Wyniki wyszukiwania - Biblioteka Nauki

1

Zastosowanie teledetekcji do oceny stanu zagospodarowania przestrzennego w Polsce

100%

Zagajewski B.

Biuletyn PAN. Komitet Przestrzennego Zagospodarowania Kraju

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2013

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nr 252

2

Ocena przydatności sieci neuronowych i danych hiperspektralnych do klasyfikacji roślinności Tatr Wysokich

100%

Zagajewski B.

Teledetekcja Środowiska

|

2010

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tom T. 43

1--113

EN

This research aims to discover potential of hyperspectral remote sensing data for mapping high-mountain vegetation ecosystems. First, the importance of mountain ecosystems to global system should be stressed: due to environmental fragility and location of plant species and communities at the upper levels of habitats, mountainous ecosystems form a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors that influence spatial distribution of vegetation in the mountains are producing diverse mosaic of habitats leading to high biodiversity. Mountain plants developed specific adaptations to survive at the fringe of life (pigment content, plant tissue structure etc.). These adaptations have direct impact on their reflectance properties which can be acquired and quantified using hyperspectral imagery interpretation techniques. These changes are characterised by a large number of closely spaced spectral channels. Application of remote sensing techniques allows vegetation research and mapping in areas that are otherwise inaccessible. This could be due to low accessibility of terrain, very short vegetative period and unstable weather conditions. Mapping vegetation and its condition is often constrained or even prevented using traditional, field mapping techniques. To protect a delicate balance in mountainous environments vegetation cover (a perfect indicator of all the other components of biosphere) should be researched in detail and mapped with sufficient level of accuracy. This is of particular importance for the proper management as both anthropogenic pressure and local disturbances (avalanches, solifluction after extensive rainfalls) can have significant impact on vegetation, leading to disturbance, and eventually – disintegration of plant cover. It is anticipated, that vegetation mapping and condition analysis can be achieved using hyperspectral, high ground resolution imagery and digital and field remote sensing techniques. Artificial Neural Network (NN/ANN) algorithms use whole object characteristics (spectral, structural and/or textural properties, where the relationship between pixels are also taken into account). These relationships among the spatial patterns of the image frequently appear over natural biotopes and plant communities with closed coverage. Traditional classification methods that use parametrical approaches do not show satisfying results. The implemented neural network is the fuzzy ARTMAP (FAM) simulator. For training the neural network, particular layers of the covering vegetation classes were used that were identified via field mapping while the aircraft was operating. In the same time separate field data was collected for validation purposes too. For hyperspectral data compression the Minimum Noise Fraction transformation (MNF) was used. This method may be especially useful to separate and classify vegetation or land cover units. The High Tatras are located within the MAB Biosphere Reserve and encompasses alpine and subalpine zones of the Tatra National Park (TPN). The area extends within: 49°10’30’’–49°16’00’’ N and 19°45’30’’–20°07’30’’ E rectangle, encompassing approximately 110 km2 . However, in this publication only Polish part of the Tatra Mountains (so called “High Tatras”) was analysed (Figure 15). Vegetation in the area has been well researched (since the 1920’s), however most of the research has been carried out on transects or glades. Plant species have been well identified and described, however detailed maps of vegetation are available only for selected areas. The most of the research area is covered by natural and seminatural key units: peaty and boggy communities, avalanche meadows, tall herb communities ( Adenostylion ), grassland communities after grazing, subalpine dwarf scrub communities, willow thicket ( Chamaenerion angustifolium-Salix silesiaca community), mountain-pine scrub on silikat substrate ( Pinetum mugho carpaticum silicicolum ), mountain-pine scrub ( Pinetum mugho carpaticum silicicolum ) in a complex with epilitic lichen communities, mountain-pine scrub on calcareus substrate ( Pinetum mugho carpaticum calcicolum ), montane spruce forest ( Plagiothecio-Piceetum ) and lakes. Assessment of neural networks and Imaging Spectroscopy for vegetation classification of the High Tatras In this study a DAIS 7915 hyperspectral data was classified that was acquired on 04 August 2002 by the German Aerospace Center (DLR) in the frame of the HySens PL02_05 project. This instrument is a 79-channel imaging spectrometer operating in the wavelength range 0.4-12.5 μ m with 15 bit radiometric resolution. After preprocessing the obtained ground resolution was 3 meters. The classification procedures (Figure 21) began with a preparation of reference layers of 42 dominant classes for the fuzzy ARTMAP teaching (Figure 22A). This stage based on terrain acquired data. For validation’s map Spectral Angle Mapper (SAM) was used; in the first step, basing on field sampled polygons and endmembers obtained from DAIS data (corresponding to the key areas from the ground mapping) a pre-validation map was created. In the second step, basing on terrain mapping validation polygons of each analysed class were reselected (Figure 22B, Table 4). Parallel to this procedure, an exploration from all 79 bands covering the VIS-TIR regions of the spectrum was made. The first step was a band’s information analysis and the reselection of 60 spectral bands was made (Figures 23 and 24). The second step was to reduce the data dimensionality to 40 original and 20 MNF bands. For the actual classification of the plant communities, a fuzzy ARTMAP simulator was used. In order to obtain the desired results 5000 and 10 000 iterations were used while training the Neural Net. Each set of image bands and reference layer contained a detailed DEM of analysed area. Classification accuracy was measured using ENVI software’s algorithms based on test and training sets. The overall accuracy was measured throughout a pixel by pixel comparison post classification images to ground truth map (prepared from SAM and field’ verified mapping). The final results of the High Tatras polygon are shown in Tables 5-24, and the classification images present in Figures 28-35. Generally, the forty-band set of input data offered higher accuracy (1-2%) than the twenty-MNF-band set (Tables 23 and 24). In the first case, the overall accuracy value achieved was 88.6%, and kappa coefficient was 0.8740. In the case of 20 MNF bands, the overall accuracy was 82.6%, and kappa coefficient 0.8310. Two of fourt-two analysed classes weren’t classified properly: Salicetum herbaceae in a complex with Empetro-Vaccinietum (class# 6) and grassland communities after grazing in a complex with ruderal communities (#32). The worst classification results were achieved in the range of 44-80% for Oreochloo distichae-Juncetum trifidi scree form with Juncus trifidus (#14), Festuca picta community (#30), Vaccinium myrtilus community in a complex with tall herb communities (#36) and willow thicket – Chamaenerion angustifolium-Salix silesiaca community (#37). The best results were achieved for: Oreochloo distichae-Juncetum trifidi typicum (#8); Oreochloo distichae-Juncetum tri fi di sphagnetosum (#11), Oreochloo distichae-Juncetum trifidi subalpine anthropogenic form (#16), Caricetum fuscae subalpinum (#21), Empetro-Vaccinietum in a complex with Pinetum mugho (34), mountain-pine scrub on silikat substrate (38) and waters Hyperspectral data showed significant potential for discriminating different vegetation types. The use of an artificial neural network is a proper method for mapping plant communities; it should be a supporting tool for traditional vegetation mapping. The increased number of bands while classification is being done (more than 40) does not offer a significantly better overall accuracy, but the worst results are not so low like in the case of twenty-MNF band sets. The processing time of MNF-transformed data was significantly shorter while provides less accurate classification results (3-6% less overall accuracy compared to using forty-band sets). A long training time is the most inconvenient aspect of this kind of classification.

3

Zastosowanie teledetekcji do oceny stanu zagospodarowania przestrzennego w Polsce

100%

Zagajewski B.

Biuletyn Komitetu Przestrzennego Zagospodarowania Kraju PAN

|

2013

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tom No 252

13--28

PL

Potencjał teledetekcji nie jest obecnie należycie wykorzystywany do oceny stanu zagospodarowania przestrzennego w Polsce. Wydaje się, że kluczową przyczyną jest brak należytej świadomości oraz przygotowania decydentów. Wynika to również z bardzo szybkiego rozwoju technologii teledetekcyjnej i jej powszechnego dostępu do wysokorozdzielczych oraz wielospektralnych danych (szczególnie po przystąpieniu Polski do NATO). Wcześniej dane lotnicze były traktowane jako materiał poufny i dostępność zdjęć była ograniczona, natomiast zobrazowania satelitarne były bardzo drogie, a procedury przetwarzania obrazów wymagały zaawansowanych algorytmów i specjalistycznego oprogramowania. Po przystąpieniu Polski do NATO coraz szerszym strumieniem zaczęły płynąć do kraju zarówno dane, jak i specjalistyczne oprogramowanie oraz sprzęt komputerowy. Jeszcze na początku 2000 r. komputery osobiste klasy PC w wielu przypadkach nie gwarantowały możliwości przetwarzania danych, które zapisane były w dużych plikach. Większość profesjonalistów wykorzystywało stacje robocze, najczęściej działające pod systemem operacyjnym UNIX. Ograniczało to niewątpliwie popularyzację oraz rozwój teledetekcji. Znaczne zmiany nastąpiły wraz z upowszechnieniem Internetu oraz zwiększeniem możliwości operacyjnych komputerów klasy PC.

EN

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.

4

Zastosowanie geoinformacji w badaniach wpływu abiotycznych komponentów środowiska na rozmieszczenie roślinności w Narwiańskim Parku Narodowym i jego otulinie

100%

Zagajewski B.

Geoinformatica Polonica

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2004

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tom T. 6

123-136

PL

Publikacja prezentuje metody ilościowej analizy wpływu abiotycznych komponentów środowiska na stan i zróżnicowanie występowania zbiorowisk roślinnych w Narwiańskim Parku Narodowym i jego otulinie (NPN). Dotyczy to ustalenia jakości tego wpływu, jego hierarchii, a przede wszystkim ilościowej analizy związków łączących typy zbiorowisk ze spadkami terenu, utworami powierzchniowymi, głębokością do pierwszego poziomu wodonośnego, wilgotnością i typami gleb, spadkami i ekspozycją terenu. Materiały źródłowe pochodzą z badań terenowych i kartowań przeprowadzonych pod koniec lat osiemdziesiątych ubiegłego wieku na obszarze ówczesnego Narwiańskiego Parku Krajobrazowego. Uzupełnieniem są zdjęcia lotnicze i satelitarne oraz mapy topograficzne z tamtego okresu. Efektem pracy jest: statystyczna analiza stanu roślinności rzeczywistej NPK; określenie naturalności roślinności, wykazanie stopnia jej odkształcenia; wykazanie powiązań pomiędzy abiotycznymi komponentami środowiska a występowaniem zbiorowisk roślinnych, hierarchizacja omawianych zależności oraz określenie amplitudy siedliskowej poszczególnych zbiorowisk.

EN

The paper presents the influence and analysis of the abiotical components on the vegetation distribution and transformation. The research area is the Narew River National Park (Polish acronym: NPN) along with the surrounding protective zone, located in the north-east part of Poland. The valley of he Narew river is characterised by the dense network of river beds. In the period of the spring and summer is the flood frequency high. The consequence is the water-and-swamp setting of the natural environment, with the specific ecological conditioning, different from those prevailing in other river valleys. This leads to a rich mosaic of the ecosystems having arisen from the aquatic, aquatic-and-meadow, land-and-swamp, as well as land environments. It's conducted quite an intensive anthropogenic pressure of the 1970s and 1980s. After them the economic crisis stopped human impact to the environment and followed by the advancement of the renaturalisation works. Currently, after the National Park has been established, there has arisen the institutional capacity of conducting the environmental damage studies and proper environmental monitoring, because this area plays a significant role in the international system of migrations of living organisms. The area corresponding to the NPN was considered an international core area (251M) in the European programme ECONET.

5

Biblioteka spektralna gatunków zielnych Ogrodu Botanicznego Uniwersytetu Warszawskiego

63%

Zagajewski B. , Sędzikowski Z.

Teledetekcja Środowiska

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2009

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tom T. 41

121--133

EN

An objective of this paper is to form a spectral library of endmembers of the Polish Lowland vegetation species, which were collected in the Botanic Garden of the University of Warsaw, which is one of the oldest (it was founded in 1818) and the smallest (5 ha) botanic gardens in Poland. For the data acquisition ASD FieldSec3 JR, Chlorophyll Content Meter CCM-200 and a digital camera were used. Each spectral library set contains: 300 separate spectrometric measurements (100 dark current, 100 white reference and 100 ASD Plant Probe Leaf Clip); Chlorophyll Content Index and biometric information (e.g. LAI, fAPAR); 3 digital photos, time and localisation data. The spectral library contains 73 characteristics of the most important plant species (from the “red list” of protected plants and the most famous plants of the Polish Lowland Flora). Now all these data will be upgrading the Swiss SPECCHIO library as a local Polish input to the European spectral database.

6

Remote sensing tools for analyzing state and condition of vegetation

63%

Jarocińska A. , Zagajewski B.

Roczniki Geomatyki

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2009

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tom T. 7, z. 2

47-53

EN

Hyperspectral data, which are characterized by very high spectral, spatial and radiometric resolutions, allow the analysis of the biometric properties of plants in different wavelengths of the electromagnetic spectrum. This kind of data can be applied to interpretation of vegetation, land cover forecast biomass and crops and also for analyzing plant condition, because vegetation cover is a very good indicator of environmental condition. All the spectral characteristics of plants can be measured and analyzed quantitatively using different vegetation indices, which are a mathematical combination of various bands. The most frequently used regions of the spectrum are visible, red-near infrared edge, near and middle infrared. In these regions it is possible to measure chlorophyll, carotenoids and other pigment content, fresh and dry biomass, water and nutrient content, internal leaf structure, soil moisture and plant surface temperature. In this study, four of the vegetation indices have been analysed: Normalized Difference Vegetation Index (Rouse et al., 1973; Griffith et al., 2002), Soil Adjusted Vegetation Index (Huete, 1988), Leaf Area Index (Surlock, 2001; Haboudane et al., 2004) and fAPAR - fraction of Absorbed Photosynthetically Active Radiation (Moreau, Li, 1996). These indices measure the condition of plants and estimate the quantity of biomass. Correctly calculated indices offer much information about the functionality of an ecosystem. Such vegetation indices are broadly used for vegetation monitoring. The main purpose of the research was an analysis of plant condition using remote sensing methods. Maps of spatial distribution of the NDVI, SAVI, LAI and fAPAR were prepared using ground and airborne measurements (DAIS 7915 products were corrected and verified by field measurements). Indices from airborne and ground level measurements were also correlated. The studies took place in the Low Beskid Mountains., which constitute one of the most natural parts of the Polish Carpathian Mountains (Fig. 3). The area extends from 49o34'- 49o41'N to 21o01'-21o09'E, with an altitude range of 400-750 m. The study area focuses on the Bystrzanka catchment around the town of Szymbark. This catchment has an area of around 13.5 km2. The largest part of the area, 40%, is covered by forest. Meadows and pasture comprise 28% of the area. A small fragment of the area is covered by arable land. The area is defined as a natural and seminatural environment. The human influence is relatively low and natural processes are not disturbed, so that vegetation can be used here as an indicator of other ecosystem components (soils, microclimate etc.)

PL

Techniki teledetekcyjne umożliwiają prowadzenie monitoringu przyrodniczego roślinności, w tym dokładną analizę fizjologii oraz właściwości biometrycznych. W artykule przedstawiony jest sposób badania kondycji roślinności wykorzystujący teledetekcyjne wskaźniki roślinności oraz związki mię dzy wskaźnikami mierzonymi z poziomu naziemnego i pułapu lotniczego. Badania były prowadzone na terenach naturalnych i ekstensywnie wykorzystywanych rolniczo zlewni Bystrzanki w Beskidzie Niskim. W badaniach wykorzystano dwa rodzaje danych: wartości wskaźników NDVI, SAVI, LAI i fAPAR pobranych na poziomie terenowym oraz obraz hiperspektralny ze skanera lotniczego DAIS 7915. Pobrano dane z poziomu terenowego. Następnie utworzono obrazy wskaźników w dwóch progra mach ATCOR i ENVI 4.3 (obraz wskaźnika NDVI). Obrazy wskaźników SAVI, LAI i fAPAR uzyskane z pierwszego programu były w jednostkach niezgodnych dla wskaźników, dlatego wymagały dalszych transformacji. Pobrano wartości wskaźników z obrazów. Następnie przeprowadzono analizy staty styczne porównując wartości z obrazów z danymi terenowymi, uzyskując równania regresji, których użyto do transformacji obrazów. Ostatnim etapem było utworzenie map przestrzennego rozkładu czterech wskaźników oraz mapy kondycji roślinności biorącej pod uwagę wartości wskaźników SAVI, LAI i fAPAR. Stwierdzono, że użycie teledetekcyjnych wskaźników roślinności ułatwia pozyskiwanie informacji o stanie roślinności i obiektywizuje te dane. Zanotowano korelacje między wskaźnikami NDVI i LAI oraz NDVI i fAPAR, są one zdecydowanie silniejsze na poziomie lotniczym. Na ścisłość korelacji wpływa sposób pobierania danych oraz sposób użytkowania terenu. Techniki hiperspektralne stwa rzają dodatkowe możliwości pozyskiwania informacji przez analizę krzywej odbicia spektralnego, a nie jedynie jej wycinków, tak jak w przypadku technik wielospektralnych. Wykorzystując tak utworzo ne wskaźniki możliwa jest dokładniejsza analiza roślinności. Stwierdzono, że na badanym terenie wskaźniki NDVI, SAVI, LAI i fAPAR mają wysokie wartości. Na podstawie mapy kondycji roślinności stwierdzono, że na przeważającym obszarze roślinność była w dobrym stanie.

7

Wpływ wybranych elementów środowiska przyrodniczego na zróżnicowanie emisyjności promieniowania termalnego Doliny Gąsienicowej

63%

Metrycka J. , Zagajewski B.

Teledetekcja Środowiska

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2009

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tom T. 41

105--120

EN

Hyperspectral remote sensing is still being discovered as a tool about analytical possibilities for the research on areas about diversifi ed character, like mountain areas. This study investigated the relationship between spatial variability of surface temperature of the Gąsienicowa Valley (the Tatra Mountains) and chosen components of the natural environment, such as: near-surface lithology layers, soil surfaces, land cover types, altitudes, slopes and aspects. Image of the surface radiation temperature was processed basing on the Digital Airborne Imaging Spectrometer (DAIS 7915) data. Thematic layers were: acquired from the Tatra National Park GIS Office (geology, lithology and soil layers), generated from DTM (altitude, slopes and aspects) and created from the DAIS RGB compositions data (land cover). The analysis of relationship between components and surface temperatures were measured by the power connection index (Richling, 1983) and connection index (Zagajewski, 2003). It has been stated that the greatest power of connections occurred between the radiation temperature and the soil surface, however on the majority surface of the Gąsienicowa Valley temperature responses most strongly to the land cover type.

8

Korelacje naziemnych i lotniczych teledetekcyjnych wskaźników roślinności dla zlewni Bystrzanki

63%

Jarocińska A. , Zagajewski B.

Teledetekcja Środowiska

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2008

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tom T. 40

100--124

9

Wykorzystanie obrazów hiperspektralnych do klasyfikacji pokrycia terenu zlewni Bystrzanki

63%

Olesiuk D. , Zagajewski B.

Teledetekcja Środowiska

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2008

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tom T. 40

125--148

10

Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend

51%

Golenia M. , Zagajewski B. , Ochtyra A. , Hościło A.

Polish Cartographical Review

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2015

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tom Vol. 47, No. 4

203--212

EN

Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified. The paper presents a method of land cover classification based on a fuzzy artificial neural network simulator and Landsat TM satellite images. The latest CORINE Land Cover 2012 polygons were used as reference data. Three satellite images acquired 21 April 2011, 5 June 2010, 27 August 2011 over Warsaw and surrounding areas were processed. As an outcome of classification procedure, the maps, error matrices and a set of overall, producer and user accuracies and a kappa coefficient were achieved. The classification accuracy oscillates around 76% and confirms that artificial neural networks can be successfully used for forest, urban fabric, arable land, pastures, inland waters and permanent crops mapping. Low accuracies were obtained in case of heterogenic land cover units.

11

Badania górskich zbiorowisk roślinnych z użyciem technik hiperspektralnych

51%

Zagajewski B. , Sobczak M. , Wrzesień M.

Przegląd Geofizyczny

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2004

|

tom Z. 3-4

115-129

EN

Mountain plant species have very specific environmental adaptations (like for example increased carotenoid content) to protect them from harsh conditions: e.g. excessive sun radiation, high amplitudes of temperature, strong winds etc. Those adaptations result from plant physiology that is chemical and physical properties of the green, vegetative matter. Laboratory and field analyses of spectral properties of plants have shown, that identification of plants and vegetation communities in mountains is possible. Spectral signature of a plant is distinctive and variable with wavelength, its characteristic absorption features being a direct result of its physiological process. Photosynthesis is based on conversion of radiation absorbed in blue and red parts of electromagnetic spectrum to energy. Chlorophyll a is the main factor in photosynthesis while chlorophyll b plays a secondary function, supporting chlorophyll a in light absorption. Carotenoids' role is to protect chlorophyll from photooxidation and thylakoid membranes from destruction resulting from excess sun radiation. High content of carotenoids is noted for plants subjected to extensive sun radiation (e.g. alpine species). Quantity of carotenoids increases also with plant senescence. Other pigments such as xanthophyslls and anthocyanins can also contribute to absorption of visible radiation. In addition to spectral characteristics of vegetation, there exists a wide range of supporting indices used in vegetation research, like: NDVI, SR, WDVI, SAVI, MSAVI, NLI and NLI2, AVI, PRI. The principal of a vegetation index is to define a simple relationship between the reflectance measured by a sensor in particular wavelengths and parameter directly characterising a plant (e.g. condition of a photosynthesising apparatus, efficiency of evapotranspiration process) or vegetation stand (biomass or canopy structure). Other commonly used biophysical vegetation characteristics which can be directly derived from remote sensing measurements include, among others: LAI (Leaf Area Index), fAPAR (fraction of Absorbed Photosynthetically Active Radiation) and plant-air temperature difference. In this paper methodology of vegetation monitoring using field remote sensing techniques is presented. This is the first stage of the assessment of the potential of hyperspectral data for analysis and monitoring of mountain environments with a special focus on vegetation mapping and condition investigation. The research aims advanced field measurements, laboratory analysis of pigments (chlorophyll a, b and carotenoids), dry/fresh biomass and large scale, hyperspectral imagery (DAIS 7915, ROSIS). The study was conducted in Tatra National Park ("High Tatras"). Field remote sensing measurements were carried out on July and August 2002. The year has been exceptionally good for vegetation development and the state of the researched vegetation was good. No vegetation in poor condition or under stress has been detected. Four sets of measurements characterising different aspects of vegetation and its habitat condition were carried out at both sites: spectrometric measurements; survey of Leaf Area Index (LAI); measurements of Accumulated Photosynthetic Active Radiation (APAR); plant heat and water balance assessment; fluorescence; biomass, water content in leaves, absorption of photosynthetic plant pigments; sun radiation and GPS measurements and detailed land-use and vegetation mapping. Results of field campaign can be outlined: the qualitative and quantitative analysis of photosynthetising pigments showed significant differences between analysed species; field radiometric measurements confirmed the results achieved in the laboratory analysis of leaf pigment content; spectral signatures of researched communities are characteristic for plants in good condition; LAI index measured for all researched communities oscillates around an optimal value; productivity defined as an APAR/ PAR0 ratio for all researched communities was very high; temperature differences between plant and air temperature for all plant communities were negative, which indicates good performance of the process of evapotranspiration of the plant species building the communities. The applied methods of field measurements and laboratory analysis show a potential of remote sensing techniques for research and mapping of vegetation in mountainous environments. The next stages of research (analysis of the hyperspectral data) should show proper recognition and state of alpine plants and communities.

12

Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains

51%

Jędrych M. , Zagajewski B. , Marcinkowska-Ochtyra A.

Polish Cartographical Review

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2017

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tom Vol. 49, No. 3

107--119

EN

Effective assessment of environmental changes requires an update of vegetation maps as it is an indicator of both local and global development. It is therefore important to formulate methods which would ensure constant monitoring. It can be achieved with the use of satellite data which makes the analysis of hard-to-reach areas such as alpine ecosystems easier. Every year, more new satellite data is available. Its spatial, spectral, time, and radiometric resolution is improving as well. Despite significant achievements in terms of the methodology of image classification, there is still the need to improve it. It results from the changing needs of spatial data users, availability of new kinds of satellite sensors, and development of classification algorithms. The article focuses on the application of Sentinel-2 and hyperspectral EnMAP images to the classification of alpine plants of the Karkonosze (Giant) Mountains according to the: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood (ML) algorithms. The effects of their work is a set of maps of alpine and subalpine vegetation as well as classification error matrices. The achieved results are satisfactory as the overall accuracy of classification with the SVM method has reached 82% for Sentinel-2 data and 83% for EnMAP data, which confirms the applicability of image data to the monitoring of alpine plants.

13

Wykorzystanie teledetekcji hiperspektralnej w analizie roślinności zanieczyszczonej metalami ciężkimi

51%

Zagajewski B. , Lechnio J. , Sobczak M.

Teledetekcja Środowiska

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2007

|

tom T. 37

82--100

14

Zastosowanie sztucznych sieci neuronowych do aktualizacji map pokrycia terenu Corine

51%

Golenia M. , Zagajewski B. , Ochtyra A. , Hościło A.

Polski Przegląd Kartograficzny

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2015

|

tom T. 47, nr 3-4

257--266

PL

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.

EN

Modern land cover maps are the basis of many scientific disciplines and they are widely applied. One of the problems connected with the revision of maps is the data updating procedure. Remote Sensing daily provides us with the new satellite images, that can meet the needs of database updates. In this article the method of classification for land cover with the artificial, neural, fuzzy ARTMAP networks is presented by the authors in accordance with the objectives and legend of the CORINE Land Cover Map on the basis of the Landsat satellite data, which are used to elaborate the land cover maps. The latest CORINE Land Cover map 2012 polygons are used as the reference and verification data. Three satellite Landsat TM images of 21.04.2011, 05.06.2010, 27.08.2011 are processed by a fuzzy, artificial, neural network classificatory simulator. The area of research was Warsaw and its surrounding area. The results of this research are the classificatory land cover maps and error matrices. Acquired results confirm that the artificial neural networks can be successfully used for land cover updating.

15

Zróżnicowanie spektralne wybranych gatunków muraw wysokogórskich Doliny Gąsienicowej narażonych na wydeptywanie

51%

Kycko M. , Zagajewski B. , Kozłowska A. , Oprządek M.

Teledetekcja Środowiska

|

2012

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tom T. 47

75--86

EN

This paper presents analysis of plant cover condition in Gasienicowa Valley in the Tatra Mts. depending on various trampling intensity. Measurements were taken with ASD FieldSpec 3 spectrometer (its spectral range is 350-2500nm) on 8 dominant plant species of alpine swards: Juncus trifidus, Oreochloa disticha, Agrostis rupestris, Deschampsia flexuosa, Festuca airoides, Festuca picta, Luzula alpino-pilosa, Nardus stricta. These plant species were located: 0-5m, 5-10m and more than 10m distant from a touristic trail (control point). Spectral characteristics as well as vegetation indices were analyzed with ANOVA test, which showed differiential resistance to trampling of investigated plant species. The most resistant species were: Nardus stricta and Deschampsia flexuosa, whereas Oreochloa disticha and Festuca airoides appeared to be vulnerable to trampling. However, all vegetation indices for plant species were in its optimum range, so it proves that they are in a good condition. The analysis of vegetation indices enabled choosing those groups, which are the most useful in the research of mountain vegetation condition. They are: NDVI, ARVI, EVI from the broadband greeness group and mSR705 and mNDVI from narrowband greenness group (measuring chlorophyll content and cell structure), as well as WBI, NDWI, NDII from canopy water content group. The most important factor that effects investigated plant species condition is water content. The research showed that hyperspectral analysis is useful in studying human impact on vegetation cover and needs to be developed.

16

Wpływ geometrii źródło promieniowania-roślina-detektor na wartość teledetekcyjnych wskaźników roślinności

51%

Kycko M. , Zagajewski B. , Podbielska K.

Teledetekcja Środowiska

|

2013

|

tom T. 49

15--26

EN

The aim of this study is an analysis of an influence of geometry electromagnetic radiation (lamp or sun) - research target (leaves) - detector. The electromagnetic radiation was emitted by the lamp ASD ProLamp, which was installed at 30°, 45°, 90°, 135°, 150° angles. Reference measurements was a system in which the lamp and detector were set vertically. During the laboratory measurements spectral properties of Rhoeo spathacea were acquired. Based on the measured spectral curves of vegetation remote sensing indices were calculated and statistical ANOVA tests were applied. The results confirmed the relationship between the geometry of the lamp - plant - detector. The higher the angle the incident radiation results were less diverse and close to optimum values were observed. Analysis of the indicators showed that the high variability characterized by the indicators measuring water, chlorophyll contents and overall vigor parameters of plants. While the tests can be used for measuring rates of nitrogen content, the absorption of carotenoids and photosynthetically active radiation.

17

Links between vegetation and morphodynamics of high-mountain slopes in the Tatra Mountains

51%

Kozlowska A. , Raczkowska Z. , Zagajewski B.

Geographia Polonica

|

2006

|

tom 79

|

nr 1

18

Ocena kondycji drzewostanów Tatrzańskiego Parku Narodowego za pomocą metody drzewa decyzyjnego oraz wielospektralnych obrazów satelitarnych Landsat 5 TM

45%

Ochtyra A. , Zagajewski B. , Kozlowska A. , Marcinkowska-Ochtyra A. , Jarocinska A.

Sylwan

|

2016

|

tom 160

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nr 03

EN

The paper presents a method of Landsat 5 Thematic Mapper satellite image processing to assess the condition of forests in the Tatra National Park (southern Poland). Selected images were acquired on 1987/09/01, 2005/09/02 and 2011/09/03 from the same sensor with maximum time interval for the first and last scene and from similar phenological period. Firstly, the data were radiometrically corrected using the ATCOR 2/3 software and Digital Terrain Model from the ASTER mission. Quality of the correction was assessed calculating RMSE for reflectance values from images and resampled spectral characteristics collected in terrain. RMSE was in range 3−10%. Next, basing on Landsat images, Normalized Difference Infrared Index (NDII) and a Maximum Likelihood supervised classificatory, following dominant land cover types were identified: forests (including dwarf pine), grasslands, rocks, lakes, shadows (additionally clouds were distinguished on data from 1987/09/01). It allowed to select forest areas with producer accuracy not worse than 97.69% and user accuracy not worse than 98.31%. On corrected Landsat images Normalized Difference Vegetation Index (NDVI, an overall vegetation state) and Moisture Stress Index (MSI, canopy water content) were calculated. Vegetation indices discriminated forest state using the decision tree method. The worst overall condition was observed for the 1987 (about 21% of forest stands were in the worst condition and 87% were in medium condition), while the best one in 2005 (75.51% forest stands were in good condition and 10.66% were in the best condition). In case of 2011, the overall condition was quite good, but there were large areas with poor condition caused by bark beetle outbreaks. Proposed method allows for a fast and objective assessment of forest condition. It is possible to detect damaged areas or stands in poor condition. It can be complement for traditional methods of monitoring and management in forestry and nature protection.

19

Crop classification with neural networks using airborne hyperspectral imagery

45%

Olesiuk D. , Bachmann M. , Habermeyer M. , Heldens W. , Zagajewski B.

Roczniki Geomatyki

|

2009

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tom T. 7, z. 2

107-112

EN

Mainly due to size of input data, the artificial neural networks (ANNs) methods for remote sensing image classification can be expensive to use, in terms of computer resources and expert analyst time (Mahesh, Mather, 2006). In the case of hyperspectral data, neural networks training process may take weeks of time, in order to determine the number of input nodes in network structure needed by hundreds of image bands. In addition, not every neural networks package, such as the Stuttgart Neural Network Simulator (SNNS) used in this study, works with binary data, which makes dimensionality data reduction methods necessary to develop an effective classification scheme based on an ASCII text file. Despite these reservations, ANNs offer a wide field of research and investigation in crop and land cover classification, because they are a non-parametric method in the sense that they make no assumptions about the statistical distribution of the classes to be identified. As additional benefit, they can accept non-numeric inputs as well as ratio and interval-scale data. Moreover, the SNNS software provides the user a unique opportunity to design the input layers in a network structure, such as sub pattern window, which makes it possible to include texture information as additional data in the classification process (Zell et al., 1995). This method is especially useful in discrimination of non-homogeneous classes (Zagajewski, Olesiuk, 2008), and has been applied in this study. The objective of this work was to compare the results of crop classifications based on two data sets derived from hyperspectral HyMap imagery: (1) after MNF transformation, (2) vegetation and soil indices. The minimum noise fraction (MNF) transformation is used to segregate noise in the data, to determine the inherent dimensionality of the image data, and to reduce the computational requirements for subsequent processing (Boardman, Kruse, 1994). Essentially, it is two cascaded transformations. The first transformation, based on an estimated noise covariance matrix, de-correlates and rescales the noise in the data. This first step results in transformed data in which the noise has unit variance and no band-to-band correlations. The second step is a standard Principal Components transformation of the noise-whitened data. MNF bands are in a descending order of eigen values with almost no noise in the bands where the eigen values are near unity and below unity indicating signal-to-noise ratio (S/N) decreasing with decreasing order of MNF bands. The second data set contains hyperspectral indices which were selected to estimate pigment, nitrogen, cellulose and water content in vegetation, and clay and iron content in soil. The study area is located in the Demmin region in north Germany (Figure 1). This is a previously mapped agricultural area, where the main land cover/ land use types are represented by agriculture and grassland farming, with intermixed forestry and urban areas. This area is used as an agricultural and multi-disciplinary test site, and is included in the Committeee on Earth Observation Satellites (CEOS) catalogue for calibration and validation sites.

PL

Celem opracowania jest porównanie wyników klasyfikacji upraw uzyskanych ze zdjęć hiperspektralnych HyMap. Teren badań znajduje się w rolniczym regionie Demmin w północnych Niemczech. Do klasyfikacji wykorzystano dwa zestawy danych: 1) obrazy po transformacji Minimum Noise Fraction (MNF) oraz 2) mapy wskaźników roślinnych i glebowych. Transformacja MNF polega na redukcji wymiarów przestrzeni spektralnej (kompresji danych) i składa się z dwóch kaskadowych transformacji. Pierwszy etap polega na dekorelacji szumu, a drugi to standardowa transformacja PCA przeprowadzona na danych po oddzieleniu szumu. W rezultacie powstają nowe kanały, które uszeregowane są od największej do najmniejszej wariancji, przez co do dalszych prac mogą być wykorzystane najbardziej przydatne informacje. Drugi zestaw danych zawiera utworzone na podstawie obrazu hiperspektralnego wskaźniki roślinne i glebowe. Definiują one zawartość pigmentów, azotu, celulozy oraz wody w roślinność, a także iłu i żelaza w glebie. Klasyfikacja przeprowadzona została z wykorzystaniem sztucznych sieci neuronowych. Wykorzystano do tego celu oprogramowanie Stuttgart Neural Network Simulator (SNNS). Zastosowano sieć wielowarstwową, jednokierunkową, uczoną z użyciem metody wstecznej propagacji błędów (back- propagation errors). Klasyfikacje obu zestawów danych wykonano z zastosowaniem dwóch typów struktury neuronów w warstwie wejściowej. Pierwszy typ to struktura standardowa, gdzie liczba neuronów wejściowych odpowiada liczbie wykorzystywanych kanałów obrazowych. Druga struktura zaprojektowana została poprzez zdefiniowanie okna maski w postaci macierzy 3x3 piksele, dzięki czemu do procesu klasyfikacji włączona została informacja o teksturze badanego obiektu. Najlepszą dokładność całkowitą klasyfikacji wynoszącą 92,5% oszacowano dla zestawu zawierającego kanały wynikowe transformacji MNF i przeprowadzonej z wykorzystaniem struktury sieci odpowiadającej masce 3x3 piksele. Dla zestawu danych składającego się ze wskaźników roślinnych i glebowych dokładność klasyfikacji wyniosła około 80% w obydwu zastosowanych strukturach sieci.

20

Mapa geomorfologiczna województw pomorskiego i warmińsko-mazurskiego z wykorzystaniem metod geoinformatycznych

45%

Marcinkowska A. , Ochtyra A. , Olędzki J. R. , Wołk-Musiał E. , Zagajewski B.

Teledetekcja Środowiska

|

2013

|

tom T. 49

43--79

EN

The aim of this study was to prepare geomorphological maps of pomorskie and warminsko-mazurskie voivodeships in scale 1:300 000. Analysis primarily were based on the General Geomorphological Map of Poland 1:500 000 and Landsat 5 TM satellite images in RGB 453 composition, and alternatively with Geological Map of Poland 1:200 000, Topographic Map of Poland 1:100 000 and Digital Terrain Model from Shuttle Radar Topography Mission. These materials were processed into digital form and imported them PUWG 1992 coordinate system. Based on them was lead interpretation and vectorization of geomorphological forms. It was detailing the boundaries in accordance with the content of the General Geomorphological Map of Poland 1:500 000. Then polygons were coded according to the numbering of J. Borzuchowski (2010). Very important was process to design a legend and then editing maps. The last stage of this study was to prepare a composition for printing maps. The effect of studies are geomorphological maps of pomorskie and warminsko-mazurskie voivodeships in scale 1:300 000, and an interactive databases in ESRI shapefile format (*.shp).