Michael Schmidt

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—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)
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)
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)
Tropical rain forests are the most important habitat type for biodiversity conservation worldwide (Myers et al. 2000). Although they only cover about 7% of the global land surface (Hansen & DeFries 2004), they shelter a large variety of life. At least 44% of the world's vascular plants and 35% of terrestrial vertebrate species are endemic to 25 global(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)
A unique spectral unmixing technique is presented that allows endmember proportion estimation automatically without known endmember spectra. This approach, termed unsupervised multiple endmember spectral mixture analysis (UMESMA), allows a more accurate and continuous land cover detection since fractional land cover abundances per pixel are used instead of(More)
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