Michael Schmidt

Learn More
The diversity of nature, from genes to ecosystems, is an important resource we all benefit from. Yet, biodiversity is threatened by anthropogenic pressure causing habitat loss and fragmentation, climate change and its related effects (Thomas et al., 2004). This might lead to a severe decrease in ecosystem services with negative effects for human(More)
—Time series generated from remotely sensed data are important for regional to global monitoring, estimating long-term trends, and analysis of variations due to droughts or other extreme events such as El Niño. Temporal vegetation patterns including phenological states, photosynthetic activity, or biomass estimations are an essential input for climate(More)
Is the annoyance of snoring a reliable tool for the measurement of snoring or does it depend more on the sensitivity of the listener? During an automatized hearing experiment, 550 representative snoring sequences, recorded during polysomnography, were randomly presented to ten examiners for the evaluation of their annoyance (0-100). The mean annoyance score(More)
Long term observation of space-borne remote sensing data provides a means to explore temporal variation on the Earth's surface. This improved understanding of variability is required by numerous global change studies to explain annual and interannual trends and to separate those from individual events. This knowledge also can be included into budgeting and(More)
Globally acquired data from both MODIS instruments are suitable for science quality time series, because the unique concept of pixel-level quality information of each MODIS land product allows a detailed analysis of the data usability. MODIS datasets are regularly updated and reprocessed to meet present science requirements. This study compares time series(More)
1. ABSTRACT In recent years, the increasing availability of earth observation data at different spatial and temporal resolutions together with an increasing number of digitized and geo-referenced species occurrence data (museum and herbarium collections as well as field observations) has created the opportunity to model and monitor species geographic(More)
Many remote sensing projects require the utilization of sample data for training a supervised classification algorithm. In almost all instances the correctness of training data is highly important for accurate image classification. While data obtained during field studies are suitable for many small scale studies and the classification of high and very high(More)
This study presents a multi-scale procedure to derive continuous proportional cover of woody vegetation in savanna ecosystems. QuickBird data was classified to define a continuous training and validation data set of woody cover proportions. Using a regression tree algorithm based on Landsat TM data, this woody cover information was extrapolated to an area(More)