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  • 1
    In: Journal of Biogeography, December 2016, Vol.43(12), pp.2502-2512
    Description: To purchase or authenticate to the full-text of this article, please visit this link: http://onlinelibrary.wiley.com/doi/10.1111/jbi.12781/abstract Byline: Gudrun Carl, Daniel Doktor, Oliver Schweiger, Ingolf Kuhn Keywords: discrete wavelet transform; generalized linear model; multimodel inference; remote-sensing signal; spatial scales; vegetation period Abstract Aim Assessing the relationship between a spatial process and environmental variables as a function of spatial scale is a challenging problem. Therefore, there is a need for a valid and reliable tool to examine and evaluate scale dependencies in biogeography, macroecology and other earth sciences. Location Central Europe (latitude 43.99[degrees]-54.22[degrees] N, longitude 4.79[degrees]-15.02[degrees] E). Methods We present a method for applying two-dimensional wavelet analysis to a generalized linear model. This scale-specific regression is combined with a multimodel inference approach evaluating the relative importance of several environmental variables across different spatial scales. We apply this method to data of climate, topographic and land cover variables to explain variation in annual greening of vegetation (i.e. phenology) in Central Europe. Results Land use is more important to explain the variation in greening than climate at smaller resolution while climate is more important at larger resolution with a shift at c. 1000 km.sub.2. Main conclusions To the best of our knowledge, this is the first study analysing the scale dependency of an ecosystem process, clearly distinguishing between the different components of scale, namely grain, focus and extent. The obtained results demonstrate that our newly proposed method is particularly suitable for studying scale dependencies of various spatial processes on environmental drivers keeping grain and extent constant and changing focus (i.e. resolution). Article Note: Editor: Richard Pearson CAPTION(S): Appendix S1 Additional information about data sets. Appendix S2 R code for calculating scale-specific regressions.
    Keywords: Discrete Wavelet Transform ; Generalized Linear Model ; Multimodel Inference ; Remote‐Sensing Signal ; Spatial Scales ; Vegetation Period
    ISSN: 0305-0270
    E-ISSN: 1365-2699
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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(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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  • 4
    Language: English
    In: Stochastic Environmental Research and Risk Assessment, 2013, Vol.27(5), pp.1221-1230
    Description: Temporal shifts in phenology or vegetation period of plants are seen as indicators of global warming with potentially severe impacts on ecosystem functioning. In spite of increasing knowledge on drivers, it is of utmost importance to disentangle the relationship between air temperatures, phenological events, potential temporal lags (phase shifts) and time scale for certain plant species. Assessing the phase shifts as well as the scale-dependent relationship between temperature and vegetation phenology requires the development of a nonlinear temporal model. Therefore, we use wavelet analysis and present a framework for identifying scale-dependent cross-phase coupling of bivariate time series. It allows the calculation of (a) scale-dependent decompositions of time series, (b) phase shifts of seasonal components in relation to the annual cycle, and (c) inter-annual phase differences between seasonal phases of different time series. The model is applied to air temperature data and remote sensing phenology data of a beech forest in Germany. Our study reveals that certain seasonal changes in amplitude and phase with respect to the normal annual rhythm of temperature and beech phenology are coupled time-delayed components, which are characterized by a time shift of about one year.
    Keywords: Beech forest ; Bivariate time series analysis ; Coherence phase ; Morlet wavelet ; Normalized difference vegetation index ; Remote sensing signal
    ISSN: 1436-3240
    E-ISSN: 1436-3259
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  • 5
    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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  • 6
    Language: English
    In: Remote Sensing, 2014, Vol.6(12), pp.12247-12274
    Description: The machine learning method, random forest (RF), is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality and autocorrelation. Common multivariate regression approaches,...
    Keywords: Natural Sciences ; Earth And Related Environmental Sciences ; Naturvetenskap ; Geovetenskap Och Miljövetenskap ; Hyperspectral Data ; Vegetation Status ; Random Forest ; Prosail ; Crop
    ISSN: 2072-4292
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  • 7
    In: Journal of Vegetation Science, September 2016, Vol.27(5), pp.999-1011
    Description: Spatial patterns of pollination types (wind‐, insect‐, and self‐pollination) can be mapped at local scales with airborne imaging spectroscopy. We tested this potential and analyzed why information about pollination types can be retrieved from spectral data. The approach worked because pollination types differed in leaf and canopy traits that determine the spectral signal.
    Keywords: Ecosystem Services ; Functional Traits ; Grassland ; Hyperspectral ; Imaging Spectroscopy ; Mire
    ISSN: 1100-9233
    E-ISSN: 1654-1103
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  • 8
    Language: English
    In: Remote Sensing, 01 March 2017, Vol.9(3), p.254
    Description: Phenological metrics extracted from satellite data (phenometrics) have been increasingly used to access timely, spatially explicit information on crop phenology, but have rarely been calibrated and validated with field observations. In this...
    Keywords: Crop ; Phenology ; Moderate Resolution Imaging Spectrometer (Modis) ; Normalized Difference Vegetation Index (Ndvi) ; Phenological Metrics ; Field Observations ; Geography
    E-ISSN: 2072-4292
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  • 9
    Language: English
    In: Sensors, 2017, Vol.17(8), p.1855
    Description: Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions.A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and...
    Keywords: Life Sciences ; Vegetation Indexes ; Plant Phenology ; Satellite Data ; Land-Surface ; Time-Series ; Reflectance ; Models ; Calibration ; Extraction ; Climate ; Temperate Forest ; Remote Sensing ; Donnée Satellitaire ; Mesure Hyperspectrale ; Télédétection
    ISSN: 1424-8220
    E-ISSN: 1424-8220
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  • 10
    Language: English
    In: Remote Sensing of Environment, July 2017, Vol.196, pp.279-292
    Description: Numerous studies have investigated reflectance-based estimations of physico-chemical leaf traits such as the contents of light absorbing pigments, leaf mass per area, or carbon and nitrogen contents. Only few studies, however, attempted to estimate leaf traits that are more directly linked to photosynthesis. We tested the feasibility of estimating two important photosynthesis traits, the maximum carboxylation capacity ( ) and the maximum electron transport rate ( ), from in-situ leaf reflectance spectra using approaches that are applicable also on larger spatial scales. We conducted measurements of reflectance, CO response curves, leaf mass per area ( ), and nitrogen content per area ( ) for 37 temperate deciduous tree species and a total of 242 leaves from widely differing light environments. Partial least squares (PLS) regression was used to estimate , , , and from reflectance spectra. The results showed that both and can be estimated from leaf reflectance measurements with good accuracy (R = 0.64 for , R = 0.70 for ) even for a large number of tree species and varying light environments. Detailed analysis of reflectance-based PLS and linear regression models with regard to prediction performances and regression coefficients led to the conclusion that the correlation to was the dominating mechanism on which the and PLS models were based. The PLS regression coefficients of , and showed clear signatures of nitrogen-related absorption features. The finding that and estimations from leaf reflectance are predominantly based on their relationships to has important implications for large scale estimations of these photosynthesis parameters. We suggest that future studies should focus more on large scale estimation of from remote sensing and estimate and in a separate step using their respective relationships to .
    Keywords: Vcmax ; Jmax ; Nitrogen ; Photosynthesis ; Leaf Reflectance ; Pls ; Hyperspectral ; Environmental Sciences ; Geography ; Geology
    ISSN: 0034-4257
    E-ISSN: 1879-0704
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