Geoinformatics

Landslides are a major geohazard, which result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping can help communities plan and prepare for these damaging events. Digital elevation models (DEMs) are one of the most important data-sets used in landslide hazard assessment. Despite their frequent use, limited research has been completed to date on how the DEM source and spatial resolution can influence the accuracy of the produced landslide susceptibility maps. The aim of this paper is to analyse the influence of spatial resolutions and source of DEMs on landslide susceptibility mapping. For this purpose, Advanced Spaceborne Thermal Emission and Reflection (ASTER), National Elevation Dataset (NED), and Light Detection and Ranging (LiDAR) DEMs were obtained for two study sections of approximately 140 km 2 in north-west Oregon. Each DEM was resampled to 10, 30, and 50 m and slope and aspect grids were derived for each resolution. A set of nine spatial databases was constructed using geoinformation science (GIS) for each of the spatial resolution and source. Additional factors such as distance to river and fault maps were included. An analytical hierarchical process (AHP), fuzzy logic model, and likelihood ratio-AHP representing qualitative, quantitative, and hybrid landslide mapping techniques were used for generating landslide susceptibility maps. The results from each of the techniques were verified with the Cohen's kappa index, confusion matrix, and a validation index based on agreement with detailed landslide inventory maps. The spatial resolution of 10 m, derived from the LiDAR data-set showed higher predictive accuracy in all the three techniques used for producing landslide susceptibility maps. At a resolution of 10 m, the output maps based on NED and ASTER had higher misclassification compared to the LiDAR-based outputs. Further, the 30-m LiDAR output showed improved results over the 10-m NED and 10-m ASTER output, indicating that finer resolution does not necessarily result in higher predictive accuracy in landslide mapping. The source of the data-sets is an important consideration and can have significant influence on the accuracy of a landslide susceptibility analysis.

A B S T R A C T An increasing number of people in the world are living in coastal areas characterized by high geophysical and biophysical sensitivity. Thus, it is necessary to provide coastal planners with tools helping them to design efficient management plans to mitigate the negative effects caused by a growing number of coastal climate hazards that threaten life and property. We calculate an Exposure Index (EI) for the coastline of Mozambique and assess the importance of the natural habitats in reducing exposure to coastal climate hazards. We estimate, for year 2015, an increase of 276% in the number of people affected by a high, or very high, level of exposure when compared to a " Without habitats " scenario, i.e. excluding the protective effects of sand dunes, mangroves, and corals. The results of the EI are supported by the Desinventar Database, which has historic data concerning loss and damage caused by events of geological or weather related origin. These results also indicate where the most exposed areas are thereby providing useful information to design effective coastal plans that increase resilience to climate hazards and erosion in Mozambique.

Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index) can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7). The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality.

A B S T R A C T An increasing number of people in the world are living in coastal areas characterized by high geophysical and biophysical sensitivity. Thus, it is necessary to provide coastal planners with tools helping them to design efficient management plans to mitigate the negative effects caused by a growing number of coastal climate hazards that threaten life and property. We calculate an Exposure Index (EI) for the coastline of Mozambique and assess the importance of the natural habitats in reducing exposure to coastal climate hazards. We estimate, for year 2015, an increase of 276% in the number of people affected by a high, or very high, level of exposure when compared to a " Without habitats " scenario, i.e. excluding the protective effects of sand dunes, mangroves, and corals. The results of the EI are supported by the Desinventar Database, which has historic data concerning loss and damage caused by events of geological or weather related origin. These results also indicate where the most exposed areas are thereby providing useful information to design effective coastal plans that increase resilience to climate hazards and erosion in Mozambique.

The cryosphere is the frozen part of the Earth's system. Snow and ice are the main constituents of the cryosphere and may be found in different states, such as snow, freshwater ice, sea ice, perma-frost, and continental ice masses in the form of glaciers and ice sheets. The present review mainly deals with state-of-the-art applications of synthetic aperture radar (SAR) with a special emphasize on cryospheric information extraction. SAR is the most important active microwave remote sensing (RS) instrument for ice monitoring, which provides high-resolution images of the Earth's surface. SAR is an ideal sensor in RS technology, which works in all-weather and day and night conditions to provide useful unprecedented information, especially in the cryospheric regions which are almost inaccessible areas on Earth. This paper addresses the technological evolution of SAR and its applications in studying the various components of the cryosphere. The arrival of SAR radically changed the capabilities of information extraction related to ice type, new ice formation, and ice thickness. SAR applications can be divided into two broad classes-polarimetric applications and interferometric applications. Polarimetric SAR has been effectively used for mapping calving fronts, crevasses, surface structures, sea ice, detection of icebergs, etc. The paper also summarizes both the operational and climate change research by using SAR for sea ice parameter detection. Digital elevation model (DEM) generation and glacier velocity mapping are the two most important applications used in cryosphere using SAR interferometry or interferometric SAR (InSAR). Space-borne InSAR techniques for measuring ice flow velocity and topography have developed rapidly over the last decade. InSAR is capable of measuring ice motion that has radically changed the science of glaciers and ice sheets. Measurement of temperate glacier velocities and surface characteristics by using airborne and space-borne interferometric satellite images have been the significant application in glaciology and cryospheric studies. Space-borne InSAR has contributed to S. D. Jawak et al. 164 major evolution in many research areas of glaciological study by measuring ice-stream flow velocity , improving understanding of ice-shelf processes, yielding velocity for flux-gate based mass-balance assessment, and mapping flow of mountain glaciers. The present review summarizes the salient development of SAR applications in cryosphere and glaciology.