The original version of this article was revised due to a retrospective Open Access order.

Thousands or even million of pixels can be contained in a single Slope Unit. Hence, each covariate used in spatial predictive models is characterized by a distribution of values for each Slope Unit. Here, we model the whole covariates' distribution within Slope Units for landslide susceptibility purposes. This is done by finely dissecting each covariate into quantiles and then modeling the susceptibility via a LASSO penalized Binary Logistic Regression. We choose a LASSO penalization because the common Stepwise procedure is not selective enough to shrink a large number of covariates to an interpretable subset (which we also demonstrate here). LASSO mostly selects 6 covariates out of 372 to explain the spatial distribution of shallow landslides in the Upper Badia valley, Italian Alps. This allows us to verify that the selection does not include any quantile close to the median hence, nor to the mean. The latter is the common representation of the covariates' distribution within Slope Units, which we also test and report in the supplements. Overall, we suggest to always investigate the whole distribution because the mean may not be the most informative nor the most performing way to generate Slope-Unit-based susceptibility models. In this general context, we generate our landslide inventory by combining semi-automated (OBIA) and manual mapping procedures. Our inventory, quantile covariates' representation and LASSO penalization produce excellent performances and interpretable relations between covariates and landslides.

Very high spatial resolution (VHSR) stereo-imagery-derived digital surface models (DSM) can be used to generate digital elevation models (DEM). Filtering algorithms and triangular irregular network (TIN) densification are the most common approaches. Most filter-based techniques focus on image-smoothing. We propose a new approach which makes use of integrated object-based image analysis (OBIA) techniques. An initial land cover classification is followed by stratified land cover ground point sample detection, using object-specific features to enhance the sampling quality. The detected ground point samples serve as the basis for the interpolation of the DEM. A regional uncertainty index (RUI) is calculated to express the quality of the generated DEM in regard to the DSM, based on the number of samples per land cover object. The results of our approach are compared to a high resolution Light Detection and Ranging (LiDAR)-DEM, and a high level of agreement is observed-especially for non-vegetated and scarcely-vegetated areas. Results show that the accuracy of the DEM is highly dependent on the quality of the initial DSM and-in accordance with the RUI-differs between the different land cover classes.

(1) Interfaculty Department of Geoinformatics Z_GIS, University of Salzburg, Salzburg, Austria (florian.albrecht@sbg.ac.at; elisabeth.weinke@sbg.ac.at; daniel.hoelbling@sbg.ac.at; barbara.friedl@sbg.ac.at), (2) GRID-IT Gesellschaft für angewandte Geoinformatik mbH, Innsbruck, Austria (eisank@grid-it.at), (3) Geologische Bundesanstalt (GBA), Vienna, Austria (filippo.vecchiotti@geologie.ac.at; arben.kociu@geologie.ac.at)

Object-based image analysis (OBIA) has been increasingly used to map geohazards such as landslides on optical satellite images. OBIA shows various advantages over traditional image analysis methods due to its potential for considering various properties of segmentation-derived image objects (spectral, spatial, contextual, and textural) for classification. For accurately identifying and mapping landslides, however, visual image interpretation is still the most widely used method. The major question therefore is if semi-automated methods such as OBIA can achieve results of comparable quality in contrast to visual image interpretation. In this paper we apply OBIA for detecting and delineating landslides in five selected study areas in Austria and Italy using optical Earth Observation (EO) data from different sensors (Landsat 7, SPOT-5, WorldView-2/3, and Sentinel-2) and compare the OBIA mapping results to outcomes from visual image interpretation. A detailed evaluation of the mapping results per study area and sensor is performed by a number of spatial accuracy metrics, and the advantages and disadvantages of the two approaches for landslide mapping on optical EO data are discussed. The analyses show that both methods produce similar results, whereby the achieved accuracy values vary between the study areas.

National and regional authorities and infrastructure maintainers in mountainous regions require accurate knowledge of the location and spatial extent of landslides for hazard and risk management. Information on landslides is often collected by a combination of ground surveying and manual image interpretation following landslide triggering events. However, the high workload and limited time for data acquisition result in a trade-off between completeness, accuracy and detail. Remote sensing data offers great potential for mapping and monitoring landslides in a fast and efficient manner. While facing an increased availability of high-quality Earth Observation (EO) data and new computational methods, there is still a lack in science-policy interaction and in providing innovative tools and methods that can easily be used by stakeholders and users to support their daily work.

In the presented paper the technical and user-centric analysis steps for the development of a web-based landslide mapping service are discussed. The generation of the service is based on remote sensing and open-source geodata. The service will support various stakeholders to monitor landslides, to update and publish landslide information, as well as to analyze affected infrastructures. For the service development, an iterative and incremental approach of agile software development is used. With the agile approach, the users are directly involved in all software development phases. Furthermore, the software is developed in single components and its enhancement is incrementally performed.

Detailed, accurate and complete digital landslide maps are still rarely available. Nevertheless, such maps are essential for recovery and documentation after landslide-triggering events, for landslide monitoring and for hazard and risk mapping. These tasks concern national and federal authorities, owners, maintainers and insurers of infrastructure, and ultimately the public. The increased availability and quality of Earth Observation (EO) data, in combination with efficient image analysis techniques, foster semi-automated mapping approaches. Apart from the challenge of generating valid landslide information (e.g. landslides mapped as polygons including metadata, landslide-affected infrastructure) based on various optical satellite imagery, ways have to be found to make landslide information products accessible to the stakeholders. Here, we present the user requirements analysis for an innovative EO-based landslide information web service which will allow for fast and efficient provision of landslide information products. The need for such a service was confirmed by practitioners. The identified requirements were considered suitable for the planned development of a pre-operational web service for selected study areas in the Alps (Austria, Italy), which are highly susceptible to weather-induced landslides.

Nowadays, several sensors are capable of acquiring very high resolution (VHR) satellite images in stereo, and even in tri-stereo mode. These data can be efficiently exploited with photogrammetric methods, such as semi-global matching (SGM), to produce high quality 2.5D digital terrain information. Regarding the production of digital terrain models, since VHR (tri-)stereo satellite images are available at high temporal frequencies for nearly the whole globe, they can be considered an attractive alternative to restricted and expensive airborne laserscanning (ALS) data. In this study, SGM is applied to a Pléiades-1A image triplet, to generate an accurate and detailed 3D point cloud and a 2.5D digital surface model (DSM) for a high-alpine study area in South Tyrol with complex, snow- and ice-covered topography. Visual and quantitative comparison to an ALS reference DSM showed that there were only small elevation errors for large parts of the study area. Errors were higher in regions that were obscured by shade. This is one of the first studies demonstrating that SGM is not only capable of delivering reliable digital surface information for flat and hilly regions, but also for high-alpine areas with complex terrain.

Applied to a test site located in southern Taiwan, this study compares two object-based image analysis (OBIA) methods for post-failure landslide detection based on (I) polarimetric synthetic aperture radar (PolSAR) and (II) optical satellite imagery. With its day-and-night availability and almost complete weather independency, SAR has several advantages compared to optical imagery. Consequently, in most cases, SAR imagery for a dedicated area of interest is earlier available than the first cloud-free optical data. However, the high spatial and spectral resolution of multispectral optical Earth observation data may enable a more detailed and accurate landslide detection. A comparison of both methods is feasible, as the polarimetric SAR image (dual-polarimetric (HH/HV) TerraSAR-X, StripMap) and the very high spatial resolution optical imagery (QuickBird) were acquired at a temporal baseline of only 20 days. Thus, SAR and optical data show the same state of the environment. After speckle filtering of the PolSAR data using the refined Lee filter and radiometric calibration, the intensity information of both polarization channels (HH and HV) was geocoded. Next, the features of interest (i.e. landslides, debris flows and riverbed) are derived from the PolSAR imagery using a newly developed OBIA procedure, which makes use of the different backscattering behavior of forest and other vegetated areas as compared to bare soil, which was assumed to be an indication for the occurrence of mass-movements and debris/sediment transport and deposition areas. Using the normalized difference standard deviation of the calibrated intensities of both polarimetric channels, HH and HV, the OBIA procedure considers (I) the higher variation of the backscattering intensities in forest areas and (II) the relatively higher backscattering of vegetated areas in the cross-polarized channel compared to bare soil areas. The latter is characterized by a more dominant backscattering in the co-polarized channel. For the object-based detection based on the QuickBird image the Normalized Difference Vegetation Index (NDVI) was applied to detect the unvegetated areas. Based on the NDVI layer an automated threshold was computed, which divides the image into two subsets, i.e. vegetated and non-vegetated areas. To produce suitable image objects for the classification, the areas potentially affected by mass-movements were re-segmented using the multiresolution segmentation algorithm implemented in the eCognition (Trimble) software. Additionally to the QuickBird image a digital elevation model (DEM) with 5 m spatial resolution was used to support the differentiation of classes. As the spectral information alone was not sufficient for class separation, the distinction into landslides, debris flows and riverbed was mainly based on slope values. Finally, a few rules considering spatial and contextual properties were introduced to refine the classification and to remove false positives (e.g. built-up areas and fields). The reference dataset for validation includes vector data of landslides, debris flows and the river bed and was produced through manual digitization, performed by a local expert. Both results are compared to the reference data set and the pros and cons of the imagery utilized for landslide detection are evaluated. The result of the novel object-based method based on PolSAR data reveals a certain potential for landslide detection, especially for rapid assessment of affected areas after landslide triggering events.

Mapping or "delimiting" landforms is one of geomorphology's primary tools. Computer-based techniques such as land-surface segmentation allow the emulation of the process of manual landform delineation. Land-surface segmentation exhaustively subdivides a digital elevation model (DEM) into morphometrically-homogeneous irregularly-shaped regions, called terrain segments. Terrain segments can be created from various land-surface parameters (LSP) at multiple scales, and may therefore potentially correspond to the spatial extents of landforms such as drumlins. However, this depends on the segmentation algorithm, the parameterization, and the LSPs. In the present study we assess the widely used multiresolution segmentation (MRS) algorithm for its potential in providing terrain segments which delimit drumlins. Supervised testing was based on five 5-m DEMs that represented a set of 173 synthetic drumlins at random but representative positions in the same landscape. Five LSPs were tested, and four variants were computed for each LSP to assess the impact of median filtering of DEMs, and logarithmic transformation of LSPs. The testing scheme (1) employs MRS to partition each LSP exhaustively into 200 coarser scales of terrain segments by increasing the scale parameter (SP), (2) identifies the spatially best matching terrain segment for each reference drumlin, and (3) computes four segmentation accuracy metrics for quantifying the overall spatial match between drumlin segments and reference drumlins. Results of 100 tests showed that MRS tends to perform best on LSPs that are regionally derived from filtered DEMs, and then log-transformed. MRS delineated 97% of the detected drumlins at SP values between 1 and 50. Drumlin delimitation rates with values up to 50% are in line with the success of manual interpretations. Synthetic DEMs are well-suited for assessing landform quantification methods such as MRS, since subjectivity in the reference data is avoided which increases the reliability, validity and applicability of results.

We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition® software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multiresolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis.