainable urban development. This research constructed the Enhanced Nighttime Light Urban Index (ENUI), which combines NPP-VIIRS nighttime light with Landsat spectral data, and used this index for quantitative evaluation of urban expansion in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) urban agglomeration. The results showed that, between 2012 and 2018, (1) the extraction using the ENUI achieved overall accuracy of 0.93-0.94, producer's accuracy in urban areas of 0.82-0.84, user's accuracy in urban areas 0.73-0.75, and Kappa values of 0.75-0.78, showing a relatively high extraction accuracy; (2) the GBA urban expansion followed a circular and radial expansion pattern; (3) the growth trend of expansion intensity and development potential for sub-central cities was stronger than central cities; and (4) the agglomeration auto correlation of the spatial expansion in the GBA presents a continuous "dispersion" trend. Compared with previous methods, our study improved the extraction accuracy of urban expansion and effectively reduced the deficiency of overflow effect for NPP-VIIRS nighttime light. The research can thus provide valuable datasets and reference for decision-making to adjust and optimize urban development pattern and design urban sustainable development.

城市的地面沉降一直以来都是沿海城市密切关注的问题。在沿海城市对高层建筑群诱发的地面沉降进行深入研究具有重要意义。获取地面沉降监测点的高程信息,分析了厦门市地面沉降情况与空间差异性。重点探讨建设用地、建筑容积率与土地利用转化信息这3种因素对厦门市地面沉降的影响,并对厦门岛地面沉降风险进行评估,可为厦门岛的高层建筑三维空间布局与优化研究提供先验知识。结果表明:（1）在2001—2015年厦门市整体的高程存在下降的趋势且较为缓慢,同时厦门市的城市建设规模不断变大且建设面积增速不断增大,与厦门市的城市地面的沉降发展趋势相同。（2）厦门市的高层建筑密度需达到一定程度才对地面沉降有影响,厦门岛的建筑容积率与地面沉降没有存在显著的相关性。（3）在不同土地利用转化建设用地对地面沉降的影响中:原水域用地沉降最为明显,建成时间越长,总沉降量越大;原耕地用地转化成高层建筑之后沉降有明显发生,也随着建成时间的延长有逐渐增加沉降量的态势;原园地、林地用地沉降只在少部分地区发生且与建成时间无明显差别。（4）厦门岛可用于城市建设区域中的53.34%是地面沉降中风险等级以上的。此外,本研究给出防御防治城市地面沉降风险的有效建议与措施。本研究结果可为厦门市城市化进程中的地面沉降监测、规划设计与决策等提供重要的数据支撑,为城市可持续发展提供科学的依据;也可为国内外其他城市的地面沉降监测分析和可持续发展研究提供理论依据和借鉴意义。

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

The theoretical research and practice of sustainable development based on landsenses ecology are carried out through the overall framework mechanism of meliorization and process regulation. This kind of mechanism is denoted as hyperfeedback mechanism of meliorization towards sustainable development, or hyperfeedback for short. Based on the basic idea of landsenses ecology and meliorization approach, this paper expounds the hyperfeedback and related aspects, puts forward the concept and characteristics of hyperfeedback system, and illustrates a platform framework for the meliorization simulation and management of hyperfeedback system.

Timely and accurate extraction of urban built-up areas is crucial to addressing environmental problems related to fast changes in urban land cover, which is fundamental for optimizing land use patterns and supporting global sustainable development. Nighttime light (NTL) from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) offer a new data source for extracting urban information. However, this kind of data suffer from drawbacks of blooming effects. To address this problem, in this study, the Enhanced Nighttime Light Urban Index (ENUI) approach, which involves the combination of NPP-VIIRS NTL with the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), is proposed and tested. This approach was used to rapidly monitor the urban built-up areas in the Guangdong-Hong Kong-Macao Greater Bay Area in 2012, 2015, and 2018. The average overall accuracy and Map-level Image Classification Efficacy (MICE) for the extraction results are 93.56% and 0.77, respectively, while those of the Local-Optimized Thresholding (LOT) are 86.48% and 0.54, respectively; meanwhile, the average F-score values, user's accuracy and producer's accuracy for urban areas using the proposed approach increased by 9.98%, 10.90% and 8.67%, respectively, compared with the LOT. These findings suggest that this approach has a higher extraction accuracy than the LOT; this is primarily ascribed to the integration of NTL data with the NDVI, NDWI, and NDBI, which increases the variability of nighttime light in the urban core area and adequately alleviates the blooming effects of nighttime light brightness in water bodies and vegetated areas. The proposed approach shows great potentials to accurately and effectively monitor multi-temporal urban information and address environmental issues using NPP-VIIRS NTL data in global urban agglomerations.