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  • 1
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    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: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, June 2015, Vol.8(6), pp.2393-2404
    Description: Advanced classifiers, e.g., partial least squares discriminant analysis (PLS-DA) and random forests (RF), have been recently used to model reflectance spectral data in general, and of soil properties in particular, since their spectra are multivariate and highly collinear. Preprocessing transformations (PPTs) can improve the classification accuracy by increasing the variability between classes while decreasing the variability within classes. Such PPTs are common practice prior to a PLS-DA, but are rarely used for RF. The objectives of this paper are twofold: to compare the performances of PLS-DA and RF for modeling the spectral reflectance of soil in changed land-uses with different treatments and to compare the effects of nine different PPTs on the prediction accuracy of each of these classification methods. Differences in six physical, biological, and chemical soil properties of changed land-uses from the northern Negev Desert in Israel were evaluated. Significant differences were found between soil properties, which are used to classify land-uses and treatments. Depending on the dataset, different PPTs improved the classification accuracy by 11%-24% and 32%-42% for PLS-DA and RF, respectively, in comparison to the spectra without PPT. Out of the PPTs tested, the generalized least squares weighting (GLSW)-based transformations were found to be the most effective for most classifications using both PLS-DA and RF. Our results show that both PLS-DA and RF are suitable classifiers for spectral data, provided that an appropriate PPT is applied.
    Keywords: Radio Frequency ; Soil Measurements ; Accuracy ; Laboratories ; Standards ; Soil Properties ; Generalized Least Squares Weighting (Glsw) ; Land-Use Changes ; Partial Least Squares Discriminant Analysis (Pls-Da) ; Preprocessing Transformations (Ppts) ; Random Forests (Rf) ; Soil Spectroscopy ; Geology
    ISSN: 1939-1404
    E-ISSN: 2151-1535
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
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  • 4
    Language: English
    In: Ecological Indicators, February 2018, Vol.85, pp.190-203
    Description: By adding attributes of space and time to the spectral traits (ST) concept we developed a completely new way of quantifying and assessing land use intensity and the hemeroby of urban landscapes. Calculating spectral traits variations (STV) from remote sensing data and regressing STV against hemeroby, we show how to estimate human land use intensity and the degree of hemeroby for large spatial areas with a dense temporal resolution for an urban case study. We found a linear statistical significant relationship (p = 0.01) between the annual amplitude in spectral trait variations and the degree of hemeroby. It was thereof possible to separate the different types of land use cover according to their degree of hemeroby and land use intensity, respectively. Moreover, since the concept of plant traits is a functional framework in which each trait can be assigned to one or more ecosystem functions, the assessment of STV is a promising step towards assessing the diversity of spectral traits in an ecosystem as a proxy of functional diversity.
    Keywords: Spectral Traits (St) ; Spectral Trait Variations (Stv) ; Urban Land-Use-Intensity (U-Lui) ; Human-Use-Intensity ; Remote Sensing ; Hemeroby ; Ndvi ; Glcm ; Environmental Sciences
    ISSN: 1470-160X
    E-ISSN: 1872-7034
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  • 5
    Language: English
    In: Canadian Journal of Remote Sensing, 13 September 2013, Vol.39(3), pp.191-207
    Description: We describe a study using the ASIA-Eagle hyperspectral sensor to measure the spectral response of spring barley over an entire climate-controlled growing season and correlate those results with the results of 25 biophysical and biochemical parameters....
    Keywords: Geography
    ISSN: 0703-8992
    E-ISSN: 1712-7971
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  • 6
    Language: English
    In: Remote Sensing, 01 November 2015, Vol.7(11), pp.15748-15781
    Description: Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove...
    Keywords: Land-Use Change ; Imaging Spectroscopy ; Reflectance Spectroscopy ; Spectral Soil Quality Index ; Soil Quality Index ; Geography
    E-ISSN: 2072-4292
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  • 7
    Language: English
    In: Journal of Molecular Medicine, 2012, Vol.90(6), pp.625-635
    Description: To meet the growing need for tissue replacement materials for our aging population, the development of new adaptive biomaterials is essential. The tissues with the highest demand for implant materials are skin and bone. These tissues share various similarities, including signaling pathways and extracellular matrix composition. Glycosaminoglycans such as hyaluronan and chondroitin sulfate are the major organic extracellular matrix components. They modulate the attraction of skin and bone precursor cells and their subsequent differentiation and gene expression and regulate the action of proteins essential to bone and skin regeneration. The precise action of glycosaminoglycans varies according to their structural composition mainly in respect to the degree of sulfation and polymer length. Changes in the glycosaminoglycan composition are frequently seen in physiological and pathological remodeling processes, such as bone formation or scaring. Here, we review the current state of knowledge of how the most common glycosaminoglycan, chondroitin sulfate and hyaluronan, interact with bone and skin cells, and summarize their potential in tissue engineering for skeletal and skin diseases.
    Keywords: Glycosaminoglycans ; Extracellular matrix ; Biomaterial design ; Skin ; Bone ; Wound healing
    ISSN: 0946-2716
    E-ISSN: 1432-1440
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  • 8
    Language: English
    In: Schizophrenia Research, 2005, Vol.73(2), pp.357-366
    Description: Low estrogen levels leading to an elevated rate of menstrual dysfunctions such as amenorrhea and irregular menstruation have been described in women with schizophrenia and have often been attributed to antipsychotic-induced hyperprolactinemia. However, there is some evidence that “hypoestrogenism” in schizophrenic women does not occur exclusively under medication with hyperprolactinemia-inducing antipsychotics. While the precise mechanism of low estrogen levels in schizophrenic women has not been elucidated yet, “hypoestrogenism” is of clinical relevance because estrogen seems to endow an antipsychotic-like effect in schizophrenia and thus positively affect the course of illness in schizophrenic women. In addition, low levels of estrogen might have a negative effect on bone mineral density and on the cardiovascular system. To test the “hypoestrogenism hypothesis”, hormone levels in 75 women with schizophrenia diagnosed according to DSM-IV and ICD-10 were determined in the follicular, periovulatory, and luteal phases of the menstrual cycle. Levels of estradiol, prolactin, luteinizing hormone (LH), follicle-stimulating hormone (FSH), progesterone, and testosterone were assessed. The serum levels of estradiol were generally reduced during the entire menstrual cycle compared to normal reference values. With low levels of LH over the entire cycle and of progesterone in the luteal phase, anovulatory cycles were assumed. Hypoestrogenism was found in about 60% of the patients in accordance with a strict definition (estradiol serum level below 30 pg/ml in the follicular phase and below 100 pg/ml in the periovulatory phase). To rule out a possible effect of hyperprolactinemia on the gonadal axis and a subsequent effect on estradiol levels from treatment with conventional (“typical”) antipsychotics, serum estradiol levels of patients treated with certain atypical antipsychotics known to induce only a mild increase in prolactin, or no increase at all, were compared with those from patients treated with conventional antipsychotics. The data clearly indicate high prolactin levels in the latter, but low levels in the group treated with atypical antipsychotics. In both groups, however, low levels of estradiol compared to normal reference values were measured. The present findings provide evidence that hypoestrogenism in schizophrenia occurs in women with and without antipsychotic-induced hyperprolactinemia. Further research should be conducted to clarify the cause of hypoestrogenism in schizophrenic women and focus on possible clinical implications.
    Keywords: Schizophrenia ; Estrogen ; Estradiol ; Prolactin ; Sex Hormones ; Hypoestrogenism ; Hyperprolactinemia ; Antipsychotics
    ISSN: 0920-9964
    E-ISSN: 1573-2509
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  • 9
    Language: English
    In: Schizophrenia Research, 2005, Vol.74(2), pp.125-134
    Description: The expected therapeutic effect of estrogen as an adjunct treatment to antipsychotics in women suffering from schizophrenia for relapse prevention was to be tested under real-life conditions. A multicenter, randomized, placebo-controlled, double-blind, cross-over study based on an A–B–A–B (and/or B–A–B–A) design was applied. Forty-six hypoestrogenic women with schizophrenia hospitalized for the first time or repeatedly were included in the study. Their average age was 37.9 and they had been suffering from schizophrenia for 8.4 years. During the drug treatment phases, they received a three-phase estrogen–gestagen combination drug (17β-estradiol+norethisterone acetate) in addition to an antipsychotic drug. Significant effects of the adjuvant hormone replacement therapy on the estradiol levels could be observed, and high and low levels of estradiol prevailed in the active drug and placebo phases, respectively. We did not find any difference either in defined relapse events or in the psychopathology between estradiol replacement and placebo phases. Neither did the required antipsychotic doses or the tolerance data differ between the two phases. Thus, the results of our study do confirm the hypothesis that a combined estradiol/antipsychotic therapy is superior to an antipsychotic monotherapy for relapse prevention.
    Keywords: Schizophrenia ; Estrogens ; Estradiol ; Hormone Replacement Therapy ; Estrogen Protection Hypothesis ; Hypoestrogenism
    ISSN: 0920-9964
    E-ISSN: 1573-2509
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  • 10
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