Snow cover is essential for the hydrological cycle and ecological balance of the Tibetan Plateau (TP). The normalized difference snow index (NDSI) is a widely used indicator for snow detection, yet extensive cloud cover often disrupts the spatiotemporal continuity of MODIS NDSI data. Given the close link between snow cover and environmental conditions, introducing environmental factors provides a novel perspective on reconstruction. Here, we developed a LightGBM-based NDSI reconstruction method that integrates the spatiotemporal cube with environmental features—meteorological, topographical, and geographical—along with a spatiotemporal reliability assessment. This method generated a robust, long-term, cloud-free MODIS NDSI dataset over the TP (daily, 500 m). Through simulation experiments, we evaluated the numerical, spatial, and classification accuracy of our method. Results showed that this method achieved high accuracy with averaged coefficient of determination ($R^{2}$), mean absolute error (MAE), and root-mean-square error (RMSE) of 0.81, 0.090, and 0.138, respectively, while classification metrics overall accuracy (OA),$F1$-score (FS), commission error (CE), and omission error (OE) of 0.94, 0.82, 0.038, and 0.20, respectively. Notably, incorporating snow-related environmental features resulted in superior metric accuracy, image quality, and spatial detail compared to spatiotemporal interpolation (SI) alone. Furthermore, the proposed method demonstrated higher accuracy during snow cover periods and in high-altitude regions on the TP. This novel approach to NDSI reconstruction enhances the understanding of snow accumulation and melting processes on the TP, offering a robust data foundation for climate change monitoring and hydrological modeling.

Unmanned aerial vehicle (UAV) laser scanning (ULS) and backpack laser scanning (BLS) are two commonly employed technologies in precision forestry. However, data acquired by these two types of light detection and ranging (LiDAR) are distinct, with one capturing point clouds beneath the canopy and the other above. Consequently, there is minimal overlap in the point clouds collected by both methods, especially in dense forests, presenting significant challenges for data registration. Furthermore, many trees in forests (particularly broadleaf trees) have the tree tops and trunk centers not aligned vertically, which greatly increases the difficulty of the data registration methods based on tree position. To solve the above-mentioned problems, we here propose a novel and robust method to register ULS and BLS point clouds in forested areas. Our method consists of three key steps, that is, tree location extraction, quadrant search-based minimum spanning tree (MST) matching, and registration. The quadrant searching strategy dynamically searches for potential candidates in four quadrants centered on the initial tree locations. By constructing MSTs for the potential tree locations, triangle constraints require only four topologically similar tree locations to find one-to-one correspondences during the stepwise MST matching process. The proposed method was evaluated in five urban forest sample plots and one natural forest sample plot located in China, covering both coniferous and broadleaf forests. The results show that our method obtained good registration results on all six sample plots, with an averaged rotation error, translation error, pointwise error, and root-mean-square error (RMSE) of 0.012 rad, 0.354, 0.378, and 0.379 m, respectively. Comparative studies indicate that our method outperformed existing registration methods, demonstrating its effectiveness and robustness. Our method allows for the creation of a more complete picture of forest vertical structure and holds great potential for informing sustainable forest management practices and supporting critical ecological assessments.

Snow phenology is a crucial indicator that captures the dynamic changes in snow cover, which play a significant role in shaping hydrological processes and influencing ecosystem functioning. Recent climate change has affected the temporal dynamics of snow accumulation and ablation processes, particularly on the Tibetan Plateau. However, accurately quantifying how meteorological conditions influence snow phenology, especially the nonlinear and time-lagged effects, remains challenging. To address this challenge, we present a novel survival analysis model, a state-of-the-art approach used in medical research, to examine the complex effects of meteorological conditions on snow onset date (SOD) and snow end date (SED). Rigorous validation demonstrates that incorporating nonlinear and time-lagged relationships enhances both the accuracy and interpretability of the model, providing deeper insights into snow cover dynamics on the Tibetan Plateau. Specifically, our findings indicate that meteorological factors show an average delay of 11−12 days for SOD and SED across the Tibetan Plateau. A 1°C increase in temperature or a 1 W/m² increase in shortwave radiation reduces the probability of SOD by 10.7% and 1.7%, while increasing the probability of SED by 8.2% and 0.6%. Conversely, a 1 mm increase in precipitation or a 1 m/s decrease in wind speed increases the probability of SOD by 11.2% and 25.0%, and decreases the probability of SED by 12.5% and 17.6%, respectively. In addition to enhancing the quantitative understanding of how various meteorological factors influence snow phenology, the proposed model presents a promising approach for forecasting snow cover dynamics under future climate change scenarios.

The nighttime urban environment is increasingly affected by various forms of artificial light at night. Color temperature, a critical characteristic of light, has significant effects on numerous fields and industries. The widespread adoption of light-emitting diode (LED) light, a low-carbon technology, has resulted in extensive use of lights with varying color temperatures in diverse settings. However, it is crucial to recognize that different color temperatures have distinct impacts on human health and ecological systems. Therefore, understanding the spatial distribution and composition of nighttime light (NTL) with different color temperatures is essential for developing sustainable strategies that balance public safety, energy consumption, and ecosystem conservation. In response to this need, we propose a color temperature-based lighting source classification system utilizing autonomous aerial vehicle (AAV)-captured NTL images, rather than the traditional satellite-based NTL images, due to the superiority of spatial resolution (SR). We employ an object-oriented classification method to categorize lights into high-pressure sodium (HPS), warm LEDs, cool LEDs, and colored LEDs. Moreover, to evaluate the effect of flight altitude on classification accuracy, we classify lights at seven different altitudes and compare their accuracy at each level. Our results indicate that the random forest (RF) algorithm can accurately identify the four types of lights, with the highest classification accuracy achieved at a flight altitude of 350 m, where the overall accuracy (OA) and kappa coefficient were 0.957 and 0.947, respectively. Moreover, at this altitude, the highest producer’s accuracy (PA) for warm LEDs and colored LEDs was 0.971 and 0.942, respectively, while the user’s accuracy (UA) for each light type exceeded 0.9. In addition, the methodology also demonstrated strong performance in more complicated regions, as evidenced by an off-site application accuracy of 0.847 and a kappa coefficient of 0.808. This study is the first to identify NTL types based on color temperature, offering a new perspective for urban lighting planning and light pollution management.

Urban spatial interaction serves as an indicative measure for estimating the intensity and character of interurban linkages and relationships. The previous studies have utilized intercity relational data (e.g., population migration, goods trade, and information exchange) to build up urban connections directly. Besides, the nighttime light (NTL) data have also been adopted to simulate a dynamic urban intercity flow. However, the relevant studies have not clearly defined the urban spatial interaction based on the NTL data. To answer this question, we used trial-and-error testing to define the NTL-based interaction. First, five traditional urban interactions were selected as the potential definitions, including population migration, transfer of innovation, information flow, financial flow, and urban composite interaction. Second, as usual, the NTL-based urban interaction, named the Nighttime Light Interaction (NTLI) index, was simulated based on the NPP-VIIRS-like NTL data and the radiation model. Taking the Yangtze River Delta Urban Agglomerations (YRDUA) as an example, we found that the NTL-based urban interaction is more like the population migration at the urban agglomeration scale and the provincial scale withR2of 0.71 and 0.59, respectively. In addition to this, the NTLI index has a weak correlation with the Transfer of Patent (TP) index, Information Flow (IF) index, Economic Interaction (EI) index, and Composite Interaction (CI) index. To sum up, the interaction network from NTL data can be an adequate proxy of urban population interaction, rather than the knowledge network or economic network. This study provides a new thought for urban network simulation and urban population mobility research.

Geographically weighted regression (GWR) is a classical modeling method for dealing with spatial non-stationarity. It incorporates the distance decay effect in space to fit local regression models, where distance is defined as Euclidean distance. Although this definition has been expanded, it remains focused on physical distance. However, in the era of globalization and informatization, where the phenomenon of remotely close association is common, physical distance may not reflect real spatial proximity, and GWR based on physical distance has clear limitations. This paper proposes a geographically weighted regression based on a network weight matrix (NWM GWR) model. This does not rely on geographical location modeling; instead, it uses network distance to measure the proximity between two regions and weights observations by improving the kernel function to achieve distance attenuation. We adopt the population mobility network to establish a network weight matrix, modeling China’s urbanization and its multidimensional driving factors using network autocorrelation and NWM GWR methods. Results show that the NWM GWR model has more accurate fit and better stability than ordinary least squares and GWR models, and better reveals relationships between variables, which makes it suitable for modeling economic and social systems more broadly.

Nighttime light (NTL) data have been commonly used as a proxy for characterizing socioeconomic activities. Vegetation coverage has been found to be closely and inversely correlated with NTL intensity (NTLI). Although the combination of NTLI and vegetation indices has been studied in various applications, the complex relationship between NTLI and vegetation indices is not yet clear. By analyzing the relationship between NDVI and NTLI for the mainland China from 2013 to 2021, we found that the scatterplot between NDVI and NTLI exhibits a triangular shape with a physical meaning, which we called the NTLI-NDVI triangular feature space. Using the triangular feature space, we proposed the Vegetation Nighttime Condition Index (VNCI), which is defined as the ratio of NTLI differences among pixels with a specific NDVI value. VNCI is closely associated with local urban characteristics and has the ability to increase variation in NTLI conditions within urban areas. To demonstrate the application potential of the NTLI-NDVI triangular feature space and the proposed VNCI, two applications (urban area extraction and socioeconomic parameters estimation) were conducted. In terms of extracting urban areas, VNCI shows a better detection ability (with an average overall accuracy of 85.01%) than the original NPP-VIIRS NTL data and other two existing indices in extracting urban areas. Moreover, we further proposed a simple and novel NTL correcting approach to correct NTL using VNCI, which effectively eliminates the impact of vegetation and enhances the accuracy of estimating socioeconomic parameters. Our findings demonstrated that the VNCI-corrected NTL data show superior performance (with an average R2of 0.9) in estimating both the gross domestic product and electric power consumption at provincial level. We believe the NTLI-NDVI triangular feature space and VNCI hold great potential for NTL-based urban studies.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has been collecting elevation measurements of the Earth’s surface since its launch in September 2018. Although ICESat-2 was not designed for urban applications, its excellent altimetry capabilities over the globe make it possible to obtain urban height information. However, few studies have been conducted to validate ICESat-2 height measurements in urban areas. In this letter, we evaluate the heights retrieved from 20 months of ATL03 and ATL08 data using airborne LiDAR data collected in New York City (NYC). The results indicated that the heights from ATL03 product have a moderate agreement with airborne LiDAR data with vertical errors around −1.49 m (root mean squared error [RMSE] = 2.89 m, mean absolute error [MAE] = 2.11 m,$R^{2} = 0.98$, and observations = 910 497). The ATL08 product also performed a fine accuracy in terrain heights estimating (mean error [ME] = −0.01 m, RMSE = 3.63 m, MAE = 2.04 m, and$R^{2} = 0.94$). In the three categories of urban environments, ATL03 performs best in urban high-rise dense area, with an average residual error of −1.44 m, followed by urban non-high-rise dense area with an average residual error of −1.49 m. In urban forest area, ATL08 shows its performance in measuring terrain height with an RMSE of 1.78 m. We demonstrated that ICESat-2 can provide a useful source of urban heights and holds great potential to light up more urban applications related to urban change monitoring and 3-D morphology.

Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS) are two emerging mobile mapping technologies for capturing detailed spatial information near ground in urban built environments. BLS has flexible mobility and usually provides point clouds in a local coordinate system. Therefore, a mismatch between VLS and BLS point clouds data is quite common. Fusing VLS and BLS data in different coordinate systems could provide a comprehensive survey of urban built environments. Because of the complexity of urban road environments and the difference in data acquisition methods, traditional registration approaches based on point-level correspondences are likely to fail and sometimes involve substantial manual efforts. In this paper, we propose a novel registration approach that finds the optimal transformation between the respective point clouds based on a unique tree distribution pattern defined by tree trunk centers. The proposed method consists of three key steps, i.e., trunk center extraction, stepwise minimum spanning tree (MST) matching, and transformation estimation. Stepwise MST matching is an essential step in finding the one-to-one correspondences using a topological similarity between the two LiDAR datasets. We evaluated our method with five real-world datasets collected in the Shanghai city, China. The results showed that the proposed method performed well in all five experiment sites with an average rotation error of less than 0.06° and an average translation error of less than 0.05 m. Moreover, the reported mean position deviation in the five sites are 0.112 m, 0.144 m, 0.176 m, 0.148 m, and 0.184 m, respectively. Our proposed method has a great potential for registering multiplatform LiDAR data that could provide comprehensive and essential 3D information for numerous urban applications.

Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena.

Remotely sensed nighttime light (NL) data collected by the Suomi National Polar-orbiting Partnership Satellite equipped with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor have proven to be effective for evaluating fossil fuel combustion carbon emissions (CEs). However, few studies have analyzed the relationships between NL and CE originating from different sectors. The effects of impact factors on the NL-CE relationship have not been thoroughly examined and compared. Utilizing the corrected annual composite average of NPP-VIIRS data (NTL), this letter individually investigated the relationships between the NTL and CE from all types of fossil fuels total CE (TCE); CE from gasoline, diesel oil, natural gas, and cement urban carbon emission (UC); and CE from raw coal, cleaned coal, other washed coal, briquette, and coke industrial carbon emission (IC) in China at the provincial level. The impact factors governing the NTL-CE relationship were also examined. The results showed that total NLs (TNLs) may be a more effective means for estimating UC than other types of CE but may not be a good proxy for IC due to the mismatch between their amounts and brightness. The${R}^{{2}}$values from TNL and TCE analyses were higher than those of TNL and IC within the eastern, central, and western regions. Meanwhile, we found that NTL could more accurately evaluate CE in urban areas with a large population size and a relatively developed social economy. Although the urbanization rate was the most important factor in the assessment of CE from NTL, China’s urbanization rate presented an inverted U-shaped impact on the NTL-CE relationship in the long run.

The Geoscience Laser Altimeter System onboard the NASA Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) provided elevation measurements of Earth's surface between 2003 and 2009. The centroid and maximum-amplitude-peak (MAP) retracking methods have been designed and applied to process the returned laser waveforms for elevation measurements. Although these two methods work well in general, they may generate erroneous measurements when the returned waveform was complicated by adverse atmospheric conditions (clouds, ice fogs, blowing snow, and dust storms). The centroid retracking method is often more severely affected when compared with the MAP retracking method. In this study, we present a new retracking method that exploits the spatial contextual information from neighboring footprints along the satellite ground track, in addition to the single return waveform shape information. Our method uses a probabilistic relaxation (PR) algorithm to integrate the spatial contextual information and the waveform shape information to identify the waveform peak that most likely represents the true surface elevation, rather than simply detecting the peak with the maximum magnitude. For different types of land surfaces, such as inland lakes, polar tundra, ice sheet, and sand deserts, we demonstrate that our new PR retracking method is able to produce more reliable, consistent, and accurate elevation measurements than the standard NASA ICESat/GLAS data products. The root mean squares error (RMSE) is reduced from 0.85 to 0.17 m for inland lake, from 0.81 to 0.23 m for polar tundra, from 1.25 to 0.33 m for ice sheet, and from 2.48 to 2.34 m for sand desert.

Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies.

The population distribution grid at fine scales better reflects the distribution of residents and plays an important role in investigating urban systems. The recent years have witnessed a growing trend of applying the nighttime light data to the estimation of population at micro levels. However, using the nighttime light data alone to estimate population may cause the overestimation problem due to excessively high light radiance in specific types of areas such as commercial zones and transportation hubs. In dealing with this issue, this study used taxi trajectory data that delineate people’s movements, and explored the utility of integrating the nighttime light and taxi trajectory data in the estimation of population in Shanghai at the spatial resolution of 500 m. First, the initial population distribution grid was generated based on the NPP-VIIRS nighttime light data. Then, a calibration grid was created with taxi trajectory data, whereby the initial population grid was optimized. The accuracy of the resultant population grid was assessed by comparing it with the refined survey data. The result indicates that the final population distribution grid performed better than the initial population grid, which reflects the effectiveness of the proposed calibration process.

Detailed and precise information on urban building patterns is essential for urban design, landscape evaluation, social analyses and urban environmental studies. Although a broad range of studies on the extraction of urban building patterns has been conducted, few studies simultaneously considered the spatial proximity relations and morphological properties at a building-unit level. In this study, we present a simple and novel graph-theoretic approach, Extended Minimum Spanning Tree (EMST), to describe and characterize local building patterns at building-unit level for large urban areas. Building objects with abundant two-dimensional and three-dimensional building characteristics are first delineated and derived from building footprint data and high-resolution Light Detection and Ranging data. Then, we propose the EMST approach to represent and describe both the spatial proximity relations and building characteristics. Furthermore, the EMST groups the building objects into different locally connected subsets by applying the Gestalt theory-based graph partition method. Based on the graph partition results, our EMST method then assesses the characteristics of each building to discover local patterns by employing the spatial autocorrelation analysis and homogeneity index. We apply the proposed method to the Staten Island in New York City and successfully extracted and differentiated various local building patterns in the study area. The results demonstrate that the EMST is an effective data structure for understanding local building patterns from both geographic and perceptual perspectives. Our method holds great potential for identifying local urban patterns and provides comprehensive and essential information for urban planning and management.

Accurate information on urban areas at regional and global scales is required for various socioeconomic and environmental applications. The nighttime light (NTL) composite data have proven to be an effective data source for extracting urban areas. Various urban mapping methods have been proposed in the literature to extract urban built-up areas from the Defense Meteorological Satellite Program's Operational Linescan System NTL data with a variable accuracy. However, most of the previous methods cannot be directly applied to the NTL data derived from the Suomi National Polar-orbiting Partnership Satellite with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor onboard. In this letter, we introduced a logarithmic transformation to preprocess the NPP-VIIRS NTL composite data. Then, four popular methods for urban built-up area extraction were tested using the original and log-transformed NTL data, respectively. The selected methods included the thresholding technique, Sobel-based edge detection, neighborhood statistics analysis, and watershed segmentation. The accuracy of the results was evaluated through validating the urban areas derived using each method against the referenced urban areas obtained from the National Land Cover Database for the U.S.. The results indicated that logarithmic transformation is an effective procedure for enhancing the difference between urban built-up areas and nonurban areas. The selected methods for urban built-up area extraction were found to perform better on the log-transformed NTL data than the original NTL data.

Urban spatial structure affects many aspects of urban functions and has implications for accessibility, environmental sustainability, and public expenditures. During the urbanization process, a careful and efficient examination of the urban spatial structure is crucial. Different from the traditional approach that relies on population or employment census data, this research exploits the nighttime light (NTL) intensity of the earth surface recorded by satellite sensors. The NTL intensity is represented as a continuous mathematical surface of human activities, and the elemental features of urban structures are identified by analogy with earth's topography. We use a topographical metaphor of a mount to identify an urban center or subcenter and the surface slope to indicate an urban land-use intensity gradient. An urban center can be defined as a continuous area with higher concentration or density of employments and human activities. We successfully identified 33 urban centers, delimited their corresponding boundaries, and determined their spatial relations for Shanghai metropolitan area, by developing a localized contour tree method. In addition, several useful properties of the urban centers have been derived, such as 9% of Shanghai administrative area has become urban centers. We believe that this method is applicable to other metropolitan regions at different spatial scales.

Lake surface temperature (LST) is a significant indicator of lake physical states and energy fluxes. This paper integrates multi-dates Landsat 8 thermal data with extensive in situ measurements to retrieve LST of Alaska lakes, and analyses temperature patterns across spatial scales, intra and inter-lakes. Our analysis shows that inside a lake, LST is relatively homogeneous and largely isothermal under strong wind, while larger temperature difference may exist during calm days. Warmer or cooler temperature gradients along wind direction are found for both coastal and inland lakes. At regional scale, small, deep lakes and lakes in inland and/or southern latitude are more likely to have higher mean temperatures across western ACP during summer season.

Surface depressions are abundant in topographically complex landscapes, and they exert significant influences on hydrological, ecological, and biogeochemical processes at local and regional scales. The increasing availability of high-resolution topographical data makes it possible to resolve small surface depressions. By analogy with the reasoning process of a human interpreter to visually recognize surface depressions from a topographic map, we developed a localized contour tree method that is able to fully exploit high-resolution topographical data for detecting, delineating, and characterizing surface depressions across scales with a multitude of geometric and topological properties. In this research, we introduce a new concept ‘pour contour’ and a graph theory-based contour tree representation for the first time to tackle the surface depression detection and delineation problem. Beyond the depression detection and filling addressed in the previous raster-based methods, our localized contour tree method derives the location, perimeter, surface area, depth, spill elevation, storage volume, shape index, and other geometric properties for all individual surface depressions, as well as the nested topological structures for complex surface depressions. The combination of various geometric properties and nested topological descriptions provides comprehensive and essential information about surface depressions across scales for various environmental applications, such as fine-scale ecohydrological modeling, limnological analyses, and wetland studies. Our application example demonstrated that our localized contour tree method is functionally effective and computationally efficient.

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.