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  • 1
    UID:
    gbv_1794541179
    Format: 1 Online-Ressource (426 p.)
    ISBN: 9783036529578 , 9783036529561
    Content: Requests for regional soil moisture observations are increasing to parameterize complex hydrological models, to assess the impact of land-use changes, and to develop climate adaption strategies in the agricultural sector. Spatial land-use patterns have an impact on the soil water balance and groundwater recharge. Soil moisture is therefore a key parameter for the long-term monitoring and development of sustainable land-management and landscape design strategies that mitigate regional water scarcity and droughts. For example, the spatial organization of hedges or tree rows related to open land and wind direction avoids soil erosion, limits local evaporation, and increases local soil water storage. Since the early 1980s, satellite missions have been designed to monitor proxies for soil moisture, mainly at the national and global scale, with a relatively coarse pixel resolution and low accuracy. The local effects of weather and climate are very dynamic in space and time. Thus, a strong need exists for more accurate, regional-scale remote sensing products for soil moisture. The transfer of existing, proof-of-concept algorithms to region-specific monitoring frameworks is urgent. This Special Issue provides an overview of current developments on remote sensing-based soil moisture observations that are applicable at a regional scale. The compendium of research papers demonstrates the benefits of concurrently utilizing multi-source remote sensing data and in situ measurements through: - Using additional data and site-specific knowledge; - Combining empirical and physical approaches; - Developing concepts to deal with mixed pixels
    Note: English
    Language: English
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  • 2
    UID:
    b3kat_BV047903158
    Format: 1 Online-Ressource
    ISBN: 9783036529578
    Content: Requests for regional soil moisture observations are increasing to parameterize complex hydrological models, to assess the impact of land-use changes, and to develop climate adaption strategies in the agricultural sector. Spatial land-use patterns have an impact on the soil water balance and groundwater recharge. Soil moisture is therefore a key parameter for the long-term monitoring and development of sustainable land-management and landscape design strategies that mitigate regional water scarcity and droughts. For example, the spatial organization of hedges or tree rows related to open land and wind direction avoids soil erosion, limits local evaporation, and increases local soil water storage. Since the early 1980s, satellite missions have been designed to monitor proxies for soil moisture, mainly at the national and global scale, with a relatively coarse pixel resolution and low accuracy. The local effects of weather and climate are very dynamic in space and time. Thus, a strong need exists for more accurate, regional-scale remote sensing products for soil moisture. The transfer of existing, proof-of-concept algorithms to region-specific monitoring frameworks is urgent. This Special Issue provides an overview of current developments on remote sensing-based soil moisture observations that are applicable at a regional scale. The compendium of research papers demonstrates the benefits of concurrently utilizing multi-source remote sensing data and in situ measurements through: - Using additional data and site-specific knowledge; - Combining empirical and physical approaches; - Developing concepts to deal with mixed pixels.
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-0365-2956-1
    Language: English
    URL: Volltext  (kostenfrei)
    Author information: Schulz, Karsten 1964-
    Author information: Pause, Marion 1979-
    Author information: Jagdhuber, Thomas 1979-
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  • 3
    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
    URL: Volltext  (kostenfrei)
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  • 4
    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
    URL: Volltext  (kostenfrei)
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  • 5
    UID:
    edochu_18452_27133
    Format: 1 Online-Ressource (48 Seiten)
    Content: Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.
    Content: Peer Reviewed
    In: Basel : MDPI, 14,9
    Language: English
    URL: Volltext  (kostenfrei)
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  • 6
    UID:
    edochu_18452_21210
    Format: 1 Online-Ressource (52 Seiten)
    Content: 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.
    Content: Peer Reviewed
    In: Basel : MDPI, 10,7
    Language: English
    URL: Volltext  (kostenfrei)
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