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  • 1
    Language: English
    In: Environmental Monitoring and Assessment, 2013, Vol.185(11), pp.9419-9434
    Description: In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.
    Keywords: Monitoring ; Landscape heterogeneity ; Hyperspectral imagery ; Semivariogram ; Scale effects
    ISSN: 0167-6369
    E-ISSN: 1573-2959
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  • 2
    Language: English
    In: Ecological Modelling, 10 January 2015, Vol.295, pp.123-135
    Description: The aim of this paper was to create a model that predicts the different phenological BBCH macro-stages of barley in laboratory on the plot scale and to transfer the most suitable model to the landscape scale. To characterise the phenology, eight vitality and phenology-related vegetation parameters like leaf area index (LAI), Chl-SPAD content, C-content, N-content, C/N-content, canopy chlorophyll content (CCC), gravimetric water content (GWC) and vegetation height at the same time as all imaging hyperspectral measurements (AISA-EAGLE, 395–973 nm). These biochemical–biophysical vegetation parameters were investigated according to the different phenological macro-stages of barley. The predictive models were developed using four different types of vegetation indices (VI): (I) published VI’s, (II) reflectance VI’s as well as (III) VI formula combinations and (IV) a combination of all VI index types using the Library for Support Vector Machines (LibSVM) and tested with a recursive conditional correlation weighting selection algorithm (RCCW) to reduce the number of variables. To increase the performance of the model a 10-fold cross-validation was carried out for all statistical models. The GWC was found to be the most important variable for differentiating between the phenological macro-stages of barley. The most suitable model for predicting the phenological BBCH macro-stages was achieved by a model that combined all three kinds of VI’s: published VI’s, reflectance VI’s and formula combination VI’s with a classification accuracy of 84.80%. With the classification model for the reflectance VI’s = 746 nm and for the VI formula combinations = (527 + 612) nm and = (540 + 639) nm. The best predictive model was applied to the airborne AISA-EAGLE hyperspectral data to model the phenological macro-stages of barley at the landscape level. The classification error of the best predictive model of 12.80% as well as disturbance factors such as channels and areas with weeds or ruderal vegetation lead to misclassifications of BBCH macro-stages at the landscape level. By using One Sensor At Different Scales-Approach (OSADIS), sensor-specific differences in the model building and model transfer can be eliminated. The approach described in the paper for determining the phenology based on imaging hyperspectral RS data shows that in the process of plant phonological development a number of biochemical–biophysical vegetation traits in vegetation change, which can be thoroughly recorded with hyperspectral remote sensing technology. For this reason, hyperspectral RS constitutes an ideal, cost-effective and comparable approach, with whose help vegetation traits and changes can be quantified, which are key for ecological modelling.
    Keywords: Phenological Stage ; Bbch Barley ; Hyperspectral Sensor ; AISA ; Spectral Indices ; Vegetation Characteristics ; Environmental Sciences ; Ecology
    ISSN: 0304-3800
    E-ISSN: 1872-7026
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  • 3
    Language: English
    In: Environmental Monitoring and Assessment, 2013, Vol.185(2), pp.1215-1235
    Description: Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R 2 of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.
    Keywords: Hyperspectral remote sensing ; Spatiotemporal scale ; Controlled long-term laboratory experiment ; Imaging spectroscopy ; Semivariogram ; AISA-EAGLE/HAWK (DUAL)
    ISSN: 0167-6369
    E-ISSN: 1573-2959
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  • 4
    Language: English
    In: Psychoneuroendocrinology, June 2019, Vol.104, pp.49-54
    Description: Sex differences in self-control become apparent during preschool years. Girls are better able to delay their gratification and show less attention problems and overactive behavior than boys. In this context, organizational effects of gonadal steroids affecting the neural circuitry underlying self-control could be responsible for these early sex differences. In the present study testosterone levels measured in amniotic fluid (via ultra performance liquid chromatography and tandem mass spectrometry) were used to examine the role of organizational sex hormones on self-control. One hundred and twenty-two 40-month-old children participated in a delay of gratification task (DoG task) and their parents reported on their attention problems and overactive behavior. Girls waited significantly longer for their preferred reward than boys, and significantly more girls than boys waited the maximum period of time, providing evidence for sex differences in delay of gratification. Boys that were rated as suffering from more attention problems and overactive behavior waited significantly shorter in the DoG task. Amniotic testosterone measures were reliable in boys only. Most importantly, boys who waited shorter in the DoG task and boys who were reported to suffer from more attention problems and overactive behavior had higher prenatal testosterone levels. These findings extend our knowledge concerning organizational effects of testosterone on the brain circuitry underlying self-control in boys, and are of relevance for understanding how sex differences in behavioral disorders are connected with a lack of self-control.
    Keywords: Sex Differences ; Prenatal Testosterone ; Amniocentesis ; Self-Control ; Delay of Gratification ; Attention Problems ; Overactive Behavior ; Medicine ; Anatomy & Physiology
    ISSN: 0306-4530
    E-ISSN: 1873-3360
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