Christian Götze

Spectrometric analyses in comparison to the physiological condition of heavy metal stressed floodplain vegetation in a standardised experiment

Floodplain ecosystems are affected by flood dynamics, nutrient supply as well as anthropogenic activities. Heavy metal pollution poses a serious environmental challenge. Pollution transfer from the soil to vegetation is still present at the central location of Elbe River, Germany. The goal of this study was to assess and separate the current heavy metal contamination of the floodplain ecosystem, using spectrometric field and laboratory measurements. A standardized pot experiment with floodplain vegetation in differently contaminated soils provided the basis for the measurements. The dominant plant types of the floodplains are: Urtica dioica, Phalaris arundinacea and Alopecurus pratensis, these were also chemically analysed. Various vegetation indices and methods were used to estimate the red edge position, to normalise the spectral curve of the vegetation and to investigate the potential of different methods for separating plant stress in floodplain vegetation. The main task was to compare spectral bands during phenological phases to find a method to detect heavy metal stress in plants. A multi-level algorithm for the curve parameterisation was developed. Chemo-analytical and ecophysiological parameters of plants were considered in the results and correlated with spectral data. The results of this study show the influence of heavy metals on the spectral characteristics of the focal plants. The developed method (depth CR1730) showed significant relationship between the plants and the contamination.

An approach for the classification of pioneer vegetation based on species-specific phenological patterns using laboratory spectrometric measurements

Abstract This paper aims to develop a flexible decision-tree framework for the classification of nine spectrally highly similar pioneer species for monitoring heterogeneous grassland habitats, which is based on different plant phenological indicators. For typical pioneer species, laboratory spectroscopic measurements were taken. Reflectance spectra were collected to cover a complete phenological cycle. First, a combined spectral similarity measure that consists of two independent methods was applied. Second, phenological metrics were derived from time series of NDVI for each species to describe differences in annual plant development. Further, spectral vegetation indices were applied that are related to plant physiological properties. The investigated species could be grouped into three spectral separability clusters. With increasing spectral similarities, species of the particular groups can be separated into subgroups and individual species that show a similar phenological development. The remaining subgroups with the highest spectral and phenological similarities could be divided to individual species by consideration of plant physiological parameters. The results show that the combination of different spectral methods with phenological metrics enhanced the classification of pioneer vegetation and other spectrally similar species. The approach can be used as basis for a continuous monitoring of fast changing habitats.

White-reference based post-correction method for multi-source spectral libraries

ry data producers and the data archivists but most importantly the high-end data users (Milton et al. 2006). Even now practical questions are still of high importance and need to be discussed before going into more complex levels. However, to discuss the whole data chain would go beyond the capabilities of this paper. For this case a reduced but highly focused aim is advisable. This reduction leads to the very first level of the spatial data scale, the

Spectral characterization of black materials for use as background in spectrometric laboratories

ABSTRACT This paper describes a study aimed at the assessment of low-reflectance equipment in a laboratory using spectral measurements. The use of reflectance spectrometers is common for the detection of soils, minerals, vegetation, and manmade materials, but comparability remains questionable. It is well-known that several factors can have profound effects on measured reflectance spectra. In this paper, we present a low-reflectance material that eliminates the impact of light effects from sample backgrounds or the equipment and walls of dark chambers. For laboratory spectral measurements, two different setups (changes in geometry) of the experimental design with ASD FieldSpec Pro were performed. Overall, 23 visible black materials were measured. Statistical analysis revealed that only three materials were useful for the investigated topic. These materials (foam rubber, cellulose fleece, and thermal coating) exhibit low reflection, below 10%, are flat and featureless in the spectral curve and have low spectral variation by rotation. In contrast, 20 other materials showed either low reflectance in visible light, a high spectral variation by rotation or in the second setup, high specular radiation. The results indicated that in certain cases, laboratory reflectance measurements made by a non-qualified background material were not truly reproducible or suitable for a spectral library. This experiment demonstrates a problem in laboratory scenarios involving remote sensing working groups, helps to place our measurements in context, and forms the basis for comparison with other studies.

Detecting heavy metal pollution of floodplain vegetation in a pot experiment using reflectance spectroscopy

ABSTRACT Floodplain soils at the Elbe River are frequently polluted by heavy metals. Metal enrichments are linked to the composition of the floodplain’s plant community. Various studies have shown that soil characteristics, floodplain geomorphology and other factors may influence plant physiology and have demonstrated an overlapping of heavy metal pollution and growing conditions such as water and nutrient supply. The goal of this study was to assess and separate the current heavy metal contamination of the floodplain vegetation from other parameters using spectrometric laboratory measurements. A standardized pot experiment with floodplain vegetation in differently contaminated soils provided the basis for measurements. Various vegetation indices and spectral methods were used to normalize the spectral curve of the vegetation and to investigate the potential of different methods for separating plant stress in floodplain vegetation. The results of this study show the influence of heavy metals on the spectral characteristics of the focal plants. From 11 tested methods, 5 showed a significant correlation (R2 > 0.6) to heavy metal content. Most methods with a significant correlation to heavy metal also showed a high correlation (R2 > 0.5) to other investigated parameters such as chlorophyll content and nutrient content. Only the developed method (band depth at continuum removal spectra at 1725 nm (CR1725)) showed a significant relationship to the heavy metal load (R2 = 0.644) and not to other parameters, and can therefore be used as a basis for further work in field studies in the context of heavy metal stress in floodplain plants.