Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR-RF integrated model and multiresolution DEMs

© 2019 by the authors. Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR-RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR-RF integrated model showed that the prediction accuracy of FR-RF-PALSAR (0.917) was higher than FR-RF-ASTER (0.865) and FR-RF-SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures

Quality assessment of DEM derived from topographic maps for geomorphometric purposes

Digital elevation models (DEMs) play a significant role in geomorphological research. For geomorphologists reconstructing landform and drainage structure is frequently as important as elevation accuracy. Consequently, large-scale topographic maps (with contours, height points and watercourses) constitute excellent material for creating models (here called Topo-DEM) in fine resolution. The purpose of the conducted analyses was to assess the quality of Topo-DEM against freely-available globalDEMs and then to compare it with a reference model derived from laser scanning (LiDAR-DEM). The analysis also involved derivative maps of geomorphometric parameters (local relief, slope, curvature, aspect) generated on the basis of Topo-DEM and LiDAR-DEM. Moreover, comparative classification of landforms was carried out. It was indicated that Topo-DEM is characterised by good elevation accuracy (RMSE <2 m) and reflects the topography of the analyzed area surprisingly well. Additionally, statistical and percentage metrics confirm that it is possible to generate a DEM with very good quality parameters on the basis of a large-scale topographic map (1:10,000): elevation differences between Topo-DEM and: 1) topographic map amounted from−1.68 to +2.06 m,MAEis 0.10 m, RMSE 0.16 m; 2) LiDAR-DEM (MAE 1.13 m, RMSE 1.69 m, SD 1.83 m); 3) GPS RTK measurements amounted from−3.6 to +3.01 m, MAE is 0.72 m, RMSE 0.97 m, SD 0.97 m. For an area of several dozen km2 Topo-DEM with 10×10 m resolution proved more efficient than detailed (1×1 m) LiDAR-DEM

Giving gully detection a HAND:Testing the scalability and transferability of a semi-automated object-orientated approach to map permanent gullies

Gully erosion can incur on- and off-site impacts with severe environmental and socio-economic consequences. Semi-automated mapping provides a means to map gullies systematically and without bias, providing information on their location and extent. If used temporally, semi-automated mapping can be used to quantify soil loss and identify soil loss source areas. The information can be used to identify mitigation strategies and test the efficacy thereof. We develop, describe, and test a novel semi-automated mapping workflow, gHAND, based on the distinct topographic landform features of a gully to enhance transferability to different climatic regions. Firstly, topographic heights of a Digital Elevation Model are normalised with reference to the gully channel thalweg to extract gully floor elements, and secondly, slope are calculated along the direction of flow to determine gully wall elements. As the gHAND workflow eliminates the need to define kernel thresholds that are sensitive towards gully size, it is more scalable than kernel-based methods. The workflow is rigorously tested at different gully geomorphic scales, in contrasting geo-environments, and compared to benchmark methods explicitly developed for region-specific gullies. Performance is similar to benchmark methods (variance between 1.4 % and 14.8 %). Regarding scalability, gHAND produced under- and over-estimation errors below 30.6 % and 16.1 % for gullies with planimetric areas varying between 1421.6 m2 and 355403.7 m2, without editing the workflow. Although the gHAND workflow has limitations, most markedly the requirement of manually digitising gully headcuts, it shows potential to be further developed to reliably map gullies of small- to large-scales in different geo-environments

Accuracy assessment in glacier change analysis

This thesis assesses the accuracy of digital elevation models (DEM) generated from contour lines and LiDAR points (Light Detection and Ranging) employing several interpolation methods at different resolutions. The study area is Jostefonn glacier that is situated in Sogn og Fjordane county, Norway. There are several ways to assess accuracy of DEMs including simple ways such as visual comparison and more sophisticated methods like relative and absolute comparison. Digital elevation models of the Jostefonn glacier were created from contour lines for years 1966 and 1993. LiDAR data from year 2011 was used as a reference data set. Of all the interpolation methods tested Natural Neighbours (NN) and Triangular Irregular Network (TIN) algorithms rendered the best results and proved to be superior to other interpolation methods. Several resolutions were tested (the cell size of 5 m, 10 m, 20 m and 50 m) and the best outcome was achieved by as small cell size as possible. The digital elevation models were compared to a reference data set outside the glacier area both on a cell-by-cell basis and extracting information at test points. Both methods rendered the same results that are presented in this thesis. Several techniques were employed to assess the accuracy of digital elevation models including visualization and statistical analysis. Visualization techniques included comparison of the original contour lines with those generated from DEMs. Root mean square error, mean absolute error and other accuracy measures were statistically analysed. The greatest elevation difference between the digital elevation model of interest and the reference data set was observed in the areas of a steep terrain. The steeper the terrain, the greater the observed error. The magnitude of the errors can be reduced by using a smaller cell size but that this is offset by a larger amount of data and increased data processing time.Popular science Glaciers are very sensitive indicators of climate change. The major cause of melting glaciers is global warming. This rapid rate of melting has serious negative impact on the earth causing flooding, leaving impact on flora and fauna, resulting in shortage of freshwater and hydroelectricity. The long-term monitoring of glaciers and the knowledge gained from it can help governments, environmental and water resource managers to make plans to cope with impacts of climate change. Results from glacier monitoring ought to be precise, showing the actual situation compared to the situation in the past as well as predicting possible glacier changes in the future. The aim of this thesis was to investigate how sensitive the results were to different methods used in glacier change detection focusing on the quality of Digital Elevation Models (DEMs). The study area of this thesis was the Jostefonn glacier situated in Sogn and Fjordane county, Norway. Digital elevation models were created from contour lines for years 1966 and 1993. LiDAR data from year 2011 was used as a reference data set. Several techniques were employed to estimate the accuracy of digital elevation models including visualization, statistical analysis, analysing the accuracy of digital elevation models for terrain on different slopes, comparison to a reference data set outside the glacier area that was considered to be stable and where no elevation change was expected. The original contour lines (1966 and 1993) were compared with the ones generated from the created terrain models (glacier area) as well as with the contour lines from the reference data set (outside the glacier area) by visualization techniques. Accuracy measures (Root Mean Square Error, Mean Absolute Error and others) were statistically analysed. Natural Neighbours and Triangular Irregular Network interpolators proved to be superior to other algorithms used to create the terrain models. The best outcome was achieved by using as small cell size as possible. 5 m resolution rendered the best results from the resolutions tested (5 m, 10 m, 20 m and 50 m). The greatest elevation differences were observed in the areas of a steep terrain. The steeper the terrain, the greater the elevation difference. The terracing effect was noticed in the digital elevation models due to the high density of elevation points on the contour lines and hardly any points between them. Useful information can be obtained by estimating accuracy of digital elevation models. The accuracy of terrain models determines the reliability of glacier change analysis and that is why the digital elevation model must represent the terrain as accurately as possible. The different methods used in this thesis rendered very similar results and that indicated that the results were reliable and the terrain models created with Natural Neighbours and Triangular Irregular Network interpolators (resolution of 5 m) can be employed in further glacier change analysis

Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data

Publisher's version (útgefin grein)Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hítardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hítará river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that-without further post-processing-the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery.This research has been supported by the Austrian Science Fund (FWF) through the project MORPH (Mapping, monitoring and modelling the spatio-temporal dynamics of land surface morphology; FWF-P29461-N29) and the Doctoral Collage GIScience (DKW1237-N23), as well as by the Austrian Academy of Sciences (?AW) through the project RiCoLa (Detection and analysis of landslide-induced river course changes and lake formation).Peer Reviewe

Using very high resolution (VHR) imagery within a GEOBIA framework for gully mapping: An application to the Calhoun Critical Zone Observatory

Gully erosion is a form of accelerated erosion that may affect soil productivity, restrict land use, and lead to an increase of risk to infrastructure. Accurate mapping of these landforms can be difficult because of the presence of dense canopy and/or the wide spatial extent of some gullies. Even where possible, mapping of gullies through conventional field surveying can be an intensive and expensive activity. The recent widespread availability of very high resolution (VHR) imagery has led to remarkable growth in the availability of terrain information, thus providing a basis for the development of new methodologies for analyzing Earth’s surfaces. This work aims to develop a geographic object-based image analysis to detect and map gullies based on VHR imagery. A 1-meter resolution LIDAR DEM is used to identify gullies. The tool has been calibrated for two relatively large gullies surveyed in the Calhoun Critical Zone Observatory (CCZO) area in the southeastern United States. The developed procedure has been applied and tested on a greater area, corresponding to the Holcombe’s Branch watershed within the CCZO. Results have been compared to previous works conducted over the same area, demonstrating the consistency of the developed procedure

A comparison of open-source LiDAR filtering algorithms in a mediterranean forest environment

Light detection and ranging (LiDAR) is an emerging remote-sensing technology with potential to assist in mapping, monitoring, and assessment of forest resources. Despite a growing body of peer-reviewed literature documenting the filtering methods of LiDAR data, there seems to be little information about qualitative and quantitative assessment of filtering methods to select the most appropriate to create digital elevation models with the final objective of normalizing the point cloud in forestry applications. Furthermore, most algorithms are proprietary and have high purchase costs, while a few are openly available and supported by published results. This paper compares the accuracy of seven discrete return LiDAR filtering methods, implemented in nonproprietary tools and software in classification of the point clouds provided by the Spanish National Plan for Aerial Orthophotography (PNOA). Two test sites in moderate to steep slopes and various land cover types were selected. The classification accuracy of each algorithm was assessed using 424 points classified by hand and located in different terrain slopes, cover types, point cloud densities, and scan angles. MCC filter presented the best overall performance with an 83.3% of success rate and a Kappa index of 0.67. Compared to other filters, MCC and LAStools balanced quite well the error rates. Sprouted scrub with abandoned logs, stumps, and woody debris and terrain slopes over 15° were the most problematic cover types in filtering. However, the influence of point density and scan-angle variables in filtering is lower, as morphological methods are less sensitive to them

Semi-automatic identification of neogenic deposits by using high resolution digital surface models in Southeastern Brazil

The Cenozoic deposits occupy large areas of the Brazilian territory with parts subject to intense human occupation. Nevertheless they are still little known and researched. One of the most important Cenozoic sedimentary units in Brazil corresponds to Barreiras Formation (Neogene), which is located along almost all of the Brazilian coast. Geomorphologically the deposits of the Barreiras Formation are associated with a relief of tabular forms, dissected into deep valleys with steep slopes. This research aims to develop a semi-automatic method of detailed scale mapping Barreiras Formation deposits by employing Digital Surface Models (DSMs) obtained from high resolution satellite images. The justification of this research is based on both: the lack of cartographic databases on the scale of 1:25.000, as well as the lack of maps representing these deposits in more detailed scale necessary for their proper description and interpretation. The selected area is located in the northern region of the State of Rio de Janeiro, between the city of Campos dos Goytacazes and the border to the State of Espírito Santo. In this area the most significant occurrence of Barreiras Formation deposits in the State of Rio de Janeiro is found. The main goal to be achieved in this research is the identification of such deposits by evaluating their geomorphological characteristics which are modeled by specific variables that represent these aspects. The study on the potential of the use of stereoscopic capabilities of high resolution images coming from ALOS/PRISM sensor, which made the generation of DSMs possible, together with the availability of Rational Polynomial Coefficients (RPCs), contributed to minimize the costs of acquiring field data, since most of the information needed to identify these deposits were obtained directly from these DSMs. The method used in this research consisted of: (1) the automatic generation and vertical accuracy control of DSMs; (2) the orthorectification of images and horizontal accuracy control of orthoimages; (3) the preparation of DSMs for the terrain analyses; (4) the terrain analyses; and (5) the generation and qualitative and quantitative evaluation of the final map. The generation of DSMs and the orthorectification of the images were carried out by using the empirical model of rational functions, using the RPCs available in the images. The terrain analyses were based on the interpretation and combination of specific morphometric variables using two different methods: (a) combinatorial OR; and (b) maximum entropy. The best result was obtained with the first method, combining the four variables: altitude, slope, curvature and terrain roughness index. Beforehand, these variables were classified according to intervals representing the Barreiras Formation. The assessment of the final map was made by comparing such map to another one, generated by using visual interpretation method, in a scale comparable to the scale of the map generated in this research. The level of detail in drawing the boundaries of the deposits of Barreiras Formation in the final map was verified by comparing it to the map available at 1:250,000 scale, obtained from the Digital Terrain Models (DTMs) of the SRTM. The potential of the use of stereoscopic capabilities of high resolution images obtained from sensor ALOS/PRISM, together with the availability of RPCs reduced considerably the cost of acquisition of field data required for the generation of DSMs and orthoimages. The proposed methodology has brought clear benefits in terms of reducing the time spent in making a map in detail scale, compared to the time spent using the visual interpretation method

Object-based Urban Building Footprint Extraction and 3D Building Reconstruction from Airborne LiDAR Data

Buildings play an essential role in urban intra-construction, urban planning, climate studies and disaster management. The precise knowledge of buildings not only serves as a primary source for interpreting complex urban characteristics, but also provides decision makers with more realistic and multidimensional scenarios for urban management. In this thesis, the 2D extraction and 3D reconstruction methods are proposed to map and visualize urban buildings. Chapter 2 presents an object-based method for extraction of building footprints using LiDAR derived NDTI (Normalized Difference Tree Index) and intensity data. The overall accuracy of 94.0% and commission error of 6.3% in building extraction is achieved with the Kappa of 0.84. Chapter 3 presents a GIS-based 3D building reconstruction method. The results indicate that the method is effective for generating 3D building models. The 91.4% completeness of roof plane identification is achieved, and the overall accuracy of the flat and pitched roof plane classification is 88.81%, with the user’s accuracy of the flat roof plane 97.75% and pitched roof plane 100%