This study presents a comparison of data acquired from three LiDAR sensors from different manufacturers, i.e., Yellow Scan Mapper (YSM), AlphaAir 450 Airborne LiDAR System CHC Navigation (CHC) and DJI Zenmuse L1 (L1). The same area was surveyed with laser sensors mounted on the DIJ Matrice 300 RTK UAV platform. In order to compare the data, a diverse test area located in the north-western part of the Lublin Province in eastern Poland was selected. The test area was a gully system with high vegetation cover. In order to compare the UAV information, LiDAR reference data were used, which were collected within the ISOK project (acquired for the whole area of Poland). In order to examine the differentiation of the acquired data, both classified point clouds and DTM products calculated on the basis of point clouds acquired from individual sensors were compared. The analyses showed that the largest average height differences between terrain models calculated from point clouds were recorded between the CHC sensor and the base data, exceeding 2.5 m. The smallest differences were recorded between the L1 sensor and ISOK data—RMSE was 0.31 m. The use of UAVs to acquire very high resolution data can only be used locally and must be subject to very stringent landing site preparation procedures, as well as data processing in DTM and its derivatives.

Visible and near-infrared (VIS-NIR) spectroscopy is extensively used in the field of soil science to predict several soil properties, mostly in laboratory conditions. When measured in situ, contact probes are used, and, very often, time-consuming methods are applied to generate better spectra. Unfortunately, spectra obtained by these methods differ greatly from spectra remotely acquired. This study tried to address this issue by measuring reflectance spectra directly with a fibre optic or a 4° lens on bare untouched soils. C, N content and soil texture (sand, silt, and clay) prediction models were established using partial least-square (PLS) and support vector machine (SVM) regression. With spectral pre-processing, some satisfactory models were obtained, i.e., for C content (R² = 0.57; RMSE = 0.09%) and for N content (R² = 0.53; RMSE = 0.02%). Some models were improved when using moisture and temperature as auxiliary data for the modelling. Maps of C, N and clay content generated with laboratory and predicted values were presented. Based on this study, VIS-NIR spectra acquired with bare fibre optic and/or a 4° lens could be used to build prediction models in order to obtain basic preliminary information on soil composition at the field scale. The predicting maps seem suitable for a fast but rough field screening.

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

Most studies on habitat selection among animals are conducted at local scales, whereas reliable determination of species requirements at larger spatial scales can be problematic. We used data available for NATURA 2000 sites to determine the habitat requirements of two relatively widespread and common species—pine marten Martes martes and stone marten Martes foina—in Southern Europe. Using presence-absence data, we applied statistical models at two spatial scales. At the local scale (within the dispersal distances of the species), sites occupied by martens were compared with unoccupied sites using buffers of different sizes, whereas at the regional scale, unoccupied sites were selected randomly. To adjust for spatial autocorrelation of data, penalised quasi-likelihood approximations were used. Both species of martens demonstrated preferences for areas with higher proportions of forest cover and steeper terrain. At the local, but not at the regional, scale, pine martens occurred at lower elevations, whilst stone martens occurred at higher elevations. We found that climatic variables (mean temperature, precipitation) had no significant effect on the presence of the martens. The results of our analyses generally confirmed findings of previous studies on habitat selection of both marten species in Southern Europe. This demonstrates the utility of data collected for NATURA 2000 sites for use in various analyses such as conservation planning and evaluating the impact of climate change on the distribution of animal species.

The geodiversity of loess areas is reflected in the characteristic dataset of loess landforms, with the dominance of several types of valley forms (mainly gullies). The availability of satellite elevation data and high-resolution aerial topography scanning data provides an opportunity for quantitative assessment of geomorphodiversity. This is done through the analysis of topographic texture, delimitation and statistical characterization of the topographic parameters of erosional landforms, such as volume and degree of dissection (density of valleys) or the degree of ‘coverage’ of the area by valley forms. An important factor affecting the accuracy of the estimation is the accuracy of the underlying digital terrain model (DTM). This study compares three digital terrain models, with cell sizes of 30, 10 and 1 m, generated from satellite altimetry data and airborne laser scanning (ALS) data. The subregion of Szczebrzeszyn Roztocze (Western Roztocze Region, SE Poland), i.e., one of the most typical loess relief regions in Poland, was selected as the study area. Selected topographic texture analyses were carried out using the SLRM (Simple Local Relief Model) algorithm. Delimitation of valleys was performed by delineating the extent of slope change in two key steps: (1) detection of areas below the average topographic surface; (2) delimitation using supervised classification of DTMs. The results of the study show that the accuracy of delimitation of valleys increases inversely proportional to the DTMs resolution. Automated topographic texture analysis allows delimitation and extraction, as well as statistical analysis of parameters of valleys. Finally, two indicators have been proposed, Relative Valley Area (RVA) and Area-normalised Valley Cubature (AVC), which can be used in geomorphodiversity studies of a geologically homogeneous area. The dimensionless RVA index can also be expressed as a percentage (%) of the area of valley forms in a basic field of 1 km². Furthermore, the AVC index shows the dynamic character of the main relief features of the analysed area.

A pot experiment was conducted to determine the impact of water availability on the discriminatory status of nitrogen (N) in plants using hyperspectral imaging. Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability. Five different classification algorithms were used to discriminate between nitrogen concentrations in plants at different levels of water availability. Several statistical parameters, including kappa and overall classification accuracy for calibration and prediction, were used to determine the efficiency and accuracy of the models. The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery. Additionally, characteristic electromagnetic wavelengths were identified in which reflectance correlated with nitrogen and water content in plants could be recorded. It was also noted that the spectral resolution between the N and High Water (HW)/Low Water (LW) treatments was lower in the short-wave infrared (SWIR) region than in the visible and near-infrared (VNIR) region.