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  • Merbach, Ines  (4)
Type of Medium
  • 1
    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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  • 2
    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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  • 3
    In: Photogrammetrie - Fernerkundung - Geoinformation, October 2012, Vol.2012(5), pp.589-601
    Description: Hyperspectral remote-sensing data can contribute significantly to data analysis in research, opening up a wide spectrum for fields of application due to geometrical as well as spectral characteristics, e.g. in water status analysis, in the classification of vegetation types, in the classification of physical-biochemical vegetation parameters, in classifying soil composition and structure, and in determining large-scale soil contamination. Hence, there is a tremendous demand for hyperspectral information. However the use of commercial hyperspectral data is associated with a number of problems and a great deal of time and effort is required for research using hyperspectral data that spans different spatial and/or hierarchical as well as temporal scales. As a result few investigations have been conducted on the causal relationships between imaging hyperspectral signals and meaningful vegetation variables over a longer monitoring period. At the Helmholtz Centre for Environmental Research (UFZ) Leipzig a scale-specific hyperspectral remote sensing based on the sensors AISAEAGLE (400-970 nm) and AISA-HAWK (970-2500 nm) has been set up. On three different scales (plot, local and regional) intensive investigations are being carried out on the spatio-temporal responses of biophysical and biochemical state variables of vegetation, soil and water compared to the hyperspectral response. This paper introduces and discusses the scale approach and demonstrates some preliminary examples from its implementation.
    Keywords: Imaging Hyperspectral Remote Sensing ; Vegetation Monitoring ; Multi - Scale Analyses
    ISSN: 1432-8364
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  • 4
    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. The spectrum of each hyperspectral image...
    Keywords: Geography
    ISSN: 0703-8992
    E-ISSN: 1712-7971
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