This study explores the use of the Unmanned Aerial Systems (UAS) with multispectral sensors and Normalized Difference Vegetation Index (NDVI) to evaluate the health of urban green tramways, a key element in sustainable city planning. It addresses the challenge of maintaining these green spaces, particularly under urban stressors like water scarcity and road salt damage. By applying NDVI, the research identifies resilient plant species on different sections of track and assesses various vegetation types' maintenance needs. This approach promises to enhance green tramway maintenance, reduce water consumption, and guide the selection of suitable vegetation. The findings offer significant implications for eco-friendly urban development, demonstrating how advanced monitoring techniques can optimize the environmental benefits of urban green spaces. This study sets the groundwork for such integrated approaches by demonstrating the practical application of NDVI in urban tramway contexts, suggesting a novel integration of these data with urban environmental management strategies

This study presents a comprehensive analysis of ecological corridors within the buffer zone of Kampinos National Park (KNP), Poland, a UNESCO-recognized Kampinos Forest Biosphere Reserve and part of the NATURA 2000 network. Facing threats from urbanization and groundwater intake, the study aims to additionally help in protection of the rich biodiversity and wetlands of KNP by mapping and valorizing ecological corridors using advanced remote sensing techniques.Utilizing a combination of high-resolution PlanetScope satellite data and UAV-based laser scanning (ULS) technologies, the research focuses on 19 identified ecological corridors, varying from 20 to 600 hectares. The primary tools include the DJI Matrice 350 RTK with an L1 laser scanner and multispectral PlanetScope images. These methodologies allowed for the creation of detailed digital terrain models (DTM) and digital surface models (DSM), essential for identifying anthropogenic barriers and natural features impacting wildlife migration.The study employs a pixel-based valorisation method, considering factors like biologically active surface, moisture levels, natural vegetation, landscape morphology, and anthropogenic barriers. The results, derived from a random forest classification of PlanetScope images, reveal significant impacts of development on the ecological corridors. The overall classification accuracy was found to be 86%.This research underscores the importance of high-resolution spatial data in identifying biologically active areas, vegetation cover, and potential conflict sites for animal migration. The findings provide crucial insights for the conservation and management of ecological corridors in KNP, highlighting the role of remote sensing in ecological studies and environmental monitoring.

Using hyperspectral and multispectral data captured from a UAV platform is becoming an increasingly popular method for research data acquisition. In the case of a hyperspectral camera, one of the noted problems is the geometrical shift of high-wavelength channels registered during a flight. This paper proposed two different method for processing such data. Our approaches, can be summarized briefly as:• Automatic method: aligning bands from each frame before mosaicking.• Manual method: separate mosaicking of each band and manual alignment of mosaicked bands.We address the question of which method generates smaller errors in the data mosaicking process of the photogrammetric processing of SENOP data.

The aim of this study was to determine the possibility of identifying cyanobacterial blooms and selected water quality parameters in inland lakes on the example of the Lake Gluszyńskie using remote sensing. The study was carried out using ASD Field Spec 3, SENOP HSP II camera mounted on the UAV, Secchi disk and microscopic analysis, taking data from the northern part of the lake. Chlorophyll a, total suspended solids, BOD5, DOC, and cyanobacterial biomass were determined. Correlation coefficients were calculated using LOOCV method. Based on the previous satellite image investigation was already known that the intensive cyanobacterial blooms are observed in Lake Gluszynskie in its northern part. Measurement campaign was realized in the nortnern part of the lake. Correlation between results of laboratory analyses and results of spectral measurements is strong, sometimes exceeding 0.8 for all examined water quality parameters. Wavelength ranges of 697-923 nm and 1044-1121 nm, and in the case of cyanobacteria biomass also 350-393 nm. Analysis for SENOP HSP II camera confirms potential use of UAV multispectral camera for water quality monitoring.

The paper analyzes trends of the Crop Water Stress Index (CWSI) and meteorological parameters in the Upper Biebrza riparian wetlands in Poland from 1990 to 2021. Wetlands are vital ecosystems providing various ecosystem goods and services but are vulnerable to human activities and climate change. The CWSI, a method orginally developed for monitoring vegetation hydrological stress in agricultural environments, assesses water stress in wetlands based on canopy temperature and meteorological parameters. The study examines the interaction between CWSI and basic meteorological parameters, including air temperature, relative humidity, and precipitation. Satellite imagery and meteorological data are utilized to analyze the long-term trends. The results indicate a decreasing trend in CWSI, although not statistically significant, along with increasing temperature and relative humidity trends. Precipitation shows a slightly increasing trend but is not statistically significant. Further comprehensive analysis considering additional CWSI drivers and habitat types is needed to understand the relationship between water stress and meteorological factors in wetlands.

Using ALS and UAS LiDAR DEM we study the geomorphology of fluvial islands in the channel of braided river which was affected by summer drought. On the basis of DoD (DEM of difference) method we analyse the dynamics of geomorphological processes in two periods: 2019 to 2020 and 2020 to 2022. Bank erosion is analysed on the basis of difference between DTM. Bank erosion reduces the area of riverine islands. The difference of CHM reveals a process of succession of riparian vegetation (mostly willow shrubs - Salix alba) on sandbars. The riparian succession makes these landforms anchored by newly developed shrubs, what induces accretion of sandbars. The sandbars therefore transform into riverine islands.

In this study we developed an algorithm for spontaneous green space identification in the cities. The algorithm was developed based on NDVI analyses (calculated from Sentinel-2 data) in Warsaw's green spaces, Poland. We analysed NDVI values in nearly 2000 points which allowed us to develop an algorithm for a distinction between managed and spontaneous vegetation, the algorithm results show that in all vegetation classes NDVI values obtained for managed vegetation are lower than for spontaneously occurring one. Application of algorithm requires a time series of satellite images, for all each of them, the NDVI threshold value is calculated. Finally, based on the number of exceedances of the threshold value each green space pixel is assigned to managed or spontaneous vegetation class. The validation shows that the algorithm achieves 70% accuracy for both classes.

This study is focused on detecting water stress on natural wetland ecosystems. In the Upper Biebrza Basin (NE Poland), on the meteorological tower, continuous measurements of plant surface temperature and basic meteorological parameters (air temperature and humidity) were conducted. Based on this measurements Crop Water Stress Index (CWSI) was calculated. Also Landsat 8 thermal images were used for similar calculation. Firstly, correlation of Landsat 8 based and tower based CWSI was evaluated. The high agreement of values regards this two sources was obtained (R2= 0.99, p-value <; 0.01, slope of adjusted model is equal to 0.989 and its intercept is equal to 0.0003). Secondly, the Landsat based CWSI was calculated for all wetland ecosystems in the Upper Biebrza Basin for selected images from the period of 2014-2016 years. Results show positive verification of satellite images as a source for CWSI calculation for natural wetland ecosystem, additionally, water stress was indicated frequently during analyzed years.

This study shows results of parallel measurements of greenhouse gases fluxes (carbon dioxide and water vapour) and canopy reflectance of alkaline fen. Fluxes were measured using eddy-covariance technique on micrometeorological station located in the Upper Biebrza Basin (NE Poland) in Rogożynek Village. Study site is located in the Biebrza National Park which was established to protect one of the biggest coherent lowland wetland area in the Central Europe. Statistical relations of reflectance and spectral indices with fluxes were calculated based on measurements during two growing season (2015 and 2016). Four types of functions were examined: linear, quadratic, exponential and logarithmic and for two timestamps: half-hour and day. The correlation between carbon dioxide fluxes and reflectance is better than for water vapour fluxes. For both carbon dioxide and water vapour we obtained higher correlations coefficients using selected spectral indices than using reflectance and higher correlations for daily timestamp than for half-hour fluxes.

This study is focused on adaptation of the water stress index to make use of it on bogs and mires of temperate transitional climate zone. Investigated object is located in Janowskie Forest Landscape Park (south-east Poland), area protected within Natura 2000 network and recognized as habitat 7140 (transition mires and quaking bogs). In order to obtain dataset UAV flights with thermal camera and airborne mission with hyperspectral sensor were performed. Field measurements included collection of both meteorological and biophysical parameters. It is shown that CWSI is a promising method for monitoring of water stress on Natura 2000 habitats.

In this study we develop a spatial model for interception capacity of vegetation based on LiDAR data. The study is conducted in the natural wetland river valley dominated meadows, reeds and small bushes. The multiple regression model was chosen to relate the field measurements of interception capacity and LiDAR statistics at 2m grid. The optimal model was chosen by stepwise selection and further manual variables selection resulting in the r2of 0.52 and the residual standard error of 0.27 mm. The model preserved the vegetation pattern spatially and showed reasonable estimates for both vegetation covered and not covered by field sampling. The model was, however, affected by LiDAR measurements corrupted by river inundation. The results show good perspective for using LiDAR data for interception capacity estimation.

This paper presents a validation of a recently developed method for simulating new spectral bands from multispectral images. We use the method to simulate CHRIS/PROBA bands from a Landsat ETM+ image. Next we validate the simulated bands by RMSE, band-band scatter plots and visual comparison with original CHRIS bands acquired 4 days before the ETM+ image. Finally, we calculate NDVI from the original and simulated CHRIS bands and from the ETM+ bands. Result show that the simulated CHRIS NDVI in the dominant vegetation types in the study area is more correlated to the original CHRIS NDVI, than ETM+ NDVI. For not dominant vegetation types, not included into the simulation process, the correlation is lower for simulated CHRIS NDVI than for ETM+ NDVI.

The runoff amount and intensity on catchment scale is strongly related to the spatial distribution of impervious area cover, which is the predominant cover type in urbanized area. This can only be taken effectively into account when a fully- distributed hydrological model is used. In this paper we investigate the assessment of imperviousness by a multi- resolution remote sensing technique. The remote sensing approach uses a classified high resolution (HR) Ikonos image that covers part of the research area to train a neural network based sub-pixel classification model that estimates impervious surface cover proportions within the pixels of a medium-resolution (MR) Landsat ETM+ image that covers the entire area. The GIS based distributed WetSpa model was used for studying the influence of different imperviousness scenarios on runoff generation with an hourly time step. It shows that estimates of imperviousness derived from satellite data may strongly improve those made by experts, as well as the necessity of application of fully-distributed grid-based hydrological models for urban runoff simulation.