: A system is described which: (a) replaces existing sets of diverse terrain indices with a group of statistics for point-characteristics; (b) calculates all of these statistics in a single computer run from a single data set; and (c) utilizes available altitude matrix data The procedures are applicable to Altitude matrix data at any grid mesh From altitudes in each 3 x 3 submatrix, a quadratic surface is fitted and solved for its first and second horizontal and vertical derivatives at the central point This yields the slope gradient, slope aspect, profile convexity, and plan convexity at every point in the original matrix, except for the peripheral rows and columns These 'point' descriptors are presented as: (1) line-printer shaded maps; (2) histograms; (3) scatter plots of each pair; (4) matrix of pair-wise correlations, plus circular regressions on aspect, and several multiple regressions, and (5) summary (moment-based) statistics In general, the five basic descriptors have little relation to each other, except that gradient is usually a quadratic function of altitude A comparison is made with other approaches, such as spectral analysis and fractal modelling The long-distance persistence properties of terrain mean that considerable extra variance at long wavelengths is usually incorporated when the study area is extended Hence the auto correlation function varies with the length of series or size of area Variance of derivatives are also affected, but means, skews, and kurtoses are not

Statistical Characterization of Altitude Matrices by Computer. Report 6. An Integrated System of Terrain Analysis and Slope Mapping.

Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling

Landslide susceptibility prediction (LSP) has been widely and effectively implemented by machine learning (ML) models based on remote sensing (RS) images and Geographic Information System (GIS). However, comparisons of the applications of ML models for LSP from the perspectives of supervised machine learning (SML) and unsupervised machine learning (USML) have not been explored. Hence, this study aims to compare the LSP performance of these SML and USML models, thus further to explore the advantages and disadvantages of these ML models and to realize a more accurate and reliable LSP result. Two representative SML models (support vector machine (SVM) and CHi-squared Automatic Interaction Detection (CHAID)) and two representative USML models (K-means and Kohonen models) are respectively used to scientifically predict the landslide susceptibility indexes, and then these prediction results are discussed. Ningdu County with 446 recorded landslides obtained through field investigations is introduced as case study. A total of 12 conditioning factors are obtained through procession of Landsat TM 8 images and high-resolution aerial images, topographical and hydrological spatial analysis of Digital Elevation Modeling in GIS software, and government reports. The area value under the curve of receiver operating features (AUC) is applied for evaluating the prediction accuracy of SML models, and the frequency ratio (FR) accuracy is then introduced to compare the remarkable prediction performance differences between SML and USML models. Overall, the receiver operation curve (ROC) results show that the AUC of the SVM is 0.892 and is slightly greater than the AUC of the CHAID model (0.872). The FR accuracy results show that the SVM model has the highest accuracy for LSP (77.80%), followed by the CHAID model (74.50%), the Kohonen model (72.8%) and the K-means model (69.7%), which indicates that the SML models can reach considerably better prediction capability than the USML models. It can be concluded that selecting recorded landslides as prior knowledge to train and test the LSP models is the key reason for the higher prediction accuracy of the SML models, while the lack of a priori knowledge and target guidance is an important reason for the low LSP accuracy of the USML models. Nevertheless, the USML models can also be used to implement LSP due to their advantages of efficient modeling processes, dimensionality reduction and strong scalability.

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Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30

This article is centred on analysing the state of the art of the conflation processes applied to geospatial databases (GDBs) from heterogeneous sources. The term conflation is used to describe the procedure for the integration of these different data, and conflation methods play an important role in systems for updating GDBs, derivation of new cartographic products, densification of digital elevation models, automatic features extraction and so on. In this article we define extensively each conflation process, its evaluation measures and its main application problems and present a classification of all conflation processes. Finally, we introduce a bibliography which the reader may find useful to further explore the field. It tries to serve as a starting point and direct the reader to characteristic research in this area.

Digital map conflation: a review of the process and a proposal for classification

This paper reports an investigation into the generalization of a grid-based digital elevation model (DEM) for the purpose of terrain analysis. The focus is on the method of restructuring the grid-based surface elevation data to form a triangulated irregular network (TIN) that is optimized to keep the important terrain features and slope morphology with the minimum number of sample points. The critical points of the terrain surface are extracted from the DEM based on their significance, measured not only by their local relief, but also by their importance in identifying inherent geomorphological and drainage features in the DEM. A compound method is proposed by integrating the traditional point-additive and feature-point methods to construct a drainage-constrained TIN. The outcome is then compared with those derived from other selected methods including filtering, point-additive or feature-point algorithms. The results show that the compound approach is capable of taking advantage of both point-additive and feature-point algorithms to maximally keep the terrain features and to maintain RMSE at an acceptable level, while reducing the elevation data points by over 99%. The analytical result also shows that the proposed method outperforms the compared methods with better control in retaining drainage features at the same level of RMSE.

Generalization of DEM for terrain analysis using a compound method

Landslides lead to a great threat to human life and property safety. The delineation of landslide-prone areas achieved by landslide susceptibility assessment plays an important role in landslide management strategy. Selecting an appropriate mapping unit is vital for landslide susceptibility assessment. This paper compares the slope unit and grid cell as mapping unit for landslide susceptibility assessment. Grid cells can be easily obtained and their matrix format is convenient for calculation. A slope unit is considered as the watershed defined by ridge lines and valley lines based on hydrological theory and slope units are more associated with the actual geological environment. Using 70% landslide events as the training data and the remaining landslide events for verification, landslide susceptibility maps based on slope units and grid cells were obtained respectively using a modified information value model. ROC curve was utilized to evaluate the landslide susceptibility maps by calculating the training accuracy and predictive accuracy. The training accuracies of the grid cell-based susceptibility assessment result and slope unit-based susceptibility assessment result were 80.9 and 83.2%, and the prediction accuracies were 80.3 and 82.6%, respectively. Therefore, landslide susceptibility mapping based on slope units performed better than grid cell-based method.

A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment

This study attempts to develop a method for the simulation of surface flow paths on a digital elevation model (DEM). The objective is to use a facet-based algorithm to estimate the surface flow paths on a raster DEM. A grid DEM was used to create a triangular facet network (TFN) over which the surface flow paths were determined. Since each facet in the network has a constant slope and aspect, the estimations of, for example, flow direction and divergence/convergence are less complicated compared to traditional raster-based solutions. Experiments were undertaken by estimating the specific catchment area (SCA) over a number of mathematical surfaces, as well as on a real-world DEM. Comparisons were made between the derived SCA by the TFN algorithm with some algorithms reported in the literature. The results show that the TFN algorithm produced the closest outcomes to the theoretical values of the SCA compared with other algorithms, deriving more consistent outcomes and being less influenced by surface shapes. The real-world DEM test also shows that the TFN was capable of modeling flow distribution without noticeable "artifacts," and its ability of tracking flow paths makes it an appropriate platform for dynamic surface flow simulation. (Less)

Estimating surface flow paths on a digital elevation model using a triangular facet network

Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

A scale-adaptive digital elevation model (S-DEM) method is proposed for multi-scale terrain analysis using a single high-resolution digital elevation model (DEM) database The motivation is to construct a DEM that is self-adaptive to a given scale of an application, rather than letting the application fit into the built-in scale of the DEM The method is based on an adaptive compound point extraction (CPE) algorithm that extracts surface ‘significant points’ from a high-resolution DEM according to their degree of importance (DOI) to the scale of an application A data structure can be established to match the demand from an application at a coarser scale Based on the data structure, a triangulated irregular network (TIN) model can be generated to support the terrain analysis at the desired scale The aim of the S-DEM is to support multi-scale applications in three aspects, namely, ‘one database for all scales and scale-adaptive’ (ie matching any application scale using a single high-resolution DEM), ‘c

Yumin Chen

Papers

Generalization of DEM for terrain analysis using a compound method

A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment

Estimating surface flow paths on a digital elevation model using a triangular facet network

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

A scale-adaptive DEM for multi-scale terrain analysis