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Modelowanie wymiany netto ekosystemu (NEE) na obszarach bagiennych z zastosowaniem danych satelitarnych

Dąbrowska-Zielińska K., Budzyńska M., Tomaszewska M., Malińska A., Bartold M., Gatkowska M., Turlej K., Małek I., Turbiak J., Jaszczyński J.

Woda-Środowisko-Obszary Wiejskie

2017

T. 17, z. 1

31--51

W artykule przedstawiono wyniki badań nad modelowaniem wymiany netto ekosystemu (NEE – ang. Net Ecosystem Exchange) bagiennego na przykładzie doliny Biebrzy z wykorzystaniem zdjęć satelitarnych i danych z pomiarów naziemnych z lat 2011–2015. Przeprowadzono szczegółową analizę zastosowania zdjęć optycznych i radarowych do uzyskania charakterystyk roślinno-wilgotnościowych wpływających na wymianę węgla. W wyniku przeprowadzonych analiz opracowano modele szacowania NEE, w których zastosowano opracowane na podstawie danych satelitarnych następujące parametry: wilgotność gleby (WG), zawartość wody w roślinach (WR). Do analizy WG i WR został zastosowany współczynnik wstecznego rozpraszania (σº) obliczony z sygnału zarejestrowanego w zakresie mikrofalowym przez urządzenia SAR (ang. Synthetic Aperture Radar) dla różnych polaryzacji fal. Prace badawcze zmierzające do określenia wielkości wymiany węgla oraz jego zróżnicowania przestrzennego i czasowego, przeprowadzone z uwzględnieniem informacji o pokrywie roślinnej i wilgotności gleby uzyskanych z danych satelitarnych, są ważne dla monitorowania ekosystemów bagiennych.

The article presents results of the study on modeling Net Ecosystem Exchange (NEE) in the wetland ecosystem using remote sensing and in-situ data. The study has been conducted in Biebrza Valley for the years 2011–2015. The analysis of application of optical and microwave images for the assessment of vegetation-moisture conditions influenced carbon exchange has been performed. The impact of soil moisture and type of vegetation habitat on CO2 flux in wetland ecosystems has been analyzed to develop NEE models. Soil moisture (WG) and vegetation water content (WR) have been correlated with backscattering coefficient (σº) calculated from the signal registered by microwave satellites in different wave polarization. The research was focused on the assessment of carbon balance in time and space taking into account vegetation cover and soil moisture derived from satellite data. The research is important for monitoring wetland ecosystem.

Analiza zależności między zawartością wody w roślinach zmierzoną w terenie a teledetekcyjnymi wskaźnikami roślinności

Niedzielko J., Milczarek M., Szepietowska M., Pokrzywnicka M., Boral B., Łach G., Kaźmierczak M., Jarocińska A.

Teledetekcja Środowiska

2012

T. 47

43--57

Monitoring the plant moisture has a significant role in geographical research. It may be used, among the others, for climate modelling, agricultural predicting, rational water management, drought monitoring and determining vulnerability to the occurrence of the fire. Traditional methods, based on field measurements, are the most accurate, but also time-consuming. Therefore these methods can be applied only in a limited area. In order to explore bigger areas remote sensing methods are useful. To analyse plant condition and water content vegetation indices can be used. Their calculations are based on the reflectance in different bands. Despite many studies conducted on the development of remote sensing indices, still there is a need for verification of their accuracy and usefulness by comparing the results obtained through remote sensing tools with the results of field measurements. In this paper three indices are used: Moisture Stress Index (MSI), Normalized Difference Infrared Index (NDII) and transformation Tasseled Cap (the Wetness band). The aim of this study was to compare the value of vegetation indices calculated using images from Landsat 5 Thematic Mapper with the results of field measurement from five test areas of different type of land cover: cereal crops, non-cereal crops, forests, meadows and pastures. Research was carried out in province Ontario (Canada) and consisted of two stages. The first stage was the fi eld measurements, where the specified number of plant samples was collected and water content was calculated. The second stage consisted of the preparation of relevant satellite images (atmospheric correction and making the mosaic) and the calculation of vegetation indices. The study has shown, that statistical relationships between data sets obtained through remote sensing indices and calculated on the basis of field measurements are diverse for different indices. MSI and NDII values are significantly correlated with the water content in plants (R= -0.62 and 0.56, respectively). The correlation of TCW was rated as moderate (R=0.30). Spatial distribution of water content based on maps created using NDII and MSI is similar. It was noticed that TC Wetness transformation overestimates water content in cereal plants (smaller water content) and underestimates it in natural green plant ecosystems, which generally have higher water content. As a result, the range of water content values obtained from TCW is more narrow (dominates the class of 60-70% water in plants) than the range of values calculated using NDII and MSI. Both indices have more uniform distribution dominated by the classes of moderate water content (50-60%), rather wet plants (60-70%) and very wet plants (70-80%). Each index is characterized by different distribution of the water content. In general values calculated on the basis of NDII and MSI are higher than calculated using TCW. In order to perform more accurate analysis between values calculated using satellite images and the results of field measurements, the values of particular types of land cover should be compared.