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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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 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]

Nighttime light data play an important role in the research on cities, while the urban centers over a large spatial scale are still far from clearly understood. Aiming at the current challenges in monitoring the spatial structure of cities using nighttime light data, this paper proposes a new method for identifying urban centers for massive cities at the large spatial scale based on the brightness information captured by the Suomi National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor. Based on the method for extracting the peak point based on digital elevation model (DEM) data in terrain analysis, the maximum neighborhood and difference algorithms were applied to the NPP-VIIRS data to extract the pixels with the peak nighttime light intensity to identify the potential locations of urban centers. The results show 7239 urban centers in 2200 cities in China in 2017, with an average of 3.3 urban centers per city. Approximately 68% of the cities had significant polycentric structures. The developed method in this paper is useful for identifying the urban centers and can provide the reference to the city planning and construction. [ABSTRACT FROM AUTHOR]

ABSTRACT Li, H.; Gong Z.; Dai, W.Q.; Lu, C.Z.; Zhang, X.Y; Cybele, S., and Guo, H.T., 2018. Feasibility of elevation mapping in muddy tidal flats by remotely sensed moisture (RSM) method. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 291–295. Coconut Creek (Florida), ISSN 0749-0208. It is hard for traditional ways to measure muddy intertidal topography because of its difficult access and limitation of exposure time. As a result, this paper proposes a new way, called remotely sensed moisture (RSM) method, to retrieve elevation in muddy tidal flats. When the tide ebbs, the intertidal flat exposes gradually from the land to the sea, thus forming a phenomenon of ‘low moisture in high-lying areas and high moisture in low-lying areas’. It can be seen that there may exist the negative correlation between the moisture and elevation of intertidal flat. By means of TM Image at low tide, this paper retrieved the moisture of muddy intertidal flat and found out that there does exist the negative correlation, but relatively low (R2=0.55). Further analysis showed that the remnant water is a critical factor to affect the correlation. After excluding regions nearby waterlines, the correlation between moisture and elevation is improved significantly (R2=0.78). The research demonstrates that RSM method has a potential to map topography of muddy tidal flats. [ABSTRACT FROM AUTHOR]

The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and Digital Elevation Model (DEM). Furthermore, residual analysis was performed to remove the impact of natural factors and separately assess the impact of human activities on vegetation restoration. The experiment was carried out in Shangwan Mine, China, and following results were obtained: (1) During the period of 2000 to 2020, intensified huan activities corresponded to positive vegetation changes (NDVI-HA) that exhibited an upward trend over time. (2) The spatial heterogeneity of vegetation restoration was attributed to the DEM. It is negatively correlated with NDVI in natural conditions, while under the environment of mining activities, there is a positive correlation between NDVI-HA and DEM. (3) The contribution of human activities to vegetation restoration in mining areas has been steadily increasing, surpassing the influences of temperature and precipitation since 2010. The results of this study can provide important references for the assessment of vegetation restoration to some extent in mining areas. [ABSTRACT FROM AUTHOR]

The time-effective mapping of erosion gullies is crucial for monitoring and early detection of developing erosional progression. However, current methods face challenges in obtaining large-scale erosion gully networks rapidly due to limitations in data availability and computational complexity. This study developed a rapid method for extracting erosion gully networks by integrating interferometric synthetic aperture radar (InSAR) and the relative elevation algorithm (REA) within the Huangfuchuan Basin, a case basin in the northern Loess Plateau, China. Validation in the study area demonstrated that the proposed method achieved an F1 score of 81.94%, representing a 9.77% improvement over that of the reference ASTER GDEM. The method successfully detected small reliefs of erosion gullies using the InSAR-refined DEM. The accuracy of extraction varied depending on the characteristics of the gullies in different locations. The F1 score showed a positive correlation with gully depth (R2 = 0.62), while the fragmented gully heads presented a higher potential of being missed due to the resolution effect. The extraction results provided insights into the erosion gully networks in the case study area. A total of approximately 28,000 gullies were identified, exhibiting pinnate and trellis patterns. Most of the gullies had notable intersecting angles exceeding 60°. The basin's average depth was 64 m, with the deepest gully being 140 m deep. Surface fragmentation indicated moderate erosive activity, with the southeastern loess region showing more severe erosion than the Pisha sandstone-dominated central and northwestern regions. The method described in this study offers a rapid approach to map gullies, streamlining the workflow of erosion gully extraction and enabling efficiently targeted interventions for erosion control efforts. Its practical applicability and potential to leverage open-source data make it accessible for broader application in similar regions facing erosion challenges. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to investigate the factors influencing ecosystem service trade-offs/synergies (TOSs) in major agricultural production areas in the southern hilly region and to propose optimization strategies to promote ecosystem sustainability in agricultural areas. Methods: The study used a geographical detector to analyze the determinants influencing the ecosystem service trade-offs as well as correlation analysis, geographically weighted regression (GWR), and a geographical detector to analyze the spatial and temporal evolution of ecosystem service and TOS relationships from 2000 to 2020 in Hunan Province. Results: The results showed that the comprehensive value of ecosystem services in Hunan Province showed an increasing trend from 2000 to 2020. With spatial heterogeneity, the areas with high values were mainly distributed in the hilly areas in the west, south, and east of Hunan Province, and the areas with low values were mainly distributed in the Dongting Lake Plain and the Xiangzhong Hilly Basin. There was a trade-off relationship between food production (FP) and all other ecosystem services, of which FP has the strongest trade-off effect with habitat quality (HQ). The synergy effect between HQ, water yield (WY), carbon storage (CS), and soil conservation (SC) shows an increasing trend. Gross domestic product (GDP) and SLOPE are the dominant factors for the strength of trade-offs between food supply and other ecosystem services, and Digital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) are the dominant factors for the strength of synergy effects among ecosystem services. The strength of TOS effects of ecosystem services is determined by interactions or co-influences between the two services rather than by a single component. Discussion: The results of this study can provide a reference basis for the enhancement of ecosystem services and the sustainable planning of agricultural landscapes in the southern hilly areas. [ABSTRACT FROM AUTHOR]

On July 23, 2019, a high-speed and long-runout landslide occurred in Jichang Town, Shuicheng County, Guizhou Province, China, causing 42 deaths and 9 missing. This paper used the discrete element software MatDEM to construct a three-dimensional discrete element model based on digital elevation data and then simulated and analyzed the movement and accumulation process of the landslide. The maximum average velocity of the source area elements reached 14 m/s when passed through the scraping area; meanwhile, the velocity of the scraping area elements increased rapidly. At 90 s, the maximum displacement of the source area elements reached 1358.5 m. The heat generated during the movement of the landslide was mainly the frictional heat, and the frictional heat increased sharply when the source area elements passed through the scraping area. The change of frictional heat has a certain positive correlation with the velocity of the scraping area elements. Finally, the volume of the scraping area elements was 2.4 times greater than the source area elements in the deposits. The scraping effect increases the volume of the sliding body and expands the impact area of the landslide disaster. Additionally, by setting different compressive and tensile strengths as well as internal friction coefficients to analyze the influences of their value changes on the landslide movement process, the results show that the smaller the strengths and internal friction coefficient of the model, the greater the depth and area of the scraping area, which will result in a thicker accumulation; meanwhile, the average displacement, average velocity, and heat will also increase. [ABSTRACT FROM AUTHOR]

From the perspective of landscape, it is important to evaluate the landscape sustainability of ecologically fragile areas and explore temporal and spatial evolution laws to promote their sustainable development. Presently, most studies on the analysis of landscape Boltzmann entropy (also called configurational entropy) are based on a single landscape, and most of these studies are theoretical discussions. However, there are few case studies on landscape ecology. The main objectives of this paper are to explore a quantitative relationship between Boltzmann entropy and landscape sustainability, to propose a method for evaluating landscape sustainability based on Boltzmann entropy, and to evaluate the sustainability of diverse landscapes in Mizhi County, Shaanxi Province, China. This article uses digital elevation model (DEM) data with a spatial resolution of 30 m in Mizhi County. The remote sensing data on Mizhi County from 2000 were obtained by the Landsat Enhanced Thematic Mapper (ETM) + sensor, and the high-resolution image of Mizhi County from 2015 was obtained by the Gaofen-1 satellite. In this article, the subbasins are taken as the evaluation unit, and the Boltzmann entropy of Mizhi County is calculated based on the experts' scoring of landscape sustainability in the study area. Through the analysis of landscape sustainability results from 216 subbasins in Mizhi County in 2000 and 2015, the following conclusions are drawn: (1) the evaluation matrix proposed in this paper is effective, and the Boltzmann entropy obtained by this method can directly reflect the level of landscape sustainability; (2) during the research period, the landscape sustainability of Mizhi County showed a good trend overall, especially the three townships of Taozhen, Shadian, and Shigou, which were significantly improved, and these findings were consistent with the field investigation; (3) on the spatial level, the landscape sustainability of mid-eastern Mizhi County is relatively poor compared to that in other regions, but the sustainability is also slowly increasing. [ABSTRACT FROM AUTHOR]

Benggang are characterized by deep-cut slopes with various shapes and depressions on the vast weathered crust slopes in southern China. The gully heads have been continuously collapsed and eroded to form a chair-like erosion landforms. It develops rapidly, and leads to large amounts of erosion, with the hazards of damaging land resources, destroying basic farmland, and deteriorating ecological environment. To study and manage Benggang, the primary task is to discover it. Traditional methods based on local in-situ investigations, which are not only labour-consuming but also inefficient. These methods are difficult to meet the needs of large-scale investigations of Benggang. This paper proposes a method for automatic Benggang recognition based on Ultra-High Resolution (UHR) DOM (Digital Orthophoto Map) and DSM (Digital Surface Model) obtained from UAV (Unmanned Aerial Vehicle) survey. This method adopts a Bag of Visual-Topographical Words (BoV-TW) model. The local features extracted from DOM and DSM are represented based on BoV-TW, and fused by Latent Dirichlet Allocation (LDA). Finally Support Vector Machine (SVM) is adopted as a supervised classifier to achieve high-precision automatic Benggang recognition. Experimental results prove that the total accuracy of our method can be maintained at about 95%, with recall and precision above 80% (the highest are 97.22% and 94.44%, respectively), which are significantly higher than the methods of using only DOM local features and using only BoV-TW. [ABSTRACT FROM AUTHOR]

Tsunami is one of the world's most dangerous marine disaster. In this paper, freely available remote sensing data are applied to study the hazard, vulnerability, and evacuation in the event that a tsunami strikes the district of Tianya in the city of Sanya. Tsunami inundation is calculated using a tsunami numerical model, and the tsunami vulnerability and evacuation in the inundation area are analyzed. Aster Global Digital Elevation Model elevation data are applied to provide input data for the tsunami numerical model and thus obtain tsunami inundation areas, while they are also used to study the surface slope for evacuation. Landsat satellite imagery is used to analyze land–water borders and land cover in both hazard assessment and evacuation analysis. Visible Infrared Imaging Radiometer Suite nighttime lights data provide information of the socioeconomic activity for vulnerability analysis. The analysis results show that the remote sensing data is suitable for tsunami assessment and evacuation analysis of China's county-level region. We can get a general understanding about tsunami vulnerability and evacuation situation. One kind of remote sensing data can accomplish several tasks, avoiding the error caused by different source data. Remote sensing can play an important role in tsunami assessment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mangrove species of multiple natural reserves was implemented fine classifications. • Two adaptive stacking algorithms achieve classification accuracies of 90.4%-96.2%. • ACE and AMO stackings outperform traditional stacking in classifying mangrove species. • MPViT has excellent mangrove species classification performance. • Interpretation of the sensitivity of remote sensing features to identify mangrove species. Mangrove is the highest productive ecosystems in the global coastal zone, which has high blue carbon sink function and carbon neutrality potential. Fine species classification is essential for mangrove conservation and sustainable development, and has attracted much attention in recent years using ensemble learning and multi-dimensional data. However, the current mangrove species classification based on traditional stacking ensemble learning still faces challenges due to the correlation between base classifiers, differences in meta -classifier capabilities, the subjectivity of parameter tuning, and data redundancy. To address these issues, this paper utilized unmanned aerial vehicle (UAV) multispectral images of three mangrove nature reserves in Beibu Gulf, south China, to examine the classification and generalization ability of our proposed Adaptive Stacking Ensemble Learning (ASEL) algorithm for different mangrove species. We also aim to verify the feasibility of the Multi-Path Vision Transformer for Dense Prediction (MPViT) algorithm for mangrove species mapping, and compare its performance with the ASEL algorithm for mangrove species classification. Finally, we used the SHapley Additive Explanations (SHAP) method to measure the contribution of feature variables to the model, exploring the sensitivity of different image features to mangrove species mapping. This study highlights that: (1) The two ASEL algorithms achieved high accuracy classification of mangrove species with the overall classification accuracy ranging from 79.8% to 96.2%. The ACE-Stacking and AOM-Stacking algorithms performed better classification ability than the traditional stacking algorithm, with the mean overall classification accuracy increasing from 0.9% to 3.3%. The McNemar test further indicated that the differences in classification results derived from three algorithms were significant at the 95% confidence interval, demonstrating the better classification and generalization ability of the ASEL algorithm. (2) The MPViT algorithm achieved better classification accuracy in the three reserves, with an overall accuracy of 95.5%-97.3%, which was 0.6%-5.4% higher than the two ASEL algorithms. The average accuracy of identifying mangrove species was over 95.3% in all reserves, demonstrating the desirable mangrove classification performance of MPViT algorithm. (3) The identification accuracies (F1 scores) of mangrove species in different reserves were ranged from 0.848 to 0.984. Cyperus malaccensi had the highest identification accuracy. (4) The SHAP method interpreted the great contribution of digital surface model and variable atmospherically resistant index features on mangrove species classification. The red band and ratio vegetation index was sensitive to Aegiceras corniculatum and to Cyperus malaccensi. [ABSTRACT FROM AUTHOR]

Measuring surface deformation is crucial for a better understanding of spatial-temporal evolution and the mechanism of mining-induced deformation, thus effectively assessing the mining-related geohazards, such as landslides or damage to surface infrastructures. This study proposes a method of retrieving surface deformation by combining multi-temporal digital surface models (DSMs) with image homonymous features using China's ZY-3 satellite stereo imagery. DSM is generated from three-line-array images of ZY-3 satellite using a rational function model (RFM) as the imaging geometric model. Then, elevation changes in deformation are extracted using the difference of DSMs acquired at different times, while planar displacements of deformation are calculated using image homonymous features extracted from multi-temporal digital orthographic maps (DOMs). Scale invariant feature transform (SIFT) points and line band descriptor (LBD) lines are selected as two kinds of salient features for image homonymous features generation. Cross profiles are also extracted for deformation in typical regions. Four sets of stereo imagery acquired in 2012 to 2022 are used for deformation extraction and analysis in the Fushun coalfield of China, where surface deformation is quite distinct and coupled with rising and descending elevation together. The results show that 21.60% of the surface in the study area was deformed from 2012 to 2017, while a decline from 2017 to 2022 meant that 17.19% of the surface was deformed with a 95% confidence interval. Moreover, the ratio of descending area was reduced to 6.44% between 2017 and 2022, which is lower than the ratios in other years. The slip deformation area in the west open pit mine is about 1.22 km2 and the displacement on the south slope is large, reaching an average of 26.89 m and sliding from south to north to the bottom of the pit between 2012 and 2017, but elevations are increased by an average of about 16.35 m, involving an area of about 0.86 km2 between 2017 and 2022 due to the restoration of the open pit. The results demonstrate that more quantitative features and specific surface deformation can be retrieved in mining areas by combining image features with DSMs derived from ZY-3 satellite stereo imagery. [ABSTRACT FROM AUTHOR]

Cirques are typical erosional landforms of glaciers and have been used as bases for paleoclimate and paleoenvironment reconstruction and for understanding the interactions between glacial erosion, climate, and topography. The availability of high-resolution digital elevation models (DEMs) provides the opportunity to map large populations of cirques for regional and global scale analysis. However, cirque outlines are still mainly determined based on manual digitization, which is time consuming and labor intensive. This paper introduces an automated method to recognize and delineate cirques using DEMs based on a series of hydrological and morphological analyses, including delineating stream network, filtering streams, determining potential cirque threshold points, and delineating cirque outlines. A semi-automated tool is also developed based on user-specified threshold points or cross sections. The related tools are coded in python and imported into ArcGIS as a toolbox, AutoCirque, with user friendly interfaces. Comparison in a test area of the eastern Tian Shan, China, indicated that the population statistics are relatively consistent between manually digitized and auto-delineated cirques. Detailed comparisons for 11 selected cirques indicated that the AutoCirque-delineated and manually digitized cirque outlines are similar in shape with an average boundary offset of approximately one DEM cell size (30 m) and a 70–90% overlap-fit percentage. The derived cirque metrics are also similar, especially for elevation, slope, and aspect related metrics. This toolbox can significantly speed up the analytical processes, remove the subjectivity in delineating cirque outlines, and allow for the comparison of cirque morphology and metrics at regional and global scales. • This paper presents an automated method to delineate cirque outlines from DEMs. • The developed ArcGIS toolbox incudes both fully automated and semi-automated tools. • Delineated cirques have consistent population statistics with manual-digitized ones. • Manual- and auto-derived cirques are similar in shape with high overlap percentage. • This toolbox allows for large population analyses of cirques at different scales. [ABSTRACT FROM AUTHOR]

Karst watershed refers to the total range of surface and underground recharge areas of rivers (including subterranean rivers and surface rivers) in karst areas. Karst water resources, as the primary source of domestic water supply in southwest China, are vital for the social and economic development of these regions. It is greatly significant to establish a high-precision hydrological model of karst watershed for guiding water resources management in karst areas. Choosing the Daotian river basin in the Wumeng Mountains of Southwest China as the study area, this paper proposed a method for simplifying karst subterranean rivers into surface rivers by modifying the digital elevation model (DEM) based on a field survey and tracer test. This method aims to solve the inconsistency between the topographical drainage divides and actual catchment boundaries in karst areas. The Soil and Water Assessment Tool (SWAT) model was modified by replacing the single-reservoir model in the groundwater module with a three-reservoir model to depict the constraints of multiple media on groundwater discharge in the karst system. The results show that the catchment areas beyond topographic watershed were effectively identified after simplifying subterranean rivers to surface rivers based on the modified DEM data, which ensured the accuracy of the basic model. For the calibration and two validation periods, the Nash–Sutcliffe efficiencies (NSE) of the modified SWAT model were 0.87, 0.83, and 0.85, respectively, and R2 were 0.88, 0.84, and 0.86, respectively. The NSE of the modified SWAT model was 0.09 higher than that of the original SWAT model in simulating baseflow, which effectively improved the simulation accuracy of daily runoff. In addition, the modified SWAT model had a lower uncertainty within the same parameter ranges than the original one. Therefore, the modified SWAT model is more applicable to karst watersheds. [ABSTRACT FROM AUTHOR]

Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China's first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel. [ABSTRACT FROM AUTHOR]

Salt crust is a normal landform in drying-out salt lake basins or marine regression coastlines, but the surface evolution processes over a decadal or even centenary period are not well understood due to poor data records. Microrelief characteristics control erodibility and erosivity, which will significantly influence wind erosion and dust emission. It is essential to classify the microrelief pattern of salt crust for mapping its spatial distribution and evaluating the environmental process. A desiccated inland tail-end lake would be an example of the coastline surface evolution after regression and represent a good case study of salt crust because of the fewer exogenic process interruptions. For this paper, field work was performed in the Lop Nur playa in China, about 90° E, 40° N, which used to be a salt lake half a century ago. Ground-based photos of the salt crust were acquired and imported into structure-from-motion (SfM) software to produce a fine centimeter-scale digital elevation model (DEM). Two indexes were introduced and extracted from the digital elevation model to classify various types of salt crust: roughness was calculated to evaluate the magnitude and the gray-level co-occurrence matrix (GLCM) score was derived to describe the structure pattern of the salt crust. Moreover, in this paper, sedimentary features during different parts of a playa evaporation cycle are reviewed and peculiar kinds of salt crust found on Lop Nur are further discussed. [ABSTRACT FROM AUTHOR]

Spatial distribution and population density are important parameters in studies on urban development, resource allocation, emergency management, and risk analysis. High-resolution height data can be used to estimate the total or spatial pattern of the urban population for small study areas, e.g., the downtown area of a city or a community. However, there has been no case of population estimation for large areas. This paper tries to estimate the urban population of prefectural cities in China using building height data. Building height in urban population settlement (Mdiffs) was first extracted using the digital surface model (DSM), digital elevation model (DEM), and land use data. Then, the relationships between the census-based urban population density (CPD) and the Mdiffs density (MDD) for different regions were regressed. Using these results, the urban population for prefectural cities of China was finally estimated. The results showed that a good linear correlation was found between Mdiffs and the census data in each type of region, as all the adjusted R2 values were above 0.9 and all the models passed the significance test (95% confidence level). The ratio of the estimated population to the census population (PER) was between 0.7 and 1.3 for 76% of the cities in China. This is the first attempt to estimate the urban population using building height data for prefectural cities in China. This method produced reasonable results and can be effectively used for spatial distribution estimates of the urban population in large scale areas. [ABSTRACT FROM AUTHOR]