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The Qinghai-Tibetan Plateau (QTP) is the highest plateau in the world. Under the background of global change, it is of unique significance to study the net primary productivity (NPP) of vegetation on the QTP. Based on the Google Earth Engine (GEE) cloud computing platform, the spatio-temporal variation characteristics of the NPP on the QTP from 2001 to 2017 were studied, and the impacts of climate change, elevation and human activity on the NPP in the QTP were discussed. The mean and trend of NPP over the QTP were “high in the southeast and low in the northwest” during 2001–2017. The trend of NPP was mostly between 0 gC·m−2·yr−1 and 20 gC·m−2·yr−1 (regional proportion: 80.3%), and the coefficient of variation (CV) of NPP was mainly below 0.16 (regional proportion: 89.7%). Therefore, NPP was relatively stable in most regions of the QTP. Among the correlation coefficients between NPP and temperature, precipitation and human activities, the positive correlation accounted for 81.1%, 48.6% and 56.5% of the QTP area, respectively. Among the two climatic factors, the influence of temperature on NPP was greater than that of precipitation. The change of human activities and the high temperature at low altitude had positive effects on the increase of NPP.

The landslide susceptibility assessment based on machine learning can accurately predict the probability of landslides happening in the region. However, there are uncertainties in machine learning applications. In this paper, Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) are used to assess the landslide susceptibility in order to discuss the model uncertainty. The model uncertainty is explained in three ways: landslide susceptibility zoning result, risk area (high and extremely high) statistics, and the area under Receiver Operating Characteristic Curve (ROC). The findings indicate that: (1) Landslides are restricted by influence factors and have the distribution law of relatively concentrated and strip-shaped distribution in space. (2) The percentage of real landslide in risk area is 86%, 87%, 82%, and 61% in SVM, RF, LR, and ANN, respectively. The area under ROC of RF, SVM, LR, and ANN, respectively, is 90.92%, 80.45%, 73.75%, and 71.95%. (3) Compared with the prediction accuracy of the training set and test set from the same earthquake, the accuracy of landslide prediction in the different earthquakes is reduced.

Scan matching, an approach to recover the relative position and orientation of two laser scans, is a very important technique for indoor positioning and indoor modeling. The iterative closest point (ICP) algorithm and its variants are the most well-known techniques for such a problem. However, ICP algorithms rely highly on the initial guess of the relative transformation, which will reduce its power for practical applications. In this paper, an initial-free 2D laser scan matching method based on point and line features is proposed. We carefully design a framework for the detection of point and line feature correspondences. First, distinct feature points are detected based on an extended 1D SIFT, and line features are extracted via a modified Split-and-Merge algorithm. In this stage, we also give an effective strategy for discarding unreliable features. The point and line features are then described by a distance histogram; the pairs achieving best matching scores are accepted as potential correct correspondences. The histogram cluster technique is adapted to filter outliers and provide an accurate initial value of the rigid transformation. We also proposed a new relative pose estimation method that is robust to outliers. We use the lq-norm (0 < q < 1) metric in this approach, in contrast to classic optimization methods whose cost function is based on the l2-norm of residuals. Extensive experiments on real data demonstrate that the proposed method is almost as accurate as ICPs and is initial free. We also show that our scan matching method can be integrated into a simultaneous localization and mapping (SLAM) system for indoor mapping.

Scientifically and accurately assessing the interaction between changes in human activity intensity and the surrounding ecological environment along the Qinghai–Tibet Railway is of great significance for the optimized construction of the railway and the restoration of the regional ecological environment. Based on different spatial distribution scales and construction phases of the Qinghai–Tibet Railway, this study integrates multi-source remote sensing data to construct a long-term spatiotemporal dataset of human activity intensity in the region. Drawing on analytical methods from production theory, a coupling theoretical framework based on remote sensing ecological models is proposed to quantitatively reveal the coupling relationships between the ecological environment and human activities across varying spatiotemporal scales along the Qinghai–Tibet Railway. The study finds that (1) the spatiotemporal distribution of human activity intensity along the Qinghai–Tibet Railway demonstrates clear patterns, with expansion primarily radiating from transportation corridors and their intersections, and marked spatial heterogeneity across different segments. Overall, human activity intensity increased slowly between 1990 and 2002, followed by a significant rise during the construction and opening of the Golmud–Lhasa section (2001–2007). From 2013 to 2020, the growth rate began to slow. Within a 0–30 km buffer zone centered on railway station locations (with a 15 km radius), the growth rate of human activity intensity generally decreased with increasing distance from the railway. In the 30–60 km buffer zone, this trend tended to stabilize. (2) The coupling process between ecological quality and human activity intensity across different spatiotemporal scales along the railway exhibits considerable spatial and temporal heterogeneity and complexity. The decoupling relationship is dominated by strong and weak decoupling patterns, with strong decoupling being the most prevalent. Weak decoupling is mainly distributed along the sides of the railway. Overall, in most areas along the railway, ecological quality has shown a certain degree of improvement alongside increasing human activity intensity; however, the rate of ecological improvement is generally lower than the rate of increase in human activity intensity. In some areas adjacent to the railway, intensified human activities have led to a decline in ecological quality, though the resulting ecological pressure remains relatively low.

Transmission towers are easily affected by various meteorological and geological disasters. In this paper, a transmission tower tilt state assessment approach—based on high precision and dense point cloud from UAV LiDAR—was proposed. First, the transmission tower point cloud was rapidly located and extracted from the 3D point cloud obtained by UAV-LiDAR line patrol. A robust histogram local extremum extraction method with additional constraints was proposed to achieve adaptive segmentation of the tower head and tower body point cloud. Second, an accurate and efficient extraction and simplification strategy of the contour of the feature plane point cloud was proposed. The central axis of the tower was constrained by the contour of the feature plane through the four-prism structure to calculate the tilt angle of the tower and evaluate the state of the tower. Finally, the point cloud of tower head from UAV-based LiDAR was accurately matched with the designed tower head model from database, and a tower head state evaluation model based on matching offset parameters was proposed to evaluate tower head tilt state. The experimental results of simulation and measured data showed that the calculation accuracy of the tilt parameters of transmission tower body was better than 0.5 degrees, that the proposed method can effectively evaluate the risk of tower head with complex structure, and improve the rapid and mass intelligent perception level of the risk state of the transmission line tower, which has a wide prospects for application.

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention.

Street trees are an important part of urban facilities, and they can provide both aesthetic benefits and ecological benefits for urban environments. Ecological benefits of street trees now are attracting more attention because of environmental deterioration in cities. Conventional methods of evaluating ecological benefits require a lot of labor and time, and establishing an efficient and effective evaluating method is challenging. In this study, we investigated the feasibility to use mobile laser scanning (MLS) data to evaluate carbon sequestration and fine particulate matter (PM2.5) removal of street trees. We explored the approach to extract individual street trees from MLS data, and street trees of three streets in Nantong City were extracted. The correctness rates and completeness rates of extraction results were both over 92%. Morphological parameters, including tree height, crown width, and diameter at breast height (DBH), were measured for extracted street trees, and parameters derived from MLS data were in a good agreement with field-measured parameters. Necessary information about street trees, including tree height, DBH, and tree species, meteorological data and PM2.5 deposition velocities were imported into i-Tree Eco model to estimate carbon sequestration and PM2.5 removal. The estimation results indicated that ecological benefits generated by different tree species were considerably varied and the differences for trees of the same species were mainly caused by the differences in morphological parameters (tree height and DBH). This study succeeds in estimating the amount of carbon sequestration and PM2.5 removal of individual street trees with MLS data, and provides researchers with a novel and efficient way to investigate ecological benefits of urban street trees or urban forests.

Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis characterized by clinical features of high fever, hemorrhage, and renal damage. China has the largest number of HFRS cases worldwide, accounting for over 90% of the total reported cases. In this paper, we used surveyed HFRS data and satellite imagery to conduct geostatistical analysis for investigating the associations of rapid urbanization, water bodies, and other factors on the spatiotemporal dynamics of HFRS from year 2005 to 2018 in Xi’an City, Northwest China. The results revealed an evident epidemic aggregation in the incidence of HFRS within Xi’an City with a phenomenal fluctuation in periodic time series. Rapid urbanization was found to greatly affect the HFRS incidence in two different time phases. HFRS caused by urbanization influences farmers to a lesser extent than it does to non-farmers. The association of water bodies with the HFRS incidence rate was found to be higher within the radii of 696.15 m and 1575.39 m, which represented significant thresholds. The results also showed that geomatics approaches can be used for spatiotemporally investigating the HFRS dynamic characteristics and supporting effective allocations of resources to formulate strategies for preventing epidemics.

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots.

The Qinghai–Tibet Railway is located in the most fragile and sensitive terrestrial ecosystem of the Qinghai–Tibet Plateau in China, and once the ecological environment is damaged, it is difficult to restore. This study, based on the Google Earth Engine platform, focuses on the section of the Qinghai–Tibet Railway from Xining to Jianghe. It utilizes Landsat series satellite imagery data from 1986 to 2020 to calculate the Remote Sensing Ecological Index (RSEI). This approach enables large-scale and long-term dynamic monitoring, analysis, and assessment of the ecological changes along the Qinghai–Tibet Railway corridor. The results indicate that (1) the average RSEI of the study area increased from 0.37 in 1986 to 0.53 in 2020, showing an overall trend of improvement. The ecological environment quality is mainly categorized as medium and good. (2) The quality of the ecological environment in the areas along the railway experienced fluctuations during different periods of railway construction and operation. From 1986 to 1994, after the first phase of the railway opened, the overall ecological environment showed a relative decline in quality. From 1994 to 2002, the ecological quality of 60% of the region saw slight improvements. During the extension construction of the second phase of the railway from 2002 to 2007, the regional ecology fluctuated again. However, from 2013 to 2020, during the operational period, a stable recovery trend was observed in the ecological environment. (3) The ecological environment in the study area is influenced by multiple factors. Different railway station areas exhibit strong spatial heterogeneity. The impact of single factors is significant, with the existence of spatial stratification and enhanced interactions among multiple factors. The strongest interactive effects are observed between land use types, the intensity of human activities, and temperature.