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Poverty has always been a global concern that has restricted human development. The first goal (SDG 1) of the United Nations Sustainable Development Goals (SDGs) is to eliminate all forms of poverty all over the world. The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1. This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework. The temporal and spatial distribution characteristics of China's poverty areas and their SDG 1 evaluation values in 2012, 2014, 2016, and 2018 have been analyzed. Based on the SDGs global indicator framework, this paper first constructed SDG 1 China's district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images, land cover data, and digital elevation model data. Secondly, we establish SDG 1 China's localized partial least squares estimation model and SDG 1 China's localized machine learning estimation model. Finally, we analyze and verify the spatiotemporal distribution characteristics of China's poverty areas and counties and their SDG 1 evaluation values. The results show that SDG 1 China's district and county localization indicator system proposed in this study and SDG 1 China's localized partial least squares estimation model can better reflect the poverty level of China's districts and counties. The estimated model R2 is 0.65, which can identify 72.77% of China's national poverty counties. From 2012 to 2018, the spatial distribution pattern of SDG evaluation values in China's districts and counties is that the SDG evaluation values gradually increase from western China to eastern China. In addition, the average SDG 1 evaluation value of China's districts and counties increased by 23% from 2012 to 2018. This paper is oriented to the United Nations SDGs framework, explores the SDG 1 localized evaluation method of China's districts and counties based on multisource remote sensing data, and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals. [ABSTRACT FROM AUTHOR]

The PIESAT-01 constellation is the world's first multi-baseline distributed synthetic aperture radar (SAR) constellation with a "Cartwheel" formation. The "Cartwheel" formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains at the center, with three auxiliary satellites orbiting around it. Due to this unique configuration of the PIESAT-01 constellation, four images of the same region and six pairs of baselines can be obtained with each shot. So far, there has been no imaging and interference research based on four-satellite constellation measured data, and there is an urgent need to explore algorithms for the "Cartwheel" configuration imaging and digital surface model (DSM) production. This paper introduces an improved bistatic SAR imaging algorithm under the four-satellites interferometric mode, which solves the problem of multi-orbit nonparallelism in imaging while ensuring imaging coherence and focusing ability. Subsequently, it presents an interferometric processing method for the six pairs of baselines, weighted fusion based on elevation ambiguity from different baselines, to obtain a high-precision DSM. Finally, this paper selects the Dingxi region of China and other regions with diverse terrains for imaging and DSM production and compares the DSM results with ICESat-2 global geolocated photon data and TanDEM DSM data. The results indicate that the accuracy of PIESAT-01 DSM meets the standards of China's 1:50,000 scale and HRTI-3, demonstrating a high level of precision. Moreover, PIESAT-01 data alleviate the reliance on simulated data for research on multi-baseline imaging and multi-baseline phase unwrapping algorithms and can provide more effective and realistic measured data. [ABSTRACT FROM AUTHOR]

Hainan Province has the largest ocean areas in China. After decades of efforts, it has accumulated abundant marine basic data, and has built various types, different topics and large-scale marine databases. The construction of Hainan provincial digital ocean environment parameters 3D visualization system can effectively integrate, utilize and share these marine information resources, and support marine management and marine ecological environment protection. The multi-dimensional dynamic visualization of information can provide scientific and effective tools for comprehensive marine management and macro-decision-making in a timely and accurate manner. This paper introduces a digital ocean environment parameters three-dimensional (3D) visualization system based on digital earth sphere model suitable for marine environment parameters three-dimensional visualization display, studies the key technologies of multi-dimensional marine environment parameters visualization based on basic geographic data and remote sensing images, which integrated high-precision Digital Elevation Model (DEM) and satellite images to realize the three-dimensional integration and visualization of marine environmental parameters in the South China Sea, providing data display, multi-temporal dimension comparison and spatial overlay analysis. The functions of statistics and data management provide interactive information services for integrated marine management and decision-making support. [ABSTRACT FROM AUTHOR]

Roads are a type of typical artificial terrain, and are key components of urban terrain. Road networks formed by connections between different roads not only form the skeleton of urban terrain, but also plays an important role in transmitting energy and matter on the urban surface. Therefore, how to consider characteristics when constructing the digital road elevation model (DEM) has become an important research topic in the field of geographic information and mapping. Using high-definition unmanned aerial vehicle (UAV) images as the basic data source, this study proposes a new method for constructing the road DEM by analyzing semantic features such as road shape and function. This method first takes the sideline and centerline of the road as the macroscopic undulation morphological constraints. It uses the shape control equation of the local domain to constrain the morphological change characteristics of the road surface in the transverse and longitudinal directions, in order to construct the road DEM with high fidelity to the surface shape characteristics. Then, in terms of the water catchment function of the road surface, a road DEM correction method considering surface flow direction characteristics is designed to ensure that the water catchment path of the road surface conforms to the actual situation. For this paper, several typical roads in Chuzhou University in Anhui Province, China, were selected as the experimental objects to carry out a DEM construction experiment. The results indicate the following: (1) compared with the traditional construction method, the DEM shape of the road constructed by this research method is more consistent with the actual road shape, and the smoothness of the road surface is better; (2) due to the high density and high elevation accuracy of the point cloud used in modeling, the elevation adjustment strategy of the sideline and centerline of the road implemented in this study does not reduce elevation accuracy, indicating that an adjustment to the elevation information is necessary for constructing the DEM of special artificial terrain; and (3) the DEM correction method proposed in this paper to find the correct catchment path can ensure that the processed DEM can accurately simulate the surface catchment process, and the correction of the elevation of the road DEM is also controlled within a small range without affecting the elevation accuracy of the regional DEM. This study has reference value for implementing projects such as urban terrain expression in the construction of 3D China. [ABSTRACT FROM AUTHOR]

Digital Surface Model (DSM) is a three-dimensional model presenting the elevation of the Earth's surface, which can be obtained by the along-track or cross-track stereo images of optical satellites. This paper investigates the DSM extraction method using Gaofen-6 (GF-6) high-resolution (HR) cross-track images with a wide field of view (WFV). To guarantee the elevation accuracy, the relationship between the intersection angle and the overlap of the cross-track images was analyzed. Cross-track images with 20–40% overlaps could be selected to conduct DSM extraction. First, the rational function model (RFM) based on error compensation was used to realize the accurate orientation of the image. Then, the disparity map was generated based on the semi-global block matching (SGBM) algorithm with epipolar constraint. Finally, the DSM was generated by forward intersection. The GF-6 HR cross-track images with about 30% overlap located in Taian, Shandong Province, China, were used for DSM extraction. The results show that the mountainous surface elevation features were retained completely, and the details, such as houses and roads, were presented in valleys and urban areas. The root mean square error (RMSE) of the extracted DSM could reach 6.303 m, 12.879 m, 14.929 m, and 19.043 m in valley, ridge, urban, and peak areas, respectively. The results indicate that the GF-6 HR cross-track images with a certain overlap can be used to extract a DSM to enhance its application in land cover monitoring. [ABSTRACT FROM AUTHOR]

China's first sub-metre stereo satellite, GaoFen-7, was launched on 7 November 2019. One of the main criteria for a stereo mapping satellite is the geometric accuracy of the images. In this paper, we present a systematic evaluation of the geometry accuracy of Gaofen-7 on two scenes over the centre of Munich, Germany. The geometry accuracy is evaluated in a three-step workflow: 1) direct georeferencing accuracy; 2) image orientation using bundle adjustment with ground control points; 3) height accuracy of the generated digital surface model (DSM). In addition to dense LiDAR point clouds, ground control points were measured in the field. These were used as references. The results show that RPC bundle adjustment with 0 order bias correction is sufficient to achieve sub-metre absolute accuracy. The height accuracy of the generated digital surface models varies with land cover type, ranging from 0.9m (NMAD) in open areas to 4.5m in urban areas. [ABSTRACT FROM AUTHOR]

Extracting a channel network based on the Digital Elevation Model (DEM) is one of the key research topics in digital terrain analysis. However, when the channel area is wide and flat, it is easy to form parallel channels, which seriously affect the accuracy of channel network extraction. To solve this problem, this study proposes a method to identify and eliminate parallel channels extracted by classical methods. First, the channel level in the study area is marked based on the flow accumulation data, and the parallel channels are then identified using the positional relationship between the different channel levels. Finally, the modification point of the identified parallel channels is determined to eliminate the parallel channels, with the help of the change relationship between the parallel channel and its upper-level channel. In this study, two watersheds in southeast China are selected as examples for method verification and analysis. Experimental results show that the parallel channel identification method proposed in this paper can accurately identify all parallel channels and eliminate the identified parallel channels one by one. The location relationship of the modified channels is consistent with the actual situation, indicating that the proposed method has good application potential in DEM-based channel extraction networks. [ABSTRACT FROM AUTHOR]

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC) detection and modeling play an important role in global environmental change research, in particular, policy-making to mitigate climate change, support land spatial planning, and achieve sustainable development. For the time being, a couple of hybrid models, such as cellular automata–Markov (CM), logistic–cellular automata-Markov (LCM), multicriteria evaluation (MCE), and multicriteria evaluation–cellular automata–Markov (MCM), are available. However, their disadvantages lie in either dependence on expert knowledge, ignoring the constraining factors, or without consideration of driving factors. For this purpose, we proposed in this paper a new hybrid model, the logistic–multicriteria evaluation–cellular automata–Markov (LMCM) model, that uses the fully standardized logistic regression coefficients as impact weights of the driving factors to represent their importance on each land use type in order to avoid these defects but is able to better predict the future land use pattern with higher accuracy taking Hefei, China as a study area. Based on field investigation, Landsat images dated 2010, 2015, and 2020, together with digital elevation model (DEM) data, were harnessed for land use/cover (LUC) mapping using a supervised classification approach, which was achieved with high overall accuracy (AC) and reliability (AC > 95%). LUC changes in the periods 2010–2015 and 2015–2020 were hence detected using a post-classification differencing approach. Based on the LUC patterns of the study area in 2010 and 2015, the one of 2020 was simulated by the LMCM, CM, LCM, and MCM models under the same conditions and then compared with the classified LUC map of 2020. The results show that the LMCM model performs better than the other three models with a higher simulation accuracy, i.e., 1.72–5.4%, 2.14–6.63%, and 2.78–9.33% higher than the CM, LCM, and MCM models, respectively. For this reason, we used the LMCM model to simulate and predict the LUC pattern of the study area in 2025. It is expected that the results of the simulation may provide scientific support for spatial planning of territory in Hefei, and the LMCM model can be applied to other areas in China and the world for similar purposes. [ABSTRACT FROM AUTHOR]

Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications. [ABSTRACT FROM AUTHOR]

Rapid flow-like landslides frequently occur in mountainous regions. To mitigate the disasters caused by these landslides, it is crucial to develop robust numerical models that can accurately predict their run-out processes. Models based on the material point method (MPM) offer significant advantages in simulating large deformation issues in geomaterials, including landslides. However, applying these models to accurately simulate real-world rapid flow-like landslides remains a challenge, primarily due to the complexities involved in handling the three-dimensional (3D) sliding bed boundary. This paper introduces a novel 3D MPM model specifically designed for simulating rapid flow-like landslides that propagate across curved beds. The constraints of the sliding bed on the landslide are imposed by the boundary nodes close to the bed. These boundary nodes carry information about the normal vector of the sliding bed, derived directly from the digital terrain model (DTM). Furthermore, the model integrates a hybrid formulation that combines the Full Lagrangian Implicit Particle (FLIP) method with the Particle In Cell (PIC) method, facilitating a stable solution for the velocity and position of material points. The effectiveness of the proposed model is confirmed through a numerical analysis of a rigid block sliding down an inclined plane and an experiment of sand flow on a curved bed. The simulation results from these two benchmark scenarios align closely with both analytical and experimental data, attesting to the validity of the model. The model is then applied to analyze a rapid flow-like landslide that occurred in Gansu Province, China, characterized by a curved sliding bed. The outcomes illustrate the model's capability to efficiently capture the landslide's climbing and turning motions induced by the meandering topography. Moreover, it successfully reproduces the main deposition characteristics observed in the field, demonstrating the model's strong suitability for simulating the propagation of rapid flow-like landslides on naturally curved beds. [ABSTRACT FROM AUTHOR]

The cold atom gravimeter (CAG) has proven to be a powerful quantum sensor for the high-precision measurement of gravity field, which can work stably for a long time in the laboratory. However, most CAGs cannot operate in the field due to their complex structure, large volume and poor environmental adaptability. In this paper, a home-made, miniaturized CAG is developed and a truck-borne system based on it is integrated to measure the absolute gravity in the field. The measurement performance of this system is evaluated by applying it to measurements of the gravity field around the Xianlin reservoir in Hangzhou City of China. The internal and external coincidence accuracies of this measurement system were demonstrated to be 35.4 μGal and 76.7 μGal, respectively. Furthermore, the theoretical values of the measured eight points are calculated by using a forward modeling of a local high-resolution digital elevation model, and the calculated values are found to be in good agreement with the measured values. The results of this paper show that this home-made, truck-borne CAG system is reliable, and it is expected to improve the efficiency of gravity surveying in the field. [ABSTRACT FROM AUTHOR]

In the context of global warming, snow cover changes have an extremely important impact on the hydrological cycle and the redistribution of water resources in arid and semi-arid regions. In this paper, based on the daily cloud-free snow area remote sensing product data in the source region of Yangtze River (SRYR) from 2000 to 2019, the snow phenology variables such as the snow cover day (SCD), snow onset date (SOD), snow end date (SED), and snow duration day (SDD) were extracted separately for each hydrological year, and the vertical distribution of snow cover area (SCA) in the SRYR was analyzed by combining with the digital elevation model (DEM). In addition, we also combined climate factors and land cover types to further explore the spatiotemporal variation characteristics of snow phenology in response to different influencing factors, in order to reveal the spatiotemporal variation patterns of snow cover in the SRYR. The results showed that: (1) The SCA in the SRYR has a more obvious vertical distribution, with the maximum SCA reaching 61.58% at high elevation, while at low elevation, the SCA is mostly below 20%. The distribution of SCD in the study area showed a significant exponential correlation with DEM (R2 = 0.87). (2) The area of SOD in the SRYR showed an advanced trend of about 63.37%, while the area of SED showed a delayed trend of about 69.59%, and the area which showed significant trends is 4.29% and 4.36%, respectively. Therefore, the trends of both SOD and SED showed insignificant changes. (3) Temperature change is the main factor affecting the change of snow cover in the SRYR. Among them, 90.9% of the regions showed a significant positive correlation between temperature and SCD, while precipitation showed a significant negative correlation with SCD in about 75.3% of the total area of SRYR. Under the stable snow area (SCD > 60), the land cover type is glacial or permanent snow about 1.5 × 103 km2, which covers almost the entire glacial or permanent snow of the SRYR. [ABSTRACT FROM AUTHOR]

[ Objective] The paper uses Geographic Information System (ArcGIS) as a platform to discuss the study of DEM data on the digital construction of rural landscape databases and the coupling relationship in geographic space of rural landscape. The aim is to further expand the coverage of China's land space digitization and provide practical theoretical basis and development possibility of quantitative planning for village level administrative zoning research. [Method] In this paper, Xiangyu Village, Junzhuang Town, Mentougou, West Beijing, is selected as the research object, and the document space database of the study area is constructed by building digital elevation model (DEM), ArcGIS stacked map weighting and visual interpretation methods. Moreover, its elevation equivalence, slope direction, and water flow direction are visually analyzed. [Result] The mountainous terrain and hydrological characteristics of Xiangyu Village are roughly consistent with the village layout; the living environment of Xiangyu Village is consistent with the terrain and texture, and it belongs to the rural landscape system of mountains, valleys and small watersheds. [ Conclusion] The results obtained through data visualization analysis are basically consistent with the traditional village planning pattern of Xiangyu Village; the rural landscape based on village level administrative divisions is still in the traditional planning stage, and there is great development space for village level geographic spatial survey. Therefore, it is necessary for relevant practitioners to make more contributions to fill the full coverage of China's land space digitization. [ABSTRACT FROM AUTHOR]

The Wudaoliang–Changshagongma fault is one of the NW-trending faults located within the southern Bayan Har Block of the Tibetan Plateau in China. In this paper, we used high-resolution imagery and digital elevation model data to study the geomorphological and geological characteristics of the fault. Furthermore, the result also determined the fault trace and estimated the average horizontal slip rate of the fault since the late Quaternary to have been 2.6 ± 0.6 mm/a. This slip rate is approximately equivalent to that of the Awancang, Madoi–Garde, and Dari faults, which are also located within the block. Furthermore, the slip rates of these faults obtained by remote sensing and geological methods are consistent with GPS observations. It indicates that tectonic deformation within the block is continuous and diffuse. Using trenching study results and sedimentary radiocarbon dating, we identified four paleoearthquake events that occurred at 42,378–32,975, 33,935–20,663, 5052–4862, and after 673–628 cal BP, respectively. The recurrence intervals of large earthquakes on the faults within the block are much longer than those of the boundary faults, and the slip rates are also smaller, indicating that faults within the block play a regulatory role in the tectonic deformation of the Bayan Har Block. [ABSTRACT FROM AUTHOR]

This paper describes the construction of a 0.25° × 0.25° daily soil temperature (TS) data set at six soil depths (i.e., 0, 5, 10, 15, 20, and 40 cm) based on a homogenized TS at more than 2,000 meteorological stations across mainland of China for 1960–2017. First, at each station, the observed TS is split into a daily climatology of 1981–2010 and a daily anomaly for 1960–2017. Then, they are respectively interpolated to 0.25° × 0.25° horizontal resolution by using the thin‐plain spline and the angular distance weight interpolation methods. Finally, a gridded multilayer TS data set is constructed from the sum of the above two gridded products. Latitude, longitude, and high‐resolution digital elevation models are explicitly incorporated into the interpolation processes. Selectivity tests indicate that the optimal interpolation distance for the TS anomaly field is 500 km, where the gridded data set may have maximum coverage of the entire Chinese mainland. The results show that the gridded TS climatology is slightly lower than the station observations. The differences between gridded and the station‐based TS varies ±3°C, with relatively large in SW and TIBET. For the gridded anomaly fields, the cross‐validation shows that the mean absolute errors between gridded and station‐based TS are generally less than 0.6°C in the YZ, SW, and SC, but they are above 1.0°C in the NW and TIBET due to the sparse station distribution and large topographic relief. From 1961 to 2016, the seasonal variability of the gridded TS is generally consistent with that of the surface air temperature. The gridded TS over the majority of land displays significantly positive trends in both summer and winter (p > 95%), indicating an overall warming land surface in China. The long‐term gridded TS data not only preserves the spatial features represented in the station observations, but also provides a spatio‐temporal continuous long‐term product of mainland China. Thus, it may be used in future studies, for example, evaluation of land surface model simulations, validation of satellite retrievals, land‐atmosphere interactions, and climate change. [ABSTRACT FROM AUTHOR]

Monitoring of river discharge is a key process for water resources management, soil and water conservation, climate change, water cycling, flood or drought warning, agriculture and transportation, especially for the sustainable development of rivers and their surrounding ecological environment. Continuous and comprehensive discharge monitoring was usually impossible before, due to sparse gauges and gauge deactivation. Satellite remote sensing provides an advanced approach for estimating and monitoring river discharge at regional or even global scales. River morphology is generally considered to be a direct factor that affects the accuracy of remote sensing estimation, but the specific indicators and the extent to which it affects the estimation accuracy have not yet been explored, especially for medium to small rivers (width < 100 m). In this paper, six sites with hydrological gauges in the upper Heihe River Basin (HRB) of northwestern China and the Murray Darling Basin (MDB) of southeastern Australia were selected as the study cases. River discharge was estimated from Landsat imagery using the C/M method accordingly. River gradient, sinuosity, and width were obtained from Digital Elevation Model data for each site. Global Surface Water Dataset (GSWD) was also employed for indicating the dynamic status of river morphology. A series of methods were applied to analyze the influence of river morphology on estimation accuracy qualitatively and quantitatively, based on which we established inference about the theory of selecting satellite virtual gauges (SVGs). The results confirm the feasibility of the C/M method for discharge estimation, with the accuracy affected by multiple river morphological indicators. Among them, river width was found to be the most significant one. Moreover, water occurrence and water extent extracted from GSWD also have impact on the discharge estimation accuracy. Another independent river section in MDB was set as an example to demonstrate the reasonability of the established theory. It is anticipated that this study would promote the application of remote sensing for discharge estimation by providing practical guidance for establishing appropriate SVGs. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) technology is an important method to generate digital surface model (DSM). The past studies on space-borne derived DSM most focused on the elevation correction, due to the relative low resolution of DSM product. As a large number of high-resolution satellite data emerge, the horizontal discrepancies are needed to be considered. This paper proposes a DSM block adjustment method with horizontal constraints, aimed at eliminating the horizontal errors that exist between multiple DSM scenes with overlaps, achieving high precision and consistency in both the horizontal and vertical dimensions. Using ICESat-2 ATL08 point clouds as absolute elevation control and a reference DSM for horizontal control, the adjustment equations are constructed based on the constraint of tie points and controls. The experiment selects 7 image pairs of China TH2-01 SAR satellite, corresponding ICESat-2 ATL08 point and AW3D30 as reference DEM. The block adjustment results show that the proposed method improves the absolute vertical accuracy from 3.78 m to 2.56 m and reduces the average horizontal standard deviation between the InSAR derived DSMs and the reference AW3D30 from 15.31 m to 9.08 m. [ABSTRACT FROM AUTHOR]

Topographic variables derived from digital elevation models (DEM) are the most commonly used predictor in soil organic carbon (SOC) prediction. The predictability of these variates for the spatial heterogenity of SOC is influenced by the size of DEM resolution. This paper aimed to investigate the resolution effect of the topography at multiple DEM resolutions and select the most important predictors and the optimal resolution for SOC prediction in a large-scale and morphologically complex area. The study area, covering 15 600 km2, was located in a mountainous province in China. A total of 3901 soil samples and three machine learning algorithms including random forest, gradient boosted regression tree, and artificial neural network were used to construct the SOC predictive models at resolutions of 30, 90, 150, 210, 270, 330, and 390 m. Topographic factors were derived from the DEM with an original resolution of 30 m and were subsequently resampled to varying resolutions by using the bilinear interpolation algorithm. The importance of each variate in each predictive model was computed for the most important predictor selection, and the determination coefficient (R2) and Root Mean Square Error (RMSE) of each predictive model were computed for the accuracy evaluation. Comparative analysis on point-based topographic representation, the accuracy of predictive models, and the variable importance was conducted among different resolutions to investigate the resolution effect on the SOC prediction and to select the optimal resolution for this study area. The results showed that topography was the key factor influencing the SOC distribution in this large-scale area due to the region's mountainous nature, and the relative contribution of the topography to predict SOC distribution varied with DEM resolutions. Elevation was the most important topographic factor at all DEM resolutions. Accuracies of predictive models built on the three machine learning algorithms were all dependent on DEM resolution, indicating that DEM resolution had an important effect on SOC prediction. Overall, the random forest predictive models outperformed the other two algorithms, and its most accurate result was obtained at the 210-m resolution with an R2 of 0.25 and an RMSE of 4.77 g kg–1. In conclusion, we found that finer resolutions did not necessarily produce more accurate SOC predictions even in a large-scale and morphologically complex area. Therefore, investigating the resolution effect is suggested to select an optimal resolution for the SOC prediction in other regions characterized by similar geomorphological conditions as this study area. [ABSTRACT FROM AUTHOR]

Accurate and error-free digital elevation model (DEM) data are a basic guarantee for the safe flight of unmanned aerial vehicles (UAVs) during surveys in the wild, especially in moun-tainous areas with large topographic undulations. Existing free and open-source DEM data gen-erally cover large areas, with relatively high spatial resolutions (~90, 30, and even 12.5 m), but they do not have the advantage of timeliness and cannot accurately reflect current and up-to-date topographical information in the survey area. UAV pre-scanning missions can provide highly accurate and recent terrain data as a reference for UAV route planning and ensure security for subsequent aerial survey missions; however, they are time consuming. In addition, being limited to the electric charge of the UAV, pre-scanning increases the human, financial, and time consumption of field missions, and it is not applicable for field aerial survey missions in reality, unless otherwise specified, especially in harsh environments. In this paper, we used interferometric synthetic aper-ture radar (InSAR) technology to process Sentinel-1a data to obtain the DEMs of the survey area, which were used for route planning, and other free and open-source DEMs were also used for flightline plans. The digital surface models (DSMs) were obtained from the structure of the UAV pre-scan mission images, applying structure for motion (SfM) technology as the elevation reference. Comparing the errors between the InSAR-derived DEMs and the four open-source DEMs based on the reference DSM to analyze the practicability of flight route planning, the results showed that among the four DEMs, the SRTM DEM with a spatial resolution of 30 m performed best, which was considered as the first reference for UAV route plans when the survey area in complex mountainous regions is covered with a poor or inoperative network. The InSAR-derived DEMs from the Sentinel-1 images have great potential value for UAV flight planning, with a large perpendicular baseline and short temporal baseline. This work quantitatively analyzed the errors among the different DEMs and provided a discussion regarding UAV flightline plans based on external DEMs. This can not only effectively reduce the manpower, materials, and time consumption of field operations, improving the efficiency of UAV survey tasks, but it also broadens the use of InSAR technology. Furthermore, with the launch of high-resolution SAR satellites, InSAR-derived DEMs with high spatial and temporal resolutions provide an optimistic and credible strategy for UAV route planning with small errors. [ABSTRACT FROM AUTHOR]

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the "beautiful countryside" construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide. [ABSTRACT FROM AUTHOR]

In recent years, extreme weather events caused by global climate change have occurred frequently, intensifying the frequency of flood disasters. For flood hazard analysis, high-quality data and a reasonable weight assignment of the relevant factors are critical. This study conducts four rainfall fusion methods, to fuse the Tropical Rainfall Measuring Mission (TRMM) 3B42 and the observations in Dazhou City, China. Then, the random forest was applied to obtain the weights of various factors to facilitate a comprehensive flood hazard analysis under four rainfall durations. The results show that (1) the linear regression performs best out of the four fusion methods, with a correlation coefficient of 0.56; (2) the Digital Elevation Model (DEM) is the most impact factor with a weight of more than 0.2; and (3) the proposed flood analysis system performs well, as 70% of historical flood points are distributed in high and sub-high hazard areas and more than 93% of historical flood points are distributed in medium hazard areas. This study identified the flood hazard grade and distribution in Dazhou City, which could provide a valuable methodology to contribute to flood hazard analysis and disaster management with satellite rainfall. Furthermore, the results of this paper are profound for future work on the high-resolution flood risk assessment and management in Dazhou City. [ABSTRACT FROM AUTHOR]

Selecting an appropriate hillslope transport model and calibrating model parameters are essential for morphological dating of fault and fluvial scarps. In this paper, we refine the method of profile‐based morphologic dating by updating the representation of nonlinearity in sediment flux dependence on the hillslope gradient. We apply this revised method to fluvial scarps bounding fluvial terraces offset along the Kongur Normal Fault in the semi‐arid high‐altitude Pamir mountains, northwestern China. One of these terraces, the T3 surface, is dated to 7.0+1.9/−1.6 kyr using 10Be cosmogenic depth profile analysis. Well‐preserved, dated terraces make this an ideal site to test the utility of morphological modelling in constraining ages of the young terrace risers. To do this, 35 topographic swath profiles across the terrace risers are extracted from a 0.2 m‐resolution digital elevation model produced using structure from motion from photos collected by an unmanned aerial vehicle. The best estimates of morphological age are 13.9 ± 1.3 m2 for the riser T3/T4 and 11.9 ± 1.3 m2 for T2/T3 using a linear diffusion approach. These two morphological ages overlap within uncertainty and fail to distinguish between two young terrace risers. Alternatively, we employed a nonlinear diffusion model, calibrated with transport constant k = 1 m2 kyr−1, nonlinearity n = 2, and critical gradient Sc = tan(33°). This nonlinear model produces ages of 7.3 ± 0.5 kyr for T3/T4 and 4.0 ± 0.2 kyr for T2/T3; these ages are consistent with terrace surface ages deduced by using vertical offset divided by independently determining average throw rate. This comparison shows the advantage of a nonlinear model in defining ages of young scarps. Furthermore, we explored the minor effect of heterogeneous degradation along steep sections of the scarp profiles. The nonlinear scarp modelling scheme we develop in this paper is suitable for studying scarp degradation in other regions. © 2020 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The Gaofen-3 (GF-3) satellite can provide digital elevation model (DEM) data from its interferogram outputs. However, the accuracy of these data cannot be ensured without applying a surveying and mapping (SAM) calibration process, thus necessitating geometric and interferometric calibration technologies. In this paper, we propose an independent parameter decomposition (IPD) method to conduct SAM calibration on GF-3 data and generate high-accuracy DEMs. We resolved the geometric parameters to improve the location accuracy and resolved the interferometric parameters to improve the height accuracy. First, we established a geometric calibration model, analyzed the Range–Doppler (RD) model and resolved the initial imaging time error as well as the initial slant range error. Then, we established a three-dimensional reconstruction (TDR) model to analyze the height error sources. Finally, the interferometric phase error and baseline vector error were precisely estimated to ensure the vertical accuracy of the interferometric results by establishing the interferometric calibration model. We then used the GF-3 interferometric data derived on the same orbit in a north–south distribution to conduct the calibration experiment. The results show that the plane positioning accuracy was 5.09 m following geometric calibration, that the vertical accuracy of the interferometric results was 4.18 m following interferometric calibration and that the average absolute elevation accuracy of the derived DEM product was better than 3.09 m when using the GF-3 SAR data, thus confirming the correctness and effectiveness of the proposed GF-3 IPD calibration method. These results provide a technical basis for SAM calibration using GF-3 interferograms at the 1:50,000 scale in China. [ABSTRACT FROM AUTHOR]

The information unavailability of a global navigation satellite system (GNSS) for geo‐localization in many military and tourism applications implies the need to employ other applicable types of information, such as automatic image‐based location recognition. Unlike most existing localization methods that use GNSS information to locate an initial position, this paper proposes an automatic geo‐localization framework that does not require GNSS data. The proposed framework is a two‐stage pipeline that uses a query image and digital elevation model (DEM) data as input. The authors frame automatic geo‐localization without GNSS recordings as a skyline matching problem. By extracting the skyline from the DEM data and query images, the query image can be localized by matching the query skyline feature to the DEM skyline database. It has been demonstrated that this low‐cost approach can perform efficiently in mountainous or hilly areas to produce reliable localization results. The system was tested on 50 testing site points within a large‐scale area (China, 202.6 km2), and an average position error of 43.13 m was detected within 4.5 s. [ABSTRACT FROM AUTHOR]

On 24 June 2017, a rockslide-debris avalanche occurred in Xinmo Village, Maoxian County, Sichuan Province, China. The displaced materials traveled approximately 2.6 km and caused 83 casualties. Combined with the field investigation, seismic signal, and numerical simulation, the dynamic process and deposit characteristics of the Xinmo landslide were analyzed. Based on the Arias intensity and Hibert time–frequency analysis of the seismic signal, the Xinmo landslide is determined to have a total duration of 120 s and with three-stage dynamic processes, namely, the initial accelerating stage (50 s), erosion impact stage (50 s), and accumulation stage (20 s). Then, taking the field investigation results, the total duration, and kinetic parameters inverted from the seismic signal as criteria, the DAN3D (Dynamic Analysis of Landslides) was employed to reconstruct the dynamic process of the Xinmo landslide. The results show that the erosion and deposit depth distributions obtained from the simulation are in agreement with the pre- and post-landslide DEM (digital elevation model) data and field results. Further, the three-stage dynamic processes (40 s, 60 s, 20 s) obtained from the numerical simulation are also close to the seismic analysis results. In this study, the results of the field investigation and seismic analysis provide a more detailed set of inversion criteria for the numerical simulation work, which contributes to reconstructing the dynamic processes of the Xinmo landslide more accurately. Based on the approach of multi-source data collaboration in this paper, those landslides that lack direct observation data can be better understood and the numerical parameters used for landslide dynamics inversion are better calibrated, which can be applied to predict the runout behavior and disaster scope of potential landslides in the immediate area. [ABSTRACT FROM AUTHOR]

Landslide extraction is one of the most popular topics in remote sensing. Numerous techniques have been proposed to manage the landslide identification problem. However, most aim to extract landslides that have already occurred or delineate the potential landslide manually. It is greatly important to identify and delineate potential landslides automatically, which has not been investigated. In this paper, we propose an automatic identification and delineation method, i.e., object-based image analysis (OBIA) of potential landslides by integrating optical imagery with a deformation map. We applied a deformation map generated by the interferometric synthetic aperture radar (InSAR) technique, rather than the digital elevation model (DEM) for landslide segmentation. Then, we used a classification and regression tree (CART) model with the spectral, spatial, contextual and deformation characteristics for landslide classification. For accuracy assessment, we implemented the evaluation indicators of recall and precision. The proposed method is verified in both specific landslide-prone regions (Jinpingzi and Shuanglongtan landslides) and a large catchment of the Jinsha River, China. By comparing our results with the ones using purely optical imagery, the precision of the Jinpingzi landslide is improved by 14.12%, and the recall and precision of the Shuanglongtan landslide are improved by 3.1% and 3.6%, respectively, and the recall for the large catchment is improved by 9.95%. Our method can improve delineation of potential landslides significantly, which is crucial for landslide early warning and prevention. [ABSTRACT FROM AUTHOR]

On January 3, 2019, the Chang'e-4 (CE-4) probe successfully landed in the Von Kármán crater inside the South Pole-Aitken (SPA) basin. With the support of a relay communication satellite "Queqiao" launched in 2018 and located at the Earth-Moon L2 liberation point, the lander and the Yutu-2 rover carried out in-situ exploration and patrol surveys, respectively, and were able to make a series of important scientific discoveries. Owing to the complexity and unpredictability of the lunar surface, teleoperation has become the most important control method for the operation of the rover. Computer vision is an important technology to support the teleoperation of the rover. During the powered descent stage and lunar surface exploration, teleoperation based on computer vision can effectively overcome many technical challenges, such as fast positioning of the landing point, high-resolution seamless mapping of the landing site, localization of the rover in the complex environment on the lunar surface, terrain reconstruction, and path planning. All these processes helped achieve the first soft landing, roving, and in-situ exploration on the lunar farside. This paper presents a high-precision positioning technology and positioning results of the landing point based on multi-source data, including orbital images and CE-4 descent images. The method and its results have been successfully applied in an actual engineering mission for the first time in China, providing important support for the topographical analysis of the landing site and mission planning for subsequent teleoperations. After landing, a 0.03 m resolution DOM was generated using the descent images and was used as one of the base maps for the overall rover path planning. Before each movement, the Yutu-2 rover controlled its hazard avoidance cameras (Hazcam), navigation cameras (Navcam), and panoramic cameras (Pancam) to capture stereo images of the lunar surface at different angles. Local digital elevation models (DEMs) with a 0.02 m resolution were routinely produced at each waypoint using the Navcam and Hazcam images. These DEMs were then used to design an obstacle recognition method and establish a model for calculating the slope, aspect, roughness, and visibility. Finally, in combination with the Yutu-2 rover mobility characteristics, a comprehensive cost map for path search was generated.By the end of the first 12 lunar days, the Yutu-2 rover has been working on the lunar farside for more than 300 days, greatly exceeding the projected service life. The rover was able to overcome the complex terrain on the lunar farside, and travelled a total distance of more than 300 m, achieving the "double three hundred" breakthrough. In future manned lunar landing and exploration of Mars by China, computer vision will play an integral role to support science target selection and scientific investigations, and will become an extremely important core technology for various engineering tasks. [ABSTRACT FROM AUTHOR]

The GaoFen-7 (GF-7) satellite is successfully launched on November 3, 2019, and its laser altimeter system is officially and firstly employed as the main payload for earth observations in China, which includes two sets of laser altimeters and laser footprint cameras. The Laser Footprint Image (LFI) is used to capture laser spots on the ground. In order to make up for the shortcomings of high cost field work for the traditional laser altimeter ground detector-based calibration method, this paper proposes a novel laser altimeter calibration method based on LFI. Firstly, the spaceborne laser calibration model and the Laser Footprint Camera (LFC) geolocation model are established. Secondly, the image coordinates of laser spot centroid are extracted from LFI, and the ground location of is obtained by ray intersecting with the reference Digital Surface Model (DSM). Finally, the centroid of laser spot is considered as Ground Control Point (GCP), and the pointing bias of GF-7 laser altimeter is calibrated by the Least Squares Estimation (LSE). The ALOS Global Digital Surface Model "ALOS World 3D-30m" (AW3D30) is used to evaluate the elevation accuracy of GF-7 laser altimeter before and after the calibration. The results indicate that elevation accuracy of the GF-7 laser altimeter is improved significantly after calibration. The proposed method can be effectively applied for high-frequency geometric calibration of GF-7 laser altimeter. [ABSTRACT FROM AUTHOR]

The topographic skeleton is the primary expression and intuitive understanding of topographic relief. This study integrated a topographic skeleton into deep learning for terrain reconstruction. Firstly, a topographic skeleton, such as valley, ridge, and gully lines, was extracted from a global digital elevation model (GDEM) and Google Earth Image (GEI). Then, the Conditional Generative Adversarial Network (CGAN) was used to learn the elevation sequence information between the topographic skeleton and high-precision 5 m DEMs. Thirdly, different combinations of topographic skeletons extracted from 5 m, 12.5 m, and 30 m DEMs and a 1 m GEI were compared for reconstructing 5 m DEMs. The results show the following: (1) from the perspective of the visual effect, the 5 m DEMs generated with the three combinations (5 m DEM + 1 m GEI, 12.5 m DEM + 1 m GEI, and 30 m DEM + 1 m GEI) were all similar to the original 5 m DEM (reference data), which provides a markedly increased level of terrain detail information when compared to the traditional interpolation methods; (2) from the perspective of elevation accuracy, the 5 m DEMs reconstructed by the three combinations have a high correlation (>0.9) with the reference data, while the vertical accuracy of the 12.5 m DEM + 1 m GEI combination is obviously higher than that of the 30 m DEM + 1 m GEI combination; and (3) from the perspective of topographic factors, the distribution trends of the reconstructed 5 m DEMs are all close to the reference data in terms of the extracted slope and aspect. This study enhances the quality of open-source DEMs and introduces innovative ideas for producing high-precision DEMs. Among the three combinations, we recommend the 12.5 m DEM + 1 m GEI combination for DEM reconstruction due to its relative high accuracy and open access. In regions where a field survey of high-precision DEMs is difficult, open-source DEMs combined with GEI can be used in high-precision DEM reconstruction. [ABSTRACT FROM AUTHOR]

Efforts to evaluate the susceptibility of debris flows in large areas, especially in mountainous regions, are often hampered by the alpine and canyon terrain. This paper proposes a convolution neural network (CNN) model named dense residual shuffle net (DRSNet). It is successfully applied to Nujiang Prefecture in Yunnan Province of China, a typical alpine area with frequent debris flows. DRSNet uses digital elevation model, remote sensing, lithology, soil type and precipitation data as input. First, dense connection and residual structure were used to extract the shallow features of various data. Next, channel shuffle, fuse block and fully connection were applied to strengthen the correlation between different shallow features and give inner danger scores. Finally, precipitation as the activation factor was introduced giving the valleys susceptibility. To verify the feasibility of DRSNet, comparative tests were conducted on 7 CNN models and 3 other machine learning (ML) methods. Experimental results show that DRSNet can achieve 78.6% accuracy in debris flow valley classification, which is at least 7.4% higher than common CNN models and 15.2% higher than other ML methods. This article provides new ideas for debris flow susceptibility evaluation. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs) have a significant and negative impact on the urban ecological environment and on human health, and it is imperative to examine factors that lead to UHIs. Although numerous studies have been conducted in this field, little research has considered seasonal variations in UHIs in coastal cities. Moreover, parametric statistical analyses, such as regression and correlation analyses, have been typically applied to examine the influential factors. Such analyses are flawed because they cannot uncover the complicated relationships between UHIs and their factors. Taking Dalian, a coastal city in China, as an example, this paper reveals the dynamic mechanism of the UHI effect for different seasons using the cubist regression tree algorithm. Analyses suggest that the UHI effect only exists in spring and summer, and no obvious UHIs can be found in autumn and winter. The adjacency to the sea leads to moderate UHI effects in spring and summer and no UHI or urban cooling island (UCI) effects in autumn and winter. The distance to the coastline, however, does not play a role in the UHI effect. Furthermore, as one of the most important factors, the vegetation coverage plays a significant role in the UHI effect in spring and summer and significantly mediates the UHI in autumn and winter. Comparatively, the elevation (e.g., digital elevation models (DEMs)) is consistently negatively associated with the land surface temperature in all seasons, although a stronger relationship was found in spring and summer. In addition, the surface slope is also a significant factor in spring and winter, and the population density impacts the UHI distribution in summer as well. [ABSTRACT FROM AUTHOR]

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis, digital geomorphological mapping, and other fields. Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area. This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines: convexity mean index (CM-index), convexity standard deviation (CSD-index), and convexity imbalance index (CIB-index). We develop computation methods to extract peaks from digital elevation model (DEM). Subsequently, the initial peaks extracted by neighborhood statistics are classified using the proposed indices. The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China. An ASTER Global DEM (ASTGTM2 DEM) with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions. DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau. The mountain peak extraction and classification results obtained from the different resolution DEM are compared. The experimental results show that: (1) The CM-index and the CSDindex accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices. (2) The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak (SR-Peak) and multiple ridge intersection peak (MRI-Peak). The visual inspection results show that the classification accuracy in the different study areas exceeds 75%. (3) The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution. [ABSTRACT FROM AUTHOR]

Hydrological models are a conceptual representation of a simplified hydrological cycle. The hydrological cycle is the water cycle that circulates water from the land surface to the atmosphere and back again to the land. With the use of Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), an event-based model and a continous hydrological model were established in Zijinguan watershed of Daqinghe River basin. To study the loss methods provided by HEC-HMS and its appropriateness for model fitting, is the main objective of this paper. The watershed was delineated with HEC-GeoHMS in ArcGIS, and its properties were extracted from a Digital Elevation Model (DEM) of 30 m × 30 m. The HEC-HMS includes a soil conservation service curve number (SCS-CN) method and a soil moisture calculation (SMA) loss method, simulating the event and continuous runoff, respectively. SCS Unit hydrograph and Muskingum were used for flow routing. Specifically, eight rainfall events were selected for calibrating (6 events) and verifying (2 events) the event-based model. Similarly, for the continuous model the wet seasons of eight different years were used for calibration and verification. The calibrated parameters of the events model were used in the continuous model. The soil moisture and evapotranspiration data were decoded from the satellite data to set in the continuous model. The performance of SCS-CN and SMA models was compared. During the calibration period, the values of NSE, PEV and PEPF range from 0.605 to 0.744, 3.1 to 13.58%, and 11.104 to 27.72%, and during validation they are 0.527 to 0.634, 4.35% to 5.01%, and 13.66% to 27.88%, for SCS-CN model. For the SMA model, the values of NSE, PEV and PEPF during calibration range from 0.434 to 0.604, 2.879 to 34.326%, −4.831 to 57.48%, and during validation they are 0.094 to 0.624, −19.52% to −12.55%, and 40.213% to 50.15%. Overall, the performance of SCS-CN model is found more satisfactory than that of SMA model. [ABSTRACT FROM AUTHOR]

Landslides, collapses and cracks are the main types of geological hazards, which threaten the safety of human life and property at all times. In emergency surveying and mapping, it is time-consuming and laborious to use the method of field artificial investigation and recognition and using satellite image to identify ground hazards, there are some problems, such as time lag, low resolution, and difficult to select the map on demand. In this paper, a 10 cm per pixel resolution photogrammetry of a geological hazard -prone area of Taohuagou, Shanxi Province, China is carried out by DJ 4 UAV. The digital orthophoto model (DOM), digital surface model (DSM) and three-dimensional point cloud model (3 DPCM) are generated in this region. The method of visual interpretation of cracks based on DOM (as main) -3DPCM (as auxiliary) and landslide and collapse based on 3DPCM (as main) — DOM and DSM (as auxiliary) are proposed. Based on the low altitude remote sensing image of UAV, the shape characteristics, geological characteristics and distribution of the identified hazards are analyzed. The results show that using UAV low altitude remote sensing image, the method of combination of main and auxiliary data can quickly and accurately identify landslide, collapse and crack, the accuracy of crack identification is 93%, and the accuracy of landslide and collapse identification is 100%. It mainly occurs in silty clay and mudstone geology and is greatly affected by slope foot excavation. This study can play a great role in the recognition of sudden hazards by low altitude remote sensing images of UAV. [ABSTRACT FROM AUTHOR]

The security and socioeconomic development of China's border areas are of great significance to the nation and the wider world. Using census, statistical, digital elevation model (DEM) and network data, this paper employs visual analysis to capture population distribution patterns in China's 131 border counties from 1982 to 2010. Multiple stepwise regression is carried out to identify the influencing factors of population dynamics in border regions. The main findings include: China's most heavily populated border areas are primarily in the northeast, northwest, and the Guangxi-Yunnan region, while rapid growth of population is found in western Inner Mongolia, southwest Xinjiang, northwest Tibet, and southern Yunnan. Given the increasingly market-oriented migration mechanism, the national reclamation policy has been no longer effective in population attraction in the new century. Education has significantly lowered and will continuously lower the fertility rate in remote border areas. The factors influencing population growth show a remarkable regional heterogeneity along China's long border. [ABSTRACT FROM AUTHOR]

In China, flood inundation modeling is often limited by the lack of high quality topographic dataset, especially in small cities where hydrologic informatization is relatively backward. Coarse-resolution digital elevation models (DEMs) often lead to lower accuracy in simulation results due to the loss of small-scale building features in terrain modeling. Increasing the DEM resolution by resampling techniques may be an available solution to building representation problems in data-sparse areas, which has not yet received much attention and needs to be tested in conjunction with urban inundation models. This paper evaluated the impact of DEM resolution and resampling techniques on the building treatment method (BTM) and the output of urban inundation model to determine whether satisfactory inundation simulation can be achieved based on resampled DEMs with fine resolution. Using the 2016 flood event in Wuhan as the study case, a detailed comparison among 19 modeling scenarios differing in DEM resolutions, resampling techniques and BTMs was subsequently conducted. The outcome of the comparison analysis revealed the sensitivity of the simulated inundation depth to the resampled DEM resolution. Furthermore, the scenario using resampled DEMs with high resolution showed better performance in urban inundation modeling because the building features and the flow paths within building gaps were adequately represented by the BTM. The findings of this study may facilitate the application of BTMs for urban inundation modeling to predict (with useful accuracy) the inundation extent and depth in areas where fine topography data are unavailable. [ABSTRACT FROM AUTHOR]

When using a Terrestrial 3D laser scanning (TLS) to monitor the mining subsidence in a mining area, the conventional methods are to set up the public target manually, or to set up the ground control network in advance. However, these have the disadvantages of high labor intensity and low efficiency. In order to overcome the shortcomings of the conventional methods, this paper presents the subsidence monitoring method using TLS without a target in the mining area. Feature points are used to splice the multi-station point clouds in the same phase, and a few station coordinates are used to uniformly transform the multiphase data into a geodetic coordinate system. Afterwards, the surface digital elevation model (DEM) is established by using the point clouds after coordinate transformation, and the dynamic subsidence value and subsidence basin during the period of observation are obtained by DEM subtraction. Compared to traditional methods, this method simplifies the field measurement process, reduces the labor intensity, and transfers the main work to the internal industry. It is suitable for surface subsidence monitoring with a harsh external environment and complicated topographic conditions. Surface deformation values are important basic data for mining area subsidence parameters acquisition, mining area environmental assessment, land reclamation, and ecological restoration. As a consequence, it is of great value to popularize and apply in mining area for subsidence monitoring. [ABSTRACT FROM AUTHOR]

With high spatial resolution, on-demand-flying ability, and the capacity for obtaining three-dimensional measurements, unmanned aerial vehicle (UAV) photogrammetry is widely used for detailed investigations of single landslides, but its effectiveness for landslide detection and monitoring in a large area needs to be investigated. The Heifangtai terrace in the Loess Plateau of China is a loess terrace that is extremely susceptible to irrigation-induced loess landslides. This paper used UAV-based photogrammetry for a series of highresolution images spanning over 30 months for landslide detection and monitoring of the terrace with an area of 32 km2. Dense and evenly distributed ground control points were established and measured to ensure the high accuracy of the photogrammetry results. The structure-from-motion (SfM) technique was used to convert overlapping images into orthographic images, 3D point clouds, digital surface models (DSMs) and mesh models. Using multitemporal differential mesh models, landslide vertical movements and potential landslides were detected and monitored. The results indicate that a combination of UAV-based orthophotos and differential mesh models can be used for flexible and accurate detection and monitoring of potential loess landslides in a large area. [ABSTRACT FROM AUTHOR]

In the investigation of karstic form and underground karst water, determination of the location and geometric properties of karstic conduits and underground watersheds is one of the greatest problems not yet adequately resolved. In the karst terrain of the Yunnan–Guizhou Plateau of China, karst depressions are a product of the formation and evolution of the groundwater system, and the distribution of these depressions is a direct result of crustal uplift and the development of karstic conduits. This terrain is characterized by differences in morphological characteristics of depressions between the region of recharge and the region of outflow/discharge: the region of recharge is marked extensively by circular and random depressions, and is characterized by depressions with higher bottom elevations (referred to here as 'higher depressions'). Conversely, the region of outflow and discharge is marked extensively by depressions that form long strips or irregular alignments that are distributed along with the karstic conduits, and is characterized by depressions with lower bottom elevations (referred to here as 'lower depressions'). Here, a methodology has been put forward to determine the possible location and principal directions of karstic conduits and underground hydrogeological watersheds based on surface morphological features of depressions in the region of the Yunnan–Guizhou Plateau, China. The methodology assumes that the contour map that results from determining the bottom elevations of karstic depressions is sufficient to determine the positions of underground hydrogeological divisions and karstic channels. Taking the two main conduits systems in Guizhou Province as an example, a digital elevation model (DEM) was derived from 1:10,000 scale topographic maps with ArcGIS 10.2 and the bottom elevations of depressions were marked. Then a contour map of the bottom elevations of the depressions was produced. The directions of conduits and underground watersheds in the research area were identified from the valleys and ridges of the contour lines. The methodology introduced in this paper offers a new means of determining the location and geometric properties of karstic conduits and extracting the spatial distribution of water systems in the region of the Yunnan–Guizhou Plateau, China. This methodology will be appropriate for investigating karstic areas with continuous crustal uplift and significant variation in the bottom elevation of depressions between the region of recharge and region of discharge. [ABSTRACT FROM AUTHOR]

Quantitative estimation of rill erosion research plays a significant role in the understanding of rill formation and their development mechanisms, which is lacking presently. In this paper, rill erosion is quantified to analyze and evaluate the spatial and temporal distribution in typical gully heads of the Loess Plateau of China. Although several studies have carried rill erosion quantification, the variability analysis of rill erosion rates at different slope position is limited primarily because of the unavailability of high resolution elevation models. Using the low-altitude photography with Unmanned Aerial Vehicles (UAVs), digital surface models (DSMs) with centimeter accuracy were generated, and then geometrical parameters of a total of 1,129 rills at tableland-slope (T-S), hillslope (S), and slopes affected by artificial vegetation (SV) in the study were counted. Then, spatial and temporal development as well as erosion rates of the rills at the three sites were compared. Although many studies revealed the erosion law at the S position, few studies have linked the development of rills at the T-S position, and artificial afforestation at the SV position exacerbates the rill erosion. Furthermore, based on the centimeter-resolution DSMs obtained by the two-stage UAV photography, we found that the slope gradient which was the most susceptible to rill erosion were 5-10°, 35-50°, and 65-80° at T-S, S and SV sites, respectively. At the SV, rill incision was the most sensitive to slope changes, and rill incision potential proved to be the greatest. Generally, the higher the rill erosion depth, the smaller the frequency of occurrence, but this trend was mitigated at the SV position. Therefore, urgent action is needed to control rill erosion caused by artificial afforestation on hillslopes to alleviate and eliminate the adverse effects of vegetation planting on rill erosion on the Loess Plateau of China. [ABSTRACT FROM AUTHOR]

Monitoring and analyzing changes in the extent of cultivated land may inform strategic decisions on issues of environmental and food security. The dry cropland area of 12000 km2 in the Three Gorges Reservoir Region (TGRR) of China is essential for feeding the local population of ≈20 million, but is highly prone to soil erosion, leading to the delivery of excessive amounts of sediment and associated pollutants to the Three Gorges Reservoir (TGR), and causing serious eco-environmental consequences. Against this background, this paper used Landsat images and a digital elevation model to analyze the altitudinal distribution of, and dynamic changes in, the area of dry cropland during the period 1990 to 2015. The results suggest that dry cropland was mainly distributed in the elevation range of 200-600 m. The dry cropland area decreased from 12525.37 km2 to 11796.27 km2 during the 25-year study period, including a particularly significant decrease in the rate of decrease from 6.93 km2/yr to 43.99 km2/yr after 2000. The largest decline in the dry cropland area occurred in the elevation range of 600-900 m. The transformations between dry cropland and forest revealed the impact of the TGR operation on the extent of dry cropland. A total of 528.79 km2 of dry cropland with slopes >25° were converted to forest after 2000, whereas a total of 642 km2 of forest was converted to dry cropland during the study period, and these conversions mainly occurred between the elevation of 200–900 m. These spatiotemporal changes in the dry cropland area are likely to raise new issues concerning food security in the TGRR. [ABSTRACT FROM AUTHOR]

In March 15, 2019, a small-scale loess landslide occurred at Zaoling, Shanxi Province, China, destroying several buildings and killing 20 people. Based on a digital orthophoto map (DOM) and digital elevation model (DEM) derived from Dajiang Pro4 UAV aerial images, coupled with field investigations, this paper describes the geometric features of this slope failure and analyzes its possible inducing factors. Results show that this landslide has a circular chair shape with a sliding direction of 355°. The total projected area of the landslide is 11,800 m2, in which the source is 9880 m2, and the main scarp is 1995 m2. The volume of the source material and loose deposit debris are about 10 × 104 m3 and 12 × 104 m3, respectively. Our research suggests that the inducing factors of this event may include the low strength and structure of the loess under a wet condition, special landforms of the slope, and human activities such as water discharge from a nearby bath center building and coal mining. [ABSTRACT FROM AUTHOR]

Usually, the rational polynomial coefficient (RPC) model of spaceborne synthetic aperture radar (SAR) is fitted by the original range Doppler (RD) model. However, the radar signal is affected by two-way atmospheric delay, which causes measurement error in the slant range term of the RD model. In this paper, two atmospheric delay correction methods are proposed for use in terrain-independent RPC fitting: single-scene SAR imaging with a unique atmospheric delay correction parameter (plan 1) and single-scene SAR imaging with spatially varying atmospheric delay correction parameters (plan 2). The feasibility of the two methods was verified by conducting fitting experiments and geometric positioning accuracy verification of the RPC model. The experiments for the GF-3 satellite were performed by using global meteorological data, a global digital elevation model, and ground control data from several regions in China. The experimental results show that it is feasible to use plan 1 or plan 2 to correct the atmospheric delay error, no matter whether in plain, mountainous, or plateau areas. Moreover, the geometric positioning accuracy of the RPC model after correcting the atmospheric delay was improved to better than 3 m. This is of great significance for the efficient and high-precision geometric processing of spaceborne SAR images. [ABSTRACT FROM AUTHOR]

Estimation of inundation extent is critical for the flood control system. It helps decision-maker determine the scope of damage to save residents' lives and property. In this paper, we analyzed the drawbacks of two kinds of methods of inundation estimation, hydrodynamic method and space-filling method, and divided inundation extent into water-body extent and water-invasion extent for the purpose of enhancing estimation accuracy. Based on Mathematical Morphology, an inundation estimation method suitable for flood mapping around natural and artificial lakes was put forward by integrating remote sensing (RS) data and digital elevation model (DEM) data. This method consists of three aspects, including the subdivision of water-body extent, the deduction of water-invasion extent which is viewed as the expansion of water-body extent, and the smoothing the boundaries of water-invasion extent. With subdividing DEM data horizontally into cross sections to build equiangular subsets, the expansion operation was implemented by structuring elements which were established based on set operations and scalar flexibilities. Thus, water-invasion extent was easily described by the structuring elements under given water volume, so that inundation extent was conveniently estimated by spatial data. The experiment results show that the method has the advantages of high accuracy and expressiveness and low time consumption. It had been used to analyze the inundation caused by the water conservancy project named Hanjiang-to-Weihe River Water Transfer Project in China. The method served as decision support for building an efficient emergency response mechanism and helped implement flood disaster forecast and simulation. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the classic snowmelt runoff model (SRM) is evaluated in a daily discharge simulation with two different melt models, the empirical temperature-index melt model and the energy-based radiation melt model, through a case study from the data-sparse mountainous watershed of the Urumqi River basin in Xinjiang Uyghur Autonomous Region of China. Theclassic SRM, which uses the empirical temperature-index method, and a radiation-based SRM, incorporating shortwave solar radiation and snow albedo, were developed to simulate daily runoff for the spring and summer snowmelt seasons from 2005 to 2012, respectively. Dailymeteorological and hydrological data were collected from three stations located in the watershed. Snowcover area (SCA) was extracted from satellite images. Solarradiation inputs were estimated based on a digital elevation model (DEM). Theresults showed that the overall accuracy of the classic SRM and radiation-based SRM for simulating snowmelt discharge was relatively high. Theclassic SRM outperformed the radiation-based SRM due to the robust performance of the temperature-index model in the watershed snowmelt computation. Nosignificant improvement was achieved by employing solar radiation and snow albedo in the snowmelt runoff simulation due to the inclusion of solar radiation as a temperature-dependent energy source and the local pattern of snowmelt behavior throughout the melting season. Ourresults suggest that the classic SRM simulates daily runoff with favorable accuracy and that the performance of the radiation-based SRM needs to be further improved by more ground-measured data for snowmelt energy input. [ABSTRACT FROM AUTHOR]

The LuTan-1 (LT-1) mission, China's first civilian bistatic spaceborne Synthetic Aperture Radar (SAR) mission, comprises two L-band SAR satellites. These satellites operate in bistatic InSAR strip map mode, maintaining a formation flight with an adjustable baseline to generate global digital elevation models (DEMs) with high accuracy and spatial resolution. This research introduces a dedicated interferometric calibration model for LT-1, tackling the unique challenges of the bistatic system, such as interferometric parameter coupling and the π -ambiguity problem caused by synchronization phase errors. This study validates the model using SAR images from LT-1 and Xinjiang corner reflector data, achieving interferometric phase accuracy better than 0.1 rad and baseline accuracy better than 2 mm, thereby producing high-precision DEMs with a height accuracy meeting the 5 m requirement. [ABSTRACT FROM AUTHOR]

Excluding rough areas with surface rocks and craters is critical for the safety of landing missions, such as China's Chang'e-7 mission, in the permanently shadowed region (PSR) of the lunar south pole. Binned digital elevation model (DEM) data can describe the undulating surface, but the DEM data can hardly detect surface rocks because of median-averaging. High-resolution images from a synthetic aperture radar (SAR) can be used to map discrete rocks and small craters according to their strong backscattering. This study utilizes the You Only Look Once version 7 (YOLOv7) tool to detect varying-sized craters in SAR images. It also employs the Markov random field (MRF) algorithm to identify surface rocks, which are usually difficult to detect in DEM data. The results are validated by optical images and DEM data in non-PSR. With the assistance of the DEM data, regions with slopes larger than 10° are excluded. YOLOv7 and MRF are applied to detect craters and rocky surfaces and exclude regions with steep slopes in the PSRs of craters Shoemaker, Slater, and Shackleton, respectively. This study proves SAR images are feasible in the selection of landing sites in the PSRs for future missions. [ABSTRACT FROM AUTHOR]

The focus of this paper is on two large sequential landslides which formed a dam and the resulting lake that occurred along the Jinsha River on October 11 and November 3, 2018. About 24 and 9 × 106 m3 of material failed and rushed into the river, respectively. Both landslides totally blocked the river and induced hazard chains. Four multi-temporal unmanned aerial vehicle (UAV)-based high-resolution images and digital elevation models (DEMs) before and after the landslides were obtained and merged. The initial deformation of the two landslides can be clearly captured by optical remote-sensing images and interferometric synthetic aperture radar (InSAR) analysis. The depth-integrated continuum method was adopted to analyze the dynamic process of both landslides, using the same basal friction angle and cohesion. The computational results matched well with field measurements and observations. It is shown that the evaluation of potentially landslide-prone areas by the depth-integrated continuum method is feasible and is able to provide significant information before a possible event. [ABSTRACT FROM AUTHOR]

The China Meteorological Administration has deployed the China New Generation Weather Radar (CINRAD) network for severe weather detection and to improve initial conditions for numerical weather prediction models. The CINRAD network consists of 217 radars comprising 123 S-band and 94 C-band radars over mainland China. In this paper, a high-resolution digital elevation model (DEM) and beam propagation simulations are used to compute radar beam blockage and evaluate the effective radar coverage over China. Results show that the radar coverage at a height of 1 km above ground level (AGL) is restricted in complex terrain regions. The effective coverage maps at heights of 2 km and 3 km AGL indicate that the Yangtze River Delta, the Pearl River Delta, and North China Plain have more overlapping radar coverage than other regions in China. Over eastern China, almost all areas can be sampled by more than 2 radars within 5 km above mean sea level (MSL), but the radars operating in Qinghai-Tibet Plateau still suffer from serious beam blockage caused by intervening terrain. Overall, the radars installed in western China suffer from much more severe beam blockage than those deployed in eastern China. Maps generated in this study will inform users of the CINRAD data of their limitations for use in precipitation estimation, as inputs to other weather and hydrological models, and for satellite validation studies. [ABSTRACT FROM AUTHOR]