Wyniki wyszukiwania - Biblioteka Nauki

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Próba integracji satelitarnych obrazów hiperspektralnych z nieobrazowymi naziemnymi danymi spektrometrycznymi na przykładzie Zbiornika Dobczyckiego

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Hejmanowska B. , Drzewiecki W. , Głowienka E. , Mularz S. , Zagajewski B. , Sanecki J.

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2006

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tom Vol. 16

207--216

PL

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). Przetwarzanie obrazów hiperspektranych za pomocą oprogramowania ENVI polegało, na wstępnej korekcji wpływu atmosfery i próbie porównania krzywych spektrometrycznych z krzywymi spektralnymi z obrazów satelitarnych. Ostatnim etapem była analiza porównawcza wyników pomiaru bezpośredniego wody nad osadowej z przebiegiem krzywych spektralnych uzyskanych teledetekcyjnie. W artykule opublikowano wstępne rezultaty badań prowadzonych w ramach projektu KBN 3T 09D 09429 pt. „Badania procesów akumulacji i przemian związków chemicznych w osadach Dobczyckiego Zbiornika wody pitnej dla miasta Krakowa w celu oceny jego stanu jako ekosystemu”. Uzyskane w omawianym eksperymencie wyniki stanowią potencjalnie znacznie większy materiał badawczy niż zostało to zaprezentowane w publikacji. W przyszłości planowane są dalsze prace w kierunku lepszej wstępnej kalibracji obrazów satelitarnych, co umożliwiłoby wiarygodne porównanie pomiarów naziemnych i obrazów satelitarnych.

EN

In June 2006, a remote sensing experiment for Dobczyce Reservoir monitoring, was performed. The following data was gathered: hyperspectral images – HYPERION, multispectral images – ALI, ASD spectrometer measurements, laboratory measurements of water probe in 6 points of the reservoir. Point position was measured by GPS. Images were processed using ENVI software, initial correction and data extraction was performed. For integration, data in different formats and Geomedia coordinate systems was applied. In the paper, some results of laboratory measurements area are presented. The data was analyzed on the satellite composition to test the qualitative correlations between images and laboratory measurements. A coincidence was obtained in about 70 % (its reliability is limited because of amount of measurement points). Reflection coefficient in upper part of reservoir (more suspended matter) was ca. 0.06 and in lower part it was ca. 0.02, which confirms the quantitatively visual interpretation of the satellite composition. Unfortunately, comparison between spectrometric measurements with the spectral curve from satellite image was not successful. Image correction of the atmospheric effect was probably not satisfactory. In this paper, only initial results of the experiment are presented. In the future, the improvement of the initial correction is planned to make the comparison between spectrometer and image spectral curves possible.

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Metodyka wykorzystania super- i hiperspektralnych danych satelitarnych w analizie jakości wód śródlądowych

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Osińska-Skotak K.

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tom z. 47

3-153

PL

Różnorodność danych satelitarnych, które są obecnie dostępne, daje szerokie możliwości prowadzenia monitoringu jakości wód powierzchniowych. Dotyczy to zarówno wielkości monitorowanego obszaru (wielkość sceny satelitarnej), szczegółowości określania rozkładu przestrzennego (wielkość piksela), jak również doboru parametrów opisujących jakość wody. Do niedawna panowało przekonanie, że zastosowanie technik teledetekcyjnych do badania jakości wód śródlądowych jest ograniczone, bo wiele zanieczyszczeń nie powoduje zmiany barwy wody, która jest widoczna bezpośrednio na zdjęciach satelitarnych. Dostępne obecnie satelitarne zdjęcia super- i hiperspektralne oferują nowe możliwości, co pozwala zrewidować poglądy, dotyczące przydatności danych satelitarnych do oceny jakości wód jeziornych. Teledetekcyjny monitoring jakości wód od wielu lat jest prowadzony w odniesieniu do wód morskich i oceanicznych. W przypadku wód śródlądowych opracowano szereg formuł obliczeniowych dla konkretnych jezior lub typów jezior, ale jak dotąd brak uniwersalnej formuły obliczeniowej, niezależnej od rodzaju zbiornika wodnego. W niniejszej pracy przedstawiono metodykę przetwarzania danych super- i hiperspektralnych, pozwalającą na uzyskanie rozkładu wybranych parametrów jakości wód śródlądowych. Zaprezentowano syntetyczne wyniki badań prowadzonych od 2003 roku dla dwóch obszarów testowych, "Mazury" oraz "Zalew Wiślany". Celem tych badań było poszukiwanie takich zależności między danymi satelitarnymi a parametrami jakości wód, które nie zależałyby od rodzaju zbiornika i daty pomiaru. W wyniku tych badań uzyskano formuły obliczeniowe dla widzialności krążka Secchiego, zawartości zawiesiny ogółem, zawartości chlorofilu-a, zawartości fosforu ogółem oraz zawartości azotu ogółem, które wykazują uniwersalny charakter. Weryfi kacja wybranych formuł obliczeniowych wykazała, że zależności prezentowane w ramach niniejszej pracy pozwalają w sposób operacyjny uzyskać rozkład wybranych parametrów jakości wód śródlądowych z wystarczającą dokładnością.

EN

The variety of satellite data available at present offers great possibilities for surface water quality monitoring in terms of size of the monitored area (the size of satellite scene), possibility of obtaining various water quality parameters (different spectral bands) and precision of spatial distribution of those parameters (pixel size). An existing recent conviction shows the idea that using remote sensing techniques for investigation of inland water quality for many pollutants is limited due to changing water color, which is not visible on satellite images directly. Currently available satellite super- and hyperspectral images offer new possibilities, which permits to revise opinions on the usefulness of satellite data for assessment of lake water quality. Remote sensing monitoring of water quality in case of sea and oceanic waters has been made for many years. Many computational formulae in reference to inland waters were elaborated for individual lakes or types of lakes, but there is no universal formula independent of the kind of water basin. The elaboration of the processing methodology of superspectral CHRIS satellite data for inland waters quality assessment was the main aim of this study. The thesis presents results of a study carried out since 2003 for two tested areas, the Mazurian lakes and the Vistula Lagoon. An attempt to establish relationships between satellite data and water quality parameters which would not depend on the kind of reservoir and date of measurement was the aim of these investigations. As a result of these investigations, computational formulae were elaborated which show the universal character Secchi Disc Depth, chlorophyll-a concentration, total suspensed sediment content, total phosphorus content and total nitrogen content. Verification of selected computational formulae has shown that the relationships obtained as part of the present work give the possibility to get the distribution of chosen inland water quality parameters in operational way with sufficient accuracy.

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

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Zagajewski B.

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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.