Bogdan Zagajewski - Profile on Academia.edu (original) (raw)
Papers by Bogdan Zagajewski
Mapping spatiotemporal mortality patterns in spruce mountain forests using Sentinel-2 data and environmental factors
Ecological Informatics, 2025
Climate change is increasing the frequency of extreme events, including those in forests. One of ... more Climate change is increasing the frequency of extreme events, including those in forests. One of the major drivers of forest change in Europe is the bark beetle, which causes large-scale annual changes in spruce forest areas. Mountain forests are particularly vulnerable as changes occur rapidly and require long-term monitoring of ongoing ecological changes. For this purpose, a 10-year time series of Sentinel-2 optical satellite data fused with Sentinel-1 radar and topographic derivatives was applied to the natural forests of the Tatra Mountains in Central Europe. Based on machine learning algorithms and iterative methods, overall classification accuracies of 0.94–0.96 and snags with an F1-score of 0.81–0.98 were achieved. The highest spruce mortality rate was observed in 2018, with extensive snag areas persisting until 2024. This study revealed that smaller infestation patches (< 0.1 ha) consistently dominated the landscape, peaking in 2018, whereas larger patches (> 0.5 ha) showed a declining trend, particularly after 2020. The variable importance analysis revealed that topographic factors are critical for predicting forest disturbance patterns. Elevation emerged as the most significant predictor with a Mean Decrease in Accuracy ranging from 95 to 150, followed by slope and aspect. Snag occurrence was strongly influenced by elevation, ranging from 700 to 1700 m a.s.l., with the median elevation increasing from 1150 m in 2015 to 1400 m in 2024. The slope also played an important role, with the median slopes for snag occurrences ranging from 15° to 25°, indicating a tendency for mortality on moderate inclines, although mortality on steeper slopes (up to 50°) was occasionally observed, particularly in 2017 and 2023. Regarding the slope orientation, the southeastern and eastern aspects consistently experienced higher proportions of spruce mortality (particularly between 2017 and 2021). A strong correlation between spruce mortality and temperature-related variables was identified, particularly degree days above 5 °C and 8.3 °C during key months (April, June, and July). Median yearly air temperature showed a correlation, whereas precipitation-related variables, including the Standardised Precipitation Evapotranspiration Index (SPEI), exhibited negative correlations, particularly the SPEI 01 median. These findings improve the understanding of long-term forest changes caused by disturbances and provide key insights for the data-driven management of protected forests in a changing climate.
Remote sensing, Feb 8, 2024
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Remote Sensing, 2024
A proliferation of invasive species is displacing native species, occupying their habitats and de... more A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm.
Roczniki Geomatyki - Annals of Geomatics, 2009
SNNS application for crop classification using HyMap data
The goal of this paper is the presentation of a method and results for artificial neural networks... more The goal of this paper is the presentation of a method and results for artificial neural networks crops classification based on HyMap hyperspectral data. The method that uses an ANNs does not only depend on statistical parameters of particular class and hence makes it possible to include texture information. To experiment with variable pattern size two data sets were chosen with 10 bands obtained after MNF and 5 hyperspectral vegetation indicies. Next to post classification crops maps, additional quality layers were generated to check which classes are “problematic” because of spectral similarity or errors in the training/reference data. The best accuracy was achieved using the 10 MNF bands with the 3×3 pixel sub pattern size -94,8 %.
Climate change-induced snow thaw and subsequent accumulation of ice on the ground is a potential,... more Climate change-induced snow thaw and subsequent accumulation of ice on the ground is a potential, major threat to snow-dominated ecosystems. While impacts of ground-ice on arctic wildlife are well explored, the impacts on tundra vegetation is far from understood. We therefore tested the vulnerability of two high-arctic plants, the prostrate shrub Salix polaris and the graminoid Luzula confusa, to ice encasement for 60 days under full environmental control. Both species were tolerant, showing only minor negative responses to the treatment. Subsequent exposure to simulated late spring frost increased the amount of damaged tissue, particularly in S. polaris, compared to the pre-frost situation. Wilting shoot tips of S. polaris increased nearly tenfold, while the proportion of wilted leaves of L. confusa increased by 15%. During recovery, damaged plants of S. polaris responded by extensive compensatory growth of new leaves that were much smaller than leaves of non-damaged shoots. The results suggest that S. polaris and L. confusa are rather tolerant to arctic winter-spring climate change, and this may be part of the reason for their wide distribution range and abundance in the Arctic.
Remote Sensing, Mar 1, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Archiwum Fotogrametrii, Kartografii i Teledetekcji, 2006
STRESZCZENIE: W czerwcu 2006 został przeprowadzony eksperyment teledetekcyjny w rejonie Zbiornika... more STRESZCZENIE: W czerwcu 2006 został przeprowadzony eksperyment teledetekcyjny w rejonie Zbiornika Dobczyckiego, w ramach którego dokonano rejestracji hiperspektralnych obrazów satelitarnych Hyperion i ALI. Równocześnie przeprowadzono pomiary naziemne za pomocą spektrometru FieldSpec HH firmy ASD Inc., (Analytical Spectral Device) oraz pobrano próby osadów dennych ze zbiornika i wody nad osadowej. Miejsce pobrania prób wyznaczano za pomocą odbiornika GPS. Do przetwarzania obrazów satelitarnych oraz ich porównania z pomiarami spektrometrycznymi wykorzystano oprogramowanie ENVI. Ostatecznie wybrane z obrazów z HYPERION kompozycje barwne oraz wyniki analiz zostały zintegrowane z innymi warstwami istniejącymi już w bazie danych GIS (archiwalne obrazy satelitarne, lotnicze, mapy topograficzne, mapa sozologiczna, mapa glebowa, DTM) w środowisku Geomedia. Wykorzystano możliwość integracji różnych formatów i układów współrzędnych (1992-ortofotomapa, DTM, mapa sozologiczna, 1942-mapa glebowa, UTM-, archiwalne obrazy satelitarne, pomiar GPS).
Remote Sensing, Feb 5, 2020
Invasive and expansive plant species are considered a threat to natural biodiversity because of t... more 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.
Use of laboratory hyperspectral reflectance data of soils for predicting their diurnal albedo dynamics accomodating their roughness
The objective of this study was to assess the relationship between the hyperspectral reflectance ... more The objective of this study was to assess the relationship between the hyperspectral reflectance of soils and its albedo, measured under various roughness conditions. 108 soil surfaces measurements were conducted in Poland and Israel. Each surface was characterized by its diurnal albedo variation in the field as well as its reflectance spectra that was obtained in the laboratory. The best fit to the model was achieved by postprocessing manipulation of the spectra, namely second derivate transformation. Using stepwise elimination process, four spectral wavelengths, as well as roughness index, were selected for modeling. The resulted models allow predicting the albedo of a soil at specific roughness for any solar zenithal angle, provided that hyperspectral reflectance data is available.
Miscellanea geographica, Mar 1, 2000
Remote Sensing, Aug 21, 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Remote Sensing, Jul 12, 2018
Knowledge of tree species composition is obligatory in forest management. Accurate tree species m... more 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.
Wykorzystanie obrazów hiperspektralnych do klasyfikacji pokrycia terenu zlewni Bystrzanki
ABSTRACT
Polski Przegląd Kartograficzny, 2015
Z a r y s t r e ś c i. Aktualne mapy pokrycia terenu są podstawą wielu dyscyplin nauki oraz mają ... more Z a r y s t r e ś c i. Aktualne mapy pokrycia terenu są podstawą wielu dyscyplin nauki oraz mają szerokie zastosowanie aplikacyjne. Jednym z problemów aktualizacji map jest proces aktualizacji danych. Teledetekcja dostarcza codziennie nowych zobrazowań satelitarnych, które mogą zaspokoić potrzeby aktualizacji baz danych. W niniejszym artykule autorzy przedstawiają metodę klasyfikacji pokrycia terenu sztucznymi sieciami neuronowymi fuzzy ARTMAP zgodnie z założeniami i legendą Corine Land Cover na podstawie danych satelitarnych Landsat, które wykorzystywane są do opracowania map pokrycia terenu. W artykule użyto jako danych referencyjnych i weryfikacyjnych najnowszą mapę Corine Land Cover (CLC) 2012. Do przeprowadzenia klasyfikacji symulatorem wykorzystano trzy zdjęcia satelitarne Landsat TM (21.04.2011, 05.06.2010, 27.08.2011). Obszarem badań były okolice Warszawy. Wynikami pracy symulatora są mapy klasyfikacji pokrycia terenu oraz macierze błędów klasyfikacji. Uzyskane wyniki potwierdzają, że sztuczne sieci neuronowe mogą z powodzeniem być wykorzystywane do aktualizacji map pokrycia terenu. S ł o w a k l u c z o w e: klasyfikacja, Corine Land Cover, Landsat, sztuczne sieci neuronowe, Warszawa
SNNS classification of hyperspectral data of extensively used agricultural areas
ABSTRACT
SAM and ANN classification of hyperspectral data of seminatural agriculture used areas
... Vegetation reflectance registered by remote sensing instruments is the average of the reflect... more ... Vegetation reflectance registered by remote sensing instruments is the average of the reflectance of photosynthetic active parts, non-photosynthetic active parts (ie branches, dry leaves), shadow and ground. ... DAIS 7915 hyperspectral data used in this study was acquired on ...
Biuletyn Polska Akademia Nauk. Komitet Przestrzennego Zagospodarowania Kraju, 2013
Application of Remote Sensing for an Evaluation of Spatial Organization in Poland. Modern space m... more Application of Remote Sensing for an Evaluation of Spatial Organization in Poland. Modern space management is based on actual and high-quality data. Such solutions offers remote sensing technology information and techniques that are used for land cover monitoring and inventory. For less experienced users are particularly useful high-resolution images (e.g. QuickBird, Ikonos or Google Map) that allow visual interpretation of the earth's surface. More advanced users are particularly recommended multispectral images (e.g. Landsat, Spot, IRS, or new going Sentinel series) that allow the classification of land cover and an analysis of the environment, such as vegetation. These data can be processed and modeled in GIS. This paper presents a basic set of information for independent remote sensing data acquisition and assessment of possible measures for land cover inventory. Satellite systems allow the continuous acquisition of information, which is used in the long-term monitoring. This element can have practical importance, such as verification of illegal construction and evaluation of the land use plan.
Mapping spatiotemporal mortality patterns in spruce mountain forests using Sentinel-2 data and environmental factors
Ecological Informatics, 2025
Climate change is increasing the frequency of extreme events, including those in forests. One of ... more Climate change is increasing the frequency of extreme events, including those in forests. One of the major drivers of forest change in Europe is the bark beetle, which causes large-scale annual changes in spruce forest areas. Mountain forests are particularly vulnerable as changes occur rapidly and require long-term monitoring of ongoing ecological changes. For this purpose, a 10-year time series of Sentinel-2 optical satellite data fused with Sentinel-1 radar and topographic derivatives was applied to the natural forests of the Tatra Mountains in Central Europe. Based on machine learning algorithms and iterative methods, overall classification accuracies of 0.94–0.96 and snags with an F1-score of 0.81–0.98 were achieved. The highest spruce mortality rate was observed in 2018, with extensive snag areas persisting until 2024. This study revealed that smaller infestation patches (< 0.1 ha) consistently dominated the landscape, peaking in 2018, whereas larger patches (> 0.5 ha) showed a declining trend, particularly after 2020. The variable importance analysis revealed that topographic factors are critical for predicting forest disturbance patterns. Elevation emerged as the most significant predictor with a Mean Decrease in Accuracy ranging from 95 to 150, followed by slope and aspect. Snag occurrence was strongly influenced by elevation, ranging from 700 to 1700 m a.s.l., with the median elevation increasing from 1150 m in 2015 to 1400 m in 2024. The slope also played an important role, with the median slopes for snag occurrences ranging from 15° to 25°, indicating a tendency for mortality on moderate inclines, although mortality on steeper slopes (up to 50°) was occasionally observed, particularly in 2017 and 2023. Regarding the slope orientation, the southeastern and eastern aspects consistently experienced higher proportions of spruce mortality (particularly between 2017 and 2021). A strong correlation between spruce mortality and temperature-related variables was identified, particularly degree days above 5 °C and 8.3 °C during key months (April, June, and July). Median yearly air temperature showed a correlation, whereas precipitation-related variables, including the Standardised Precipitation Evapotranspiration Index (SPEI), exhibited negative correlations, particularly the SPEI 01 median. These findings improve the understanding of long-term forest changes caused by disturbances and provide key insights for the data-driven management of protected forests in a changing climate.
Remote sensing, Feb 8, 2024
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Remote Sensing, 2024
A proliferation of invasive species is displacing native species, occupying their habitats and de... more A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm.
Roczniki Geomatyki - Annals of Geomatics, 2009
SNNS application for crop classification using HyMap data
The goal of this paper is the presentation of a method and results for artificial neural networks... more The goal of this paper is the presentation of a method and results for artificial neural networks crops classification based on HyMap hyperspectral data. The method that uses an ANNs does not only depend on statistical parameters of particular class and hence makes it possible to include texture information. To experiment with variable pattern size two data sets were chosen with 10 bands obtained after MNF and 5 hyperspectral vegetation indicies. Next to post classification crops maps, additional quality layers were generated to check which classes are “problematic” because of spectral similarity or errors in the training/reference data. The best accuracy was achieved using the 10 MNF bands with the 3×3 pixel sub pattern size -94,8 %.
Climate change-induced snow thaw and subsequent accumulation of ice on the ground is a potential,... more Climate change-induced snow thaw and subsequent accumulation of ice on the ground is a potential, major threat to snow-dominated ecosystems. While impacts of ground-ice on arctic wildlife are well explored, the impacts on tundra vegetation is far from understood. We therefore tested the vulnerability of two high-arctic plants, the prostrate shrub Salix polaris and the graminoid Luzula confusa, to ice encasement for 60 days under full environmental control. Both species were tolerant, showing only minor negative responses to the treatment. Subsequent exposure to simulated late spring frost increased the amount of damaged tissue, particularly in S. polaris, compared to the pre-frost situation. Wilting shoot tips of S. polaris increased nearly tenfold, while the proportion of wilted leaves of L. confusa increased by 15%. During recovery, damaged plants of S. polaris responded by extensive compensatory growth of new leaves that were much smaller than leaves of non-damaged shoots. The results suggest that S. polaris and L. confusa are rather tolerant to arctic winter-spring climate change, and this may be part of the reason for their wide distribution range and abundance in the Arctic.
Remote Sensing, Mar 1, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Archiwum Fotogrametrii, Kartografii i Teledetekcji, 2006
STRESZCZENIE: W czerwcu 2006 został przeprowadzony eksperyment teledetekcyjny w rejonie Zbiornika... more STRESZCZENIE: W czerwcu 2006 został przeprowadzony eksperyment teledetekcyjny w rejonie Zbiornika Dobczyckiego, w ramach którego dokonano rejestracji hiperspektralnych obrazów satelitarnych Hyperion i ALI. Równocześnie przeprowadzono pomiary naziemne za pomocą spektrometru FieldSpec HH firmy ASD Inc., (Analytical Spectral Device) oraz pobrano próby osadów dennych ze zbiornika i wody nad osadowej. Miejsce pobrania prób wyznaczano za pomocą odbiornika GPS. Do przetwarzania obrazów satelitarnych oraz ich porównania z pomiarami spektrometrycznymi wykorzystano oprogramowanie ENVI. Ostatecznie wybrane z obrazów z HYPERION kompozycje barwne oraz wyniki analiz zostały zintegrowane z innymi warstwami istniejącymi już w bazie danych GIS (archiwalne obrazy satelitarne, lotnicze, mapy topograficzne, mapa sozologiczna, mapa glebowa, DTM) w środowisku Geomedia. Wykorzystano możliwość integracji różnych formatów i układów współrzędnych (1992-ortofotomapa, DTM, mapa sozologiczna, 1942-mapa glebowa, UTM-, archiwalne obrazy satelitarne, pomiar GPS).
Remote Sensing, Feb 5, 2020
Invasive and expansive plant species are considered a threat to natural biodiversity because of t... more 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.
Use of laboratory hyperspectral reflectance data of soils for predicting their diurnal albedo dynamics accomodating their roughness
The objective of this study was to assess the relationship between the hyperspectral reflectance ... more The objective of this study was to assess the relationship between the hyperspectral reflectance of soils and its albedo, measured under various roughness conditions. 108 soil surfaces measurements were conducted in Poland and Israel. Each surface was characterized by its diurnal albedo variation in the field as well as its reflectance spectra that was obtained in the laboratory. The best fit to the model was achieved by postprocessing manipulation of the spectra, namely second derivate transformation. Using stepwise elimination process, four spectral wavelengths, as well as roughness index, were selected for modeling. The resulted models allow predicting the albedo of a soil at specific roughness for any solar zenithal angle, provided that hyperspectral reflectance data is available.
Miscellanea geographica, Mar 1, 2000
Remote Sensing, Aug 21, 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Remote Sensing, Jul 12, 2018
Knowledge of tree species composition is obligatory in forest management. Accurate tree species m... more 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.
Wykorzystanie obrazów hiperspektralnych do klasyfikacji pokrycia terenu zlewni Bystrzanki
ABSTRACT
Polski Przegląd Kartograficzny, 2015
Z a r y s t r e ś c i. Aktualne mapy pokrycia terenu są podstawą wielu dyscyplin nauki oraz mają ... more Z a r y s t r e ś c i. Aktualne mapy pokrycia terenu są podstawą wielu dyscyplin nauki oraz mają szerokie zastosowanie aplikacyjne. Jednym z problemów aktualizacji map jest proces aktualizacji danych. Teledetekcja dostarcza codziennie nowych zobrazowań satelitarnych, które mogą zaspokoić potrzeby aktualizacji baz danych. W niniejszym artykule autorzy przedstawiają metodę klasyfikacji pokrycia terenu sztucznymi sieciami neuronowymi fuzzy ARTMAP zgodnie z założeniami i legendą Corine Land Cover na podstawie danych satelitarnych Landsat, które wykorzystywane są do opracowania map pokrycia terenu. W artykule użyto jako danych referencyjnych i weryfikacyjnych najnowszą mapę Corine Land Cover (CLC) 2012. Do przeprowadzenia klasyfikacji symulatorem wykorzystano trzy zdjęcia satelitarne Landsat TM (21.04.2011, 05.06.2010, 27.08.2011). Obszarem badań były okolice Warszawy. Wynikami pracy symulatora są mapy klasyfikacji pokrycia terenu oraz macierze błędów klasyfikacji. Uzyskane wyniki potwierdzają, że sztuczne sieci neuronowe mogą z powodzeniem być wykorzystywane do aktualizacji map pokrycia terenu. S ł o w a k l u c z o w e: klasyfikacja, Corine Land Cover, Landsat, sztuczne sieci neuronowe, Warszawa
SNNS classification of hyperspectral data of extensively used agricultural areas
ABSTRACT
SAM and ANN classification of hyperspectral data of seminatural agriculture used areas
... Vegetation reflectance registered by remote sensing instruments is the average of the reflect... more ... Vegetation reflectance registered by remote sensing instruments is the average of the reflectance of photosynthetic active parts, non-photosynthetic active parts (ie branches, dry leaves), shadow and ground. ... DAIS 7915 hyperspectral data used in this study was acquired on ...
Biuletyn Polska Akademia Nauk. Komitet Przestrzennego Zagospodarowania Kraju, 2013
Application of Remote Sensing for an Evaluation of Spatial Organization in Poland. Modern space m... more Application of Remote Sensing for an Evaluation of Spatial Organization in Poland. Modern space management is based on actual and high-quality data. Such solutions offers remote sensing technology information and techniques that are used for land cover monitoring and inventory. For less experienced users are particularly useful high-resolution images (e.g. QuickBird, Ikonos or Google Map) that allow visual interpretation of the earth's surface. More advanced users are particularly recommended multispectral images (e.g. Landsat, Spot, IRS, or new going Sentinel series) that allow the classification of land cover and an analysis of the environment, such as vegetation. These data can be processed and modeled in GIS. This paper presents a basic set of information for independent remote sensing data acquisition and assessment of possible measures for land cover inventory. Satellite systems allow the continuous acquisition of information, which is used in the long-term monitoring. This element can have practical importance, such as verification of illegal construction and evaluation of the land use plan.
by Julia M. Chyla, Nazarij Buławka, Magdalena Grzegorczyk, Marlena Kycko, Bogdan Zagajewski, Jerzy Lechnio, Monika Mierczyk, Renata Kępińska, Julian Podgórski, Daniel Zaszewski, Izabela Wyszpolska, Anna Gul, Jagoda Kobuszewska, and Adrian Ochtyra
Mamy ogromną przyjemność oddać do rąk czytelnika długo oczekiwaną publikację prezentującą opracow... more Mamy ogromną przyjemność oddać do rąk czytelnika długo oczekiwaną publikację prezentującą opracowania użytkowników licencji SITE oprogramowania ArcGIS na Uniwersytecie Warszawskim. Wspólną i spójną tematyką niniejszego wydania jest wykorzystanie technik Systemów Informacji Przestrzennej (GIS) w różnych dziedzinach: zaczynając od nauk przyrodniczych i kończąc na cyfrowej humanistyce. Niniejsza publikacja, choć nieco w innej odsłonie, stanowi kontynuację pomysłu stworzenia forum wymiany informacji na UW prowadzącego do rozwoju i upowszechniania warsztatu badawczego wykorzystującego narzędzia GIS w różnych dziedzinach nauki. W zamyśle ma zachęcać do propagowania interdyscyplinarności
projektów realizowanych na Uczelni. Pierwszy tom serii, pod redakcją Jerzego Lechnio, powstał w 2015 i nosił tytuł „GIS w UW. Pierwsze forum użytkowników licencji edukacyjnej SITE oprogramowania ArcGIS na Uniwersytecie Warszawskim. Materiały pokonferencyjne„. Stanowił bardzo ciekawą relację z postępów prac studentów i doktorantów, którzy wzięli udział w pierwszej ogólnouniwersyteckiej konferencji Forum Użytkowników Licencji SITE oprogramowania ArcGIS na Uniwersytecie Warszawskim. Konferencja ta, w założeniu jako impreza cykliczna, ostała zainicjowana przez Wydział Geografii i Studiów Regionalnych UW i odbyła się po raz pierwszy 6 lutego 2014 roku. Konwencję „Forum” zaproponował wówczas mgr Jerzy Lechnio i dr Maciej Lenartowicz
Niniejsze wydanie zawiera owoce prac dwóch kolejnych edycji konferencji: drugiej zorganizowanej na Wydziale Geologii UW (w dniu 18 lutego 2015 roku), oraz trzeciej, która odbyła się w Instytucie Archeologii UW (10-12 grudnia 2015 roku). Sprawozdania z wspomnianych konferencji zostały włączone do niniejszego woluminu. Niniejsza publikacja zawiera przede wszystkim artykuły prezentujące wybrane i najciekawsze wystąpienia z dwóch kolejnych konferencji, a stąd zachowuje porządek chronologiczny, czyli podział na tom II i III.
Wspomniane tomy obejmują ogółem dwanaście artykułów. Ich tematyka koncentruje się na takich zagadnieniach, jak: GIS w badaniach środowiskowych, zróżnicowanie tematyczne i rola danych przestrzennych w nauce i praktyce oraz komunikacji społecznej, analiza zdjęć satelitarnych i jej zastosowania, GIS w archeologii i humanistyce. Szerokie spektrum poruszanych tematów i różnorodność zastosowań technik GIS świadczą o dużych kompetencjach i potencjale absolwentów
UW na rynku pracy.
Artykuły poświęcone zastosowaniu metod i technik GIS w ocenie stanu środowiska poruszają problemy istotne z perspektywy Polski, jak i skali globu. Ich zanieczyszczenia wód podziemnych, ewapotranspiracji (parowanie z powierzchni gruntu), analiz przepuszczalność gruntu w miastach, problematyki globalnego ocieplenia, podatność lasów na uszkodzenia silnym wiatrem, czy też procesów urbanizacji.
Przykłady wykorzystania analiz zdjęć satelitarnych obejmują zarówno analizy bazujące na zastosowaniach wysokiej rozdzielczości zobrazowań panchromatycznych CORONA, jak i multispektralnych z misji Landsat i Worldview-2.
Ważnym zagadnieniem poruszanym przez autorów jest kwestia dostępności danych z zasobów publicznych, w tym Centralnej Bazy Danych Geologicznych, bazy otworów geologicznych PITAKA, Corine Land Cover i Urban Atlas. W wspomnianym nurcie mieści się prezentacja projektu udostępniania danych geograficznych w postaci Regionalizacji geomorfologicznej Karpat.
W publikacji znajdziemy, również przykłady zastosowań technologii GIS w dokumentacji archeologicznej, które rozwijane są z powodzeniem w Uniwersytecie Warszawskim.
Prezentowane opracowania obejmują przede wszystkim wyniki prac magisterskich i licencjackich studentów i absolwentów oraz badań bardziej doświadczonych badaczy z UW. Dowodzą dobitnie, że na naszych oczach dokonuje się rewolucja za sprawą szerokiego i multidyscyplinarnego wykorzystania metod i technik GIS oraz danych przestrzennych, która wpływa na pomnażanie wiedzy o otaczającym świecie, a także rozwój nowych pól badawczych w dziedzinie humanistyki, czy dystrybucję informacji w dobie społeczeństwa informacyjnego.
Rangę publikacji podnosi fakt, że wszystkie z prezentowanych artykułów podlegały recenzji naukowej i opracowaniu redakcyjnemu.