Leibniz University of Hanover

About: Leibniz University of Hanover is a education organization based out in Hanover, Niedersachsen, Germany. It is known for research contribution in the topics: Finite element method & Population. The organization has 14283 authors who have published 29845 publications receiving 682152 citations.

The role of amino acid metabolism during abiotic stress release.

Abstract: Plant responses to abiotic stress include various modifications in amino acid metabolism. By using a hydroponic culture system, we systematically investigate modification in amino acid profiles and the proteome of Arabidopsis thaliana leaves during initial recovery from low water potential or high salinity. Both treatments elicited oxidative stress leading to a biphasic stress response during recovery. Degradation of highly abundant proteins such as subunits of photosystems and ribosomes contributed to an accumulation of free amino acids. Catabolic pathways for several low abundant amino acids were induced indicating their usage as an alternative respiratory substrate to compensate for the decreased photosynthesis. Our results demonstrate that rapid detoxification of potentially detrimental amino acids such as Lys is a priority during the initial stress recovery period. The content of Pro, which acts as a compatible osmolyte during stress, was adjusted by balancing its synthesis and catabolism both of which were induced both during and after stress treatments. The production of amino acid derived secondary metabolites was up-regulated specifically during the recovery period, and our dataset also indicates increased synthesis rates of the precursor amino acids. Overall, our results support a tight relationship between amino acid metabolism and stress responses.

Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

Abstract: Wetlands are important ecosystems around the world, although they are degraded due both to anthropogenic and natural process. Newfoundland is among the richest Canadian province in terms of different wetland classes. Herbaceous wetlands cover extensive areas of the Avalon Peninsula, which are the habitat of a number of animal and plant species. In this study, a novel hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a sub-region located in the north eastern portion of the Avalon Peninsula. Particularly, multi-polarization and multi-frequency SAR data, including X-band TerraSAR-X single polarized (HH), L-band ALOS-2 dual polarized (HH/HV), and C-band RADARSAT-2 fully polarized images, were applied in different classification levels. First, a SAR backscatter analysis of different land cover types was performed by training data and used in Level-I classification to separate water from non-water classes. This was followed by Level-II classification, wherein the water class was further divided into shallow- and deep-water classes, and the non-water class was partitioned into herbaceous and non-herbaceous classes. In Level-III classification, the herbaceous class was further divided into bog, fen, and marsh classes, while the non-herbaceous class was subsequently partitioned into urban, upland, and swamp classes. In Level-II and -III classifications, different polarimetric decomposition approaches, including Cloude-Pottier, Freeman-Durden, Yamaguchi decompositions, and Kennaugh matrix elements were extracted to aid the RF classifier. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. The importance of input features was also determined using the variable importance obtained by RF. It was found that the Kennaugh matrix elements, Yamaguchi, and Freeman-Durden decompositions were the most important parameters for wetland classification in this study. Using this new hierarchical RF classification approach, an overall accuracy of up to 94% was obtained for classifying different land cover types in the study area.