Multispectral Satellite Images Research Articles

UNSUPERVISED WINTER WHEAT MAPPING BASED ON MULTI-SPECTRAL AND SYNTHETIC APERTURE RADAR OBSERVATIONS

Abstract. Annual meteorological variations and the impact of climate change in recent years impacted on agricultural production and distribution. Since wheat is a main food resource and the most widely grown crop in the world, it is essential to ensure the sustainability of its production. Therefore, accurate wheat mapping is essential for agricultural production forecasts. Multi-spectral satellite image analysis, including supervised machine learning (ML) methods, has been applied to wheat and other crop mapping, but such passive, optical imaging approaches are strongly influenced by weather conditions and cloud cover, whilst the supervised ML algorithms are highly reliant on manual labelling and ground control data. To avoid the limitation of weather and cloud, this research integrates Sentinel-1 Synthetic Aperture Radar (SAR) data with Sentinel-2 multi-spectral image products to achieve more reliable and accurate winter wheat mapping. Normalised Difference Vegetation Index (NDVI) retrieved from multi-spectral imagery and Sentinel-1’s dual-polarisation radar signals, VV, VH, acquired in different time series, are used as key inputs to an unsupervised ML model based on Dynamic Time Warping (DTW) and hierarchical clustering to prevent time-consuming manual labelling. The chosen study area lies in Norfolk, UK. The result of winter wheat classification with NDVI time series data in this study reaches 72% accuracy, but the improved classification integrating NDVI, VH, VV and VH/VV values achieves 86% accuracy. Future research will focus on optimizing the ML model with multi data integration in addition additional research sites will test more complicated scenarios and multiple crop classification.

E-TRAINEE: OPEN E-LEARNING COURSE ON TIME SERIES ANALYSIS IN REMOTE SENSING

Abstract. Time series (TS) analysis has always been an important topic in Remote Sensing (RS). Open access to archives of Earth Observation missions, periodically acquired nation-wide aerial imagery and LiDAR point clouds, and various research datasets, together with open tools for TS data processing, have brought new possibilities in RS research but also challenges in education. The topic of TS analysis in RS has become an essential part of MSc curricula in geoinformatics, geography, and related fields. Open learning materials systematically addressing the issue of TS at the master's level are rare and their development is time and resource demanding. Thus, based on previous collaboration, the four research groups from Charles University, Heidelberg University, University of Innsbruck, and University of Warsaw joint their specific expertise and developed an open E-learning course on Time Series Analysis in RS for Understanding Human-Environment Interactions (E-TRAINEE). The course consists of four Modules covering the topics of TS from general approaches (M1) to specific methods of processing TS of satellite multispectral images (M2), 3D/4D point clouds (M3), and aerial image and laboratory spectroscopy (M4). Theoretical parts are supported with exercises/tutorials and case studies based on research activities of the involved teams. The course is accessible via a web site and is published under the CC-BY SA 4.0 license. The primary target group are MSc and PhD students of geoinformatics and geography, but it is also relevant to students of environmental studies, ecology, or geology, as well as potential users from the public and private sectors dealing with applications of RS.

Estimation of wheat kernel moisture content based on hyperspectral reflectance and satellite multispectral imagery

The wheat kernel moisture content (KMC) plays a pivotal role in determining the quality, storage viability, and economic profitability of wheat. Monitoring wheat KMC in-field before harvest provides decision-making information to ensure harvested wheat meeting trade standards. The study aims to expand the feasibility of spectroscopy to estimate wheat KMC at multi-scales. In addition to using ground hyperspectral reflectance for single-point measurement of wheat KMC, satellite multispectral images were also utilized to monitor the field distribution of wheat KMC on large-scale. For each estimation task, we extracted sensitive spectral features and compared the accuracy of three machine learning methods including Ridge, Support Vector Regression (SVR) and Random Forest Regression (RFR). We also adopted Two-stage Tradaboost.R2 to address the issue of uneven distribution of samples corresponding to satellite imagery. Experimental results showed that, based on optimized spectral features, the R2 of RFR model outperformed other machine learning models (Ridge and SVR) for wheat KMC estimation (R2 > 0.85). Among all spectral features, the B5 band and PSRI vegetation index from PlanetScope satellite, as well as the B11, and B12 bands and VARI vegetation index from Sentinel-2, proved to be effective features for wheat KMC estimation. Moreover, the incorporation of hyperspectral reflectance and multispectral imagery can improve the estimation accuracy on large-scale through the transfer learning algorithm of Two-stage Tradaboost.R2. Leveraging its superior temporal and spatial resolution, PS imagery emerges as an ideal data source for monitoring rapid fluctuations in wheat KMC during the physiological maturity period, which enabled a multi-day wheat KMC simulation. Overall, This study contributes a reliable and convenient spectroscopy tool for wheat KMC estimation for multi-scenes, offering scientific insights to support agricultural water management and wheat quality.

Mapping Small-Scale Irrigation Areas Using Expert Decision Rules and the Random Forest Classifier in Northern Ethiopia

The mapping of small-scale irrigation areas is essential for food security and water resource management studies. The identification of small-scale irrigation areas is a challenge, but it can be overcome using expert knowledge and satellite-derived high-spatial-resolution multispectral information in conjunction with monthly normalized difference vegetation index (NDVI) time series, and additional terrain information. This paper presents a novel approach to characterize small-scale irrigation schemes that combine expert knowledge, multi-temporal NDVI time series, multispectral high-resolution satellite images, and the random forest classifier in the Zamra catchment, North Ethiopia. A fundamental element of the approach is mapping small-scale irrigation areas using expert decision rules to incorporate the available water resources. We apply expert decision rules to monthly NDVI composites from September 2020 to August 2021 along with the digital elevation model (DEM) data on the slope, drainage order, and distance maps to derive the sample set. The samples were based on the thresholds obtained by expert knowledge from field surveys. These data, along with the four spectral bands of a cloud-free Planet satellite image composite, 12 NDVI monthly composites, slope, drainage order, and distance map were used as input into a random forest classifier which was trained to classify pixels as either irrigated or non-irrigated. The results show that the analysis allows the mapping of small-scale irrigation areas with high accuracy. The classification accuracy for identifying irrigated areas showed a user accuracy ranging from 81% to 87%, along with a producer accuracy ranging from 64% to 79%. Furthermore, the classification accuracy and the kappa coefficient for the classified irrigation schemes were 80% and 0.70, respectively. As a result, these findings highlight a substantial level of agreement between the classification results and the reference data. The use of different expert knowledge-based decision rules, as a method, can be applied to extract small-scale and larger irrigation areas with similar agro-ecological characteristics.

Quantification of land use/land cover dynamics and urban growth in rapidly urbanized countries: The case Hawassa city, Ethiopia

ABSTRACT Land Use and Land Cover Changes (LULC) are some of the worldwide factors that have the most impact on city growth. The aim of this study could be a quantification of the land use/land cover dynamic over three decades in Hawassa City, Ethiopia. This study used multi-spectral satellite images of the years 1990 and 2020. The six category classifications within the study area were agriculture, bare land, built-up, bushland, forest, and water body. The data for the study was acquired from a satellite image of Landsat 5TM 1990, 2000, and 2010 and Landsat 8 OLI and SWIR 2020. Packages like QGIS version 3.2, ArcGIS 10.3, ENVI 4.2, and ERDAS Imagine 2013 were accustomed to performing image classification. From this finding, it is clear that the study space is underneath serious land use conversion of bare land to built-up space and bush land to agricultural. The general LULCC between the years 1990 and 2020, satellite results show that between 1990 and 2020, the quantify of built-up land increased from 12.76% to 16.85% of the study area as bare land and bush decreased from 4.77% to 2.48% and 14.29% to 10.16%, respectively, from the entire study..

INFORMATION EXTRACTION FROM MULTISPECTRAL SATELLITE IMAGES

The article analyzes various methods and approaches of modern remote sensing that can be used in the processing of multispectral satellite images in order to effectively extract visual information about territories for which preliminary data is not available. Attention is paid to the creation of new derivative images (synthesized and indexed) and to performing pixel-oriented computer non supervised classification. A series of experiments have been made that clearly reveal the advantages and conveniences of remote retrieval of information from multispectral satellite images in a territory for which reference objects and other data acquired in situ are not available.

Satellite image fusion using cyclic spatio-spectral GAN model

ABSTRACT Preserving spectral and spatial information in the satellite image fusion problem is one of the essential challenges. This paper presents a GAN-based method for fusing panchromatic and multispectral satellite images. The proposed method utilises the idea of Cycle-GAN with two generators, for spectral and spatial information preservation, based on residual in residual dense block super-resolution architecture. First, generator-1 translates panchromatic and multispectral satellite images to a high-resolution fused image and then preserves details by utilising generator-2. The goal is to reach the spatial and spectral details of panchromatic and multispectral satellite images, respectively. Two discriminators are employed, one for spectral and the other for spatial transformations, and weighted L1 loss is used for both as cycle losses. By leveraging the unique capabilities of the two generators, the proposed method achieves high-quality image fusion results with improved spectral and spatial resolutions that are evaluated over two different datasets. The experimental results demonstrate the effectiveness of the proposed method, with enhancements ranging from approximately 2% to 30% and 0.5% to 35% in WorldView2 and GeoEye-1 images, respectively, in five metrics including PSNR. Moreover, we show significant enhancements of approximately 7% to 50% and 22% to 29% in the same datasets in metrics like SAM and ERGAS.

An On-Orbit Relative Sensor Normalization for Unbalance Images from the Ice Pathfinder Satellite (BNU-1)

The Ice Pathfinder satellite (code: BNU-1) is the first Chinese microsatellite, designed for monitoring polar climate and environmental changes. The major payload of BNU-1 is the wide-field camera which provides multispectral satellite images with a 73.69 m spatial resolution and a 739 km swath width. However, the color misrepresentation issue can be observed as the BUN-1 image appears yellowish as it gets farther towards the center field of view (FOV). The blue band of the image appears to be higher near the center FOV and declines generously towards both the edge areas of the image, which may cause the color misrepresentation issue. In this study, we develop a relative sensor normalization method to reduce the radiance errors of the blue band of BNU-1 images. This method uses the radiometric probability density distribution of the BNU-1 panchromatic band as a reference, correcting the probability density distribution of the blue band radiance first. Then, the mean adjustment is used to correct the mean of the blue band radiance after probability density function (PDF) correction, obtaining the corrected radiance in the blue band. Comparisons with the ground measurements and the Landsat8 image reveal the following: (1) The radiances of snow surfaces also have good consistency with ground observations and Landsat-8 images in the red, green, and blue bands. (2) The radiance errors of the uncorrected BNU-1 images are eliminated. The RMSE decreases from 80.30 to 32.54 W/m2/μm/sr. All these results indicate that the on-orbit relative correction method proposed in this study can effectively reduce the radiance errors of the BNU-1 images.

Riverine litter monitoring from multispectral fine pixel satellite images

Mismanaged litter or debris in aquatic systems can pose threats to water quality and blue economic activities. Monitoring strategies like remote sensing can complement and support the gathering of relevant descriptors useful for understanding challenges related to leakage litter. We present a robust technique for detecting and quantifying floating riverine litter, a soup of natural and anthropogenic materials. Spectral information of GeoEye, PlanetScope and Skysat fine resolution satellite imagery was statistically transformed into spatial anomalies correlated to fractional-pixel riverine litter abundance. Algorithm development also involved techniques that accounted for variation in satellite data characteristics. The detected fractional abundance was converted into a unit surface area in a region-of-interest. Intercomparison of derived area coverage from matching images captured by the various unique sensors in the PlanetScope and Skysat constellation were consistent (R² = 0.98). Likewise, the litter surface area derived manually had a very strong linear relationship to the algorithm estimates (R² > 0.99). The prospect of time series observations over several years at sub-daily to near daily intervals were also demonstrated using the cloud-free PlanetScope and Skysat imagery. Transferability as well as easy adaptation of the algorithm was further showcased by application over water bodies in Guatemala and Slovakia.

Improving Cloud Detection in WFV Images Onboard Chinese GF-1/6 Satellite

We have developed an algorithm for cloud detection in Chinese GF-1/6 satellite multispectral images, allowing us to generate cloud masks at the pixel level. Due to the lack of shortwave infrared and thermal infrared bands in the Chinese GF-1/6 satellite, bright land surfaces and snow are frequently misclassified as clouds. To mitigate this issue, we utilized MODIS standard snow data products for reference data to determine the presence of snow cover in the images. Subsequently, our algorithm was utilized to correct misclassifications in snow-covered mountainous regions. The experimental area selected was the perpetually snow-covered Western mountains in the United States. The results indicate the accurate labeling of extensive snow-covered areas, achieving an overall cloud detection accuracy of over 91%. Our algorithm enables users to easily determine whether pixels are affected by cloud contamination, effectively improving accuracy in annotating data quality and greatly facilitating subsequent data retrieval and utilization.

Deep and machine learning methods for the (semi-)automatic extraction of sandy shoreline and erosion risk assessment basing on remote sensing data (case of Jerba island -Tunisia)

Against the backdrop of the environmental crisis, the socio-economic, ecological and cultural importance of the coastal zone calls for greater awareness of how coastal resources function, evolve, are managed and enhanced. This study aims to develop a high-performance (semi-)automatic coastal monitoring method based on Landsat-5 and Sentinel-2 multispectral satellite images for spatiotemporal analysis of shoreline changes and erosion risk assessment along Jerba Island (Tunisia) using remote sensing data and geospatial tools. A comparative study between the band ratioing (BR) method and the pixel-based image analysis (PBIA) and object-based image analysis (OBIA) methods has led to the development of machine learning (ML), random forest (RF), deep learning (DL) and convolutional neural network (CNN) algorithms. Using these classification methods, 15 different shorelines were successively detected in 1989, 2015 and 2023 and then compared with a digitized reference shoreline from the Landsat-5 and Sentinel-2 images. Following a quantitative evaluation, the accuracy of the classification model shows that the combined CNN-OBIA approach provided the least accurate results, with an overall accuracy (OA) index of 67%, while the OBIA-RF classification method provided the most accurate results (OA of 95%). This comparative study identified an accurate and improved extraction method for quantifying changes in the position of the shoreline on the east coast of Jerba Island, enabling managers to make better decisions on coastal protection and adaptation to climate change.

Improving the Accuracy of Satellite-Derived Bathymetry Using Multi-Layer Perceptron and Random Forest Regression Methods: A Case Study of Tavşan Island

The spatial and spectral information brought by the Very High Resolution (VHR) and multispectral satellite images present an advantage for Satellite-Derived Bathymetry (SDB), especially in shallow-water environments with dense wave patterns. This work focuses on Tavşan Island, located in the Sea of Marmara (SoM), and aims to evaluate the accuracy and reliability of two machine learning (ML) regression methods, Multi-Layer Perceptron (MLP) and Random Forest (RF), for bathymetry mapping using Worldview-2 (WV-2) imagery. In situ bathymetry measurements were collected to enhance model training and validation. Pre-processing techniques, including water pixel extraction, sun-glint correction, and median filtering, were applied for image enhancement. The MLP and RF regression models were then trained using a comprehensive dataset that included spectral bands from the satellite image and corresponding ground truth depth values. The accuracy of the models was assessed using metrics such as Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), and R2 value. The RF regression model outperformed the MLP model, with a maximum R2 value of 0.85, lowest MAE values from 0.65 to 1.86 m, and RMSE values from 0.93 to 2.41 m at depth intervals between 6 and 9 m. These findings highlight the effectiveness of ML regression methods, specifically the RF model, for SDB based on remotely sensed images in wave-dense shallow-water environments.

Poster] Aircraft Detection and State Estimation in Satellite Images

Unidentified flying objects are aircraft that do not continuously broadcast ADS-B. They pose a risk to air traffic safety. In this study, we introduce a method for detecting and estimating the state of aircraft in Sentinel-2 multispectral satellite images. We construct a dataset of 579 ADS-B annotated aircraft from 69 Sentinel-2 images. A CNN is trained on the dataset to aircraft state vector i.e. position, velocity, heading, and altitude. This work allows real-time monitoring of flying objects in satellite images.

A discussion on the estimation of apparent colour of water with the Forel-Ule index for medium-width sections of rivers with high hydrodynamic activity

Apparent colour has been used as an essential optical variable for surface waters. Expressing the apparent colour of waters is usually performed using the Forel-Ule Index (FUI) colour comparator scale, defined by 21 classes. The use of multispectral satellite images to extract the apparent colour of water expands the applicability of the FUI, as satellite images are characterized by mapping wide areas with a fixed revisit period, enabling monitoring of the evolution of the apparent colour of water.The use of satellite imagery has been explored for open ocean waters, mainly to characterize the optical properties of water. Less explored is their application to river environments, especially to sections where variations in geometry related to the emergence of islets and sandbanks, caused by water flow variations or high hydrodynamic activity. That is the case with river sections that are around 100 m wide, a dimension that is compatible with the spatial resolution of available multispectral imagery, e.g. Sentinel-2, meaning that the river section features can be mapped as continuous swaths of pixels.In this paper, we discuss the importance of the clear definition of land/river boundary geometry for the FUI classification of river sections, in order to prevent its application to the out-of-water area, a problem that may arise when fluvial hydrodynamic activity is high. We propose a solution based on a segmentation that uses a water index obtainable by multispectral band composition of Sentinel-2 images. An advantage of the process is the use of a unique image for both the water index segmentation and the FUI classification. The case study is the lower/medium section of the Tagus River in Portugal, susceptible to high levels of spatial changes in its water/land boundary geometry. Results show that an adequate delimitation of the water/land boundary is critical to prevent the consideration of land area in the process and bias in the FUI interpretation.

A2DWQPE: Adaptive and automated data-driven water quality parameter estimation

Accurate remote sensing estimation of inland water quality parameters (WQPs) plays a crucial role in guiding water resource management. To achieve this, researchers have explored various data-driven approaches utilizing machine learning (ML) techniques. However, there are two major challenges in WQPs estimation for inland waters. Firstly, current data-driven approaches focus on building a unified estimation model for an entire study area, which underestimates the complex dynamics of water constituents and optical properties. Secondly, ML models, particularly neural networks, require extensive hyperparameter tuning and are not user-friendly for researchers lacking relevant background and experience. In this paper, we propose an innovative method called adaptive and automated data-driven water quality parameter estimation (A2DWQPE) to address both challenges. Our method operates under the assumption that water bodies with similar spectral characteristics should share the same WQP estimation model. A2DWQPE is composed of three phases. Firstly, water types are automatedly classified by unsupervised hierarchical clustering according to spectral similarity. Then, optimal Deep Neural Network (DNN) models for estimating WQPs from multi-spectral satellite images are customized for each water type utilizing Bayesian optimization (BO). Finally, the target WQP is estimated based on the type-specific estimates and degree of membership of each water type. To evaluate the effectiveness of A2DWQPE, we applied it to estimate Secchi disk depth (SDD) in Lake Erie with in situ measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The results demonstrate that A2DWQPE outperforms the traditional approaches of developing a unified model for the entire study area. A2DWQPE achieved high accuracy with coefficient of determination (R2) over 0.72 and root mean square error (RMSE) below 1.4 m. Our method also outperforms the methods that applied Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) instead of BO, and several traditional ML algorithms. We firmly believe that A2DWQPE holds great potential for accurate inland water quality estimation and will contribute significantly to various applications in water quality monitoring and pollution prevention.

Exploring Bangladesh's Soil Moisture Dynamics via Multispectral Remote Sensing Satellite Image

Accurate monitoring and mapping of soil moisture are essential for sustainable agricultural practices, water resource management, and climate studies. This study aims to explore the mapping of soil moisture in Bangladesh using multispectral remote-sensing satellite images. The purpose of this study is to prepare a map of soil moisture aiming to help government authorities in developing agricultural activities to accelerate the sustainable development of the rural economy in Bangladesh. A total of 14 Landsat scenes of paths 135-139 and rows 42-46 covers the entire Bangladesh. Thus, a set of Landsat imagery (a total of 14 scenes) for the year 2022 was used in this study to map the soil moisture of Bangladesh through the application of Geographical Information System (GIS) and Remote Sensing. Satellite Image preprocessing, correction, and analysis were done with ENVI software (version 5.1, developed by Research Systems, Inc., USA) and the ArcGIS software (version 10.6, developed by Environmental Systems Research Institute, USA). For the study of the long-term variation of soil moisture over Bangladesh and its seasonal characteristics, a soil moisture map can be used. In addition, to improve the climate model over Bangladesh, an up-to-date soil moisture map will be very helpful. The objective of this study is to provide accurate and detailed up-to-date spatial soil moisture information at reduced cost and time which is essential for environment modeling, risk assessment, decision-making for different government agencies and development partners, and help toward socio-economic development. In this study, the map shows soil moisture as very wet, wet, dry, and very dry soils of Bangladesh. The overall land cover classification accuracy was 92.56%, with a Kappa value 0.90 for Random Forest and the overall soil classification accuracy was 87.27%, with a Kappa value 0.858 for maximum likelihood classification indicating good consistency.

Assessing the impact of villagization program on land use land cover dynamics in Benishangul-Gumuz, Western Ethiopia

<p>Planning for continuing natural resource management requires current information on the dynamics of land use and land cover. The aim of this paper was to analyze the impacts of the villagization program on land use land cover dynamics in<strong> </strong>Benishangul-Gumuz region, western Ethiopia. The study has employed a mixed-method research design using both primary and secondary sources. Multispectral LANDSAT satellite images with a 30 m resolution were acquired for land use land cover change detection between the years 1999, 2009, and 2022. Arc GIS 10.8, QGIS 3.28, ERDAS Imagine 2014, and Microsoft Excel software were used for image classification, accuracy assessment, and change detection. Six different land use land cover types: forest land, shrub and grassland, cultivated land, residential, bare land, and water bodies were identified between 1999 and 2022. The trends indicated a dramatic decrease at the rate of 27.2 ha of forestland, 17.1 ha of shrub and grassland, and 4.6 ha of water bodies per year, while the share of cultivated land, residential, and bare land has expanded at an average rate of 34.3 ha, 11.7 ha, and 2.9 ha per year respectively between 1999 and 2022. The phenomenon was caused by added population pressure due to villagization program, which in turn triggered farmland expansion and deforestation. It is recommended that raising local community awareness, reforestation, practicing land use plans, and promoting successful livelihood diversification could help to alleviate the issue and reroute the course of events in order to achieve sustainable natural resource management.</p>

Global and direct solar irradiance estimation using deep learning and selected spectral satellite images

To fully exploit the spectral information of modern geostationary satellites, this work proposes a deep learning framework using convolutional neural networks (CNNs) and attention mechanism for 5-min ground-level global horizontal irradiance (GHI) and direct normal irradiance (DNI) estimations. The inputs are spectral satellite images with the target ground station in the center, and the labels are irradiance measurements normalized by their clear-sky estimations. The use of CNNs and attention mechanism aims to better extract the spatial information for estimating ground-level solar irradiance. To improve the modeling efficiency, only a subset of spectral bands is selected based on correlation analysis, which has comparable performance with the usage of all satellite bands. The results show that the proposed method produces GHI estimation with a normalized root mean squared error (nRMSE) of 20.57% and a normalized mean bias error (nMBE) of −2.04%, and the DNI estimation has an nRMSE of 23.63% and the nMBE is 0.36%. Compared with the national solar radiation database (NSRDB), GHI and DNI estimations of the proposed method has the nRMSE reduction of 5.15% and 13.77%, respectively. Meanwhile, the proposed models generally yield better GHI and DNI estimations under different intervals of clear-sky index than NSRDB. The combination of deep learning and remote sensing shows potential in better extracting the cloud information via multispectral satellite images, which can better support solar resource assessment, especially for cloudy conditions.

Dynamics of forest stands changes on the territory of Skole Beskydy National Nature Park

The relevance of the study is determined by the issue of assessing the condition and utilization of forest cover within the territory of the Skole Beskydy National Nature Park, as well as the necessity to develop effective methods for monitoring and conserving forest ecosystems. The purpose of the study was to investigate the dynamics of forest cover changes within the Skole Beskydy National Nature Park, as well as to assess the losses of forest resources in this area and recommend the obtained series of images to enhance the forest management system. The research was conducted using a comprehensive approach combining various methods of studying forest cover changes and their comparison. The basis for applying remote sensing technologies was the use of specific multispectral satellite images combined with attribute information on forest land geospatial data. The most progressive methods of studying changes in forest plantations based on remote sensing data using geographic information systems have been demonstrated. Satellite images were obtained from the Landsat 8 satellite data set through the United States Geological Survey data portal. The normalized burn ratio and normalized difference vegetation index are calculated and compared. An analysis was also carried out using the Global Forest Watch online resource. As a result of the study, changes in forest cover were identified, an assessment of the changes was provided, and a map reflecting forest changes that occurred in the Skole Beskydy National Nature Park from 2000 to 2020 was developed. According to the results of the analysis, a low level of losses of forest plantations due to natural factors, including losses from fires, was established. The main part of forest losses is caused by anthropogenic factors. The practical significance of the results lies in the possibility of using the obtained series of images within public control and improving the forest management system

Modelling extreme water levels using intertidal topography and bathymetry derived from multispectral satellite images

Abstract. Topographic and bathymetric data are essential for accurate predictions of flooding in estuaries because water depth and elevation data are fundamental components of the shallow-water hydrodynamic equations used in models for storm surges and tides. Where lidar or in situ acoustic surveys are unavailable, recent efforts have centred on using satellite-derived bathymetry (SDB) and satellite-derived topography (SDT). This work is aimed at (1) determining the accuracy of SDT and (2) assessing the suitability of the SDT and SDB for extreme water level modelling of estuaries. The SDT was created by extracting the waterline as it tracks over the topography with changing tides. The method was applied to four different estuaries in Aotearoa / New Zealand: Whitianga, Maketū, Ōhiwa and Tauranga harbours. Results show that the waterline method provides similar topography to the lidar with a root-mean-square error equal to 0.2 m, and it is slightly improved when two correction methods are applied to the topography derivations: the removal of statistical bias (0.02 m improvement) and hydrodynamic modelling correction of waterline elevation (0.01 m improvement). The use of SDT in numerical simulations of surge levels was assessed for Tauranga Harbour in eight different simulation scenarios. Each scenario explored different ways of incorporating the SDT to replace the topographic data collected using non-satellite survey methods. In addition, one of these scenarios combined SDT (for intertidal zones) and SDB (for subtidal bathymetry), so only satellite information is used in surge modelling. The latter SDB is derived using the well-known ratio–log method. For Tauranga Harbour, using SDT and SDB in hydrodynamic models does not result in significant differences in predicting high water levels when compared with the scenario modelled using surveyed bathymetry.