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  • GBV  (35)
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  • 1
    UID:
    (DE-627)668893052
    Format: X, 103 S. , Ill., graph. Darst
    Note: München, Univ., Diss., 2011
    Language: English
    Keywords: Hochschulschrift
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  • 2
    UID:
    (DE-627)1779345348
    Format: X , Ill., graph. Darst
    Note: Dissertation München Univ., Diss 2011
    Language: English
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  • 3
    UID:
    (DE-627)670330698
    Format: Online-Ressource
    Note: München, Ludwig-Maximilians-Universität, Diss., 2011
    Additional Edition: Druckausg Soil moisture retrieval using high spatial resolution polarimetric L-band multibeam radiometer (PLMR) data at the field scale
    Language: English
    Keywords: Hochschulschrift
    URL: Volltext  (kostenfrei)
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  • 4
    UID:
    (DE-627)1823175864
    Format: Diagramme
    ISSN: 2072-4292
    Content: Environmental remote sensing has faced increasing satellite data availability, advanced algorithms for thematic analysis, and novel concepts of ground truth. For that reason, contents and concepts of learning and teaching remote sensing are constantly evolving. This eventually leads to the intuition of methodologically linking academic learning assignments with case-related scopes of application. In order to render case-related learning possible, smart teaching and interactive learning contexts are appreciated and required for remote sensing. That is due to the fact that those contexts are considered promising to trigger and gradually foster students' comprehensive interdisciplinary thinking. To this end, the following contribution introduces the case-related concept of applying simulation games as a promising didactic format in teaching/learning assignments of remote sensing. As to methodology, participating students have been invited to take on individual roles bound to technology-related profiles (e.g., satellite-mission planning, irrigation, etc.) Based on the scenario, stakeholder teams have been requested to elaborate, analyze and negotiate viable solutions for soil moisture monitoring in a defined context. Collaboration has been encouraged by providing the protected, specifically designed remoSSoil-incubator environment. This letter-type paper aims to introduce the simulation game technique in the context of remote sensing as a type of scholarly teaching; it evaluates learning outcomes by adopting certain techniques of scholarship of teaching and learning (SoTL); and it provides food for thought of replicating, adapting and enhancing simulation games as an innovative, disruptive next-generation learning environment in remote sensing.
    In: Remote sensing, Basel : MDPI, 2009, 12(2020), 4, Artikel-ID 735, 2072-4292
    In: volume:12
    In: year:2020
    In: number:4
    In: elocationid:735
    Language: English
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  • 5
    UID:
    (DE-627)1823194079
    Format: Illustrationen
    ISSN: 1866-6299
    Content: 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.
    In: Environmental earth sciences, Berlin : Springer, 2009, 69(2013), 2, Seite 317-333, 1866-6299
    In: volume:69
    In: year:2013
    In: number:2
    In: pages:317-333
    Language: English
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  • 6
    UID:
    (DE-627)1823249205
    Format: Illustrationen
    ISBN: 9783944101781
    Content: Die Umweltfernerkundung hat in den vergangenen zwei Jahrzehnten enorme Fortschritte erzielt und bietet Potenzial zur Erweiterung behördlicher Geodatenbestände und vielfältiger Anwendung. Insbesondere abbildende Hyperspektraldaten und Thermaldaten bieten die Möglichkeit für die Bereitstellung neuer thematische Informationen (GIS-Layer). Ein zentrales Einsatzgebiet ist die Analyse, Visualisierung und Bereitstellung von Informationen zu den Wirkungsketten grüner und blauer Infrastruktur in Landschaften und Siedlungen. Der Beitrag vermittelt einen Überblick zu Anwendungen, Aspekten und Anforderungen an die Akquise von Hyperspektraldaten und Thermaldaten.
    In: Dresdner Flächennutzungssymposium (12. : 2020 : Dresden), Flächennutzungsmonitoring XII, Berlin : Rhombos-Verlag, 2020, (2020), Seite 241-250, 9783944101781
    In: year:2020
    In: pages:241-250
    Language: German
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  • 7
    UID:
    (DE-627)1823178545
    Format: Diagramme, Illustrationen
    ISSN: 1872-7034
    Content: Impacts of human civilization on ecosystems threaten global biodiversity. In a changing environment, traditional in situ approaches to biodiversity monitoring have made significant steps forward to quantify and evaluate BD at many scales but still, these methods are limited to comparatively small areas. Earth observation (EO) techniques may provide a solution to overcome this shortcoming by measuring entities of interest at different spatial and temporal scales. This paper provides a comprehensive overview of the role of EO to detect, describe, explain, predict and assess biodiversity. Here, we focus on three main aspects related to biodiversity − taxonomic diversity, functional diversity and structural diversity, which integrate different levels of organization − molecular, genetic, individual, species, populations, communities, biomes, ecosystems and landscapes. In particular, we discuss the recording of taxonomic elements of biodiversity through the identification of animal and plant species. We highlight the importance of the spectral traits (ST) and spectral trait variations (STV) concept for EO-based biodiversity research. Furthermore we provide examples of spectral traits/spectral trait variations used in EO applications for quantifying taxonomic diversity, functional diversity and structural diversity. We discuss the use of EO to monitor biodiversity and habitat quality using different remote-sensing techniques. Finally, we suggest specifically important steps for a better integration of EO in biodiversity research. EO methods represent an affordable, repeatable and comparable method for measuring, describing, explaining and modelling taxonomic, functional and structural diversity. Upcoming sensor developments will provide opportunities to quantify spectral traits, currently not detectable with EO, and will surely help to describe biodiversity in more detail. Therefore, new concepts are needed to tightly integrate EO sensor networks with the identification of biodiversity. This will mean taking completely new directions in the future to link complex, large data, different approaches and models.
    In: Ecological indicators, Amsterdam [u.a.] : Elsevier Science, 2001, 70(2016), Seite 317-339, 1872-7034
    In: volume:70
    In: year:2016
    In: pages:317-339
    Language: English
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  • 8
    UID:
    (DE-627)182319737X
    Content: Spatial distributed information of isochronal surface soil moisture is very important to compensate the inaccuracy of initial conditions and the uncertainty of parameters in hydrological models at the landscape scale. In this paper the conceptual procedure to derive spatial distributed surface soil moisture values from synthetic aperture radar data by using ancillary optical remote sensing data is presented. Different biophysical vegetation parameters like vegetation water content and leaf area index overlay the soil moisture information on the microwave signal and hamper the application of many existing models. Therefore the objective of the proposed study is to test the performance of artificial neural networks to extract soil moisture information from radar data. Multi- and hyperspectral remote sensing data provide spatial distributed information about above ground vegetation parameters and can thereby used as ancillary network input to support the soil moisture extraction. The results of the study are expected to provide an improved database (initial conditions, plant parameters, etc.) for hydrological models.
    In: International Environmental Modelling and Software Society, Proceedings of the IEMSS Forth Biennial Meeting ; Vol. 1: Integrating sciences and information technology for environmental assessment and decision making, Barcelona : Universidad Politécnica de Cataluña, 2008, (2008), Seite 415-421
    In: year:2008
    In: pages:415-421
    Language: English
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  • 9
    UID:
    (DE-627)1823176364
    Format: Diagramme, Illustrationen
    ISSN: 2072-4292
    Content: Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes.
    In: Remote sensing, Basel : MDPI, 2009, 12(2020), 3, Artikel-ID 551, 2072-4292
    In: volume:12
    In: year:2020
    In: number:3
    In: elocationid:551
    Language: English
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  • 10
    UID:
    (DE-627)1823176984
    Format: Diagramme, Illustrationen
    ISSN: 2072-4292
    Content: 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 time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring.
    In: Remote sensing, Basel : MDPI, 2009, 11(2019), 21, Artikel-ID 2541, 2072-4292
    In: volume:11
    In: year:2019
    In: number:21
    In: elocationid:2541
    Language: English
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