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Cambiophagous insects, fires and windthrow cause significant forest disturbances, generating ecological changes and economical losses. The bark beetle (Ips typographus L.), inhabiting coniferous forests and eliminating weakened trees, plays a key role in posing a threat to tree stands, which are dominated by Norway spruce (Picea abies) and covers a large part of mountain areas, as well as the lowlands of Northern, Central and Eastern Europe. Due to the dynamics of the phenomena taking place, the EU recommends constant monitoring of forests in terms of large-area disturbances and factors affecting tree stands’ susceptibility to destruction. The right tools for this are multispectral satellite images, which regularly and free of charge provide up-to-date information on changes in the environment. The aim of this study was to develop a method of identifying disturbances of spruce stands, including the identification of bark beetle outbreaks. Sentinel 2 images from 2015–2018 were used for this purpose; the reference data were high-resolution aerial images, satellite WorldView 2, as well as field verification data. Support Vector Machines (SVM) distinguished six classes: deciduous forests, coniferous forests, grasslands, rocks, snags (dieback of standing trees) and cuts/windthrow. Remote sensing vegetation indices, Multivariate Alteration Detection (MAD), Multivariate Alteration Detection/Maximum Autocorrelation Factor (MAD/MAF), iteratively re-weighted Multivariate Alteration Detection (iMAD) and trained SVM signatures from another year, stacked band rasters allowed us to identify: (1) no changes; (2) dieback of standing trees; (3) logging or falling down of trees. The overall accuracy of the SVM classification oscillated between 97–99%; it was observed that in 2015–2018, as a result of the windthrow and bark beetle outbreaks and the consequences of those natural disturbances (e.g., sanitary cuts), approximately 62.5 km2 of coniferous stands (29%) died in the studied area of the Tatra Mountains.
Robert Migas-Mazur; Marlena Kycko; Tomasz Zwijacz-Kozica; Bogdan Zagajewski. Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains. Remote Sensing 2021, 13, 3314 .
AMA StyleRobert Migas-Mazur, Marlena Kycko, Tomasz Zwijacz-Kozica, Bogdan Zagajewski. Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains. Remote Sensing. 2021; 13 (16):3314.
Chicago/Turabian StyleRobert Migas-Mazur; Marlena Kycko; Tomasz Zwijacz-Kozica; Bogdan Zagajewski. 2021. "Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains." Remote Sensing 13, no. 16: 3314.
Mountain forests are exposed to extreme conditions (e.g., strong winds and intense solar radiation) and various types of damage by insects such as bark beetles, which makes them very sensitive to climatic changes. Therefore, continuous monitoring is crucial, and remote-sensing techniques allow the monitoring of transboundary areas where a common policy is needed to protect and monitor the environment. In this study, we used Sentinel-2 and Landsat 8 open data to assess the forest stands classification of the UNESCO Krkonoše/Karkonosze Transboundary Biosphere Reserve, which is undergoing dynamic changes in recovering woodland vegetation due to an ecological disaster that led to damage and death of a large portion of the forests. Currently, in this protected area, dry big trunks and branches coexist with naturally occurring young forests. This heterogeneity generates mixes, which hinders the automation of classification. Thus, we used three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to classify dominant tree species (birch, beech, larch and spruce). The best results were obtained for the SVM RBF classifier, which offered an average median F1-score that oscillated around 67.2–91.5% depending on the species. The obtained maps, which were based on multispectral satellite images, were also compared with classifications made for the same area on the basis of hyperspectral APEX imagery (288 spectral bands with three-meter resolution), indicating high convergence in the recognition of woody species.
Bogdan Zagajewski; Marcin Kluczek; Edwin Raczko; Ajda Njegovec; Anca Dabija; Marlena Kycko. Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve. Remote Sensing 2021, 13, 2581 .
AMA StyleBogdan Zagajewski, Marcin Kluczek, Edwin Raczko, Ajda Njegovec, Anca Dabija, Marlena Kycko. Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve. Remote Sensing. 2021; 13 (13):2581.
Chicago/Turabian StyleBogdan Zagajewski; Marcin Kluczek; Edwin Raczko; Ajda Njegovec; Anca Dabija; Marlena Kycko. 2021. "Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve." Remote Sensing 13, no. 13: 2581.
Land cover information is essential in European Union spatial management, particularly that of invasive species, natural habitats, urbanization, and deforestation; therefore, the need for accurate and objective data and tools is critical. For this purpose, the European Union’s flagship program, the Corine Land Cover (CLC), was created. Intensive works are currently being carried out to prepare a new version of CLC+ by 2024. The geographical, climatic, and economic diversity of the European Union raises the challenge to verify various test areas’ methods and algorithms. Based on the Corine program’s precise guidelines, Sentinel-2 and Landsat 8 satellite images were tested to assess classification accuracy and regional and spatial development in three varied areas of Catalonia, Poland, and Romania. The method is dependent on two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM). The bias of classifications was reduced using an iterative of randomized training, test, and verification pixels. The ease of the implementation of the used algorithms makes reproducing the results possible and comparable. The results show that an SVM with a radial kernel is the best classifier, followed by RF. The high accuracy classes that can be updated and classes that should be redefined are specified. The methodology’s potential can be used by developers of CLC+ products as a guideline for algorithms, sensors, and the possibilities and difficulties of classifying different CLC classes.
Anca Dabija; Marcin Kluczek; Bogdan Zagajewski; Edwin Raczko; Marlena Kycko; Ahmed Al-Sulttani; Anna Tardà; Lydia Pineda; Jordi Corbera. Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping. Remote Sensing 2021, 13, 777 .
AMA StyleAnca Dabija, Marcin Kluczek, Bogdan Zagajewski, Edwin Raczko, Marlena Kycko, Ahmed Al-Sulttani, Anna Tardà, Lydia Pineda, Jordi Corbera. Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping. Remote Sensing. 2021; 13 (4):777.
Chicago/Turabian StyleAnca Dabija; Marcin Kluczek; Bogdan Zagajewski; Edwin Raczko; Marlena Kycko; Ahmed Al-Sulttani; Anna Tardà; Lydia Pineda; Jordi Corbera. 2021. "Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping." Remote Sensing 13, no. 4: 777.
Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions.
Marta Samulowska; Szymon Chmielewski; Edwin Raczko; Michał Lupa; Dorota Myszkowska; Bogdan Zagajewski. Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping. ISPRS International Journal of Geo-Information 2021, 10, 46 .
AMA StyleMarta Samulowska, Szymon Chmielewski, Edwin Raczko, Michał Lupa, Dorota Myszkowska, Bogdan Zagajewski. Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping. ISPRS International Journal of Geo-Information. 2021; 10 (2):46.
Chicago/Turabian StyleMarta Samulowska; Szymon Chmielewski; Edwin Raczko; Michał Lupa; Dorota Myszkowska; Bogdan Zagajewski. 2021. "Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping." ISPRS International Journal of Geo-Information 10, no. 2: 46.
This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.
Adam Waśniewski; Agata Hościło; Bogdan Zagajewski; Dieudonné Moukétou-Tarazewicz. Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon. Forests 2020, 11, 941 .
AMA StyleAdam Waśniewski, Agata Hościło, Bogdan Zagajewski, Dieudonné Moukétou-Tarazewicz. Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon. Forests. 2020; 11 (9):941.
Chicago/Turabian StyleAdam Waśniewski; Agata Hościło; Bogdan Zagajewski; Dieudonné Moukétou-Tarazewicz. 2020. "Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon." Forests 11, no. 9: 941.
The United Nations (UN) sustainable development goals (SDGs), a strategy to guide the world’s social and economic transformation, highlight the issue of urban air pollution in SDG 11. Open data, as an output of citizen science (CS), are needed to supply and improve the SDG indicator system. Therefore, we propose a CS framework to extend the paradigm of urban air pollution monitoring from particulate matter concentration levels to air quality-related health symptom load, and foster the development of a tier-3 SDG indicator (which we call indicator 11.6.3). Building this new perspective for CS contributions to the achievement of SDGs, we address the problem of crowdsourced data bias as a prerequisite for better quality open data output. The aim of this study is to propose an air pollution symptom mapping framework for citizen-driven research and to find the most robust data quality assurance system (QAs) in this field. The method includes a GeoWeb application as well as data quality assurance mechanisms based on conditional statements, in order to reduce crowdsourced data bias. A four-month crowdsourcing campaign, released in Lubelskie voivodship (Poland), resulted in 1823 outdoor reports with a rejection rate of up to 28%, depending on the applied QA system (QAs). Testing the QAs variants, we find the most robust data bias solving method in survey-based symptom mapping. The framework output is shared via GeoWeb dashboards, including the 11.6.3 indicator evaluation. By familiarizing the public with citizen science, a city can track the progress of its SDG achievements and increase the transparency of the process through the use of GeoWeb.
Marta Samulowska; Szymon Chmielewski; Edwin Raczko; Michał Lupa; Dorota Myszkowska; Bogdan Zagajewski. Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator. 2020, 1 .
AMA StyleMarta Samulowska, Szymon Chmielewski, Edwin Raczko, Michał Lupa, Dorota Myszkowska, Bogdan Zagajewski. Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator. . 2020; ():1.
Chicago/Turabian StyleMarta Samulowska; Szymon Chmielewski; Edwin Raczko; Michał Lupa; Dorota Myszkowska; Bogdan Zagajewski. 2020. "Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator." , no. : 1.
Invasive and expansive plant species are considered a threat to natural biodiversity because of their high adaptability and low habitat requirements. Species investigated in this research, including Solidago spp., Calamagrostis epigejos, and Rubus spp., are successfully displacing native vegetation and claiming new areas, which in turn severely decreases natural ecosystem richness, as they rapidly encroach on protected areas (e.g., Natura 2000 habitats). Because of the damage caused, the European Union (EU) has committed all its member countries to monitor biodiversity. In this paper we compared two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to identify Solidago spp., Calamagrostis epigejos, and Rubus spp. on HySpex hyperspectral aerial images. SVM and RF are reliable and well-known classifiers that achieve satisfactory results in the literature. Data sets containing 30, 50, 100, 200, and 300 pixels per class in the training data set were used to train SVM and RF classifiers. The classifications were performed on 430-spectral bands and on the most informative 30 bands extracted using the Minimum Noise Fraction (MNF) transformation. As a result, maps of the spatial distribution of analyzed species were achieved; high accuracies were observed for all data sets and classifiers (an average F1 score above 0.78). The highest accuracies were obtained using 30 MNF bands and 300 sample pixels per class in the training data set (average F1 score > 0.9). Lower training data set sample sizes resulted in decreased average F1 scores, up to 13 percentage points in the case of 30-pixel samples per class.
Anita Sabat-Tomala; Edwin Raczko; Bogdan Zagajewski. Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sensing 2020, 12, 516 .
AMA StyleAnita Sabat-Tomala, Edwin Raczko, Bogdan Zagajewski. Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sensing. 2020; 12 (3):516.
Chicago/Turabian StyleAnita Sabat-Tomala; Edwin Raczko; Bogdan Zagajewski. 2020. "Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data." Remote Sensing 12, no. 3: 516.
The unique set of physical and chemical properties of asbestos has led to its many industrial applications worldwide, of which roofing and facades constitute approximately 80% of currently used asbestos-containing products. Since asbestos-containing products are harmful to human health, their use and production have been banned in many countries. To date, no research has been undertaken to estimate the total amount of asbestos–cement products used at the country level in relation to regions or other administrative units. The objective of this paper is to present a possible new solution for developing the spatial distribution of asbestos–cement products used across the country by applying the supervised machine learning algorithm, i.e., Random Forest. Based on the results of a physical inventory taken on asbestos–cement products with the use of aerial imagery, and the application of selected features, considering the socio-economic situation of Poland, i.e., population, buildings, public finance, housing economy and municipal infrastructure, wages, salaries and social security benefits, agricultural census, entities of the national economy, labor market, environment protection, area of built-up surfaces, historical belonging to annexations, and data on asbestos manufacturing plants, best Random Forest models were computed. The selection of important variables was made in the R v.3.1.0 program and supported by the Boruta algorithm. The prediction of the amount of asbestos–cement products used in communes was executed in the randomForest package. An algorithm explaining 75.85% of the variance was subsequently used to prepare the prediction map of the spatial distribution of the amount of asbestos–cement products used in Poland. The total amount was estimated at 710,278,645 m2 (7.8 million tons). Since the best model used data on built-up surfaces which are available for the whole of Europe, it is worth considering the use of the developed method in other European countries, as well as to assess the environmental risk of asbestos exposure to humans.
Ewa Wilk; Małgorzata Krówczyńska; Bogdan Zagajewski. Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm. Sustainability 2019, 11, 4355 .
AMA StyleEwa Wilk, Małgorzata Krówczyńska, Bogdan Zagajewski. Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm. Sustainability. 2019; 11 (16):4355.
Chicago/Turabian StyleEwa Wilk; Małgorzata Krówczyńska; Bogdan Zagajewski. 2019. "Modelling the Spatial Distribution of Asbestos—Cement Products in Poland with the Use of the Random Forest Algorithm." Sustainability 11, no. 16: 4355.
Chlorophyll fluorescence parameters can provide useful indications of photosynthetic performance in vivo. Coupling appropriate fluorescence measurements with other noninvasive techniques, such as absorption spectroscopy or gas exchange, can provide insights into the limitations to photosynthesis under given conditions. Chlorophyll content is one of the dominant factors influencing the conditions of a vegetation growing season, and can be tested using both fluorescence and remote sensing methods. Hyperspectral remote sensing and recording the narrow range of the spectrum can be used to accurately analyze the parameters and properties of plants. The aim of this study was to analyze the influence of lead ions (Pb, 5 mM Pb(NO3)2) on the growth of pea plants using spectral properties. Hyperspectral remote sensing and chlorophyll fluorescence measurements were used to assess the physiological state of plants seedlings treated by lead ions during the experiment. The plants were growing in hydroponic cultures supplemented with Pb ions under various conditions (control, complete Knop + phosphorus (+P); complete Knop + phosphorus (+P) + Pb; Knop (-P) + Pb, distilled water + Pb) affecting lead uptake via the root system. Spectrometric measurements allowed us to calculate the remote sensing indices of vegetation, which were compared with chlorophyll and carotenoids content and fluorescence parameters. The lead contents in the leaves, roots, and stems were also analyzed. Spectral characteristics and vegetation properties were analyzed using statistical tests. We conclude that: (1) pea seedlings grown in complete Knop (with P) and in the presence of Pb ions were spectrally similar to the control plants because lead was not transported to the shoots of plants; (2) lead most influenced plants that were grown in water, according to the highest lead content in the leaves; and (3) the effects of lead on plant growth were confirmed by remote sensing indices, whereas fluorescence parameters identified physiological changes induced by Pb ions in the plants.
Marlena Kycko; Elżbieta Romanowska; Bogdan Zagajewski. Lead-Induced Changes in Fluorescence and Spectral Characteristics of Pea Leaves. Remote Sensing 2019, 11, 1885 .
AMA StyleMarlena Kycko, Elżbieta Romanowska, Bogdan Zagajewski. Lead-Induced Changes in Fluorescence and Spectral Characteristics of Pea Leaves. Remote Sensing. 2019; 11 (16):1885.
Chicago/Turabian StyleMarlena Kycko; Elżbieta Romanowska; Bogdan Zagajewski. 2019. "Lead-Induced Changes in Fluorescence and Spectral Characteristics of Pea Leaves." Remote Sensing 11, no. 16: 1885.
Vegetation, through its condition, reflects the properties of the environment. Heterogeneous alpine ecosystems play a critical role in global monitoring systems, but due to low accessibility, cloudy conditions, and short vegetation periods, standard monitoring methods cannot be applied comprehensively. Hyperspectral tools offer a variety of methods based on narrow-band data, but before extrapolation to an airborne or satellite scale, they must be verified using plant biometrical variables. This study aims to assess the condition of alpine sward dominant species (Agrostis rupestris, Festuca picta, and Luzula alpino-pilosa) of the UNESCO Man&Biosphere Tatra National Park (TPN) where the high mountain grasslands are strongly influenced by tourists. Data were analyzed for trampled, reference, and recultivated polygons. The field-obtained hyperspectral properties were verified using ground measured photosynthetically active radiation, chlorophyll content, fluorescence, and evapotranspiration. Statistically significant changes in terms of cellular structures, chlorophyll, and water content in the canopy were detected. Lower values for the remote sensing indices were observed for trampled plants (about 10–15%). Species in recultivated areas were characterized by a similar, or sometimes improved, spectral properties than the reference polygons; confirmed by fluorescence measurements (Fv/Fm). Overall, the fluorescence analysis and remote sensing tools confirmed the suitability of such methods for monitoring species in remote mountain areas, and the general condition of these grasslands was determined as good.
Marlena Kycko; Bogdan Zagajewski; Samantha Lavender; Anca Dabija. In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species. Remote Sensing 2019, 11, 1296 .
AMA StyleMarlena Kycko, Bogdan Zagajewski, Samantha Lavender, Anca Dabija. In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species. Remote Sensing. 2019; 11 (11):1296.
Chicago/Turabian StyleMarlena Kycko; Bogdan Zagajewski; Samantha Lavender; Anca Dabija. 2019. "In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species." Remote Sensing 11, no. 11: 1296.
Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll concentration in three representative arctic plant species, i.e., the prostrate deciduous shrub Salix polaris, the herb Bistorta vivipara, and the prostrate semievergreen shrub Dryas octopetala. This study was conducted at the high Arctic archipelago of Svalbard, Norway. Of the 23 analyzed candidate vegetation and chlorophyll indices, the following showed the best statistical correlations with the optical measurements of chlorophyll concentration: Vogelmann red edge index 1, 2, 3 (VOG 1, 2, 3), Zarco-Tejada and Miller index (ZMI), modified normalized difference vegetation index 705 (mNDVI 705), modified normalized difference index (mND), red edge normalized difference vegetation index (NDVI 705), and Gitelson and Merzlyak index 2 (GM 2). An assessment of the results from this analysis indicates that S. polaris and B. vivipara were in good health, while the health status of D. octopetala was reduced. This is consistent with other studies from the same area. There were also differences between study sites, probably as a result of local variation in environmental conditions. All these indices may be extracted from future satellite missions like EnMAP (Environmental Mapping and Analysis Program) and FLEX (Fluorescence Explorer), thus, enabling the efficient monitoring of vegetation condition in vast and inaccessible polar areas.
Bogdan Zagajewski; Marlena Kycko; Hans Tømmervik; Zbigniew Bochenek; Bronisław Wojtuń; Jarle W. Bjerke; Andrzej Kłos. Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala. Acta Societatis Botanicorum Poloniae 2018, 87, 1 .
AMA StyleBogdan Zagajewski, Marlena Kycko, Hans Tømmervik, Zbigniew Bochenek, Bronisław Wojtuń, Jarle W. Bjerke, Andrzej Kłos. Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala. Acta Societatis Botanicorum Poloniae. 2018; 87 (4):1.
Chicago/Turabian StyleBogdan Zagajewski; Marlena Kycko; Hans Tømmervik; Zbigniew Bochenek; Bronisław Wojtuń; Jarle W. Bjerke; Andrzej Kłos. 2018. "Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala." Acta Societatis Botanicorum Poloniae 87, no. 4: 1.
Knowledge of tree species composition is obligatory in forest management. Accurate tree species maps allow for detailed analysis of a forest ecosystem and its interactions with the environment. The research presented here focused on developing methods of tree species identification using aerial hyperspectral data. The research area is located in Southwestern Poland and covers the Karkonoski National Park (KNP), which was significantly damaged by acid rain and pest infestation in the 1980s. High-resolution (3.35 m) Airborne Prism Experiment (APEX) hyperspectral images (288 spectral bands in the range of 413 to 2440 nm) were used as a basis for tree species classification. Beech (Fagus sylvatica), birch (Betula pendula), alder (Alnus incana), larch (Larix decidua), pine (Pinus sylvestris), and spruce (Picea abies) were classified. The classification algorithm used was feed-forward multilayered perceptron (MLP) with a single hidden layer. To simulate such a network, we used the R programming environment and the nnet package. To provide more accurate measurement of accuracy, iterative accuracy assessment was performed. The final tree species maps cover the whole area of KNP; a median overall accuracy (OA) of 87% was achieved, with median producer accuracy (PA) for all classes exceeding 68%. The best-classified classes were spruce, beech, and birch, with median producer accuracy of 93%, 88% and 83%, respectively. The pine class achieved the lowest median producer and user accuracies (68% and 75%, respectively). The results show great potential for the use of hyperspectral data as a tool for identifying tree species locations in diverse mountainous forest.
Edwin Raczko; Bogdan Zagajewski. Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images. Remote Sensing 2018, 10, 1111 .
AMA StyleEdwin Raczko, Bogdan Zagajewski. Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images. Remote Sensing. 2018; 10 (7):1111.
Chicago/Turabian StyleEdwin Raczko; Bogdan Zagajewski. 2018. "Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images." Remote Sensing 10, no. 7: 1111.
In the years 2014-2016 biomonitoring studies were conducted in the forest areas of south and north-eastern Poland: the Karkonosze Mountains, the Beskidy Mountains, the Borecka Forest, the Knyszyńska Forest and the Białowieska Forest. This study used epigeic moss Pleurozium schreberi and epiphytic lichens Hypogymnia physodes. Samples were collected in spring, summer and autumn. Approximately 500 samples of moss and lichens were collected for the study. In the samples, Mn, Ni, Cu, Zn, Cd, Hg and Pb concentrations were determined. Based on the obtained results, the studied areas were ranked by extent of heavy-metal deposition: Beskidy > Karkonosze Mountains > forests of north-eastern Poland. Some seasonal changes in concentrations of metals accumulated in moss and lichens were also indicated. There was observed, i.a., an increase in Cd concentration at the beginning of the growing season, which may be related to low emissions during the heating season. Analysis of the surface distribution of deposition of metals in the studied areas showed a significant contribution of nearby territorial emissions and unidentified local emission sources. The contribution of distant emission to Zn, Hg and Pb deposition levels in the Karkonosze and Beskidy region was also indicated.
Andrzej Kłos; Zbigniew Ziembik; Małgorzata Rajfur; Agnieszka Dołhańczuk-Śródka; Zbigniew Bochenek; Jarle W. Bjerke; Hans Tømmervik; Bogdan Zagajewski; Dariusz Ziółkowski; Dominik Jerz; Maria Zielińska; Paweł Krems; Piotr Godyń; Michał Marciniak; Paweł Świsłowski. Using moss and lichens in biomonitoring of heavy-metal contamination of forest areas in southern and north-eastern Poland. Science of The Total Environment 2018, 627, 438 -449.
AMA StyleAndrzej Kłos, Zbigniew Ziembik, Małgorzata Rajfur, Agnieszka Dołhańczuk-Śródka, Zbigniew Bochenek, Jarle W. Bjerke, Hans Tømmervik, Bogdan Zagajewski, Dariusz Ziółkowski, Dominik Jerz, Maria Zielińska, Paweł Krems, Piotr Godyń, Michał Marciniak, Paweł Świsłowski. Using moss and lichens in biomonitoring of heavy-metal contamination of forest areas in southern and north-eastern Poland. Science of The Total Environment. 2018; 627 ():438-449.
Chicago/Turabian StyleAndrzej Kłos; Zbigniew Ziembik; Małgorzata Rajfur; Agnieszka Dołhańczuk-Śródka; Zbigniew Bochenek; Jarle W. Bjerke; Hans Tømmervik; Bogdan Zagajewski; Dariusz Ziółkowski; Dominik Jerz; Maria Zielińska; Paweł Krems; Piotr Godyń; Michał Marciniak; Paweł Świsłowski. 2018. "Using moss and lichens in biomonitoring of heavy-metal contamination of forest areas in southern and north-eastern Poland." Science of The Total Environment 627, no. : 438-449.
Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth’s object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy.
Prakash Nimbalkar; Anna Jarocinska; Bogdan Zagajewski. Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging. Journal of Spectroscopy 2018, 2018, 1 -15.
AMA StylePrakash Nimbalkar, Anna Jarocinska, Bogdan Zagajewski. Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging. Journal of Spectroscopy. 2018; 2018 ():1-15.
Chicago/Turabian StylePrakash Nimbalkar; Anna Jarocinska; Bogdan Zagajewski. 2018. "Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging." Journal of Spectroscopy 2018, no. : 1-15.
Mapping plant communities is a difficult and time consuming endeavor. Methods relying on field surveys deliver high quality data but are usually limited to relatively small areas. In this paper we apply airborne hyperspectral data to vegetation mapping in remote and hard to reach areas. We classified 22 vegetation communities in the Giant Mountains on 3.12-m Airborne Prism Experiment (APEX) hyperspectral images, registered in 288 spectral bands (10 September 2012). As the classification algorithm, Support Vector Machines (SVM) was used. APEX data were corrected geometrically and atmospherically, and three dimensionality reduction methods were performed to select the best dataset. As reference we used a non-forest vegetation map containing vegetation communities of Polish Karkonosze National Park from 2002, orthophotomap and field surveys data from 2013 to 2014. We obtained the post-classification maps of 22 vegetation communities, lakes and areas without any vegetation. Iterative accuracy assessment repeated 100 times was used to obtain the most objective results for individual communities. The median value of overall accuracy (OA) was 84%. Fourteen out of twenty-four classes were classified of more than 80% of producer accuracy (PA) and sixteen out of twenty-four of user accuracy (UA). APEX data and SVM with the use of iterative accuracy assessment are useful for the mountain communities classification. This can support both Polish and Czech national parks management by giving the information about diversity of communities in the whole transboundary area, helping with identification especially in changing environment caused by humans.
Adriana Marcinkowska-Ochtyra; Bogdan Zagajewski; Edwin Raczko; Adrian Ochtyra; Anna Jarocińska. Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery. Remote Sensing 2018, 10, 570 .
AMA StyleAdriana Marcinkowska-Ochtyra, Bogdan Zagajewski, Edwin Raczko, Adrian Ochtyra, Anna Jarocińska. Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery. Remote Sensing. 2018; 10 (4):570.
Chicago/Turabian StyleAdriana Marcinkowska-Ochtyra; Bogdan Zagajewski; Edwin Raczko; Adrian Ochtyra; Anna Jarocińska. 2018. "Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery." Remote Sensing 10, no. 4: 570.
This research focuses on the effect of trampling on vegetation in high-mountain ecosystems through the electromagnetic spectrum’s interaction with plant pigments, cell structure, water content and other substances that have a direct impact on leaf properties. The aim of the study was to confirm with the use of fluorescence methods of variability in the state of high-mountain vegetation previously measured spectrometrically. The most heavily visited part of the High Tatras in Poland was divided into polygons and, after selecting the dominant species within alpine swards, a detailed analysis of trampled and reference patterns was performed. The Analytical Spectral Devices (ASD) FieldSpec 3/4 were used to acquire high-resolution spectral properties of plants, their fluorescence and the leaf chlorophyll content with the difference between the plant surface temperature (ts), and the air temperature (ta) as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR) used as reference data. The results show that, along tourist trails, vegetation adapts to trampling with the impact depending on the species. A lower chlorophyll value was confirmed by a decrease in fluorescence, and the cellular structures were degraded in trampled compared to reference species, with a lower leaf reflectance. In addition, at the extreme, trampling can eliminate certain species such as Luzula alpino-pilosa, for which significant changes were noted due to trampling.
Marlena Kycko; Bogdan Zagajewski; Samantha Lavender; Elżbieta Romanowska; Magdalena Zwijacz-Kozica. The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards. Remote Sensing 2018, 10, 220 .
AMA StyleMarlena Kycko, Bogdan Zagajewski, Samantha Lavender, Elżbieta Romanowska, Magdalena Zwijacz-Kozica. The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards. Remote Sensing. 2018; 10 (2):220.
Chicago/Turabian StyleMarlena Kycko; Bogdan Zagajewski; Samantha Lavender; Elżbieta Romanowska; Magdalena Zwijacz-Kozica. 2018. "The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards." Remote Sensing 10, no. 2: 220.
This research focuses on the use of HySpex hyperspectral images for verification of two-dimensional hydrodynamic modelling of open-channel flow over loose bed (CCHE2D) and assessment of water quality in the Zegrze Reservoir. The CCHE2D hydrodynamic model results show the distribution of hydraulic parameters of water flow and the sediment concentrations in the reservoir. HySpex images were used to obtain remote sensing indices of water quality. The images were compared to the hydrodynamic model results and field measurements. The analysis of hydrodynamic model results and hyperspectral image indices show the spatial distribution of the water’s physico-chemical properties in the reservoir, and poor mixing of the Bug River and the Narew River at their confluence. This study shows that there is synergy potential in using hydrodynamic modelling results and remote sensing indices of water quality for analysis of the reservoir’s water quality.
Anita Sabat-Tomala; Anna Maria Jarocińska; Bogdan Zagajewski; Artur Stanisław Magnuszewski; Łukasz Maciej Sławik; Adrian Ochtyra; Edwin Raczko; Jerzy Ryszard Lechnio. Application of HySpex hyperspectral images for verification of a two-dimensional hydrodynamic model. European Journal of Remote Sensing 2018, 51, 637 -649.
AMA StyleAnita Sabat-Tomala, Anna Maria Jarocińska, Bogdan Zagajewski, Artur Stanisław Magnuszewski, Łukasz Maciej Sławik, Adrian Ochtyra, Edwin Raczko, Jerzy Ryszard Lechnio. Application of HySpex hyperspectral images for verification of a two-dimensional hydrodynamic model. European Journal of Remote Sensing. 2018; 51 (1):637-649.
Chicago/Turabian StyleAnita Sabat-Tomala; Anna Maria Jarocińska; Bogdan Zagajewski; Artur Stanisław Magnuszewski; Łukasz Maciej Sławik; Adrian Ochtyra; Edwin Raczko; Jerzy Ryszard Lechnio. 2018. "Application of HySpex hyperspectral images for verification of a two-dimensional hydrodynamic model." European Journal of Remote Sensing 51, no. 1: 637-649.
Szymon Chmielewski; Marta Samulowska; Michał Lupa; Danbi Lee; Bogdan Zagajewski. Citizen science and WebGIS for outdoor advertisement visual pollution assessment. Computers, Environment and Urban Systems 2018, 67, 97 -109.
AMA StyleSzymon Chmielewski, Marta Samulowska, Michał Lupa, Danbi Lee, Bogdan Zagajewski. Citizen science and WebGIS for outdoor advertisement visual pollution assessment. Computers, Environment and Urban Systems. 2018; 67 ():97-109.
Chicago/Turabian StyleSzymon Chmielewski; Marta Samulowska; Michał Lupa; Danbi Lee; Bogdan Zagajewski. 2018. "Citizen science and WebGIS for outdoor advertisement visual pollution assessment." Computers, Environment and Urban Systems 67, no. : 97-109.
Remote sensing is a suitable candidate for monitoring rapid changes in Polar regions, offering high-resolution spectral, spatial and radiometric data. This paper focuses on the spectral properties of dominant plant species acquired during the first week of August 2015. Twenty-eight plots were selected, which could easily be identified in the field as well as on RapidEye satellite imagery. Spectral measurements of individual species were acquired, and heavy metal contamination stress factors were measured contemporaneously. As a result, a unique spectral library of dominant plant species, heavy metal concentrations and damage ratios were achieved with an indication that species-specific changes due to environmental conditions can best be differentiated in the 1401–2400 nm spectral region. Two key arctic tundra species, Cassiope tetragona and Dryas octopetala, exhibited significant differences in this spectral region that were linked to a changing health status. Relationships between field and satellite measurements were comparable, e.g., the Red Edge Normalized Difference Vegetation Index (RENDVI) showed a strong and significant relationship (R2 = 0.82; p = 0.036) for the species Dryas octopetala. Cadmium and Lead were below detection levels while manganese, copper and zinc acquired near Longyearbyen were at concentrations comparable to other places in Svalbard. There were high levels of nickel near Longyearbyen (0.014 mg/g), while it was low (0.004 mg/g) elsewhere.
Bogdan Zagajewski; Hans Tømmervik; Jarle W. Bjerke; Edwin Raczko; Zbigniew Bochenek; Andrzej Kłos; Anna Jarocińska; Samantha Lavender; Dariusz Ziółkowski. Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants. Remote Sensing 2017, 9, 1289 .
AMA StyleBogdan Zagajewski, Hans Tømmervik, Jarle W. Bjerke, Edwin Raczko, Zbigniew Bochenek, Andrzej Kłos, Anna Jarocińska, Samantha Lavender, Dariusz Ziółkowski. Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants. Remote Sensing. 2017; 9 (12):1289.
Chicago/Turabian StyleBogdan Zagajewski; Hans Tømmervik; Jarle W. Bjerke; Edwin Raczko; Zbigniew Bochenek; Andrzej Kłos; Anna Jarocińska; Samantha Lavender; Dariusz Ziółkowski. 2017. "Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants." Remote Sensing 9, no. 12: 1289.
Jerzy Cierniewski; Jakub Ceglarek; Arnon Karnieli; Sławomir Królewicz; Cezary Kaźmierowski; Bogdan Zagajewski. Predicting the diurnal blue-sky albedo of soils using their laboratory reflectance spectra and roughness indices. Journal of Quantitative Spectroscopy and Radiative Transfer 2017, 200, 25 -31.
AMA StyleJerzy Cierniewski, Jakub Ceglarek, Arnon Karnieli, Sławomir Królewicz, Cezary Kaźmierowski, Bogdan Zagajewski. Predicting the diurnal blue-sky albedo of soils using their laboratory reflectance spectra and roughness indices. Journal of Quantitative Spectroscopy and Radiative Transfer. 2017; 200 ():25-31.
Chicago/Turabian StyleJerzy Cierniewski; Jakub Ceglarek; Arnon Karnieli; Sławomir Królewicz; Cezary Kaźmierowski; Bogdan Zagajewski. 2017. "Predicting the diurnal blue-sky albedo of soils using their laboratory reflectance spectra and roughness indices." Journal of Quantitative Spectroscopy and Radiative Transfer 200, no. : 25-31.