This paper presents the results of geohazards mapping and evaluation their role in the modifying of the urban landscape in Poznań, Poland. Information on geohazards specific to an urban lowland area (river floods, flash floods, mass movement, wind erosion) because of increasing climate change was compiled into a geomorphological map. The most spatially widespread geohazards in Poznań include: (a) on moraine hills/uplands: mass movement, wind erosion, and flash floods (4.9% of the city area); (b) on outwash plains: mass movement and flash floods (8.6% of the city area); (c) on fluvial landforms: mass movement and river floods (9.8% of the city area); (d) within plains of various origin: flash floods (7.6% of the city area). Mass movement affecting short but numerous (and locally steep) slopes, and flash floods are the most widespread geohazards (16 and 14%, of Poznań’s area, respectively). Extensive urbanisation especially the expansion of impervious surfaces increases the high risk of flash floods.

Low-relief areas are not fully the main subject of geomorphometric analyses. The development of the automatic classification of landforms mainly focuses on landforms related to the fluvial morphogenetic cycle. Thus, the morphogenetic diversity of the plains is not reflected in the existing classification systems. The area of Poland where the low relief area exceeds 80 % of the country's territory and results in various morphogenetic processes was selected for the analysis. The purpose of the analysis was recognition of the differentiation of surface types. The first step includes selecting appropriate morphogenetic variables, the second unsupervised classification using the Gaussian Mixture Model, and the third one encompassing the interpretation, namely the labeling process. Twenty Land Surface Types were distinguished, five belonging to uplands, and the remaining 15 types of plains were divided into four subgroups: rolling plains, dissection plains, smooth plains, and near-flat plains. Compared with other classification systems, terrain forms, morphogenetic strides, and physiographic division. The comparison showed a strong correspondence between the morphogenesis of the area and the inventory of surface types, and the high consistency of the Land Surface Types patterns within physiographic units.

Tillage of arable fields, using for instance a smoothing harrow, may increase the magnitude of albedo of such soil surfaces depending on the location, the sun’s illumination and atmospheric components. As these soil surfaces absorb less shortwave radiation compared to plowed soils, the result is an atmospheric cooling and a positive effect on the Earth’s climate. This paper is the follow-on of a previous study aimed at quantifying the seasonal dynamics of net shortwave radiation reflected by bare air-dried arable land areas located in contrasting environments, i.e. Poland and Israel. Soil tillage includes a plow, a disk harrow, and a smoothing harrow. Previous work concentrated on the estimate of net shortwave radiation under clear-sky theoretical scenarios, whereas the present study deals with a realistic atmosphere throughout the year 2014. This latter is characterized by the observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation (MSG). The variations of the net shortwave radiation for the selected bare arable land areas were assessed in combining observations from Landsat 8 images and digital maps of land use and soil, plus model equations that calculate the diurnal variations of the broadband blue-sky albedo with roughness inclusive. The daily amount of net shortwave radiation for air-dried bare arable land in Poland and Israel for the time their spatial coverage is the largest was found to be about 40–50% and 10% lower, respectively, in cloudy-sky conditions compared to clear-sky conditions.

In our work we used noninvasive point reflectance spectroscopy in the range from 400 to 2100 nm coupled with machine learning to study scales on the brown and golden iridescent areas on the dorsal side of the forewing of Diachrysia chrysitis and D. stenochrysis. We used our approach to distinguish between these species of moths. The basis for the study was a statistically significant collection of 95 specimens identified based on morphological feature and gathered during 23 years in Poland. The numerical part of an experiment included two independent discriminant analyses: stochastic and deterministic. The more sensitive stochastic approach achieved average compliance with the species identification made by entomologists at the level of 99–100%. It demonstrated high stability against the different configurations of training and validation sets, hence strong predictors of Diachrysia siblings distinctiveness. Both methods resulted in the same small set of relevant features, where minimal fully discriminating subsets of wavelengths were three for glass scales on the golden area and four for the brown. The differences between species in scales primarily concern their major components and ultrastructure. In melanin-absent glass scales, this is mainly chitin configuration, while in melanin-present brown scales, melanin reveals as an additional factor.

The paper presents an application of interpretative machine learning to identify groups of lakes not with similar features but with similar potential factors influencing the content of total phosphorus – P _tot. The method was developed on a sample of 60 lakes from North-Eastern Poland and used 25 external explanatory variables. Selected variables are stable over a long time, first group includes morphometric parameters of lakes and the second group encompass watershed geometry geology and land use. Our method involves building a regression model, creating an explainer, finding a set of mapping functions describing how each variable influences the outcome, and finally clustering objects by ’the influence’. The influence is a non-linear and non-parametric transformation of the explanatory variables into a form describing a given variable impact on the modeled feature. Such a transformation makes group data on the functional relations between the explanatory variables and the explained variable possible. The study reveals that there are five clusters where the concentration of P _tot is shaped similarly. We compared our method with other numerical analyses and showed that it provides new information on the catchment area and lake trophy relationship.