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BTU
Modified wick lysimeters for critical water use efficiency evaluation and yield crop modelling
(2011)
Als Pionierbaumart besiedelt die Robinie (Robinia pseudoacacia) schnell offene Flächen und gilt im Vergleich zu anderen mitteleuropäischen Baumarten als relativ gut angepasst an trockene Standorte mit lockeren Böden. Sie gehört zu den wenigen Baumarten Mitteleuropas, die zur biologischen Stickstoff-Fixierung (BNF) befähigt sind. In dieser Studie soll der jährliche Eintrag von Stickstoff (N) aus der biologischen N-Fixierung der Robinie in Kurzumtriebsplantagen und Agroforstsystemen abgeschätzt werden.
Ecosystems are characterized as complex systems with abiotic and biotic processes interacting between the various components that have evolved over long-term periods. Most ecosystem studies so far have been carried out in mature systems. Only limited knowledge exists on the very initial phase of ecosystem development. Concepts on the development of ecosystems are often based on assumptions and extrapolations with respect to structure–process interactions in the initial stage. To characterize the effect of this initial phase on structure and functioning of ecosystems in later stages, it is necessary to disentangle the close interaction of spatial and temporal patterns of ecosystem structural assemblages with processes of ecosystem development. The study of initial, less complex systems could help to better identify and characterize coupled patterns and processes. This paper gives an overview of concepts for the initial development of different ecosystem compartments and identifies open questions and research gaps. The artificial catchment site “Chicken Creek” is introduced as a new research approach to investigate these patterns and processes of initial ecosystem development under defined boundary conditions. This approach allows to integrate the relevant processes with related pattern and structure development over temporal and spatial scales and to derive thresholds and stages in state and functioning of ecosystems at the catchment level.
Normalized Difference Vegetation Indices (NDVIs) are typically determined using satellite or airborne remote sensing, or field portable spectrometers, which give an averaged signal on centimetre to metre scale plots. Biological soil crust (BSC) patches may have smaller sizes, and ecophysiological, hydrological as well as pedological processes may be heterogeneously distributed within this level of resolution. A ground-based NDVI imaging procedure using low-cost equipment (Olympus Camedia 5000z digital camera equipped with a Hoya R72 infrared filter) was developed in this study to fill this gap at the level of field research, where carrying costly and bulky equipment to remote locations is often the limiting factor for data collection. Method principle and field data are presented, and the field experiment was deepened comparing NDVI measurements and CO2 turnover of soil crust samples in the laboratory, backing the reliability of the approach.
A commercially available colour rendition chart with known red (600–700 nm) and NIR (800–900 nm) reflectances was placed into each scene and used for calibration purposes on a per-image basis. Generation of NDVI images involved (i) determination of red and NIR reflectances from the pixel values of the red and NIR channels, respectively, and (ii) calculation and imaging of the NDVI, where NDVI values of −1 to +1 were mapped to grey values of 0 to 255. The correlation between NDVI values retrieved from these images and NDVI values determined using field spectrometry was close (r2 = 0.91), the 95% confidence interval amounted to 0.10 NDVI units. The pixel resolution was 0.8 mm in the field and 0.2 mm in the laboratory, but can still be improved significantly with closer distance to the crust or with higher camera resolution. NDVI values obtained using the new method were related to the net CO2 uptake of BSCs, where both slope and correlation coefficient of the respective regression function conformed with literature data. Geostatistical analysis revealed that both spatial variability of net CO2 uptake as well as size of individual hot spots of this parameter increased with crust development. The latter never exceeded 4 mm in the investigated crusts, which points to the necessity of high resolution imaging for linking remote sensing with ecophysiology. Perspectively, the new method could be used for field monitoring of both biological soil crusts and vascular vegetation.
Wie beeinflussen Kiefern (Pinus sylvestris L.) die Entwicklung der Benetzungshemmung auf Sandböden?
(2011)
High resolution mapping of Normalized Difference Vegetation Indices (NDVI) of biological soil crusts
(2012)
Dew formation was investigated in three developmental stages of biological soil crusts (BSC), which were collected along a catena of an inland dune and in the initial substrate. The Penman equation, which was developed for saturated surfaces, was modified for unsaturated surfaces and used for prediction of dewfall rates. The levels of surface saturation required for this approach were predicted using the water retention functions and the thicknesses of the BSCs. During a single event, dewfall increased with crust development from 0.08 kg m−2 for the initial substrate to 0.10, 0.20 and 0.25 kg m−2 for crusts stages 1 to 3, respectively, which was well reflected by the model response. The suggested mechanism of dew formation involves a delay in water saturation in near-surface soil pores and EPS where the crusts were thicker and where the water capacity was high, resulting in elevated vapor flux towards the surface. The results also indicate that the amount of dewfall was too low to observe water flow into deeper soil. Analysis of the soil water retention curves revealed that, despite the sandy mineral matrix, moist crusts with clogged by swollen EPS pores exhibited a clay-like behavior. It is hypothesized that BSCs gain double benefit from suppressing their competitors by runoff generation and from improving their water supply by dew collection. Despite higher amounts of dew, the water availability to the crust community decreases with crust development, which may be compensated by ecophysiological adaptation of crust organisms, and which may further suppress higher vegetation or mosses.
Cyanobacteria and green algae present in biological soil crusts are able to colonize mineral substrates even under extreme environmental conditions. As pioneer organisms, they play a key role during the first phases of habitat colonization. A characteristic crust was sampled 3 years after installation of the artificial water catchment “Chicken creek”, thus representing an early successional stage of ecosystem development. Mean annual rainfall and temperature were 559 mm and 9.3°C, respectively. We combined scanning electron microscopy (SEM/EDX) and infrared (FTIR) microscopy to study the contact zone of algal and cyanobacterial mucilage with soil minerals in an undisturbed biological soil crust and in the subjacent sandy substrate. The crust was characterized by an approximately 50 μm thick surface layer, where microorganisms resided and where mineral deposition was trapped, and by an approximately 2.5 mm thick lower crust where mineral particles were stabilized by organo-mineral structures. SEM/EDX microscopy was used to determine the spatial distribution of elements, organic compounds and minerals were identified using FTIR microscopy and X-ray diffraction (XRD). The concentration of organic carbon in the crust was about twice as much as in the parent material. Depletion of Fe, Al and Mn in the lower crust and in the subjacent 5 mm compared to the geological substrate was observed. This could be interpreted as the initial phase of podzolization. Existence of bridging structures between mineral particles of the lower crust, containing phyllosilicates, Fe compounds and organic matter (OM), may indicate the formation of organo-mineral associations. pH decreased from 8.1 in the original substrate to 5.1 on the crust surface 3 years after construction, pointing to rapid weathering of carbonates. Weathering of silicates could not be detected.
Soil water
(2010)