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  • 1
    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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  • 2
    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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  • 3
    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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  • 4
    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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  • 5
    Language: English
    In: Environmental Earth Sciences, 2013, Vol.69(2), pp.317-333
    Description: Sustainable water quality management requires a profound understanding of water fluxes (precipitation, run-off, recharge, etc.) and solute turnover such as retention, reaction, transformation, etc. at the catchment or landscape scale. The Water and Earth System Science competence cluster (WESS, http://www.wess.info/ ) aims at a holistic analysis of the water cycle coupled to reactive solute transport, including soil–plant–atmosphere and groundwater–surface water interactions. To facilitate exploring the impact of land-use and climate changes on water cycling and water quality, special emphasis is placed on feedbacks between the atmosphere, the land surface, and the subsurface. A major challenge lies in bridging the scales in monitoring and modeling of surface/subsurface versus atmospheric processes. The field work follows the approach of contrasting catchments, i.e. neighboring watersheds with different land use or similar watersheds with different climate. This paper introduces the featured catchments and explains methodologies of WESS by selected examples.
    Keywords: Water and solute fluxes ; Water quality ; Catchments ; Land-surface atmosphere exchange ; Processes and feedbacks ; Modeling ; Monitoring
    ISSN: 1866-6280
    E-ISSN: 1866-6299
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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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