Monitoring of boreal forests in Siberia : integrating ground and satellite (C- and L-band SAR) data

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 terrest...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Stelmaszczuk-Gòrska, Martyna - 1981- [verfasserIn] Schmullius, Christiane - 1960- [akademischer betreuerIn] Thiel, Christian - 1975- [akademischer betreuerIn]
Körperschaften:	Friedrich-Schiller-Universität Jena [Grad-verleihende Institution]
Hochschulschrift:	Dissertation ; Friedrich-Schiller-Universität Jena ; 2020

Format:	E-Book
Sprache:	Englisch ; Deutsch

Erschienen:	Jena: 2020?

Schlagwörter:	Sibirien / Wald / Biomasse / Monitorüberwachung / Fernerkundung / Radar / Synthetische Apertur
Formangabe:	Hochschulschrift

Anmerkung:	Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020
Umfang:	1 Online-Ressource (234 Seiten) ; Illustrationen, Diagramme

Weitere Ausgabe:	Erscheint auch als Druck-Ausgabe Stelmaszczuk-Gòrska, Martyna, 1981 -: Monitoring of boreal forests in Siberia - Jena, 2020

Links:	Link aufrufen Link aufrufen Link aufrufen Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	urn:nbn:de:gbv:27-dbt-20210223-111321-006 10.22032/dbt.47862

Katalog-ID:	1749161621

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topic_title	551.307 DE-101 550 DE-101 48.40 bkl 38.03 bkl 31 00 DE-27 00 Universitätsbibliographie Jena 31 00 DE-27 01 Chemisch-Geowissenschaftliche Fakultät Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data von Martyna Anna Stelmaszczuk-Gòrska, M.Sc g (DE-588)4054780-2 (DE-627)104253134 (DE-576)209110546 Sibirien gnd s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4006877-8 (DE-627)106375830 (DE-576)208867597 Biomasse gnd s (DE-588)4291501-6 (DE-627)104224908 (DE-576)210840870 Monitorüberwachung gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4176765-2 (DE-627)105353906 (DE-576)209972866 Radar gnd s (DE-588)4195782-9 (DE-627)105209155 (DE-576)210103388 Synthetische Apertur gnd (DE-627)
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title_full	Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data von Martyna Anna Stelmaszczuk-Gòrska, M.Sc
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title_sort	monitoring of boreal forests in siberiaintegrating ground and satellite (c- and l-band sar) data
title_auth	Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data
abstract	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. Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020
abstractGer	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. Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020
abstract_unstemmed	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. Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020
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bklnumber	48.40 38.03
allfields	1228432228 DE-101 urn:nbn:de:gbv:27-dbt-20210223-111321-006 urn 10.22032/dbt.47862 doi (DE-627)1749161621 (DE-599)KXP1749161621 (OCoLC)1238088594 DE-627 ger DE-627 rda eng ger XA-DE-TH 551.307 DE-101 550 DE-101 48.40 bkl 38.03 bkl Stelmaszczuk-Gòrska, Martyna 1981- verfasserin (DE-588)1216350221 (DE-627)1727609034 aut Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data von Martyna Anna Stelmaszczuk-Gòrska, M.Sc. Jena [2020?] 1 Online-Ressource (234 Seiten) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020 Dissertation Friedrich-Schiller-Universität Jena 2020 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. Zusammenfassungen in deutscher und englischer Sprache Archivierung/Langzeitarchivierung gewährleistet XA-DE-TH pdager DE-27 Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content g (DE-588)4054780-2 (DE-627)104253134 (DE-576)209110546 Sibirien gnd s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4006877-8 (DE-627)106375830 (DE-576)208867597 Biomasse gnd s (DE-588)4291501-6 (DE-627)104224908 (DE-576)210840870 Monitorüberwachung gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4176765-2 (DE-627)105353906 (DE-576)209972866 Radar gnd s (DE-588)4195782-9 (DE-627)105209155 (DE-576)210103388 Synthetische Apertur gnd (DE-627) Schmullius, Christiane 1960- akademischer betreuerin (DE-588)1024689867 (DE-627)720315395 (DE-576)36936080X dgs Thiel, Christian 1975- akademischer betreuerin (DE-588)129324469 (DE-627)707211182 (DE-576)297599054 dgs Friedrich-Schiller-Universität Jena Grad-verleihende Institution (DE-588)36164-1 (DE-627)100833012 (DE-576)190344695 dgg Jena (DE-588)4028557-1 (DE-627)104814411 (DE-576)208977872 uvp Erscheint auch als Druck-Ausgabe Stelmaszczuk-Gòrska, Martyna, 1981 - Monitoring of boreal forests in Siberia Jena, 2020 216 Seiten (DE-627)174915935X https://doi.org/10.22032/dbt.47862 2022-02-07 Langzeitarchivierung Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2021-04-08 Resolving-System https://d-nb.info/1228432228/34 2021-04-08 Langzeitarchivierung Nationalbibliothek https://www.db-thueringen.de/receive/dbt_mods_00047862 application/pdf 2021-04-08 Verlag kostenfrei kostenfrei https://doi.org/10.22032/dbt.47862 2021-04-08 Resolving-System GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER 48.40 Forstwirtschaft (DE-627)106416006 38.03 Methoden und Techniken der Geowissenschaften (DE-627)106411284 BO 045F 551.307 20 01 0084 3932706811 x 01-06-21 21 01 0046 3932767586 z 01-06-21 22 01 0018 3932828208 SUBolrd xu 01-06-21 23 01 0830 3932886291 olr-d x 01-06-21 30 01 0104 3932924266 z 01-06-21 31 01 0027 387081523X z 23-02-21 40 01 0007 3932961587 xsn 01-06-21 60 01 0705 3933021634 OLRD z 01-06-21 63 01 3401 3933078563 ORD x 01-06-21 70 01 0089 3933111471 zdo 01-06-21 105 01 0841 3933435951 z 01-06-21 110 01 3110 3933150132 x 01-06-21 132 01 0959 3933192730 OLR-DISS x 01-06-21 151 01 0546 3933246768 OLR-ODISS z 01-06-21 161 01 0960 3933273099 ORD z 01-06-21 293 01 3293 3933389674 ORD z 01-06-21 370 01 4370 3933420792 x 01-06-21 2403 01 DE-LFER 3883165719 00 --%%-- --%%-- n --%%-- l01 09-03-21 20 01 0084 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 21 01 0046 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 22 01 0018 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 23 01 0830 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 30 01 0104 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 31 01 0027 https://doi.org/10.22032/dbt.47862 LF 40 01 0007 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 60 01 0705 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 63 01 3401 E-Book https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 LF 70 01 0089 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 105 01 0841 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 110 01 3110 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 132 01 0959 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 151 01 0546 Volltext https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 161 01 0960 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 293 01 3293 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 370 01 4370 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2403 01 DE-LFER https://doi.org/10.22032/dbt.47862 31 00 DE-27 00 Universitätsbibliographie Jena 31 00 DE-27 01 Chemisch-Geowissenschaftliche Fakultät 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2021-06-01:01:02:00
spelling	1228432228 DE-101 urn:nbn:de:gbv:27-dbt-20210223-111321-006 urn 10.22032/dbt.47862 doi (DE-627)1749161621 (DE-599)KXP1749161621 (OCoLC)1238088594 DE-627 ger DE-627 rda eng ger XA-DE-TH 551.307 DE-101 550 DE-101 48.40 bkl 38.03 bkl Stelmaszczuk-Gòrska, Martyna 1981- verfasserin (DE-588)1216350221 (DE-627)1727609034 aut Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data von Martyna Anna Stelmaszczuk-Gòrska, M.Sc. Jena [2020?] 1 Online-Ressource (234 Seiten) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020 Dissertation Friedrich-Schiller-Universität Jena 2020 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. Zusammenfassungen in deutscher und englischer Sprache Archivierung/Langzeitarchivierung gewährleistet XA-DE-TH pdager DE-27 Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content g (DE-588)4054780-2 (DE-627)104253134 (DE-576)209110546 Sibirien gnd s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4006877-8 (DE-627)106375830 (DE-576)208867597 Biomasse gnd s (DE-588)4291501-6 (DE-627)104224908 (DE-576)210840870 Monitorüberwachung gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4176765-2 (DE-627)105353906 (DE-576)209972866 Radar gnd s (DE-588)4195782-9 (DE-627)105209155 (DE-576)210103388 Synthetische Apertur gnd (DE-627) Schmullius, Christiane 1960- akademischer betreuerin (DE-588)1024689867 (DE-627)720315395 (DE-576)36936080X dgs Thiel, Christian 1975- akademischer betreuerin (DE-588)129324469 (DE-627)707211182 (DE-576)297599054 dgs Friedrich-Schiller-Universität Jena Grad-verleihende Institution (DE-588)36164-1 (DE-627)100833012 (DE-576)190344695 dgg Jena (DE-588)4028557-1 (DE-627)104814411 (DE-576)208977872 uvp Erscheint auch als Druck-Ausgabe Stelmaszczuk-Gòrska, Martyna, 1981 - Monitoring of boreal forests in Siberia Jena, 2020 216 Seiten (DE-627)174915935X https://doi.org/10.22032/dbt.47862 2022-02-07 Langzeitarchivierung Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2021-04-08 Resolving-System https://d-nb.info/1228432228/34 2021-04-08 Langzeitarchivierung Nationalbibliothek https://www.db-thueringen.de/receive/dbt_mods_00047862 application/pdf 2021-04-08 Verlag kostenfrei kostenfrei https://doi.org/10.22032/dbt.47862 2021-04-08 Resolving-System GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER 48.40 Forstwirtschaft (DE-627)106416006 38.03 Methoden und Techniken der Geowissenschaften (DE-627)106411284 BO 045F 551.307 20 01 0084 3932706811 x 01-06-21 21 01 0046 3932767586 z 01-06-21 22 01 0018 3932828208 SUBolrd xu 01-06-21 23 01 0830 3932886291 olr-d x 01-06-21 30 01 0104 3932924266 z 01-06-21 31 01 0027 387081523X z 23-02-21 40 01 0007 3932961587 xsn 01-06-21 60 01 0705 3933021634 OLRD z 01-06-21 63 01 3401 3933078563 ORD x 01-06-21 70 01 0089 3933111471 zdo 01-06-21 105 01 0841 3933435951 z 01-06-21 110 01 3110 3933150132 x 01-06-21 132 01 0959 3933192730 OLR-DISS x 01-06-21 151 01 0546 3933246768 OLR-ODISS z 01-06-21 161 01 0960 3933273099 ORD z 01-06-21 293 01 3293 3933389674 ORD z 01-06-21 370 01 4370 3933420792 x 01-06-21 2403 01 DE-LFER 3883165719 00 --%%-- --%%-- n --%%-- l01 09-03-21 20 01 0084 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 21 01 0046 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 22 01 0018 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 23 01 0830 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 30 01 0104 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 31 01 0027 https://doi.org/10.22032/dbt.47862 LF 40 01 0007 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 60 01 0705 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 63 01 3401 E-Book https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 LF 70 01 0089 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 105 01 0841 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 110 01 3110 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 132 01 0959 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 151 01 0546 Volltext https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 161 01 0960 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 293 01 3293 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 370 01 4370 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2403 01 DE-LFER https://doi.org/10.22032/dbt.47862 31 00 DE-27 00 Universitätsbibliographie Jena 31 00 DE-27 01 Chemisch-Geowissenschaftliche Fakultät 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2021-06-01:01:02:00
allfields_unstemmed	1228432228 DE-101 urn:nbn:de:gbv:27-dbt-20210223-111321-006 urn 10.22032/dbt.47862 doi (DE-627)1749161621 (DE-599)KXP1749161621 (OCoLC)1238088594 DE-627 ger DE-627 rda eng ger XA-DE-TH 551.307 DE-101 550 DE-101 48.40 bkl 38.03 bkl Stelmaszczuk-Gòrska, Martyna 1981- verfasserin (DE-588)1216350221 (DE-627)1727609034 aut Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data von Martyna Anna Stelmaszczuk-Gòrska, M.Sc. Jena [2020?] 1 Online-Ressource (234 Seiten) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020 Dissertation Friedrich-Schiller-Universität Jena 2020 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. Zusammenfassungen in deutscher und englischer Sprache Archivierung/Langzeitarchivierung gewährleistet XA-DE-TH pdager DE-27 Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content g (DE-588)4054780-2 (DE-627)104253134 (DE-576)209110546 Sibirien gnd s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4006877-8 (DE-627)106375830 (DE-576)208867597 Biomasse gnd s (DE-588)4291501-6 (DE-627)104224908 (DE-576)210840870 Monitorüberwachung gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4176765-2 (DE-627)105353906 (DE-576)209972866 Radar gnd s (DE-588)4195782-9 (DE-627)105209155 (DE-576)210103388 Synthetische Apertur gnd (DE-627) Schmullius, Christiane 1960- akademischer betreuerin (DE-588)1024689867 (DE-627)720315395 (DE-576)36936080X dgs Thiel, Christian 1975- akademischer betreuerin (DE-588)129324469 (DE-627)707211182 (DE-576)297599054 dgs Friedrich-Schiller-Universität Jena Grad-verleihende Institution (DE-588)36164-1 (DE-627)100833012 (DE-576)190344695 dgg Jena (DE-588)4028557-1 (DE-627)104814411 (DE-576)208977872 uvp Erscheint auch als Druck-Ausgabe Stelmaszczuk-Gòrska, Martyna, 1981 - Monitoring of boreal forests in Siberia Jena, 2020 216 Seiten (DE-627)174915935X https://doi.org/10.22032/dbt.47862 2022-02-07 Langzeitarchivierung Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2021-04-08 Resolving-System https://d-nb.info/1228432228/34 2021-04-08 Langzeitarchivierung Nationalbibliothek https://www.db-thueringen.de/receive/dbt_mods_00047862 application/pdf 2021-04-08 Verlag kostenfrei kostenfrei https://doi.org/10.22032/dbt.47862 2021-04-08 Resolving-System GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER 48.40 Forstwirtschaft (DE-627)106416006 38.03 Methoden und Techniken der Geowissenschaften (DE-627)106411284 BO 045F 551.307 20 01 0084 3932706811 x 01-06-21 21 01 0046 3932767586 z 01-06-21 22 01 0018 3932828208 SUBolrd xu 01-06-21 23 01 0830 3932886291 olr-d x 01-06-21 30 01 0104 3932924266 z 01-06-21 31 01 0027 387081523X z 23-02-21 40 01 0007 3932961587 xsn 01-06-21 60 01 0705 3933021634 OLRD z 01-06-21 63 01 3401 3933078563 ORD x 01-06-21 70 01 0089 3933111471 zdo 01-06-21 105 01 0841 3933435951 z 01-06-21 110 01 3110 3933150132 x 01-06-21 132 01 0959 3933192730 OLR-DISS x 01-06-21 151 01 0546 3933246768 OLR-ODISS z 01-06-21 161 01 0960 3933273099 ORD z 01-06-21 293 01 3293 3933389674 ORD z 01-06-21 370 01 4370 3933420792 x 01-06-21 2403 01 DE-LFER 3883165719 00 --%%-- --%%-- n --%%-- l01 09-03-21 20 01 0084 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 21 01 0046 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 22 01 0018 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 23 01 0830 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 30 01 0104 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 31 01 0027 https://doi.org/10.22032/dbt.47862 LF 40 01 0007 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 60 01 0705 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 63 01 3401 E-Book https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 LF 70 01 0089 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 105 01 0841 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 110 01 3110 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 132 01 0959 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 151 01 0546 Volltext https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 161 01 0960 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 293 01 3293 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 370 01 4370 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2403 01 DE-LFER https://doi.org/10.22032/dbt.47862 31 00 DE-27 00 Universitätsbibliographie Jena 31 00 DE-27 01 Chemisch-Geowissenschaftliche Fakultät 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2021-06-01:01:02:00
allfieldsGer	1228432228 DE-101 urn:nbn:de:gbv:27-dbt-20210223-111321-006 urn 10.22032/dbt.47862 doi (DE-627)1749161621 (DE-599)KXP1749161621 (OCoLC)1238088594 DE-627 ger DE-627 rda eng ger XA-DE-TH 551.307 DE-101 550 DE-101 48.40 bkl 38.03 bkl Stelmaszczuk-Gòrska, Martyna 1981- verfasserin (DE-588)1216350221 (DE-627)1727609034 aut Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data von Martyna Anna Stelmaszczuk-Gòrska, M.Sc. Jena [2020?] 1 Online-Ressource (234 Seiten) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020 Dissertation Friedrich-Schiller-Universität Jena 2020 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. Zusammenfassungen in deutscher und englischer Sprache Archivierung/Langzeitarchivierung gewährleistet XA-DE-TH pdager DE-27 Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content g (DE-588)4054780-2 (DE-627)104253134 (DE-576)209110546 Sibirien gnd s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4006877-8 (DE-627)106375830 (DE-576)208867597 Biomasse gnd s (DE-588)4291501-6 (DE-627)104224908 (DE-576)210840870 Monitorüberwachung gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4176765-2 (DE-627)105353906 (DE-576)209972866 Radar gnd s (DE-588)4195782-9 (DE-627)105209155 (DE-576)210103388 Synthetische Apertur gnd (DE-627) Schmullius, Christiane 1960- akademischer betreuerin (DE-588)1024689867 (DE-627)720315395 (DE-576)36936080X dgs Thiel, Christian 1975- akademischer betreuerin (DE-588)129324469 (DE-627)707211182 (DE-576)297599054 dgs Friedrich-Schiller-Universität Jena Grad-verleihende Institution (DE-588)36164-1 (DE-627)100833012 (DE-576)190344695 dgg Jena (DE-588)4028557-1 (DE-627)104814411 (DE-576)208977872 uvp Erscheint auch als Druck-Ausgabe Stelmaszczuk-Gòrska, Martyna, 1981 - Monitoring of boreal forests in Siberia Jena, 2020 216 Seiten (DE-627)174915935X https://doi.org/10.22032/dbt.47862 2022-02-07 Langzeitarchivierung Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2021-04-08 Resolving-System https://d-nb.info/1228432228/34 2021-04-08 Langzeitarchivierung Nationalbibliothek https://www.db-thueringen.de/receive/dbt_mods_00047862 application/pdf 2021-04-08 Verlag kostenfrei kostenfrei https://doi.org/10.22032/dbt.47862 2021-04-08 Resolving-System GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER 48.40 Forstwirtschaft (DE-627)106416006 38.03 Methoden und Techniken der Geowissenschaften (DE-627)106411284 BO 045F 551.307 20 01 0084 3932706811 x 01-06-21 21 01 0046 3932767586 z 01-06-21 22 01 0018 3932828208 SUBolrd xu 01-06-21 23 01 0830 3932886291 olr-d x 01-06-21 30 01 0104 3932924266 z 01-06-21 31 01 0027 387081523X z 23-02-21 40 01 0007 3932961587 xsn 01-06-21 60 01 0705 3933021634 OLRD z 01-06-21 63 01 3401 3933078563 ORD x 01-06-21 70 01 0089 3933111471 zdo 01-06-21 105 01 0841 3933435951 z 01-06-21 110 01 3110 3933150132 x 01-06-21 132 01 0959 3933192730 OLR-DISS x 01-06-21 151 01 0546 3933246768 OLR-ODISS z 01-06-21 161 01 0960 3933273099 ORD z 01-06-21 293 01 3293 3933389674 ORD z 01-06-21 370 01 4370 3933420792 x 01-06-21 2403 01 DE-LFER 3883165719 00 --%%-- --%%-- n --%%-- l01 09-03-21 20 01 0084 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 21 01 0046 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 22 01 0018 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 23 01 0830 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 30 01 0104 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 31 01 0027 https://doi.org/10.22032/dbt.47862 LF 40 01 0007 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 60 01 0705 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 63 01 3401 E-Book https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 LF 70 01 0089 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 105 01 0841 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 110 01 3110 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 132 01 0959 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 151 01 0546 Volltext https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 161 01 0960 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 293 01 3293 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 370 01 4370 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2403 01 DE-LFER https://doi.org/10.22032/dbt.47862 31 00 DE-27 00 Universitätsbibliographie Jena 31 00 DE-27 01 Chemisch-Geowissenschaftliche Fakultät 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2021-06-01:01:02:00
allfieldsSound	1228432228 DE-101 urn:nbn:de:gbv:27-dbt-20210223-111321-006 urn 10.22032/dbt.47862 doi (DE-627)1749161621 (DE-599)KXP1749161621 (OCoLC)1238088594 DE-627 ger DE-627 rda eng ger XA-DE-TH 551.307 DE-101 550 DE-101 48.40 bkl 38.03 bkl Stelmaszczuk-Gòrska, Martyna 1981- verfasserin (DE-588)1216350221 (DE-627)1727609034 aut Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data von Martyna Anna Stelmaszczuk-Gòrska, M.Sc. Jena [2020?] 1 Online-Ressource (234 Seiten) Illustrationen, Diagramme Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Kumulative Dissertation, enthält Zeitschriftenaufsätze Tag der Verteidigung: 02.12.2020 Dissertation Friedrich-Schiller-Universität Jena 2020 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. Zusammenfassungen in deutscher und englischer Sprache Archivierung/Langzeitarchivierung gewährleistet XA-DE-TH pdager DE-27 Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content g (DE-588)4054780-2 (DE-627)104253134 (DE-576)209110546 Sibirien gnd s (DE-588)4064354-2 (DE-627)104141573 (DE-576)209152923 Wald gnd s (DE-588)4006877-8 (DE-627)106375830 (DE-576)208867597 Biomasse gnd s (DE-588)4291501-6 (DE-627)104224908 (DE-576)210840870 Monitorüberwachung gnd s (DE-588)4016796-3 (DE-627)104213418 (DE-576)208917411 Fernerkundung gnd s (DE-588)4176765-2 (DE-627)105353906 (DE-576)209972866 Radar gnd s (DE-588)4195782-9 (DE-627)105209155 (DE-576)210103388 Synthetische Apertur gnd (DE-627) Schmullius, Christiane 1960- akademischer betreuerin (DE-588)1024689867 (DE-627)720315395 (DE-576)36936080X dgs Thiel, Christian 1975- akademischer betreuerin (DE-588)129324469 (DE-627)707211182 (DE-576)297599054 dgs Friedrich-Schiller-Universität Jena Grad-verleihende Institution (DE-588)36164-1 (DE-627)100833012 (DE-576)190344695 dgg Jena (DE-588)4028557-1 (DE-627)104814411 (DE-576)208977872 uvp Erscheint auch als Druck-Ausgabe Stelmaszczuk-Gòrska, Martyna, 1981 - Monitoring of boreal forests in Siberia Jena, 2020 216 Seiten (DE-627)174915935X https://doi.org/10.22032/dbt.47862 2022-02-07 Langzeitarchivierung Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2021-04-08 Resolving-System https://d-nb.info/1228432228/34 2021-04-08 Langzeitarchivierung Nationalbibliothek https://www.db-thueringen.de/receive/dbt_mods_00047862 application/pdf 2021-04-08 Verlag kostenfrei kostenfrei https://doi.org/10.22032/dbt.47862 2021-04-08 Resolving-System GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER 48.40 Forstwirtschaft (DE-627)106416006 38.03 Methoden und Techniken der Geowissenschaften (DE-627)106411284 BO 045F 551.307 20 01 0084 3932706811 x 01-06-21 21 01 0046 3932767586 z 01-06-21 22 01 0018 3932828208 SUBolrd xu 01-06-21 23 01 0830 3932886291 olr-d x 01-06-21 30 01 0104 3932924266 z 01-06-21 31 01 0027 387081523X z 23-02-21 40 01 0007 3932961587 xsn 01-06-21 60 01 0705 3933021634 OLRD z 01-06-21 63 01 3401 3933078563 ORD x 01-06-21 70 01 0089 3933111471 zdo 01-06-21 105 01 0841 3933435951 z 01-06-21 110 01 3110 3933150132 x 01-06-21 132 01 0959 3933192730 OLR-DISS x 01-06-21 151 01 0546 3933246768 OLR-ODISS z 01-06-21 161 01 0960 3933273099 ORD z 01-06-21 293 01 3293 3933389674 ORD z 01-06-21 370 01 4370 3933420792 x 01-06-21 2403 01 DE-LFER 3883165719 00 --%%-- --%%-- n --%%-- l01 09-03-21 20 01 0084 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 21 01 0046 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 22 01 0018 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 23 01 0830 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 30 01 0104 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 31 01 0027 https://doi.org/10.22032/dbt.47862 LF 40 01 0007 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 60 01 0705 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 63 01 3401 E-Book https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 LF 70 01 0089 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 105 01 0841 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 110 01 3110 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 132 01 0959 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 151 01 0546 Volltext https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 161 01 0960 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 293 01 3293 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 370 01 4370 https://nbn-resolving.de/urn:nbn:de:gbv:27-dbt-20210223-111321-006 2403 01 DE-LFER https://doi.org/10.22032/dbt.47862 31 00 DE-27 00 Universitätsbibliographie Jena 31 00 DE-27 01 Chemisch-Geowissenschaftliche Fakultät 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2021-06-01:01:02:00
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authorswithroles_txt_mv	Stelmaszczuk-Gòrska, Martyna @@aut@@ Schmullius, Christiane @@dgs@@ Thiel, Christian @@dgs@@
genre_facet	Hochschulschrift
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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. 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title	Monitoring of boreal forests in Siberia integrating ground and satellite (C- and L-band SAR) data
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title_short	Monitoring of boreal forests in Siberia
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up_date	2024-07-04T19:17:03.715Z
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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. 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Monitoring of boreal forests in Siberia : integrating ground and satellite (C- and L-band SAR) data

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