To map the aquatic vegetation of Bavarian (Germany) freshwater lakes in a large-scaled and quick way, remote sensing is a helpful tool. For interpretation of the data, a spectral library of different macrophyte and sediment reflectances is under development. Therefore, multi-temporal in situ remote sensing reflectances were sampled from May to October 2011 with hyperspectral RAMSES spectroradiometers. Occurring spectral variations during the growing season could be linked to biometric and phenological data of the particulate species. Principal component analyses showed that, by applying the presented method, differentiation of the macrophytes from sediment and among each other is possible and can be improved by multi-temporal data.
Abstract:In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440) CDOM ) and absorption slope (S 300-500 ) in lakes using field sampling and optical remote sensing data for an area of 350 km 2 in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a(λ) CDOM data from 18 lakes sampled in the field to 356 lakes in the study area (model R 2 = 0.79). Values of a(440) CDOM in 356 lakes varied from 0.48 to 8.35 m −1 with a median of 1.43 m −1 . This a(λ) CDOM dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R 2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440) CDOM = 5.3 m −1 ). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440) CDOM = 3.8 m −1 ) compared to lakes located on higher terraces.
Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
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