Geoflux GbR

Halle (Saale), Germany

Geoflux GbR

Halle (Saale), Germany
Time filter
Source Type

Moller M.,Martin Luther University of Halle Wittenberg | Koschitzki T.,Geoflux GbR | Hartmann K.-J.,State Institute for Geology and Natural Resources Saxony Anhalt | Jahn R.,Martin Luther University of Halle Wittenberg
Catena | Year: 2012

The motivation for this article results from the fact that conceptual soil maps show oftentimes inaccuracies with regard to soil unit boundaries or misfits between original paper and actual soil-related information. Using the example of a German conceptual soil map (CSM), we introduce a procedure which could be considered as a framework for testing the terrain-related plausibility applied within a genetic based soil-ordering system. Framework means that all tests and the underlying methods can be adapted to specific targets. The procedure enables both reproducible integration of expert knowledge and application of statistically sound methods. The CSM of the German Federal State of Saxony-Anhalt was tested regarding the plausibility of colluvial and fluvial process domains. The plausibility test consists of four steps and was exemplified on a study area of 100km2. First, basic relief parameters were combined to the explaining relief parameters Floodplain Index (FPI) and Mass Balance Index (MBI) enabling a classification of process domains by relative descriptions. Second, relief parameters and aggregated CSM soil units were integrated to soil-terrain objects (STO) executing a region-growing segmentation algorithm. In the third step, the one-dimensional MBI or FPI feature space of STO entities were clustered by using the K-means algorithm. The fourth step comprises the expert-based selection of reference clusters (RC) representing colluvial and fluvial process domains accepted as being true. Then, empirical cumulative distribution functions (ECDF) of RC and remaining soil unit-related STO clusters were compared by a traditional goodness-of-fit test whose suitability for estimation of terrain-related CSM plausibility is shown. Finally, the resulting ECDF distances were visualized. The testing procedure could also be used for the supervised selection of appropriate samples for automatic classification algorithms. The data integration approach is generally suitable for adopting existing data in computer-based systems. © 2011 Elsevier B.V.

Haring T.,BASF | Haring T.,TU Munich | Dietz E.,Bavarian State Institute of Forestry | Osenstetter S.,Bavarian State Institute of Forestry | And 3 more authors.
Geoderma | Year: 2012

Detailed knowledge on the spatial distribution of soils is crucial for environmental monitoring, management, and modeling. However soil maps with a finite number of discrete soil map units are often the only available information about soils. Depending on the map scale or the detailing of the map legend this information could be too imprecise. We present a method for the spatial disaggregation of map units, namely the refinement of complex soil map units in which two or more soil types are aggregated. Our aim is to draw new boundaries inside the map polygons to represent a single soil type and no longer a mixture of several soil types. The basic idea for our method is the functional relationship between soil types and topographic position as formulated in the concept of the catena. We use a comprehensive soil profile database and topographic attributes derived from a 10. m digital elevation model as input data for the classification of soil types with random forest models. We grouped all complex map units which have the same combination of soil types. Each group of map units is modeled separately. For prediction of the soil types we stratified the soil map into these groups and apply a specific random forest model only to the associated map units. In order to get reliable results we define a threshold for the predicted probabilities at 0.7 to assign a specific soil type. In areas where the probability is below 0.7 for every possible soil type we assign a new class "indifferent" because the model only makes unspecific classification there. Our results show a significant spatial refinement of the original soil polygons. Validation of our predictions was estimated on 1812 independent soil profiles which were collected subsequent to prediction in the field. Field validation gave an overall accuracy of 70%. Map units, in which shallow soils were grouped together with deep soils could be separated best. Also histosols could be predicted successful. Highest error rate were found in map units, in which Gleysoils were grouped together with deep soils or Anthrosols. To check for validity of our results we open the black box random forest model by calculating the variable importance for each predictor variable and plotting response surfaces. We found good confirmations of our hypotheses, that topography has a significant influence on the spatial arrangement of soil types and that these relationships can be used for disaggregation. © 2012 Elsevier B.V.

Moller M.,Martin Luther University of Halle Wittenberg | Gerstmann H.,Martin Luther University of Halle Wittenberg | Wurbs D.,Geoflux GbR | Glaber C.,Martin Luther University of Halle Wittenberg
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2014

The DynaC framework aims at the up-to-date derivation of the parcel-specific cover management factor as a temporally dynamic input parameter for soil erosion modeling. Parcel boundaries and parcels' fractional vegetation coverages (FV C) result from the analysis of multi-spectral and multitemporal satellite imagery. The FV C modeling results are validated by digital pictures taken from representative samples on selected parcels. The samples' locations are chosen within classified topographic positions for which a relation to FV C degrees is assumed. © 2014 IEEE.

Volk M.,Helmholtz Center for Environmental Research | Moller M.,Geoflux GbR | Wurbs D.,Geoflux GbR
Land Use Policy | Year: 2010

This paper presents a methodological framework for scale-specific assessment of soil erosion by water. The framework enables the definition of hierarchical, functional and modular nested reference units which result from the integrated consideration of policy, process and model hierarchies. The framework is applied on three planning levels: at first, large scale zones are designated that show a defined risk potential for soil erosion (first level: catchments and drainage areas in the German Federal State of Saxony-Anhalt, ca. 20,000km2). By both increasing model complexity and spatio-temporal resolution of input data, the results are locally specified within these risk zones (second level: designated farms and fields in a study area of 141km2). This is the basis for the prediction of soil erosion areas and sediment transport to hydrologic drainage networks as well as for small scale management and measure planning (third level: designated field blocks in the study area). On this level, the mitigation of soil erosion and sediment entry to the river system is demonstrated by simulating the introduction of conservation management practices, vegetation and riparian buffer strips.We used a modified version of the empirical Universal Soil Loss Equation (USLE) ABAGflux, which includes functions to better describe sedimentation and sediment transport to hydrologic drainage networks. Aggregation and statistical methods like SICOM and k means cluster analysis were applied for objective ranking and classification of the simulation results. The study aims at contributing to an improved applicability of data and methods for the assessment of soil erosion by water and soil protection on relevant planning scales. In addition, the results are considered to be important for an improved transfer of methods developed in science to their application in soil erosion risk management. © 2010 Elsevier Ltd.

Loading Geoflux GbR collaborators
Loading Geoflux GbR collaborators