Thesis ; Online: Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation
2019
Abstract: Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have ... ...
| Abstract | Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed: ? Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases? ? How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations? ? How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization? To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained. Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both groundbased and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum. Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigmentdriven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments. Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satellite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infrared. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale. Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using nonpeak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales. Die arktische Erwärmung beeinflusst Produktivität, Wachstums, Artenzusammensetzung, Phänologie und den Reproduktionserfolg arktischer Vegetation, mit Auswirkungen auf die Ökosystemfunktionen sowie auf den globalen Kohlenstoff- und Energiehaushalt. Feldbasierte Messungen und spektrale Charakterisierungen der räumlichen und zeitlichen Heterogenität arktischer Vegetationsgemeinschaften sind limitiert und die Genauigkeit fernerkundlicher Methoden im Landschaftsmaßstab eingeschränkt. Um diese Forschungslücke zu schließen und aktuelle und zukünftige Satellitenmissionen zu unterstützen, wurden drei zentrale Forschungsfragen entwickelt: 1) Wie unterscheidet sich die spektrale Variabilität des Kronendaches zwischen dominanten Vegetationsgemeinschaften der niederen Arktis und wie verändert sich diese Variabilität zwischen den wichtigsten phänologischen Phasen? 2) Wie hängen Aufnahmen der Vegetationsfarbe des Kronendaches von hoch und niedrig auflösenden Geräten mit phänologischen Veränderungen des photosynthetischen Pigmentgehalts auf Blattebene zusammen? 3) Wie beeinflusst die räumliche Aggregation von Daten mit hoher spektraler Auflösung von der Boden- bis zur Satelliten-Skala die arktischen Vegetationssignale der Tundra und welches Potenzial haben zukünftige hyperspektraler Satellitensysteme für die arktische Vegetationscharakterisierung? Zur Beantwortung dieser Fragen wurde eine detaillierte Datenbank aus feldbasierten Daten erstellt und mit hyperspektralen Luftbildern sowie multispektralen Sentinel-2 und simulierten hyperspektralen EnMAP Satellitendaten verglichen. Die Ergebnisse zeigten, dass die Spätsai-son am besten geeignet ist um dominante Vegetationsgemeinschaften mit Hilfe von hyper-spektralen Daten zu identifizieren. Ebenfalls konnte gezeigt werden, dass die mit handelsüb-lichen Digitalkameras aufgenommene Vegetationsfarbe pigmentgesteuerte Spektralindizes stark beeinflusst und den Verlauf von photosynthetischen Pigmenten nachverfolgen kann. Die räumliche Aggregation hyperspektraler Daten von der Boden- über die Luft- zur Satelli-tenskala wurde durch nicht-photosynthetische Komponenten beeinflusst und die spektralen Reflexionsvermögen der drei Skalen stimmten im roten Spektrum am höchsten und im nahen Infrarotbereich am niedrigsten überein. Die vorliegende Arbeit zeigt, dass die Integration zeitlicher, spektraler und räumlicher Daten notwendig ist, um Komplexität und Heterogenität arktischer Vegetationsreaktionen in Reaktion auf klimatische Veränderungen zu überwachen. |
|---|---|
| Subject code | 710 |
| Language | English |
| Publisher | Universität Potsdam |
| Publishing country | de |
| Document type | Thesis ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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