Yuanmao Zheng

Dongting Lake, a vital freshwater lake in China with substantial ecological, economic, and social significance, has fractional vegetation coverage (FVC) as a core indicator of regional ecological balance. To characterize the ecosystem’s health and support targeted protection, this study analyzed FVC’s spatio-temporal evolution and associated spatial factors in the Dongting Lake ecological restoration area using 2005–2020 MODIS imagery, integrating the dimidiate pixel model, slope trend analysis, and geographic detector model (noting the latter quantifies spatial explanatory power but not direct ecological causality). Results revealed distinct FVC heterogeneity: 2011 had the poorest vegetation (mean FVC = 0.60), while 2005, 2010, and 2012 showed higher FVC (mean = 0.65); summer exhibited the most vigorous growth due to favorable hydrothermal conditions. Slope was the dominant single factor with the highest spatial explanatory power for FVC (q = 0.50), its distribution strongly associated with soil moisture and erosion. The slope–soil moisture interaction had the strongest joint spatial explanatory power (q = 0.625), reflecting topographic–hydrological synergistic spatial association, implying slope may indirectly modulate vegetation water availability (inferred from spatial correlation, not causality). The slope–DEM interaction (q = 0.534) confirmed combined topographic explanatory effects. Overall, 70.3% of the region saw significant FVC improvement (notably in spring) from 2005 to 2020, with degradation in February, March, and December. Slope emerged as a key factor consistent with interannual and seasonal FVC variations. These findings provide a reliable scientific basis for targeted wetland restoration, emphasizing enhanced vegetation management in summer, autumn, and the growing season. Limitations include: MODIS’s 250 m resolution leading to mixed-pixel effects in fragmented wetlands, limited validation coverage of extreme habitats and single-year verification, and the Geodetector model’s reliance on spatial stratification and factor independence assumptions (deviating from wetland’s continuous factor variation) that preclude causal inference.

By addressing the challenges of management difficulties, insufficient integration of driver analysis, and single-dimensional analysis in the governance of illegal land use and illegal construction (collectively referred to as the “Two Illegalities”) under rapid urbanization, this study designs and implements a GIS-based governance system using Xiamen City as the study area. First, we propose a standardized data-processing workflow and construct a comprehensive management platform integrating multi-source data fusion, spatiotemporal visualization, intelligent analysis, and customized report generation, effectively lowering the barrier for non-professional users. Second, utilizing methods integrated into the platform, such as Moran’s I and centroid trajectory analysis, we deeply analyze the spatiotemporal evolution and driving mechanisms of “Two Illegalities” activities in Xiamen from 2018 to 2023. The results indicate that the distribution of “Two Illegalities” exhibits significant spatial clustering, with hotspots concentrated in urban–rural transition zones. The spatial morphology evolved from multi-core diffusion to the contraction of agglomeration belts. This evolution is essentially the result of the dynamic adaptation between regional economic development gradients, urbanization processes, and policy-enforcement synergy mechanisms. Through a modular, open technical architecture and a “Data-Technology-Enforcement” collaborative mechanism, the system significantly improves information management efficiency and the scientific basis of decision-making. It provides a replicable and scalable technical framework and practical paradigm for similar cities to transform “Two Illegalities” governance from passive disposal to active prevention and control.

Rapid urbanization complicates the precise, timely quantification of urban spatial morphology. This study examined urban spatial morphology in Fujian Province, integrating DMSP-OLS and NPP-VIIRS nighttime light imagery from 1992 to 2022 to extract the built-up urban footprint via the constructed VMNUI. This method achieved an overall accuracy >0.95 and a Kappa coefficient of 0.80 when the results were compared against land use samples. Utilizing Centroid Migration Analysis, clustering, Geographical Detector, and GTWR, we quantitatively analyzed Fujian’s urban spatial form and its driving mechanisms. The results indicate that the calibration and integration of NTL data effectively resolved saturation and overflow issues in the DMSP data, revealing an urban expansion rate of 3.79%, which centered on coastal areas. Geographical Detector analysis identified fixed-asset investment (q = 0.83), population (0.80), precipitation (0.78), and highway density (0.76) as dominant factors; GDP ∩ fixed-asset investment yielded the strongest interaction (0.873). GTWR further identified that slope aspect, GDP, and secondary industry share accelerated expansion in eastern Fujian, whereas population, urbanization rate, and mean temperature were key drivers of expansion in the west. This study analyzed the spatiotemporal evolution patterns and driving mechanisms of urban spatial form development in Fujian Province over a long period, and based on the results, actionable, science-based optimization strategies with practical implications are proposed for sustainable development in the region.

Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data have been widely used to monitor human activities and urbanization. However, the DMSP-OLS sensor has no on-board calibration, the DMSP-OLS and NPP-VIIRS data are not spatially consistent and continuous due to the differences in spatial resolution and sensor design between satellites, which makes it difficult to use both datasets at the same time for spatio-temporal consistency analysis. Based on this, this study proposed a new approach for systematically calibrating the DMSP-OLS and NPP-VIIRS NTL data, and rapidly generated a continuously consistent NTL dataset from 1992 to 2022. First, the DMSP-OLS data were calibrated using the invariant target method. Secondly, the NPP-VIIRS data were subjected to outlier elimination and time-series comparability calibration. Third, a new fourth-degree polynomial fit calibration model is proposed to calibrate the NPP-VIIRS data into the DMSP-OLS data scale. Finally, this research obtained a long-time series (1992–2022) “DMSP-OLS-like” NTL dataset, and analyzed the dynamics NTLs at different scales. Compared with other fitting methods, the “DMSP-OLS-like” dataset in this research has a clearer city hierarchy. It significantly reduces the saturation phenomenon and spillover effect, has a good spatial pattern and spatio-temporal consistency, and is highly compatible with the relevant socioeconomic reference quantities. The correlation coefficients R ² of total DN with population, GDP, and electricity consumption were 0.922, 0.922, and 0.988, respectively. The results showed that the proposed approach was effective, which is superior to those of existing researches. Our results effectually solved the problem that the two NTL datasets could not be used at the same time, and improved the calibration accuracy in the two NTL datasets, which provides a new source of data for relevant studies such as urban and environmental issues in long-time series.

In China and elsewhere, urban expansion is directly related to the important issues of social development, economic development, and the sustainable development of the ecological environment. Traditional statistical methods based on administrative regions lack geospatial information, which makes it difficult to analyze and explore in detail the development status and spatial differences of cities. In real time, nighttime light (NTL) remote sensing can reveal the spatial expansion change information of urban built-up areas (UB) on different scales, thus allowing for the analysis of urban spatial patterns and variations in urban development. Based on the long-time sequence NTL data from 1992 to 2020, this work studies the Xiamen Special Economic Zone by using the vegetation-water-adjusted NTL urban index (VWANUI) to extract the urban built-up areas and study the UB expansion patterns, the migration of the urban center of gravity, and intra-city differences. The result is a qualitative and quantitative temporal and spatial evaluation of Xiamen’s economic development characteristics. The results show that the UB of Xiamen expanded 349.219 km² from 1995 to 2020, mainly concentrated in the period 2005–2020, during which time 79.44% of the expansion of the whole study period occurred. Throughout the study period, the urban center of gravity of Xiamen city shifts 8757.15 m to the northeast at the rate of 350.29 m/year in the direction of 74.88° (the urban center of gravity shifted from the inner island to the outer island). The total brightness of nighttime lights in Xiamen is gradually increasing, indicating that the level of urban economic development continuously improved over the measurement period, that human social activities have strengthened, and that the cross-island development strategy has produced certain results. These results provide data that describe urban development and policy formulation in Xiamen.

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.

Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation.