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  • Pause, M.  (11)
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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: Canadian Journal of Remote Sensing, 02 January 2014, Vol.40(1), pp.15-25
    Description: This study presents the retrieval of near-surface soil moisture data below crop canopies (winter rye and winter barley) from airborne L-band radiometer observations using a radiative transfer model at very dry soil moisture conditions (〈15 Vol.%). Using physically based models, the roughness...
    Keywords: Geography
    ISSN: 0703-8992
    E-ISSN: 1712-7971
    Source: Taylor & Francis (Taylor & Francis Group)
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  • 5
    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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  • 6
    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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  • 7
    Language: English
    In: Vadose Zone Journal, 2013, Vol.12(4), p.0
    Description: The identification of spatial and temporal patterns of soil properties and moisture structures is an important challenge in environmental and soil monitoring as well as for soil landscape model approaches. This work examines the use of hyperspectral remote sensing techniques for quantifying geophysical parameters from the hyperspectral reflectance of the vegetation canopy. These can be used as proxies of the underlying soil and soil water conditions. Different spectral index derivatives, single band reflectance, and spectral indices from the airborne hyperspectral sensor AISA were quantified and tested in univariate and multivariate regression models for their correlation with geophysical measurements with electromagnetic induction (EMI) and gamma-ray spectrometry. The best univariate models for predicting electrical conductivity based on spectral information were based on the vertical dipole of an EM38DD with an R (super 2) = 0.54 with the spectral index Normalized Pigments Reflectance Index (NPCI) as well as for the horizontal dipole of an EM38DD with an R (super 2) = 0.65 with the spectral index NPCI. For predicting soil characteristics measured with gamma-ray spectrometry we received the best model results for gamma Th with an R (super 2) = 0.55 with the spectral index NPCI and gamma K with an R (super 2) = 0.44 with the spectral index Triangular Vegetation Index (TVI) and NPCI. The combination of variables including the geographical elevation was tested as the input for a multivariate regression analysis. For EMI and gamma-ray measurements, the "elevation" was found to be the most predictive variable and an integration of spectral indices into the elevation-based model led to only a slight improvement in the predictive power for EMI. An improvement could be made to explain the variance of gamma-ray measurement signals by combining elevation and spectral information.
    Keywords: Applied Geophysics ; Soils ; Airborne Methods ; Biochemistry ; Biophysics ; Central Europe ; Data Processing ; Elastic Waves ; Electrical Conductivity ; Electromagnetic Induction ; Electromagnetic Methods ; Elevation ; Europe ; Field Studies ; Fluvial Features ; Gamma-Ray Methods ; Gamma-Ray Spectra ; Geophysical Methods ; Geophysical Surveys ; Germany ; Global Positioning System ; Ground Methods ; Heterogeneity ; Hyperspectral Analysis ; Indicators ; Infrared Spectra ; Landform Description ; Landscapes ; Mapping ; Measurement ; Monitoring ; Multivariate Analysis ; Photochemistry ; Photosynthesis ; Quantitative Analysis ; Radioactivity Methods ; Regression Analysis ; Remote Sensing ; Rosslau Germany ; Rosslauer Oberluch ; Saxony-Anhalt Germany ; Short-Period Waves ; Soils ; Spatial Distribution ; Spectra ; Statistical Analysis ; Surveys ; Temporal Distribution ; Univariate Analysis ; Unsaturated Zone ; Vegetation;
    ISSN: Vadose Zone Journal
    E-ISSN: 1539-1663
    Source: CrossRef
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  • 8
    Language: English
    In: Remote Sensing, 01 June 2016, Vol.8(6), p.471
    Description: For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.
    Keywords: Remote Sensing ; in Situ Sampling ; Sensor Networks ; Monitoring ; Standardization ; Forest Health ; Sentinel Satellites ; Copernicus ; Geography
    E-ISSN: 2072-4292
    Source: Directory of Open Access Journals (DOAJ)
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  • 9
    Language: English
    In: Remote Sensing, 01 July 2018, Vol.10(7), p.1120
    Description: Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.
    Keywords: Forest Health ; in Situ Forest Monitoring ; Remote Sensing ; Data Science ; Digitalization ; Big Data ; Semantic Web ; Linked Open Data ; Fair ; Multi-Source Forest Health Monitoring Network ; Geography
    E-ISSN: 2072-4292
    Source: Directory of Open Access Journals (DOAJ)
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  • 10
    Language: English
    In: Remote Sensing, 01 October 2019, Vol.11(21), p.2541
    Description: In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral...
    Keywords: Ndvi ; Glyphosate ; Herbicide ; Sentinel-2 ; Broadband Spectral Indices ; Vegetation Traits ; Precision Farming ; Time-Series ; Roundup® ; Insects ; Biodiversity ; Soil Health Monitoring ; Geography
    E-ISSN: 2072-4292
    Source: Directory of Open Access Journals (DOAJ)
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