Waldemar Spallek

Regression-based air temperature spatial prediction models: an example from Poland

A Geographically Weighted Regression – Kriging (GWRK) algorithm, based on the local Geographically Weighted Regression (GWR), is applied for spatial prediction of air temperature in Poland. Hengl’s decision tree for selecting a suitable prediction model is extended for varying spatial relationships between the air temperature and environmental predictors with an assumption of existing environmental dependence of analyzed temperature variables. The procedure includes the potential choice of a local GWR instead of the global Multiple Linear Regression (MLR) method for modeling the deterministic part of spatial variation, which is usual in the standard regression (residual) kriging model (MLRK). The analysis encompassed: testing for environmental correlation, selecting an appropriate regression model, testing for spatial autocorrelation of the residual component, and validating the prediction accuracy. The proposed approach was performed for 69 air temperature cases, with time aggregation ranging from daily to annual average air temperatures. The results show that, irrespective of the level of data aggregation, the spatial distribution of temperature is better fitted by local models, and hence is the reason for choosing a GWR instead of the MLR for all variables analyzed. Additionally, in most cases (78%) there is spatial autocorrelation in the residuals of the deterministic part, which suggests that the GWR model should be extended by ordinary kriging of residuals to the GWRK form. The decision tree used in this paper can be considered as universal as it encompasses either spatially varying relationships of modeled and explanatory variables or random process that can be modeled by a stochastic extension of the regression model (residual kriging). Moreover, for all cases analyzed, the selection of a method based on the local regression model (GWRK or GWR) does not depend on the data aggregation level, showing the potential versatility of the technique.

Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles

The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red–green–blue (RGB) or near-infrared–red–green (NIRRG) or near-infrared–green–blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7–0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.

A real-time field experiment on search and rescue operations assisted by unmanned aerial vehicles

This paper reports on the performance of a novel system for supporting search and rescue activities, known as SARUAV (search and rescue unmanned aerial vehicle), in a field experiment during which a real-world search scenario was simulated. The experiment took place on March 2–3, 2017, at two sites located in southwestern Poland. Three groups acted in the experiment: (1) SARUAV and unmanned aerial vehicle (UAV) operators, (2) ground searchers, and (3) participants who simulated being lost. In the uncomplicated topography without snow cover, the system identified the lost persons, and ground searchers found them 31 min after the SARUAV report had been disseminated. In the mountainous area covered with snow, one person was found within 9 min after searchers received the SARUAV report; however, the other two persons were not identified by SARUAV. The field experiment served as a proof of concept of the SARUAV system, confirmed its potential in person identification studies, and helped to identify numerous scientific and technical problems that need to be solved to develop a mature version of the system.

Use of Remotely-Derived Bathymetry for Modelling Biomass in Marine Environments

The paper presents results on the influence of geometric attributes of satellite-derived raster bathymetric data, namely the General Bathymetric Charts of the Oceans, on spatial statistical modelling of marine biomass. In the initial experiment, both the resolution and projection of the raster dataset are taken into account. It was found that, independently of the equal-area projection chosen for the analysis, the calculated areas are very similar, and the differences between them are insignificant. Likewise, any variation in the raster resolution did not change the computed area. Although the differences were shown to be insignificant, for the subsequent analysis we selected the cylindrical equal area projection, as it implies rectangular spatial extent, along with the automatically derived resolution. Then, in the second experiment, we focused on demersal fish biomass data acquired from trawl samples taken from the western parts of ICES Sub-area VII, near the sea floor. The aforementioned investigation into processing bathymetric data allowed us to build various statistical models that account for a relationship between biomass, sea floor topography and geographic location. We fitted a set of generalised additive models and generalised additive mixed models to combinations of trawl data of the roundnose grenadier (Coryphaenoides rupestris) and bathymetry. Using standard statistical techniques—such as analysis of variance, Akaike information criterion, root mean squared error, mean absolute error and cross-validation—we compared the performance of the models and found that depth and latitude may serve as statistically significant explanatory variables for biomass of roundnose grenadier in the study area. However, the results should be interpreted with caution as sampling locations may have an impact on the biomass–depth relationship.

Przestrzenne zróżnicowanie poziomu innowacyjności regionalnej gospodarki województwa dolnośląskiego

Current development of the knowledge-based economy is dependent on the level of innovation of companies introducing new or significantly improved products, advanced technology and better work organization systems. A high level of innovativeness has a significant impact on the growth of the competitive advantage of regional economies, primarily by allowing them to use resources more efficiently. The paper presents the research and development activities in Poland which has been characterized on the basis of expenditure on research and development and the participation of R&D employees in total employment. The analysis of innovative activities in voivodeships (provinces) in Poland and in the poviats (= counties) of Lower Silesian voivodeship was based on four indicators: innovative activity, expenditure on it, its effectiveness in the form of revenues from the production of new or substantially improved products, and the level of equipment with the means of manufacturing automation. These parameters were used to construct a synthetic indicator of innovation that illustrates the intraregional competitiveness of the poviats. The analysis of innovation of the economy of Lower Silesia is presented in relation to other voivodeships what allows to determine the position of the region with regard to the rest of the country.

Rynek pracy województwa dolnośląskiego w okresie spowolnienia gospodarczego

Rynek pracy jest rozumiany jako caloksztalt zagadnien związanych z ksztaltowaniem podazy i popytu pracy. Sytuacja na wspomnianym rynku jest efektem oddzialywania wielu zlozonych procesow. Z jednej strony jest ona pochodną poziomu rozwoju i struktury gospodarki, z drugiej – wplywają na nią zmiany związane z ciągle postepującą integracją Polski z Unią Europejską, a takze procesy gospodarcze o charakterze globalnym (Hildebrandt 2003). Kryzys na rynkach finansowych i bankowych, zapoczątkowany w 2007 r., stal sie przyczyną ogolnoświatowego kryzysu gospodarczego. W wypadku Polski kryzys nie przybral charakteru typowej recesji skutkującej nierzadko spadkiem produktu narodowego brutto, lecz spowodowal znaczne obnizanie sie jego dynamiki, szczegolnie w 2009 r. W związku z tym w opracowaniu w stosunku do polskiej gospodarki w okresie 2007–2009 uzyto określenia spowolnienie gospodarcze. Obszar rynku pracy tworzy zespol roznorodnych elementow, wśrod ktorych mozemy wyroznic: zasoby kapitalu ludzkiego i jego wykorzystanie (np. z punktu widzenia grup wiekowych, • wyksztalcenia); struktury pracujących i zatrudnionych w poszczegolnych sektorach gospodarki narodowej; • wielkośc i struktury bezrobocia. • W opracowaniu zaprezentowano zagadnienia rynku pracy, ktorych analiza opierala sie na dostepnych danych (przede wszystkim z lat 2008-2009), obejmujących podmioty zatrudniające powyzej 9 osob i nieuwzgledniających rolnikow indywidualnych. Przyjecie takiej zbiorowości do badan wynika z braku publikacji przez GUS pelnych danych dotyczących pracujących i zatrudnienia w ukladach regionalnych i lokalnych.