Paper
18 October 2016 Monitoring of vegetation dynamics on the former military training area Königsbrücker Heide using remote sensing time series
Author Affiliations +
Abstract
In 1989 about 1.5 million soldiers were stationed in Germany. With the political changes in the early 1990s a substantial decline of the staff occurred on currently 200,000 employees in the armed forces and less than 60,000 soldiers of foreign forces. These processes entailed conversions of large areas not longer used for military purposes, especially in the new federal states in the eastern part of Germany. One of these conversion areas is the former military training area Konigsbruck in Saxony. For the analysis of vegetation and its development over time, the Normalized Difference Vegetation Index (NDVI) has established as one of the most important indicators. In this context, the questions arise whether MODIS NDVI products are suitable to determine conversion processes on former military territories like military training areas and what development processes occurred in the ”Konigsbrucker Heide” in the past 15 years. First, a decomposition of each series in its trend component, seasonality and the remaining residuals is performed. For the trend component different regression models are tested. Statistical analysis of these trends can reveal different developments, for example in nature development zones (without human impact) and zones of controlled succession. The presented workflow is intended to show the opportunity to support a high temporal resolution monitoring of conversion areas such as former military training areas.
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Christine Wessollek and Pierre Karrasch "Monitoring of vegetation dynamics on the former military training area Königsbrücker Heide using remote sensing time series", Proc. SPIE 10005, Earth Resources and Environmental Remote Sensing/GIS Applications VII, 100050Q (18 October 2016); https://doi.org/10.1117/12.2239944
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KEYWORDS
Vegetation

Remote sensing

MODIS

Temporal resolution

Statistical analysis

Spatial resolution

Time series analysis

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