Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

Accurate and detailed monitoring of population distribution and evolution is of great significance in formulating a population planning strategy in China. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime lights time-series (NLT) image products offer a good opportunity for detecting the population distribution owing to its high correlation to human activities. However, their detection capability is greatly limited owing to a lack of in-flight calibration. At present, the synergistic use of systematically-corrected NLT products and population spatialization is rarely applied. This work proposed a methodology to improve the application precision and versatility of NLT products, explored a feasible approach to quantitatively spatialize the population to grid units of $1\mathrm{km}\times 1\mathrm{km}$ , and revealed the spatio-temporal characteristics of population distribution from 2000 to 2010. Results indicated that, (1) after inter-calibration, geometric, incompatibility and discontinuity corrections, and adjustment based on vegetation information, the incompatibility and discontinuity of NTL products were successfully solved. Accordingly, detailed actual residential areas and luminance differences between the urban core and the peripheral regions could be obtained. (2) The population spatialization method could effectively acquire population information at per ${\mathrm{km}}^2$ with high accuracy and exhibit more details in the evolution of population distribution. (3) Obvious differences in spatio-temporal characteristics existed in four economic regions, from the aspects of population distribution and dynamics, as well as population-weighted centroids. The eastern region was the most populous with the largest increased magnitude, followed by the central, northeastern, and western regions. The population-weighted centroids of the eastern, western, and northeastern regions moved along the southwest direction, while the population-weighted centroid of the central region moved along the southeast direction. (4) The population distribution and dynamics in nine-level population density types were significantly different. In the period of 2000–2010, the population in the basic no-man and high concentration types presented a net decrease. The population in seven other regions all increased with a net increase ranging from 25 ${\mathrm{km}}^2$ (the moderate concentration type) to 245,668 ${\mathrm{km}}^2$ (the general transition type). Except those in the core concentration and extremely sparse types, the population-weighted centroids in all other population density types moved along the southwest direction.

The Belt and Road (B&R) region, a vital area with historical, economic, cultural and political significance, has undergone rapid urbanization in the past several decades, especially in the form of urban expansion. In this study, 20 megacities in the B&R region were selected to explore different spatiotemporal patterns of urban expansion. Object-oriented support vector machines (SVM), annual growth rate (AGR) models, and landscape metrics were employed to delineate the urban areas and characterize spatiotemporal characteristics and landscape patterns of these megacities during 1975–2015. All urban maps presented high overall accuracies (80.70%–95.90%) and overall Kappa coefficients (0.76–0.95). The study revealed that megacities in the B&R region have undergone different types of urban sprawl, mainly adopting a ‘concentric circle’ pattern in inland areas and a ‘sector’ pattern in coastal areas. Besides, six expansion modes were summarized according to the AGRs of individual megacities. Differences existed in megacities of the developing and developed countries and among five sub-regions. Moreover, ‘dispersion, gathering, and re-dispersion’ and ‘coalescence’ were two major landscape patterns of megacities in developing and developed countries. Results of this study can provide a scientific reference for urban planning and aid in sustainable development of local areas.

Understanding the process of urban expansion in megacities is considerably important. In this study, megacities Mumbai was selected as the study area. Based on the urban maps retrieved from Landsat images in 1973-2018, we mapped and quantified the detailed urban expansion process of Mumbai by adopting the expansion area and speed indices, centroid shift model, urban expansion type method, hot-zone identification method and landscape metrics. The results indicated that: (1) urban land remarkably expanded, and its centroid moved from the southwest to the northeast direction, mainly adopting the edge-expansion form. (2) Distinctly spatiotemporal heterogeneities existed in eight directions, faster in the north, northeast, and east directions, whereas slower in the five other directions. (3) The number of hot-zones increased from two to three and moved outward in space from urban centroid. (4) The urban landscape of Mumbai showed the “diffusion, aggregation, re-diffusion” pattern and presented differences in eight directions.

Ground filtering is an essential step in airborne light detection and ranging (LiDAR) data processing in various applications. The cloth simulation filtering (CSF) algorithm has gained popularity because of its ease of use advantage. However, CSF has limitations in topographically and environmentally complex areas. Therefore, an improved CSF (ICSF) algorithm was developed in this study. ICSF uses morphological closing operations to initialize the cloth, and estimates the cloth rigidness for providing a more accurate reference terrain in various terrain characteristics. Moreover, terrain-adaptive height difference thresholds are developed for better filtering of airborne LiDAR point clouds. The performance of ICSF was assessed using International Society for Photogrammetry and Remote Sensing urban and rural samples and Open Topography forested samples. Results showed that ICSF can improve the filtering accuracy of CSF in the samples with various terrain and non-ground object characteristics, while maintaining the ease of use advantage of CSF. In urban and rural samples, ICSF obtained an average total error of 4.03% and outperformed another eight reference algorithms in terms of accuracy and robustness. In forested samples, ICSF produced more accuracy than the well-known filtering algorithms (including the maximum slope, progressive morphology, and cloth simulation filtering algorithms), and performed better with respect to the preservation of steep slopes and discontinuities and vegetation removal. Thus, the proposed algorithm can be used as an efficient tool for LiDAR data processing.