Search

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]

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]

Benggang as a fragmented landform type, can be characterized by a deep-cut slope with various shapes and depressions on the vast weathered crust slopes in southern China. Normally, the gully heads have been continuously collapsed to form a typical landform, such as chair-like erosion. Benggang usually develops rapidly due to large amount of erosion, and thereby to threaten land resources and ecological environment. The identification of Benggang development become necessary to control erosion, and then clarify the behind mechanism. However, conventional methods have low levels of automation for a large-scale work, particularly on local inquiry, in-situ search, and manual interpretation for high-resolution images from satellite remote sensing. This paper proposes a novel Bag of Visual-Topographic Words (BoV-TW) model combining high resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) local features to represent Benggang areas, according to the classification and recognition methods in remote sensing images. The local features of DOM can be set in Harris-Affine and Maximally Stable Extremal Regions (MSER), with Scale-Invariant Feature Transform (SIFT) descriptors. The local features of DSM were extracted by a 3D Douglas-Peucker algorithm, representing by gradient direction-invariant descriptors developed in this study. A Latent Dirichlet Allocation (LDA) was used to balance latent semantic analysis, thereby to construct low-dimensional high-level semantic representations. Support Vector Machine (SVM) was used as a supervised learning training classifier to achieve high-precision and fast automatic Benggang recognition. Three Benggang areas in Tongcheng County, Hubei Province were selected as the experimental objects. The original data were collected by DJI Phantom 3 Pro micro UAV in September 2016. Photoscan processing was used to obtain the DOM and DSM of three Benggang areas. Uniform grid division can be used to achieve DOM and DSM tiles in 0.15 m resolution of 256 × 256 pixels. A high-resolution 3D Benggang model was used to mark the areas with/without Benggang. Two Benggang areas were taken as the training set, and the rest were taken as the test set, in three comparative experiments. The results show that: 1) With changing numbers of LDA topics, the proposed method can maintain a total accuracy of 95%, while the recall rate and precision of Benggang 80%, indicating the maximums were 97.22% and 94.44%, respectively. The total accuracy, recall and precision have increased by 12%, 11% and 32%, respectively, compared with that of only DOM features. The recognition performance was significantly improved after combining with DSM features. 2) With changing sizes of the BoV-TW vocabulary, the total accuracy was 90%, and the maximum was 96.10%, while the recall rate reached 90%, the maximum recall rate of 100%. Precision gradually increased as the increase of vocabulary size, with the maximum of 85.00%. Compared with that of only BoV-TW, three performance indicators increased by 13%, 12% and 30%, respectively, indicating that LDA for latent semantic analysis can greatly improve the recognition detection performance. 3) The proposed method was better than that on the ResNet50 network using DOM and DOM + DSM as the data source. It infers that Benggang as a landform type cannot be well recognized using only DOM, or the simple combination of DOM and DSM. Meanwhile, it needs to combine feature extraction and fusion strategy for efficient application. This finding can deliver a useful tool for the quantitative analysis on the identification, control and erosion mechanism for Benggang. [ABSTRACT FROM AUTHOR]

The black soil region of Northeast China is one of the most important grain production bases in China. It is also one of the four major water erosion areas in China, in which the erosion gully is the main form of water erosion. In order to analyze the causes of gully erosion and evaluate the basic situation of gully erosion in the northeast black soil area, it is necessary to analyze its morphology and distribution characteristic of gully erosion. In this paper, Nenjiang County was taken as the study area, which is located in the northwest of Heilong province, China. The aerial remote sensing images with ground resolution of 0.2 m and 1:50 000 Digital Elevation Model (DEM) were used to carry out census and statistics on the erosion gully, and the morphological characteristics such as the gully width, gully length and longitudinal slope of the erosion gully were analyzed, as well as the influence of terrain factors such as elevation, slope and aspects. The results of the second land use survey in China were used to obtain the land use situation around the erosion ditch. The results showed that: 1) The number of erosion gullies in Nenjiang County was 38 426, and the whole area of the erosion gullies were 16 277 095 m2. The erosion gully mainly occurred on hilly area, and was dominated by gully erosion, accounting for 74.50% and 51.05% of the total number and area, respectively. The next type was modern incised valley. Erosion gullies were mostly less than 500 m. 2) The width and length of the erosion gullies were positively correlated, and the slope was negatively correlated with the width and length. 3) Compared with slope, aspects generally had a little effect on the distribution of erosion gullies. Rills and ephemeral gullies mainly distributed on slopes of 2~6° and 6~15°, and gullies and modern incised valleys mainly distributed on slopes of 0~2° and 2~6°. More than 90% of rills and ephemeral gullies, and 80% of gullies and modern incised valleys were occurred on cultivated land. 4) Although the topographic threshold model of gulley in Nenjiang County was not fitted well, it could still be used to analyze the process of gully erosion. The model showed that the critical slope of the modern incised valley was small, the parameter which expressed the gully erosion process reflected the occurrence and development of the gullies might be influenced by the underground process. [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]

A catastrophic landslide occurred on 21 July 2020, 30 km from Enshi city, in Mazhe County of Hubei province, China. In this paper, we aimed to investigate the kinematic evolution and volumetric change related to this landslide using multi-source remote sensing measurements from synthetic aperture radar (SAR) and optical data. C-band Sentinel-1 and X-band TerraSAR-X SAR data are analyzed using several multi-temporal interferometry (MTI) time-series techniques including Persistent Scatterer Interferometry (PSI), Small Baseline subset (SBAS), and Combined eigenvalue maximum likelihood Phase Linking (CPL). The spatial pattern of surface deformation resulting from the interferometric analysis is then statistically analyzed to retrieve the pre-failure and post-failure displacements. Co-failure motions are analyzed using an image correlation technique applied to both the Planetscope and Sentinel-2 images. Moreover, 4 pairs of bistatic TerraSAR-X/TanDEM-X (TDX) data are utilized to generate high-precision digital elevation models (DEMs) and estimate the volumetric change related to the main slope failure. The pre-failure ground deformation analysis results suggest that the landslide was already active before the July 2020 failure, with the seasonality and hydraulic diffusivity being characteristics of a slow-moving landslide. Among the three different MTI methods applied, the CPL method results in a greater measurement points (MPs) density than the PSI and SBAS method when estimating the pre-failure movement of the Shaziba landslide. The July 2020 Shaziba disaster is divided into three main parts: (1) slightly horizontal deformation of 0.5–1.5 m within the northern part with ground and house cracks, (2) less collapse in the eastern part with horizontal motions reaching 30 m and (3) a highly eroded western part where vegetation was wholly lost in the main event, resulting in an collapse volume of approximately 4.98 million m3, out of which approximately around 3.4 million m3 was deposited and approximately 1.58 million m3 was washed away into the Qing River. After the failure, the marginal scrap of the main failure body, above crown of landslide and eastern part showed instability with rates of 20–30 mm/yr, suggesting that the failure zone may continue to expand. • Multi-sensor data integration to quantify kinematics and life cycle of the landslide. • Improvement in increasing density of scatterers using Combined eigenvalue maximum likelihood Phase Linking (CPL). • Analysis of the time lag between triggering factors and changes in background velocity. • Geodetic quantification of erosion and deposition during the main slope failure using time-series of Tandem-X data. [ABSTRACT FROM AUTHOR]

Abstract Air temperature is an important parameter affecting biosphere processes, air quality, regional climate, and human health, but is difficult to estimate, especially on a nationwide scale. Remote sensing provides high spatial resolution data that influence air temperature and can help overcome this problem in spatially heterogeneous environments. Generally, the simple linear relationships between air temperature, land surface temperature (LST), and auxiliary data, seldom consider the complex spatial variations in air temperature. The random forest (RF) regression model can characterize the complex temperature distribution by establishing the non-linear relationships among them. The main purpose of this paper is to present an RF regression method to estimate monthly average temperatures in China, using MODIS LST, normalized differential vegetation index (NDVI), night-time light, and a digital elevation model (DEM). Results based on cross-validation show that mean absolute error (MAE) and root mean square error (RMSE) at national level in different months ranged from 1.15 to 1.44 °C (mean ± standard deviation: 1.30 ± 0.10 °C) and from 1.57 to 1.99 °C (1.79 ± 0.13 °C), respectively. Potential sources of error and uncertainty were analyzed and investigated. Results show that regression model performs better in eastern (MAE: 0.68 ± 0.79 °C; RMSE: 0.75 ± 0.81 °C) and central China (MAE: 0.68 ± 0.41 °C; RMSE: 0.74 ± 0.41 °C). The model performance was strongly dependent on elevation, slope, land cover, and satellite data quality. In addition, the percentage increase in mean squared error from RF regression model was used to quantify the importance of variables. Positive importance scores indicated an improvement in accuracy with the assistance of NDVI, night-time lights, and the DEM. LST, and more auxiliary data sources, when integrated into an RF regression model, may be ways in which to improve the modeling of air temperature variations in the future. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the characteristics of the landslides developed in the epicentral area of AD 1556 M~8.5 Huaxian Earthquake, and discuss their relations to the active normal faults in the SE Weihe Graben, Central China. The results from analyzing high-resolution remote-sensing imagery and digital elevation models (DEMs), in combination with field survey, demonstrate that: (i) the landslides observed in the study area range from small-scale debris/rock falls to large-scale rock avalanches; (ii) the landslides are mostly developed upon steep slopes of ≥30°; and (iii) the step-like normalfault scarps along the range-fronts of the Huashan Mountains as well as the thick loess sediments in the Weinan area may facilitate the occurrence of large landslides. The results presented in this study would be helpful to assess the potential landslide hazards in densely-populated areas affected by active normal faulting. [ABSTRACT FROM AUTHOR]

The geological and ecological (geo-ecological) environment of the coastal coal-mining city is a basic element for human subsistence, and it connects the regional economy with social sustainable development. Using Longkou, a coastal mining city, as an example, a geological and ecological environmental quality assessment was conducted based on spatiotemporal big data. Remote sensing images, a digital elevation model (DEM), and precipitation and interpolation processing were used to generate factor layers. A synthetic evaluation index system was set up, including physical geography, geological conditions, mining intensity, ecological environmental recovery and geological hazards associated with mining. Moreover, an analytical hierarchy process was used to calculate the factor weight of each evaluation factor, and a consistency check was performed to build an assessment model of the geo-ecological environment of Longkou. The results indicate that multi-factor spatiotemporal big data provide a scientific assessment of the geo-ecological environmental quality with indispensable data and methods. The spatial distribution of geo-ecological environmental quality presented clear specialization of zonality, showing poor quality in the coastal coal mine ore concentration area and good quality in the inland and mountainous areas of Nanshan Mountain. The geo-ecological environmental quality of Longkou was divided into 5 levels as worst, poor, middle, good and better districts. The good and better districts accounted for 76.763% of the total area of the assessment region, indicating that the geo-ecological environmental quality of the study area was in good condition. The mining intensity and ecological environment recovery were major factors in determining the regional variation of the geo-ecological environment of Longkou. The possible causes inducing uncertainties and limitations in evaluation of the geo-ecological environmental quality were discussed. The model combining AHP with GIS proposed in this paper is an effective means of evaluating regional geo-ecological environmental quality. [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]

Abstract: The devastating earthquake that occurred in Wenchuan County, Sichuan Province, China, on the 12th May 2008, caused widespread damage and devastation to rural communities and economy. The terrain of the entire region has been weakened and is now highly susceptible to long-term slope instability that will trouble this region for many years to come. Before the earthquake, the maximum seismic intensity expected in the region was VI–VIII in the 12-degree Liedu scale (the intensity measure adopted in China). However, the actual intensity caused by the earthquake ranged between VIII and XI. The seismic intensity map is practical for regional guidance but lacks the detail to provide an adequate representation of the true damage level, in terms of current status and future potential in such a seismically active and populated region, especially when the hazards and risks are likely to be multiple and cascading in high relief areas. This paper presents a GIS based novel approach to earthquake damage zone modelling using satellite remote sensing and DEM data. The novelty is to take into account the co-seismic ground deformation (measured using differential SAR interferometry) as an important modulating factor in modelling the susceptibility of earthquake related geohazards, together with conventional multi-criteria factors which draw on geological and topographical variables such as rock competence, slope, proximity to drainage, and fracture density. The modulating effect of the earthquake greatly enhances the susceptibility in the areas where the majority of the ensuing landslides and debris-flows actually took place. When this susceptibility model is further modulated by the mapped surface disruption caused by the earthquake (which encompasses all forms of mass movement and urban destruction), it is directly linked to seismic intensity and we call it “earthquake damage”. The output earthquake damage map represents both the current damage status as well as the future damage (hazard) potential. The resulting earthquake damage map for the Beichuan region clearly defines a series of zones of increasing seismic intensity, approximately corresponding to Liedu values VIII–XI but with great details. [Copyright &y& Elsevier]