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  • 1
    Online Resource
    Online Resource
    Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics (2019)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_23311
    Format: 1 Online-Ressource (51 Seiten)
    Content: In the face of rapid global change it is imperative to preserve geodiversity for the overallconservation of biodiversity. Geodiversity is important for understanding complex biogeochemicaland physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystemorganization. Despite the great importance of geodiversity, there is a lack of suitable monitoringmethods. Compared to conventional in-situ techniques, remote sensing (RS) techniques providea pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, aswell as standardized monitoring of continuous geodiversity on the local to global scale. This papergives an overview of the state-of-the-art approaches for monitoring soil characteristics and soilmoisture 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 traitvariations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging),thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soilcharacteristics and soil moisture are also presented. Furthermore, the paper discusses current andfuture satellite-borne sensors and missions as well as existing data products. Due to the prospectsand limitations of the characteristics of different RS sensors, only specific geotraits and geodiversitycharacteristics can be recorded. The paper provides an overview of those geotraits.
    Content: Peer Reviewed
    In: Basel : MDPI, 11,20
    Language: English
    DOI: 10.3390/rs11202356
    DOI: 10.18452/22703
    URN: urn:nbn:de:kobv:11-110-18452/23311-5
    URL: Volltext  (kostenfrei)
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    HU Berlin
  • 2
    Online Resource
    Online Resource
    Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces (2020)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_25777
    Format: 1 Online-Ressource (61 Seiten)
    Content: The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.
    Content: Peer Reviewed
    In: Basel : MDPI, 12,22
    Language: English
    DOI: 10.3390/rs12223690
    DOI: 10.18452/25097
    URN: urn:nbn:de:kobv:11-110-18452/25777-8
    URL: Volltext  (kostenfrei)
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    HU Berlin
  • 3
    Online Resource
    Online Resource
    Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery (2021)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_23837
    Format: 1 Online-Ressource (23 Seiten)
    Content: The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.
    Content: Peer Reviewed
    In: Basel : MDPI, 13,10
    Language: English
    DOI: 10.3390/rs13101885
    DOI: 10.18452/23222
    URN: urn:nbn:de:kobv:11-110-18452/23837-0
    URL: Volltext  (kostenfrei)
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    HU Berlin
  • 4
    Article
    Article
    Minimalinvasive Direct-Push-erkundung in der Feuchtboden(geo)archäologie am Beispiel des Karlsgrabens (Fossa Carolina) (2018)
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    Details
    UID:
    b3kat_BV046025654
    Format: Illustrationen, Karten, Diagramme
    ISSN: 0342-734X
    Note: Zusammenfassungen in deutscher, englischer und französischer Sprache
    In: volume:48
    In: number:4
    In: year:2018
    In: pages:577-593
    In: Archäologisches Korrespondenzblatt / hrsg. vom Römisch-Germanischen Zentralmuseum Mainz in Verb. mit dem Präsidium der deutschen Verbände für Archäologie, Mainz, 2018, Jahrgang 48 (2018), Heft 4, Seite 577-593, 0342-734X
    Language: German
    Keywords: Fossa Carolina ; Archäologie ; Geschichte 2014-2018
    Author information: Zielhofer, Christoph 1968-
    Author information: Werban, Ulrike 1976-
    Author information: Werther, Lukas 1982-
    Author information: Dietrich, Peter 1965-
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