The aim of this study was to identify typical and specific features of land surface temperature (LST) distribution in the city of Krakow and its surroundings with the use of Landsat/ETM+ data. The paper contains a detailed description of the study area and technical properties of the Landsat program and data, as well as a complete methodology of LST retrieval. Retrieved LST records have been standardized in order to ensure comparability between satellite images acquired during different seasons. The method also enables identification of characteristic thermal regions, i.e. areas always colder and always warmer than a zonal mean LST value for Krakow. The research includes spatial analysis of the standardized LST with regard to different land cover types. Basic zonal statistics such as mean standardized LST and percentage share of hot and cold regions within 10 land cover types were calculated. GIS was used for automated data processing and mapping. The results confirmed the most obvious dependence of the LST on different land cover types. Some more factors influencing the LST were recognized on the basis of detailed investigation of the LST pattern in the urban agglomeration of Krakow. The factors are: emission of anthropogenic heat, insolation of the surfaces depending first of all on land relief and shape of buildings, seasonal changes of vegetation and weather conditions at the time of satellite image acquisition.

Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI) in the city of Wrocław, SW Poland. Seven UHI cases, measured during various meteorological conditions and characteristic of different seasons, were selected for analysis. GWR results were compared with global regression models (MLR), using various statistical procedures including corrected Akaike Information Criterion, determination coefficient, analysis of variance, and Moran’s I index. It was found that GWR is better suited for spatial modeling of UHI than MLR models, as it takes into account non-stationarity of the spatial process. However, Monte Carlo and F3 tests for spatial stationarity of the independent variables suggest that for several spatial predictors a mixed GWR–MLR approach is recommended. Both local and global models were extended by the interpolation of regression residuals and used for spatial interpolation of the UHI structure. The interpolation results were evaluated with the cross-validation approach. It was found that the incorporation of the spatially interpolated residuals leads to significant improvement of the interpolation results for both GWR and MLR approaches. Because GWR is better justified in terms of statistical specification, the combined GWR + interpolated regression residuals (GWR residual kriging; GWRK) approach is recommended for spatial modeling of UHI, instead of widely applied MLR models.

Forest fires, due to climate change, are a growing threat to human life, health, and property, especially in temperate climates. Unfortunately, the impact of individual factors on forest fires varies, depending on the geographical region and its natural and socio-economic conditions. The latter are rarely introduced into fire warning systems, which significantly reduces their effectiveness. Therefore, the main goal of this study was to quantify the impact of a wide range of anthropogenic factors on forest fires, using Poland as a representative example of a Central European country. Data were analyzed in relation to districts for the period 2007–2017, using correlation analysis and regression modeling applying global and local/mixed regression methods. It was found that almost all of the 28 variables taken for analysis significantly determined the density of forest fires, but the greatest role was played by the length of the border between forests and built-up areas, and road density. In addition, the impact of most of the analyzed variables on forest fires varied over the study area, so implementing non-stationarity in geographically weighted regression models significantly improved the goodness-of-fit compared to global models.

In cities located in concave landforms, urban heat island (UHI) is an element of a complicated thermal structure and occurs due to the common impact of urban built-up areas and orography-induced processes like katabatic flows or air temperature inversions. Kraków, Poland (760,000 inhabitants) is located in a large valley of the river Vistula. In the years 2009–2013, air temperature was measured with the 5-min sampling resolution at 21 urban and rural points, located in various landforms. Cluster analysis was used to process data for the night-time. Sodar and synoptic data analysis provided results included in the definition of the four types of night-time thermal structure representing the highest and the lowest spatial air temperature variability and two transitional types. In all the types, there are three permanent elements which show the formation of the inversion layer, the cold air reservoir and the UHI peak zone. As the impact of land use and relief on air temperature cannot be separated, a concept of relief-modified UHI (RMUHI) was proposed as an alternative to the traditional UHI approach. It consists of two steps: (1) recognition of the areal thermal structure taking into consideration the city centre as a reference point and (2) calculation of RMUHI intensity separately for each vertical zone.