Call number:
AWI G8-21-94666
Description / Table of Contents:
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
Type of Medium:
Dissertations
Pages:
234 Seiten
,
Illustrationen, Diagramme
URN:
urn:nbn:de:gbv:27-dbt-20210223-111321-006
Language:
English
,
German
Note:
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
Location:
AWI Reading room
Branch Library:
AWI Library
Permalink
