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  • English  (62)
  • 11
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    Soil moisture retrieval using high spatial resolution Polarimetric L-Band Multi-beam Radiometer (PLMR) data at the field scale (2011)
    München : Universitätsbibliothek der Ludwig-Maximilians-Universität
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    UID:
    (DE-101)1015083978
    Format: Online-Ressource
    Note: München, Ludwig-Maximilians-Universität, Diss., 2011
    Additional Edition: Druckausg. Pause, Marion, 1979- Soil moisture retrieval using high spatial resolution polarimetric L-band multibeam radiometer (PLMR) data at the field scale
    Language: English
    Keywords: Hochschulschrift
    URN: urn:nbn:de:bvb:19-132176
    URL: https://nbn-resolving.org/urn:nbn:de:bvb:19-132176
    URL: https://d-nb.info/1015083978/34
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  • 12
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    Catchments as reactors : A comprehensive approach for water fluxes and solute turnover (2013)
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    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
    DOI: 10.1007/s12665-013-2281-7
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  • 13
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    Linking Earth Observation and taxonomic, structural and functional biodiversity : Local to ecosystem perspectives (2016)
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    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
    DOI: 10.1016/j.ecolind.2016.06.022
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  • 14
    Article
    Article
    Radar-based surface soil moisture retrieval over agricultural used sites : A multi-sensor approach (2008)
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    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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  • 15
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    Monitoring glyphosate-based herbicide treatment using Sentinel-2 time series-A proof-of-principle (2019)
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    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
    DOI: 10.3390/rs11212541
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  • 16
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    Linking remote sensing and geodiversity and their traits relevant to biodiversity - Part I : Soil characteristics (2019)
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    UID:
    (DE-627)1823177514
    Format: Diagramme, Illustrationen
    ISSN: 2072-4292
    Content: 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.
    In: Remote sensing, Basel : MDPI, 2009, 11(2019), 20, Artikel-ID 2356, 2072-4292
    In: volume:11
    In: year:2019
    In: number:20
    In: elocationid:2356
    Language: English
    DOI: 10.3390/rs11202356
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  • 17
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    Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices : Prospects and case study (2020)
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    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
    DOI: 10.3390/rs12030551
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  • 18
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    Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale (2012)
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    UID:
    (DE-627)1823248101
    ISSN: 1931-3195
    Content: The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear (R2〈0.90) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good (R2 = 0.93) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.
    In: Journal of applied remote sensing, Bellingham Wash. : SPIE, 2007, 6(2012), 1, 1931-3195
    In: volume:6
    In: year:2012
    In: number:1
    Language: English
    DOI: 10.1117/1.JRS.6.063516
    URL: lizenzpflichtig
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  • 19
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    Monitoring vegetation- and geodiversity with remote sensing and traits (2024)
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    UID:
    (DE-627)1880603675
    Format: 20 , Illustrationen
    ISSN: 1471-2962
    Content: Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ, close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future.
    In: Philosophical transactions of the Royal Society. A, Mathematical, physical and engineering sciences, London : Royal Society, 1996, 382(2024), 2269, 1471-2962
    In: volume:382
    In: year:2024
    In: number:2269
    In: extent:20
    Language: English
    DOI: 10.1098/rsta.2023.0058
    DOI: 10.25673/115621
    URL: https://doi.org/10.1098/rsta.2023.0058
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  • 20
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    Advanced Detection of Invasive Neophytes in Agricultural Landscapes : A Multisensory and Multiscale Remote Sensing Approach (2024)
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    UID:
    (DE-627)1879384949
    ISSN: 2072-4292
    Content: The sustainable provision of ecological products and services, both natural and man-made, faces a substantial threat emanating from invasive plant species (IPS), which inflict considerable economic and ecological harm on a global scale. They are widely recognized as one of the primary drivers of global biodiversity decline and have become the focal point of an increasing number of studies. The integration of remote sensing (RS) and geographic information systems (GIS) plays a pivotal role in their detection and classification across a diverse range of research endeavors, emphasizing the critical significance of accounting for the phenological stages of the targeted species when endeavoring to accurately delineate their distribution and occurrences. This study is centered on this fundamental premise, as it endeavors to amass terrestrial data encompassing the phenological stages and spectral attributes of the specified IPS, with the overarching objective of ascertaining the most opportune time frames for their detection. Moreover, it involves the development and validation of a detection and classification algorithm, harnessing a diverse array of RS datasets, including satellite and unmanned aerial vehicle (UAV) imagery spanning the spectrum from RGB to multispectral and near-infrared (NIR). Taken together, our investigation underscores the advantages of employing an array of RS datasets in conjunction with the phenological stages, offering an economically efficient and adaptable solution for the detection and monitoring of invasive plant species. Such insights hold the potential to inform both present and future policymaking pertaining to the management of invasive species in agricultural and natural ecosystems.
    In: Remote sensing, Basel : MDPI, 2009, 16(2024), 3, Artikel-ID 500, 2072-4292
    In: volume:16
    In: year:2024
    In: number:3
    In: elocationid:500
    Language: English
    DOI: 10.3390/rs16030500
    URL: https://doi.org/10.3390/rs16030500
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