Guido Waldhoff

Monitoring and Modeling the Terrestrial System from Pores to Catchments: The Transregional Collaborative Research Center on Patterns in the Soil–Vegetation–Atmosphere System

AbstractMost activities of humankind take place in the transition zone between four compartments of the terrestrial system: the unconfined aquifer, including the unsaturated zone; surface water; vegetation; and atmosphere. The mass, momentum, and heat energy fluxes between these compartments drive their mutual state evolution. Improved understanding of the processes that drive these fluxes is important for climate projections, weather prediction, flood forecasting, water and soil resources management, agriculture, and water quality control. The different transport mechanisms and flow rates within the compartments result in complex patterns on different temporal and spatial scales that make predictions of the terrestrial system challenging for scientists and policy makers. The Transregional Collaborative Research Centre 32 (TR32) was formed in 2007 to integrate monitoring with modeling and data assimilation in order to develop a holistic view of the terrestrial system. TR32 is a long-term research program ...

High-resolution Crop Surface Models (CSM) and Crop Volume Models (CVM) on field level by terrestrial laser scanning

The interdisciplinary Transregional Collaborative Research Center 32 (CRC/TR 32) works on exchange processes between soil, vegetation, and the adjacent atmospheric boundary layer (SVA). Within this research project a terrestrial laser scanning sensor is used in a multitemporal approach for determining agricultural plant parameters. In contrast to other studies with phase-change or optical probe sensors, time-of-flight measurements are used. On three dates in the year 2008 a sugar beet field (4.3 ha) in Western Germany was surveyed by a terrestrial laser scanner (Riegl LMS-Z420i). Point clouds are georeferenced, trimmed, and compared with official elevation data. The estimated plant parameters are (i) surface model comparison between different crop surfaces and (ii) crop volumes as well as (iii) soil roughness parameters for SVA-Modelling. The results show, that the estimation of these parameters is possible and the method should be validated and extended.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

GIS- and RS-based land use and land cover analysis: case study Rur-Watershed, Germany

For numerous spatial applications, land use data are of central importance and have to be available in a spatial data infrastructure for regional modeling. This also counts for the research project TR32 which focuses on SVA modeling in a regional context. The land use data should be organized in a land use information system according to international data standards providing general metadata including information about data quality. Usually, land use data are available from official sources, but they lack the desired information detail for many purposes. For example, in official land use maps, agricultural land use is generally differentiated between arable land, grassland, orchards and some special land use classes like paddy fields. For detailed (agro-)ecosystem modeling, this information resolution is rather poor. Here, disaggregated land use data which provide information about the major crops and crop rotations as well as management data like date of sowing, fertilization, irrigation, harvest etc. are needed. The analysis of multispectral, hyperspectral and/or radar data from satellite or airborne sensors is a standard method to retrieve such kind of information with remote sensing methodologies. By using a Multi-Data Approach (MDA), the retrieved information from remote sensing analysis is integrated into official land use data by GIS technologies to enhance both the information level (e. g. crop rotations) of existing land use data and the quality of the land use classification.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

Abstract Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

GIS for Mapping Vegetation

GIS-based mapping of vegetation is a broadly established application with strong interconnections with remote sensing, digital surveying and mapping, as well as with more traditional approaches of mapping and cartography. It is also a major objective of vegetation sciences like agriculture, forestry, geobotany, biogeography, landscape ecology, and resource management and makes it a strongly interdisciplinary topic and task. In this context, the GIS-based mapping or monitoring results like vegetation maps, vegetation inventories, land use/land cover maps, or vegetation time series (like crop rotations) serve as a key information source for spatial decision making processes. Besides the main mapping objective, the mapping scale generally determines the mapping technology and is therefore under permanent change. For the last decades, field surveys were supported by aerial photography and satellite-based remote sensing. Consequently, the area of interest ranged from several hectares to global coverages. However, in the last ten years Unmanned Aerial Systems (UAS) or low-altitude flying manned vehicles like gyrocopters are increasingly used as sensor platforms for proximal and airborne remote sensing, providing subcentimeter resolution data. In addition to satellite-based systems (e.g. Landsat, Sentinel-2) they are combined with ground surveying techniques like field sampling, GPS, and laser scanning which already support vegetation mapping at all scales. This chapter of GIS applications on Mapping of Vegetation focuses on four major topics. In the Plant Communities and Vegetation Inventories section the technical developments, scale levels, GIS-based mapping methodologies, and the variety of available data products are handled. The follow-up section discusses official remote sensing and GIS-based datasets which contain highly valuable vegetation information, but are not intended as inventories in the first place. Multi-Data Methods which make use of such vegetation information to enhance remote sensing-based land use/land cover mapping from global to local scales are discusses thereafter. In the final section, the illustration of GIS methods for the analysis of remote sensing-based super high resolution Digital Surface Models (DSMs) in forestry and agriculture focuses the current state of the art in GIS-based vegetation mapping.

Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata

ABSTRACT Crop distribution information is essential for tackling some challenges associated with providing food for a growing global population. This information has been successfully compiled using the Multi-Data Approach (MDA). However, the current implementation of the approach is based on optical remote sensing, which fails to deliver the relevant information under cloudy conditions. We therefore extend the MDA by using Land Use/Land Cover classifications derived from six multitemporal and dual-polarimetric TerraSAR-X stripmap images, which do not require cloud-free conditions. These classifications were then combined with auxiliary, official geodata (ATKIS and Physical Blocks (PB)) data to lower misclassification and provide an enhanced LULC map that includes further information about the annual crop classification. These final classifications showed an overall accuracy (OA) of 75% for seven crop-classes (maize, sugar beet, barley, wheat, rye, rapeseed, and potato). For potatoes, however, classification does not appear to be as consistently accurate, as could be shown from repeated comparisons with variations of training and validation fields. When the rye, wheat, and barley classes were merged into a winter cereals class, the resultant five crop-class classifications had a high OA of about 90%.