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Flood protection is one of several disciplines where geospatial data is very important and is a crucial component. Its management, processing and sharing form the foundation for their efficient use; therefore, special attention is required in the development of effective, precise, standardized, and interoperable models for the discovery and publishing of data on the Web. This paper describes the design of a methodology to discover Open Geospatial Consortium (OGC) services on the Web and collect descriptive information, i.e., metadata in a geocatalogue. A pilot implementation of the proposed methodology -Geocatalogue of geospatial information provided by OGC services discovered on Google (hereinafter "Geocatalogue") -was used to search for available resources relevant to the area of flood protection. The result is an analysis of the availability of resources discovered through their metadata collected from the OGC services (WMS, WFS, etc.) and the resources they provide (WMS layers, WFS objects, etc.) within the domain of flood protection.

Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular.

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Biogeosciences and Forestry Biogeosciences and Forestry A bark beetle infestation predictive model based on satellite data in the frame of decision support system TANABBO Renata Ďuračiová (1) , Milan Muňko (1) , Ivan Barka (2) , Milan Koreň (3) , Karolina Resnerová (4) , Jaroslav Holuša (4) , Miroslav Blaženec (5) , Mária Potterf (6) , Rastislav Jakuš (4-5) The European spruce bark beetle Ips typographus L. causes significant economic losses in managed coniferous forests in Central and Northern Europe. New infestations either occur in previously undisturbed forest stands (i.e., spot initiation) or depend on proximity to previous years' infestations (i.e., spot spreading). Early identification of newly infested trees over the forested landscape limits the effective control measures. Accurate forecasting of the spread of bark beetle infestation is crucial to plan efficient sanitation felling of infested trees and prevent further propagation of beetle-induced tree mortality. We created a predictive model of subsequent year spot initiation and spot spreading within the TANABBO decision support system. The algorithm combines open-access Landsat-based vegetation change time-series data, a digital terrain model, and forest stand characteristics. We validated predicted susceptibility to bark beetle attack (separately for spot initiation and spot spreading) against beetle infestations in managed forests in the Bohemian Forest in the Czech Republic (Central Europe) in yearly time steps from 2007 to 2010. The predictive models of susceptibility to bark beetle attack had a high degree of reliability (area under the ROC curve-AUC: 0.75-0.82). We conclude that spot initiation and spot spreading prediction modules included within the TANABBO model have the potential to help forest managers to plan sanitation felling in managed forests under pressure of bark beetle outbreak.