Satellite Color Images, Vegetation index and Metabolism index from Selb, Germany from 1985 – 2023

The Germany Mosaic is a time series of Landsat satellite images and vectorized segments of the whole of Germany from 1984 to 2023. The image data are divided into the TK100 sheed sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset contains for each year optimized 6 band imagery for summer (May to July) and autumn (August to October) and the vegetation indices NDVI (Normalized Difference Vegetation Index) and NirV (Near-infrared reflectance of Vegetation) for the same time periods. In addition, vectorized zones of roughly homogeneous pixels are provided for each year. The spectral characteristics of the image data and the morphological characteristics of the zones are provided as vector attributes (see Documentation: Mosaic (1984-2023) - data description). An overview of the coverage and quality of all sheet sections is given as a vector layer at D-Mosaik_Sheet-Sections in this document. In mid-latitudes, seasonal changes in vegetation and thus in the image data, are usually much greater than changes over several years. The temporal periods of the data sets have been chosen so that together they represent the entire vegetation period (May to October), while the division into a summer and a autumn period represents the seasonal change in the metabolic rate of natural biotopes and at the same time record most of the agricultural changes due to sowing and harvesting. Depending on the weather conditions, the individual image data contain the median, the mean value or the best individual image of the specified period (see Documentation: Mosaic (1984-2023) - data description). Remote sensing has become an important tool and service for environmental research, especially for landscape analysis. Moreover, the spatial distribution and development of remotely sensed parameters can effectively complement and extend traditional biological, ecological, geographical as well as epidemiological tasks.