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.

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.

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.

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.