Klaus Granica

Landslide Susceptibility Maps for Spatial Planning in Lower Austria

Landslides threaten most parts of the provincial state of Lower Austria and cause damage to agricultural land, forests, infrastructure, settlements and people. Thus, the project “MoNOE” (Method development for landslide susceptibility modelling in Lower Austria) was initiated by the provincial government to tackle these problems and to reduce further damage by landslides. The main aim is to prepare landslide susceptibility maps for slides and rock falls and to implement these maps into the spatial planning strategies of the provincial state.

Rapid Mapping and Damage Assessment

This chapter covers two main topics. The first deals with rapid mapping of damages for generating an overview, while the second deals with detailed assessment of damages. For the topic rapid mapping, fast procedures and methods for obtaining overview maps and information from satellite imagery are the main focus. For the second topic, production of accurate and detailed information from satellite imagery is more of an issue. The chapter is based on the activities of the various partners that have contributed to the GMOSS work package handling this subject. These activities comprise both security issues as well as natural disasters

Information Services to Support Disaster and Risk Management in Alpine Areas

The concept for an operational service for natural disaster situations requires a scenario driven data access to different sensor information for all phases of a disaster management. This also includes the actual availability of image information of the earth surface concerning the specific requirements of each phase. From the temporal point of view, spaceborne data acquisition does not offer a sufficient data availability in order to support all different phases in specific crisis situations. Especially the event phase cannot be supported as required.

Different Approaches of Rockfall Susceptibility Maps in Lower Austria

Within the frame of the ongoing project MoNOE (Development of Methods for Modelling Natural Hazards in the Province of Lower Austria) the generation of an indicative multi-hazard map to be used as a scientific reference for regional policies of land use management is one of the main objectives. The applicability and reliability of two relatively simple GIS-based approaches have been analysed aiming at the identification of areas which are potentially endangered by rockfall in a fast and cost-effective manner.

Remote Sensing for Alpine Forest Monitoring

Forest areas cover large parts of the Alps. In the mountains, the main functions of the forests are referring to the protection of people and infrastructure against natural hazards such as avalanches, landslides and rockfalls. However, the protective effect of many forests is threatened by their poor condition and poor forest regeneration capabilities. Information on the state of the forest therefore is an important basis for the sustainable management of protected forests. The key forest parameters that can be mentioned in this context are the forest border line, tree species distribution, age of the forests, biomass as well as structural parameters such as vertical stand structure and forest density. The paper presents the possibilities and limits of satellite image data as well as of Airborne LiDAR data for the assessment of these parameters. The results presented are achieved by the EUFODOS project financed by the 7th Frame Work Programme of the European Commission [4] and the project ALS—Steiermark financed by Waldverband Steiermark. EUFODOS was focussing on the development of methods for pan-European identification of forest damage whereas the aim of ALS—Steiermark was to apply these methods to the entire area of the province of Styria.

Updating lidar-derived crown cover density products with sentinel-2

Crown cover density (CCD) is one important forest attribute used in forest management. With remote sensing, crown cover density maps can be derived from the spectral information of optical satellite imagery or from a normalized digital surface model (nDSM). LiDAR data based applications provide the most accurate results, but LiDAR campaigns are expensive and available data are often outdated. We propose a new method to update LiDAR-derived CCD products and to map forest change. The method is based on Sentinel-2 imagery and an outdated LiDAR nDSM used to train a kNN classifier. CCD estimations are derived for two tests sites in Austria. Results are compared with the LiDAR CCD values in unchanged forest and with the latest tree cover density product of the European Copernicus High Resolution Layers Forest. Results demonstrate the operability of the workflow. User accuracies for forest change detection are very high with 87.3% and 94.8%.

The assessment of forest parameters by combined LiDAR and satellite data over Alpine regions – EUFODOS Implementation in Austria / Hodnotenie parametrov lesa kombináciou LIDAR-u a satelitných údajov v alpských regiónoch – implementácia systému EUFODOS v Rakúsku

Abstract Regional authorities require detailed and georeferenced information on the status of forests to ensure a sustainable forest management. One of the objectives in the FP7 project EUFODOS was the development of an operational service based on airborne laser scanning and satellite data in order to derive forest parameters relevant for the management of protective forests in the Alps. The estimated parameters are forest type, stem number, height of upper layer, mean height and timber volume. RapidEye imagery was used to derive coniferous and broadleaf forest classes using a logistic regression-based method. After the generation of a normalised Digital Surface Model and a forest mask, the forest area was segmented into homogeneous polygons, tree tops were detected, and various forest parameters are calculated. The accuracy of such an assessment was comparable with some previous studies, and the R-square between the estimated and measured values was 0.69 for tree top detection, 0.82 for upper height and 0.84 for mean height. For the calculation of timber volume, the R² for modelling is 0.82, for validation with an independent set of field plots, the R² is 0.71. The results have been successfully integrated into the regional forestry GIS and are used in forest management. Abstrakt Regionálne plánovanie zabezpečujúce trvale udržateľný manažment lesa vyžaduje detailné a georeferencované informácie o stave lesov. Jedným z cieľov projektu EUFODOS (projekt 7. RP EÚ) bolo vyvinúť operatívnu službu využívajúcu údaje leteckého laserového skenovania v kombinácii so satelitnými údajmi, pomocou ktorých sú odvodené informácie potrebné pre obhospodarovanie ochranných lesov v Alpách. Zisťované parametre sú lesný typ, počet stromov, výška hornej korunovej vrstvy, priemerná výška a kmeňová zásoba. Použilo sa snímkovanie systémom RapidEye pre odvodenie tried ihličnanov a listnáčov s použitím logistického regresného modelu. Po vygenerovaní normalizovaného digitálneho modelu povrchu a masky lesa sa plocha lesa segmentovala do homogénnych polygónov, identifikovali sa vrcholce stromov a vypočítali sa požadované porastové charakteristiky. Presnosť uvedených odhadov bola porovnateľná s predošlými štúdiami - R2 medzi odhadovanými a meranými hodnotami pozícií vrcholcov stromov bol 0,69, pre hornú výšku 0,82 a pre priemernú výšku porastu 0,84. Pri výpočte objemu dreva bol R2 príslušného modelu 0,82. Pri validácii s nezávislým súborom plôch bola dosiahnutá hodnota R2 0,71. Prezentované výsledky sa úspešne integrovali do regionálnych lesníckych GIS sú využívané pri manažmente lesa.

Disaster Prevention for Alpine Routes

Natural disasters are an age-old problem that occur regularly in alpine regions, posing a major threat to the safety of settlements and transport routes. Within the project “Safety of Alpine Routes — Application of Earth Observation Combined with GIS (Hannibal)” information has been extracted from satellite remote sensing and integrated into a newly developed GIS based Decision Support System (DSS). Some of the required map information were inferred from ERS- and from SPOT5- and QUICKBIRD satellites. Forest and land cover parameters have been derived from the satellite data. Change detection techniques have been applied to monitor e.g. windfall and clear-cut areas. Methods for interferometric processing of radar data have been optimized to detect landslides and to generate maps of motion fields along the slope surfaces. The results show that this information integrated into the DSS is an efficient tool for focusing the attention to potentially endangered areas.