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  • 1
    Book
    Book
    Monitoring of boreal forests in Siberia : integrating ground and satellite (C- and L-band SAR) data (2020)
    Jena : Chemisch-Geowissenschaftliche Fakultät der Friedrich-Schiller-Universität Jena
    Details
    UID:
    kobvindex_GFZBV047284158
    Format: 234 Seiten , Illustrationen, Diagramme
    Content: Anthropogenic climate change constitutes one of the main global crises in the 21st century. It manifests itself distinctly in global warming and its effects. Forests play an essential role in mitigating the effects of climate change, improving our knowledge of the distribution and changes of terrestrial carbon stocks is vital to mitigate its consequences. Therefore, remote sensing is recommended as one of the tools to ensure systematic and operational forest monitoring. Forests in the Russian Federation are of particular importance as it is the most forested country in the world and at the same time, it is the country with the highest uncertainty when calculating global carbon stocks. Remote sensing is recommended as one of the tools to ensure systematic and operational forest monitoring. It can acquire data over large areas with a high repetition rate and at a relatively low cost. In particular, microwave sensors are recommended as they can provide weather and sun independent, systematic observations with high temporal frequency. The main goal of this cumulative dissertation was to develop methods using new algorithms for estimating parameters for boreal forests from remote sensing data acquired with Synthetic Aperture Radar (SAR). Using the SAR data acquired by the sensor with the longest wavelength available at the moment of writing this dissertation in space, the L-band, methods for estimating the above-ground forest biomass were developed. For this purpose, algorithms for machine learning (ML) were applied and validated. These methods were chosen because they are recommended for large data sets and an incomplete theoretical understanding of processes, e.g., the interaction between the forest and the radar signal, and are relatively new in forest monitoring studies. In addition, efforts have been made to establish improved mapping of large-scale forest cover change
    Note: Kumulative Dissertation, enthält Zeitschriftenaufsätze , Tag der Verteidigung: 02.12.2020 , Dissertation, Friedrich-Schiller-Universität Jena, 2020 , Content ACKNOWLEDGEMENTS APPENDED PAPERS RELATED PUBLICATIONS FIGURES TABLES I ABBREVIATIONS AND SYMBOLS ABSTRACT ZUSAMMENFASSUNG CHAPTER 1 Introduction 1.1 Importance of forest monitoring 1.2 Remote sensing for forest monitoring 1.3 Scope and structure of this thesis CHAPTER 2 2 Theoretical background & state-of-the-art 2.1 Boreal forests 2.2 Imaging radar theory 2.2.1 Radar principles 2.2.2 Radar scattering 2.2.3 SAR data processing 2.2.4 SAR lnterferometry 2.3 Radar remote sensing of boreal forests 2.3.1 Estimation of aboveground biomass 2.3.2 Monitoring of forest change 2.4 Study area and data 2.4.1 Location of study areas 2.4.2 Processing of in situ data 2.4.3 SAR L-band data: PALSAR & PALSAR-2 2.4.4 SAR C-band data: RADARSAT-2 CHAPTER 3 3 Research rationale 3.1 Research needs 3.2 Research questions CHAPTER 4 4 Research contribution 4.1 Operational forest monitoring in Siberia 4.2 Remote sensing for aboveground biomass estimation in boreal forests 4.3 Non-parametric retrieval of aboveground biomass 4.4 Multi-frequency SAR for estimation of aboveground biomass CHAPTER 5 5 Synthesis 5.1 Discussion and conclusions 5.2 Outlook REFERENCES APPENDIX A: PROCEEDINGS PAPER APPENDIX B: STUDIES ON nI0MASS ESTIMATION IN Il0REAL FORESTS MANUSCRIPT OVERVIEW STATEMENT OF AUTH0RSHIP CURRICULUM VITAE , Zusammenfassungen in deutscher und englischer Sprache
    Language: English
    Keywords: Hochschulschrift
    DOI: 10.22032/dbt.47862
    URN: urn:nbn:de:gbv:27-dbt-20210223-111321-006
    URL: Volltext
    Author information: Schmullius, Christiane 1960-
    Author information: Stelmaszczuk-Gòrska, Martyna 1981-
    Author information: Thiel, Christian 1975-
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  • 2
    Online Resource
    Online Resource
    Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements (2021)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_24570
    Format: 1 Online-Ressource (30 Seiten)
    Content: The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.
    Content: Peer Reviewed
    In: Basel : MDPI, 13,22
    Language: English
    DOI: 10.3390/rs13224659
    DOI: 10.18452/23939
    URN: urn:nbn:de:kobv:11-110-18452/24570-0
    URL: Volltext  (kostenfrei)
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    Online Resource
    Fulltext
    HU Berlin
  • 3
    Online Resource
    Online Resource
    Monitoring of boreal forests in Siberia : integrating ground and satellite (C- and L-band SAR) data (2020)
    Jena,
    Details
    UID:
    edocfu_BV047284158
    Format: 1 Online-Ressource (234 Seiten) : , Illustrationen, Diagramme.
    Content: Anthropogenic climate change constitutes one of the main global crises in the 21st century. It manifests itself distinctly in global warming and its effects. Forests play an essential role in mitigating the effects of climate change, improving our knowledge of the distribution and changes of terrestrial carbon stocks is vital to mitigate its consequences. Therefore, remote sensing is recommended as one of the tools to ensure systematic and operational forest monitoring. Forests in the Russian Federation are of particular importance as it is the most forested country in the world and at the same time, it is the country with the highest uncertainty when calculating global carbon stocks. Remote sensing is recommended as one of the tools to ensure systematic and operational forest monitoring. It can acquire data over large areas with a high repetition rate and at a relatively low cost. In particular, microwave sensors are recommended as they can provide weather and sun independent, systematic observations with high temporal frequency. The main goal of this cumulative dissertation was to develop methods using new algorithms for estimating parameters for boreal forests from remote sensing data acquired with Synthetic Aperture Radar (SAR). Using the SAR data acquired by the sensor with the longest wavelength available at the moment of writing this dissertation in space, the L-band, methods for estimating the above-ground forest biomass were developed. For this purpose, algorithms for machine learning (ML) were applied and validated. These methods were chosen because they are recommended for large data sets and an incomplete theoretical understanding of processes, e.g., the interaction between the forest and the radar signal, and are relatively new in forest monitoring studies. In addition, efforts have been made to establish improved mapping of large-scale forest cover change
    Note: Kumulative Dissertation, enthält Zeitschriftenaufsätze. - Tag der Verteidigung: 02.12.2020 , Dissertation Friedrich-Schiller-Universität Jena 2020 , Zusammenfassungen in deutscher und englischer Sprache
    Additional Edition: Erscheint auch als Druck-Ausgabe
    Language: English
    Keywords: Wald ; Biomasse ; Monitorüberwachung ; Fernerkundung ; Radar ; Synthetische Apertur ; Hochschulschrift
    DOI: 10.22032/dbt.47862
    URN: urn:nbn:de:gbv:27-dbt-20210223-111321-006
    URL: Volltext
    URL: https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20210223-111321-006
    URL: https://d-nb.info/1228432228/34
    URL: kostenfrei
    URL: https://doi.org/10.22032/dbt.47862
    Author information: Schmullius, Christiane 1960-
    Author information: Stelmaszczuk-Gòrska, Martyna 1981-
    Author information: Thiel, Christian 1975-
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Online Access
    Open Access Request
    FU Berlin
  • 4
    Online Resource
    Online Resource
    Monitoring of boreal forests in Siberia : integrating ground and satellite (C- and L-band SAR) data (2020)
    Jena,
    Details
    UID:
    edoccha_BV047284158
    Format: 1 Online-Ressource (234 Seiten) : , Illustrationen, Diagramme.
    Content: Anthropogenic climate change constitutes one of the main global crises in the 21st century. It manifests itself distinctly in global warming and its effects. Forests play an essential role in mitigating the effects of climate change, improving our knowledge of the distribution and changes of terrestrial carbon stocks is vital to mitigate its consequences. Therefore, remote sensing is recommended as one of the tools to ensure systematic and operational forest monitoring. Forests in the Russian Federation are of particular importance as it is the most forested country in the world and at the same time, it is the country with the highest uncertainty when calculating global carbon stocks. Remote sensing is recommended as one of the tools to ensure systematic and operational forest monitoring. It can acquire data over large areas with a high repetition rate and at a relatively low cost. In particular, microwave sensors are recommended as they can provide weather and sun independent, systematic observations with high temporal frequency. The main goal of this cumulative dissertation was to develop methods using new algorithms for estimating parameters for boreal forests from remote sensing data acquired with Synthetic Aperture Radar (SAR). Using the SAR data acquired by the sensor with the longest wavelength available at the moment of writing this dissertation in space, the L-band, methods for estimating the above-ground forest biomass were developed. For this purpose, algorithms for machine learning (ML) were applied and validated. These methods were chosen because they are recommended for large data sets and an incomplete theoretical understanding of processes, e.g., the interaction between the forest and the radar signal, and are relatively new in forest monitoring studies. In addition, efforts have been made to establish improved mapping of large-scale forest cover change
    Note: Kumulative Dissertation, enthält Zeitschriftenaufsätze. - Tag der Verteidigung: 02.12.2020 , Dissertation Friedrich-Schiller-Universität Jena 2020 , Zusammenfassungen in deutscher und englischer Sprache
    Additional Edition: Erscheint auch als Druck-Ausgabe
    Language: English
    Keywords: Wald ; Biomasse ; Monitorüberwachung ; Fernerkundung ; Radar ; Synthetische Apertur ; Hochschulschrift
    DOI: 10.22032/dbt.47862
    URN: urn:nbn:de:gbv:27-dbt-20210223-111321-006
    URL: Volltext
    URL: https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20210223-111321-006
    URL: https://d-nb.info/1228432228/34
    URL: kostenfrei
    URL: https://doi.org/10.22032/dbt.47862
    Author information: Schmullius, Christiane 1960-
    Author information: Stelmaszczuk-Gòrska, Martyna 1981-
    Author information: Thiel, Christian 1975-
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Online Access
    Open Access Request
    Charité
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