Dr. Doris Klein Profile

Dr. Doris Klein

at Deutsches Zentrum fur Luft- und Raumfahrt Ev

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Proceedings Article | 19 October 2012 Paper

Comparison of leaf area index derived by statistical relationships and inverse radiation transport modeling using RapidEye data in the European alpine upland

Sarah Asam, Doris Klein, Stefan Dech

Proceedings Volume 8531, 853103 (2012) https://doi.org/10.1117/12.974570

KEYWORDS: Data modeling, Vegetation, Reflectivity, Near infrared, Sensors, Spatial resolution, Statistical modeling, In situ metrology, Remote sensing, Agriculture

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Leaf Area Index (LAI) is a relevant input parameter for flux modeling of energy and matter in the biosphere. However, in a landscape such as the European alpine upland with small-scale land use patterns and high vegetation heterogeneity, existing global products are less suited and a high spatial resolution is required. Within this study two methods are compared to derive the LAI for grassland in the prealpine River Ammer catchment from high spatial resolution RapidEye data: the empirical approach based on regression functions, and the physical approach of inverted radiation transfer modeling (RTM). Established vegetation indices (VIs) as well as new ones incorporating RapidEye’s red edge band are calculated for four dates of the vegetation period 2011 and correlated with in situ LAI data. The statistical regressions between VIs and LAI of the different time steps show high correlations (R² of 0.57 up to 0.85). However, the established regressions are scene specific and the method requires excessive field work. In the physical approach the RapidEye reflectances are used as input data to an inverted RTM (PROSAIL), which is parameterized with leaf and canopy properties collected in the field. The LAI derived by the RTM have a RMSE between 2.02 and 2.28 for the different dates. Both methods capture the general LAI pattern. However, due to the broad parameterization of the RTM used to cover the heterogeneous grassland conditions, resulting LAI values are generally higher than the statistically derived LAI values.

Proceedings Article | 22 October 2010 Paper

Mapping of large irrigated areas in Central Asia using MODIS time series

M. Machwitz, J. Bloethe, D. Klein, C. Conrad, S. Dech

Proceedings Volume 7824, 782403 (2010) https://doi.org/10.1117/12.865082

KEYWORDS: MODIS, Remote sensing, Agriculture, Ecosystems, Hydrology, Current controlled current source, Vegetation, Reflectivity, Image resolution

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Remote sensing offers the opportunity to produce land cover classifications for large and remote areas on a yearly basis and is an important tool in regions that lack these information. However often training and validation data to generate annual land cover maps are not available in necessary quantity - being from one year only or covering only a small extent of the region of interest. This study was focused on land use classifications at regional scale with a special emphasize on annual updates under the constraint of limited sampling data. Often, sampling is reduced to one year or to an unrepresentative area extend within the region of interest. The investigations for the period between 2004 and 2009 were conducted in the irrigation systems of the Amu Darya Delta in Central Asia, where reliable information on crop rotations is required for sustainable land and water management. Annual training and validation data were extracted from high resolution land use classifications. For classification, statistical features based on MODIS time series of vegetation indices, reflectance and land surface temperature (LST) were calculated and a random forest algorithm was applied. By a combination of training data from different years, the accuracy could be enhanced from an overall accuracy of 70% to more than 90% for a focused subregion and also good consistency with high resolution images for the other parts of the delta, which has to be confirmed using quantitative validation. A combination of a different number of years was tested. Already two years can be sufficient to generate a robust and transferable random forest to produce yearly land use maps. The study shows the possibility to combine training data from different years for the annual classification of irrigated croplands on a regional scale.

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