Jia Li

Landslides are geological disasters that are harmful to both humans and society. Digital elevation model (DEM) time series data are usually used to monitor dynamic changes or surface damage. To solve the problem of landslide deformation monitoring without ground control points (GCPs), a multidimensional feature-based coregistration method (MFBR) was studied to achieve accurate registration of multitemporal DEMs without GCPs and obtain landslide deformation information. The method first derives the elevation information of the DEM into image pixel information, and the feature points are extracted on the basis of the image. The initial plane position registration of the DEM is implemented. Therefore, the expected maximum algorithm is applied to calculate the stable regions that have not changed between multitemporal DEMs and to perform accurate registrations. Finally, the shape variables are calculated by constructing a DEM differential model. The method was evaluated using simulated data and data from two real landslide cases, and the experimental results revealed that the registration accuracies of the three datasets were 0.963 m, 0.368 m, and 2.459 m, which are 92%, 50%, and 24% better than the 12.189 m, 0.745 m, and 3.258 m accuracies of the iterative closest-point algorithm, respectively. Compared with the GCP-based method, the MFBR method can achieve 70% deformation acquisition capability, which indicates that the MFBR method has better applicability in the field of landslide monitoring. This study provides an idea for landslide deformation monitoring without GCPs and is helpful for further understanding the state and behavior of landslides.

The number of input factors affects the prediction accuracy of a model. Factor screening plays an important role as the starting point for data input. The aim of this study is to explore the influence of different factor screening methods on the prediction results. Taking the 2014 landslide inventory of Jingdong County as an example, a landslide database was constructed based on 136 landslide events and 11 selected factors, which were randomly divided into a training dataset and a test dataset according to a ratio of 7:3. Four factor screening methods, namely, the information gain ratio (IGR), GeoDetector, Pearson correlation coefficient and multicollinearity test (MT), were selected to screen the factors. A random forest (RF) model was then used in combination with each factor set for landslide susceptibility mapping (LSM). Finally, accuracy validation was performed using confusion matrices and ROC curves. The results show that factor screening is beneficial in improving the accuracy of the resulting model compared to the original model. Second, the IGR_RF model had the highest AUC value (0.9334), which was higher than that of the MT_RF model without factor screening (0.9194), and the IGR_RF model predicted the most landslides in the very high susceptibility zone (51.22%), indicating the good prediction performance of the IGR_RF model. Finally, the factor weighting analysis revealed that NDVI, elevation and aspect had the greatest influence on landslides in Jingdong County and that curvature had the least influence on landslides. This study can provide a reference for factor screening in LSM.

Landslide traces are crucial geomorphological features of landslides. Through the recognition of landslide traces, a better grasp of the topographical features of landslides can be achieved, thereby aiding in the enhancement of capabilities for the prevention, response, and management of landslides. Aiming at the complex topographic features of landslide traces, only using a single DEM product could provide a complete and comprehensive recognition of landslide traces. A method of landslide tracing recognition based on the fusion of multi-feature information from the Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-based LiDAR) Digital Elevation Model (DEM) is proposed. First, a high-precision DEM is constructed by using the LiDAR point cloud data. Based on the DEM, four multi-feature images that can enhance the landslide geomorphology are generated: hillshading, slope, positive openness, and sky-view factor. Furtherore, the DEM multi-feature images were fused using the Visualization for Archaeological Topography (VAT) method to obtain the DEM Multi-Feature Fusion Image (DEM-DFFI). Finally, the landslide traces were extracted from the DEM-DFFI based on fractal theory. The method presented in this paper makes full use of DEM multi-feature images and fuses them, which can accurately and clearly show the topographic and geomorphological features of landslides. Based on this, it helps improve landslide trace recognition accuracy.

Due to the complex ecological effects of the interactions between natural environmental evolution and anthropogenic interference, a series of longstanding environmental problems have severely exacerbated the vulnerability of watershed ecosystems. Landscape ecological risk (LER) assessment is suitable for exploring the extent of threats and the likelihood of adverse impacts to watershed ecosystems from multiple sources of risk. However, the specific LER and the role of diverse factors on LER in the Four Lakes Basin remain unclear. Hence, it is necessary to identify the spatiotemporal evolutionary characteristics of LER and the drivers of their interactions. In this study, the variations and driving factors of LER in the Four Lakes Basin from 2000 to 2020 were evaluated. Landscape disturbance index was selected to establish the LER measurement method. Spatial autocorrelation and the standard deviation ellipse method were employed to analyze the spatiotemporal changes in LER. To overcome the uncertainties in accurately assessing the interactions, we attempted to use the geographic detector model to quantitatively analyze the driving factors. The following results are indicated: (1) In the period from 2000 to 2020, the LER had spatially uneven distribution characteristics of low in the north-central and high in the east and south. The level of LER has been increasing. (2) The LER has characteristics showing significant spatial clustering distribution. However, the spatial convergence has diminished with time. (3) The development inclination of LER was different in the two stages. The first stage (2000–2010) showed diffusion, while the second stage (2010–2020) was concentrated to the northwest. (4) The two-factor enhancement of interactions between land use index and human disturbance (q₂₀₀₀ = 0.885, q₂₀₁₀ = 0.888, q₂₀₂₀ = 0.713) was the dominant factor influencing LER. This study can provide a theoretical reference for ecological restoration and promotion of ecosystem service functions in the Four Lakes Basin.

Time series interferometric synthetic aperture radar (TS-InSAR) technique can be applied to identify the creep body. However, it has strict requirements regarding the quality of the interferograms and the interferometric baseline networks. In regions with intermittent coherence, the use of conventional methods to select interferometric pairs can easily lead to interferometric baseline disconnections, redundancy, or missing key pairs. This results in reduced accuracy and may even misidentify creep characteristics. To solve this problem, the segmented average coherence threshold method (SACTM) is proposed. SACTM is an interferometric baseline optimization method that considers monthly coherence variability. In this paper, the SACTM baseline optimization TS-InSAR technique was used to identify creep bodies in Wudongde Reservoir area located along the Jinsha River in China. Compared with the conventional methods, the SACTM method yields a more robust interferometric baseline network and a smaller standard deviation for deformation rate, which demonstrates the superiority of this method.

Lake Water storage can be estimated based on Lake Boundary and lakebed terrain. The boundary of a lake can be extracted from the remote sensing satellite image or the Unmanned Aerial Vehicle (UAV) orthophoto, but the information of the lake and its surroundings, which is supposed to be three-dimensional, is recorded in two dimensions in this way. When the tall and dense vegetation are projected on the lake, the extracted lake boundary from orthophoto would be smaller than reality, which results in lower value of the lake water storage. UAV oblique photogrammetry can solve this problem by recording multi-angle images of the lake and its surroundings. Based on aerial photogrammetry theory, a real 3D model of the lake can be constructed by combing UAV oblique photogrammetric images. The model can accurately present the actual scene of the lake, and the extracted lake boundary can be very close to reality. In this study, UAV and Unmanned Surface Boats (USB) have been employed to collect UAV oblique photogrammetric images and lakebed terrain data, respectively. The lake boundary has been extracted based on the UAV real 3D model. The Digital Elevation Model (DEM) of lakebed terrain has been built based on lakebed terrain data collected by USB. Finally, the water storage of the lake is estimated based on the boundary and DEM. Experiments show that a real 3D model based on UAV data can depict lake boundaries accurately, which could eliminate the impact of vegetation shied around the lake. Utilizing the UAV and USB can be a standard method for estimating the water storages of small lakes.

COVID-19 poses a major threat to global health care systems, and the recent surge in mortality rates confirms the importance of timely access to care. The capacity of medical service providers is reflected both in the spatial accessibility of medical institutions and in the spatial scope of their services. Therefore, this study aims to investigate the spatial scope of services and spatial accessibility of COVID-19-designated hospitals in Yunnan Province, China. Data are collected from multiple sources and included COVID-19 case data, road data, and data from designated hospitals for COVID-19 in Yunnan Province. The optimal spatial service range for designated hospitals is delineated using a weighted Voronoi diagram that takes into account the number of medical staff and the number of beds in the hospital. Traffic accessibility coefficients are introduced to analyze the spatial accessibility of COVID-19-designated hospitals, and the spatial accessibility of each designated hospital is visualized using the inverse distance weighting interpolation algorithm. The results show the following: (1) COVID-19 cases in Yunnan Province are concentrated in the central and northern regions. The largest single cells in the weighted Voronoi diagram are mainly Pu'er (59168 km²), Honghe (35569 km²), and Baoshan (46795 km²), and the time cost of attainting medical treatment is greater for residents in marginal areas. (2) Within the service space of designated hospitals, 90.24% of patients could obtain medical assistance within 2 h. Those in 52 (36.36%) counties within a municipal jurisdiction could obtain medical services within 2 h, and 76.47% of counties have above-average spatial accessibility. (3) Medical resources in Yunnan Province should be shifted toward the high-risk east-central region and the less spatially accessible in southern and western regions.