Susceptibility Assessment of Small, Shallow and Clustered Landslide in Malipo, southwest China

<p><strong>Abstract: </strong>Susceptibility assessment of landslides over a large area depends on the basic spatial unit of mapping, each unit is assumed to have unique assessment value, so the division of mapping unit is directly related to the evaluation rate, grid cell or slope unit are usually be used in many researches. Grid cell divide the study region into regular squares of predefined size, each cell is assigned a value of influence factor. Slope unit based on hydrology divides the region by ridge and valley lines, which is more related to geological environment and it is hard to identify the subbasin boundary. Both units are used in this study for the assessment of small shallow and clustered landslides in vegetated slopes in Malipo, southwest China. Google earth map on February 7, 2019 was used to interpret the landslides. ArcGIS 10.2 software was used to produce landslide inventory map and obtained 1435 landslides in the study area; most frequent landslide areas are in the range of 62m<sup>2</sup> to 900m<sup>2</sup>. Field survey was carried out to verify uncertain factors and measure moisture soil content. Soil moisture content (SMC) map was obtained by Kriging Interpolation methods based on the field measured soil moisture content of 48 sample points. Information value (IV) model was used to generate landslide susceptibility assessment map and improved information value (IIV) model was used to determine whether the mapping unit with or without landslide. Seven factors, including slope angle, slope aspect, elevation, normalized difference vegetation Index (NDVI), Soil Moisture Content (SMC), distance to river and road were used as landslide influence factors. The Area under curve (AUC) values of the slope unit IIV, IV and grid cell were 0.814, 0.802 and 0.702 respectively for success rate. For prediction rate, the AUC values of the slope unit and grid cell were 0.803(IIV), 0.790(IV) and 0.699 respectively. Slope unit is more suitable than grid cell for assessing susceptibility of Small, Shallow and Cluster Landslide (Fig.1). Improved information value model can increase the accuracy of susceptibility assessment model for this characteristic landslide.</p><p><strong>Keywords: </strong>Landslide susceptibility assessment; Slope unit; Grid cell; Information value</p><p>                                                <strong> (a)</strong>  <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.612a5aa6550062062690161/sdaolpUECMynit/12UGE&app=m&a=0&c=e934e3e9858f863f856c55ba7f923603&ct=x&pn=gepj.elif&d=1" alt="" width="289" height="206">  <strong> (b)</strong>   <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.c0a33eb6550062262690161/sdaolpUECMynit/12UGE&app=m&a=0&c=f9c48114412d0742a895968d55be3fbd&ct=x&pn=gepj.elif&d=1" alt="" width="293" height="212">                           <strong>                                                      </strong></p><p><strong>                                                              Figure 1</strong> Landslide susceptibility maps (a)Slope unit-based and (b)Grid cell-based</p>

An efficient method for automatic slope unit delineation from a huge region

<p>Slope units have great potential for hydrological and geomorphological studies, especially for landslide susceptibility modelling. Digital Elevation Models are widely used to delineate slope units by connecting drainage and divide lines. However, it is difficult to estimate reasonable scale thresholds for drainage and divide lines, which limits the application of slope units. Most existing methods for slope unit delineation determine the scales manually. Recently, several automatic methods have been proposed, but these methods encounter the problem of low computing efficiency for large-scale region. In this study, a new efficient method is presented for automatic slope unit delineation. Similar to an existing method, our method divides the region into several sub-basins and delineate slope units for every sub-basin independently. Within a sub-basin, grid cells are classified into massive units, and neighboring units are merged according to several strategies when aspect homogeneity can be satisfied. The new method is tested in Yarlung Tsangpo Basin, southeastern Tibetan Plateau. This basin has a large area of nearly 250 000 km<sup>2</sup>, and can be divided into nearly 340 000 slope units within 3 hours using a general personal computers. The rationality of our method is proved by both visual and quantitative assessments.</p>

Landslide size matters: a new spatial predictive paradigm

<p>The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geomorphological community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions.</p><p> </p><p>In this work, we initially present a review of methods developed for landslide hazard assessment since its first conception decades ago. Subsequently, we introduce for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. More specifically, we implemented a Bayesian version of a Generalized Additive Model where the maximum landslide sizes per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. These ''max'' and ''sum'' models capture the spatial distribution of landslide sizes. We tested these models on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. The two models we present are both evaluated on a suite of performance diagnostics that suggest our models suitably predict the aggregated landslide extent per slope unit. In addition to a complex procedure involving variable selection and a spatial uncertainty estimation, we built our model over slopes where landslides triggered in response to seismic shaking, and simulated the expected failing surface over slopes where the landslides did not occur in the past.  </p><p> </p><p>What we achieved is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. This information is vital in landslide hazard studies and should be combined with the estimation of landslide occurrence locations. This could ensure that governmental and territorial agencies have a complete probabilistic overview of how a population of landslides could behave in response to a specific trigger.</p><p>The predictive models we present are currently valid only for the 24 cases we tested. Statistically estimating landslide extents is still at its infancy stage. Many more applications should be successfully validated before considering such models in an operational way. For instance, the validity of our models should still be verified at the regional or catchment scale, as much as it needs to be tested for different landslide types and triggers. However, we envision that this new spatial predictive paradigm could be a breakthrough in the literature and, in time, could even become part of official landslide risk assessment protocols.</p>

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model

Landslides are one of the most extensive geological disasters in the world. The objective of this study was to assess the performances of different landslide susceptibility models information content method (ICM), analytical hierarchy process (AHP), and random forest (RF) model) and mapping unit (slope unit and grid unit) for landslide susceptibility mapping in the Helong city, Jilin province, northeastern China. First, a total of 159 landslides were mapped in the study area based on a geological hazard survey (1:50,000) of Helong city. Then, the slope units of the study area were divided by using the curvature watershed method. Next, eight influencing factors, namely, lithology, slope angle, slope aspect, rainfall, land use, seismic intensity, distance to river, and distance to fault, were selected to map the landslide susceptibility based on geological data, field survey, and landslide information. Afterward, landslide susceptibility modeling of landslide inventory data is performed for extracting and learning the symmetry latent in data patterns and relationships by three landslide susceptibility models and utilizing it to predict landslide susceptibility. Finally, the receiver operating characteristic (ROC) curve was used to compare the landslide susceptibility models. In addition, results based on grid units were calculated for comparison. The AUC (the area under the curve) result for ICM, AHP, and RF model was 87.1%, 80.5%, and 94.6% for slope units, and 83.4%, 70.9%, and 91.3% for grid units, respectively. Based on the overall assessments, the SU-RF model was the most suitable model for landslide susceptibility mapping. Consequently, these methods can be very useful for landslide hazard mitigation strategies.

Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Landslide spatial probability and size are two essential components of landslide susceptibility. However, in existing slope-unit-based landslide susceptibility assessment methods, landslide size has not been explicitly considered. This paper developed a novel slope-unit based approach for landslide susceptibility assessment that explicitly incorporates landslide size. This novel approach integrates the predicted occurrence probability (spatial probability) of landslides and predicted size (area) of potential landslides for a slope-unit to obtain a landslide susceptibility value for that slope-unit. The results of a case study showed that, from a quantitative point of view, integrating spatial probability and size in slope-unit-based landslide susceptibility assessment can bring remarkable increases of AUC (Area under the ROC curve) values. For slope-unit-based scenarios using the logistic regression method and the neural network method, the average increase of AUC brought by incorporating landslide size is up to 0.0627 and 0.0606, respectively. Slope-unit-based landslide susceptibility models incorporating landslide size had utilized the spatial extent information of historical landslides, which was dropped in models not incorporating landslide size, and therefore can make potential improvements. Nevertheless, additional case studies are still needed to further evaluate the applicability of the proposed approach.