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  • Zacharias, Steffen  (7)
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  • 1
    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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  • 2
    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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  • 3
    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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  • 4
    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...
    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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  • 5
    In: Lausch, Angela ; Borg, Erik ; Bumberger, Jan ; Dietrich, Peter ; Heurich, Marco ; Huth, Andreas ; Jung, András ; Klenke, Reinhard ; Knapp, Sonja ; Mollenhauer, Hannes ; Paasche, Hendrik ; Paulheim, Heiko ORCID: 0000-0003-4386-8195 ; Pause, Marion ; Schweitzer, Christian ; Schmulius, Christiane ; Settele, Josef ; Skidmore, Andrew K. ; Wegmann, Martin ; Zacharias, Steffen ; Kirsten, Toralf ; Schaepman, Michael E. (2018) Understanding forest health with remote sensing, part III: Requirements for a scalable multi-source forest health monitoring network based on data science approaches. Remote Sensing 10 7 1120 [Zeitschriftenartikel]
    Keywords: 004 Informatik ; 333.7 Natürliche Ressourcen, Energie und Umwelt ; 550 Geowissenschaften
    Source: Mannheim University Library
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  • 6
    Language: English
    In: Remote sensing, 2019, Vol.11(20), pp.1-51
    Description: In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits
    Keywords: Itc-Isi-Journal-Article ; Itc-Gold
    ISSN: 2072-4292
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  • 7
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