Marcin Spiralski

The paper presents recent advancements in monitoring pesticides and heavy metals using remote sensing techniques. The study is based on the pilot project “Support for Ecological Agricultural Production in Mazovia” (WEPR), which provided a scientific basis for the more comprehensive, larger-scale project “Support for Monitoring the Distribution of Pollutants in Agriculture” (WORZ). Addressing the needs of end-users, the research focuses on the development of reliable methods to assess environmental contaminants that impact soil quality. A combination of high-resolution UAV imagery, multispectral satellite data, and a suite of vegetation indices was utilized to correlate spatial variations in soil and crop conditions with pollutant concentrations. Machine learning algorithms, including Random Forest, were applied to classify contamination levels, while laboratory analyses validated the spectral findings. Despite challenges such as limited sample sizes and class imbalances, the integration of multi-source remote sensing data demonstrated promising results. The future of the study will be focused on the multi-temporal analyses, improving prediction accuracy and dataset and environmental risk mapping. Ultimately, the outcomes of this research show the potential for scalable monitoring systems that align with European sustainability and agricultural precision practices.

Advances in remote sensing technologies have revolutionized the monitoring of soil conditions in forest ecosystems, providing valuable insights into soil moisture, nutrient content, and degradation without requiring physical access to remote areas. This article explores the application of key techniques, including satellite-based L-band radiometry, UAV-enabled LiDAR, and visible–NIR spectroscopy, in assessing forest soil properties. Challenges such as canopy interference, spatial resolution limitations, and data validation are discussed, alongside innovative solutions like machine learning and high-resolution digital elevation models. Case studies highlight the effectiveness of remote sensing in addressing environmental and forestry challenges, such as tracking the effects of climate change, logging, and erosion. By integrating advanced imaging technologies with ground-based observations, remote sensing supports sustainable forest management, conservation practices, and ecological research. Future developments in sensor technology, data integration, and machine learning hold promise for even greater precision and scalability in forest soil monitoring.

One of the crucial branches of activity at the Łukasiewicz Research Network—Institute of Aviation is developing a suborbital rocket vehicle capable of launching small payloads beyond the Earth’s atmosphere, reaching over 100 km in altitude. Ensuring safety is a primary concern, particularly given the finite flight zone and impact area. Crucial to safety analysis is the wind profile, especially in the very first seconds of a flight, when rocket velocity is of the same order as the wind speed. Traditional near-ground wind data sources, ranging from wind towers to numerical models of the atmosphere, have limitations. Wind towers are costly and unfeasible at many test ranges used for launches, while numerical modeling may not reflect the specific ground profile near the launcher due to their large cell size (2 to +10 km). Meteorological balloons are not favorable for such measurements as they aim to provide the launch operator with a wind profile at high altitudes, and are launched only 1–2 times per flight attempt. Our study sought to prototype a wind measurement system designed to acquire near-ground wind profile data. It focuses on measuring wind direction and speed at near-ground altitudes with higher flight frequency, offering data on demand shortly before launch to help ensure safety. This atmosphere sounding system consists of an Unmanned Aerial Vehicle (UAV) equipped with an onboard ultrasonic wind sensor. Some reports in the literature have discussed the possibility of using UAV-borne anemometers, but the topic of measurement errors introduced by placing the anemometer onboard an UAV remains under studied. Limited research in this area underlines the need for experimental validation of design choices–for specific types of UAVs, anemometers, and mounting. This paper presents a literature review, a detailed overview of the prototyped system, and flight test results in both natural (outdoor) and controlled (indoor, no wind) conditions. Data from the UAV system’s anemometer was benchmarked against a stationary reference weather station, in order to examine the influence of the UAV’s rotor on the anemometer readings. Our findings show a wind speed Root Mean Square Error (RMSE) of 5 m/s and a directional RMSE of below 5.3° (both averaged for 1 min). The results were also compared with similar UAV-based wind measurements. The prototyped system was successfully used in a suborbital rocket launch campaign, thus demonstrating the feasibility of integrating UAVs with dedicated sensors for performing regular meteorological measurements in automatic mode.

Pedunculate oak (Quercus robur. L) is a European tree species highly sensitive to drought. If declining symptoms appear they are often detectable at the crown (such as dieback) enabling monitoring using aerial images and remote sensing methods. Here, we analyzed the impact of short and long-term drought on oaks located in western Poland, between the years of 2014 and 2017. We used both leaf nitrogen (N) and leaf phosphorus (P) concentrations measured in the laboratory, aerial images collected in the range of 460-880 nm and machine learning techniques to estimate nutrient concentrations on the > 4000 oaks growing on stagnic luvisols in the study area. We determined a negative impact on N and P concentrations during both types of drought stress (-23% and -19% for N concentration in leaves; -27% and -10% for P concentration in leaves). Decreased water availability had inconsiderable impact on N:P values (3% increase of N:P ratio during short and 7% decrease of N:P ratio during long-term drought stress). We found that the long-term drought impact was spatially diverse, possibly depending on the presence of drainage ditches and competing species.

Soil moisture content (SMC) is an important element of the environment, influencing water availability for plants and atmospheric parameters, and its monitoring is important for predicting floods or droughts and for weather and climate modeling. Optical methods for measuring soil moisture use spectral reflection analysis in the 350–2500 nm range. Remote sensing is considered to be an effective tool for monitoring soil parameters over large areas and to be more cost effective than in situ measurements. The aim of this study was to assess the SMC of bare soil on the basis of hyperspectral data from the ASD FieldSpec 4 Hi-Res field spectrometer by determining remote sensing indices and visualization based on multispectral data obtained from UAVs. Remote sensing measurements were validated on the basis of field humidity measurements with the HH2 Moisture Meter and ML3 ThetaProbe Soil Moisture Sensor. A strong correlation between terrestrial and remote sensing data was observed for 7 out of 11 selected indexes and the determination coefficient R² values ranged from 67%– 87%. The best results were obtained for the NINSON index, with determination coefficient values of 87%, NSMI index (83.5%) and NINSOL (81.7%). We conclude that both hyperspectral and multispectral remote sensing data of bare soil moisture are valuable, providing good temporal and spatial resolution of soil moisture distribution in local areas, which is important for monitoring and forecasting local changes in climate.

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The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.

The paper presents research on the near real-time atmospheric sounding system. The main objective of the research was the development and testing of the weather sounding system based on a weather balloon. The system contains a redundant system of radiosondes, a lifting platform containing weather balloon and a holding system as well as ground station. Several tests of the system were performed in August and September 2019. Altitude, reliability, resistance to weather conditions and data convergence were tested. During tests, new procedures for such missions were developed. The final test was performed for the ILR-33 Amber Rocket as a part of pre-launch procedures. The test was successful and allowed to use acquired atmospheric data for further processing. Several post-tests conclusions were drawn. The altitude of sounding by a weather balloon depends mostly on weather conditions, the amount of gas pumped and the weight of a payload. The launching place and experience of the crew play an important role in the final success of the mission, as well.

The geomorphological signature of tropical glaciers has the potential to provide important information on the response of ice masses in high‐mountain environments to climate warming. This study investigates the glacial geomorphology of Charquini Sur, Bolivia. Detailed geomorphological mapping was conducted both in the field and from satellite imagery in order to produce a 1:4000 scale geomorphological map of the glacier foreland. Sedimentological analyses (description of physical characteristics, clast shape and roundness, particle‐size distribution) provided additional insight into the landform–sediment assemblage. Glacial landforms are well preserved and include up to 11 moraine ridge suites, seven of which are cross‐valley frontal moraine arcs. These can be linked to an existing lichenometric chronology from previous work and record glacier recession since the local Little Ice Age (LIA) maximum in the late‐1600s. Lateral moraine ridges also record continuous thinning of the glacier over this time period. Smaller groups of parallel ridges are interpreted as annual moraines formed during recession. Intermorainic areas consist of flutings and a typically thin sediment cover of subglacial, supraglacial and glaciofluvial origin, with prominent ice‐moulded bedrock protuberances in places. Analysis of the landform–sediment assemblage provides an insight into the main controls on landform genesis in the basin and implies there have been temporal changes in ice‐marginal dynamics since the LIA. We present the first landsystem model for a tropical cirque glacier, documenting its behaviour since the LIA and providing an indication of glacier response in rapidly‐warming high‐mountain environments. Copyright © 2018 John Wiley & Sons, Ltd.