TY - JOUR A1 - Reinermann, Sophie A1 - Gessner, Ursula A1 - Asam, Sarah A1 - Ullmann, Tobias A1 - Schucknecht, Anne A1 - Kuenzer, Claudia T1 - Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series JF - Remote Sensing N2 - Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany. KW - earth observation KW - remote sensing KW - harvests KW - cutting events KW - grazing KW - pasture KW - meadow KW - optical KW - SAR KW - PolSAR KW - InSAR Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267164 SN - 2072-4292 VL - 14 IS - 7 ER - TY - JOUR A1 - Ullmann, Tobias A1 - Banks, Sarah N. A1 - Schmitt, Andreas A1 - Jagdhuber, Thomas T1 - Scattering characteristics of X-, C- and L-Band PolSAR data examined for the tundra environment of the Tuktoyaktuk Peninsula, Canada JF - Applied Sciences N2 - In this study, polarimetric Synthetic Aperture Radar (PolSAR) data at X-, C- and L-Bands, acquired by the satellites: TerraSAR-X (2011), Radarsat-2 (2011), ALOS (2010) and ALOS-2 (2016), were used to characterize the tundra land cover of a test site located close to the town of Tuktoyaktuk, NWT, Canada. Using available in situ ground data collected in 2010 and 2012, we investigate PolSAR scattering characteristics of common tundra land cover classes at X-, C- and L-Bands. Several decomposition features of quad-, co-, and cross-polarized data were compared, the correlation between them was investigated, and the class separability offered by their different feature spaces was analyzed. Certain PolSAR features at each wavelength were sensitive to the land cover and exhibited distinct scattering characteristics. Use of shorter wavelength imagery (X and C) was beneficial for the characterization of wetland and tundra vegetation, while L-Band data highlighted differences of the bare ground classes better. The Kennaugh Matrix decomposition applied in this study provided a unified framework to store, process, and analyze all data consistently, and the matrix offered a favorable feature space for class separation. Of all elements of the quad-polarized Kennaugh Matrix, the intensity based elements K0, K1, K2, K3 and K4 were found to be most valuable for class discrimination. These elements contributed to better class separation as indicated by an increase of the separability metrics squared Jefferys Matusita Distance and Transformed Divergence. The increase in separability was up to 57% for Radarsat-2 and up to 18% for ALOS-2 data. KW - decomposition KW - arctic KW - PolSAR KW - dual polarimetry KW - quad polarimetry KW - TerraSAR-X KW - Radarsat-2 KW - ALOS KW - ALOS-2 KW - tundra Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-158362 VL - 7 IS - 6 ER - TY - THES A1 - Ullmann, Tobias T1 - Characterization of Arctic Environment by Means of Polarimetric Synthetic Aperture Radar (PolSAR) Data and Digital Elevation Models (DEM) T1 - Charakterisierung der arktischen Landoberfläche mittels polarimetrischer Radardaten (PolSAR) und digitalen Höhenmodellen (DEM) N2 - The ecosystem of the high northern latitudes is affected by the recently changing environmental conditions. The Arctic has undergone a significant climatic change over the last decades. The land coverage is changing and a phenological response to the warming is apparent. Remotely sensed data can assist the monitoring and quantification of these changes. The remote sensing of the Arctic was predominantly carried out by the usage of optical sensors but these encounter problems in the Arctic environment, e.g. the frequent cloud cover or the solar geometry. In contrast, the imaging of Synthetic Aperture Radar is not affected by the cloud cover and the acquisition of radar imagery is independent of the solar illumination. The objective of this work was to explore how polarimetric Synthetic Aperture Radar (PolSAR) data of TerraSAR-X, TanDEM-X, Radarsat-2 and ALOS PALSAR and interferometric-derived digital elevation model data of the TanDEM-X Mission can contribute to collect meaningful information on the actual state of the Arctic Environment. The study was conducted for Canadian sites of the Mackenzie Delta Region and Banks Island and in situ reference data were available for the assessment. The up-to-date analysis of the PolSAR data made the application of the Non-Local Means filtering and of the decomposition of co-polarized data necessary. The Non-Local Means filter showed a high capability to preserve the image values, to keep the edges and to reduce the speckle. This supported not only the suitability for the interpretation but also for the classification. The classification accuracies of Non-Local Means filtered data were in average +10% higher compared to unfiltered images. The correlation of the co- and quad-polarized decomposition features was high for classes with distinct surface or double bounce scattering and a usage of the co-polarized data is beneficial for regions of natural land coverage and for low vegetation formations with little volume scattering. The evaluation further revealed that the X- and C-Band were most sensitive to the generalized land cover classes. It was found that the X-Band data were sensitive to low vegetation formations with low shrub density, the C-Band data were sensitive to the shrub density and the shrub dominated tundra. In contrast, the L-Band data were less sensitive to the land cover. Among the different dual-polarized data the HH/VV-polarized data were identified to be most meaningful for the characterization and classification, followed by the HH/HV-polarized and the VV/VH-polarized data. The quad-polarized data showed highest sensitivity to the land cover but differences to the co-polarized data were small. The accuracy assessment showed that spectral information was required for accurate land cover classification. The best results were obtained when spectral and radar information was combined. The benefit of including radar data in the classification was up to +15% accuracy and most significant for the classes wetland and sparse vegetated tundra. The best classifications were realized with quad-polarized C-Band and multispectral data and with co-polarized X-Band and multispectral data. The overall accuracy was up to 80% for unsupervised and up to 90% for supervised classifications. The results indicated that the shortwave co-polarized data show promise for the classification of tundra land cover since the polarimetric information is sensitive to low vegetation and the wetlands. Furthermore, co-polarized data provide a higher spatial resolution than the quad-polarized data. The analysis of the intermediate digital elevation model data of the TanDEM-X showed a high potential for the characterization of the surface morphology. The basic and relative topographic features were shown to be of high relevance for the quantification of the surface morphology and an area-wide application is feasible. In addition, these data were of value for the classification and delineation of landforms. Such classifications will assist the delineation of geomorphological units and have potential to identify locations of actual and future morphologic activity. N2 - Die polaren Regionen der Erde zeigen eine hohe Sensitivität gegenüber dem aktuell stattfindenden klimatischen Wandel. Für den Raum der Arktis wurde eine signifikante Erwärmung der Landoberfläche beobachtet und zukünftige Prognosen zeigen einen positiven Trend der Temperaturentwicklung. Die Folgen für das System sind tiefgehend, zahlreich und zeigen sich bereits heute - beispielsweise in einer Zunahme der photosynthetischen Aktivität und einer Verstärkung der geomorphologischen Dynamik. Durch satellitengestützte Fernerkundungssysteme steht ein Instrumentarium bereit, welches in der Lage ist, solch großflächigen und aktuellen Änderungen der Landoberfläche nachzuzeichnen und zu quantifizieren. Insbesondere optische Systeme haben in den vergangen Jahren ihre hohe Anwendbarkeit für die kontinuierliche Beobachtung und Quantifizierung von Änderungen bewiesen, bzw. durch sie ist ein Erkennen der Änderungen erst ermöglicht worden. Der Nutzen von optischen Systemen für die Beobachtung der arktischen Landoberfläche wird dabei aber durch die häufige Beschattung durch Wolken und die Beleuchtungsgeometrie erschwert, bzw. unmöglich gemacht. Demgegenüber eröffnen bildgebende Radarsystem durch die aktive Sendung von elektromagnetischen Signalen die Möglichkeit kontinuierlich Daten über den Zustand der Oberfläche aufzuzeichnen, ohne von den atmosphärischen oder orbitalen Bedingungen abhängig zu sein. Das Ziel der vorliegenden Arbeit war es den Nutzen und Mehrwert von polarimetrischen Synthetic Aperture Radar (PolSAR) Daten der Satelliten TerraSAR-X, TanDEM-X, Radarsat-2 und ALOS PALSAR für die Charakterisierung und Klassifikation der arktischen Landoberfläche zu identifizieren. Darüber hinaus war es ein Ziel das vorläufige interferometrische digitale Höhenmodel der TanDEM-X Mission für die Charakterisierung der Landoberflächen-Morphologie zu verwenden. Die Arbeiten erfolgten hauptsächlich an ausgewählten Testgebieten im Bereich des kanadischen Mackenzie Deltas und im Norden von Banks Islanld. Für diese Regionen standen in situ erhobene Referenzdaten zur Landbedeckung zur Verfügung. Mit Blick auf den aktuellen Stand der Forschung wurden die Radardaten mit einem entwickelten Non-Local-Means Verfahren gefiltert. Die co-polarisierten Daten wurde zudem mit einer neu entwickelten zwei Komponenten Dekomposition verarbeitet. Das entwickelte Filterverfahren zeigt eine hohe Anwendbarkeit für alle Radardaten. Der Ansatz war in der Lage die Kanten und Grauwerte im Bild zu erhalten, bei einer gleichzeitigen Reduktion der Varianz und des Speckle-Effekts. Dies verbesserte nicht nur die Bildinterpretation, sondern auch die Bildklassifikation und eine Erhöhung der Klassifikationsgüte von ca. +10% konnte durch die Filterung erreicht werden. Die Merkmale der Dekomposition von co-polarisierten Daten zeigten eine hohe Korrelation zu den entsprechenden Merkmalen der Dekomposition von voll-polarisierten Daten. Die Korrelation war besonders hoch für Landbedeckungstypen, welche eine double oder single bounce Rückstreuung hervorrufen. Eine Anwendung von co-polarisierten Daten ist somit besonders sinnvoll und aussagekräftig für Landbedeckungstypen, welche nur einen geringen Teil an Volumenstreuung bedingen. Die vergleichende Auswertung der PolSAR Daten zeigte, dass sowohl X- als auch C-Band Daten besonders sensitiv für die untersuchten Landbedeckungsklassen waren. Die X-Band Daten zeigten die höchste Sensitivität für niedrige Tundrengesellschaften. Die C-Band Daten zeigten eine höhere Sensitivität für mittelhohe Tundrengesellschaften und Gebüsch (shrub). Die L-Band Daten wiesen im Vergleich dazu die geringste Sensitivität für die Oberflächenbedeckung auf. Ein Vergleich von verschiedenen dual-polarisierten Daten zeigte, dass die Kanalkombination HH/VV die beste Differenzierung der Landbedeckungsklassen lieferte. Weniger deutlich war die Differenzierung mit den Kombinationen HH/HV und VV/VH. Insgesamt am besten waren jedoch die voll-polarisierten Daten geeignet, auch wenn die Verbesserung im Vergleich zu den co-polarisierten Daten nur gering war. Die Analyse der Klassifikationsgenauigkeiten bestätigte dieses Bild, machte jedoch deutlich, dass zu einer genauen Landbedeckungsklassifikation die Einbeziehung von multispektraler Information notwendig ist. Eine Nutzung von voll-polarisierten C-Band und multispektralen Daten erbrachte so eine mittlere Güte von ca. 80% für unüberwachte und von ca. 90% für überwachte Klassifikationsverfahren. Ähnlich hohe Werte wurden für die Kombination von co-polarisierten X-Band und multispektralen Daten erreicht. Im Vergleich zu Klassifikation die nur auf Grundlage von multispektralen Daten durchgeführt wurden, erbrachte die Einbeziehung der polarisierten Radardaten eine zusätzliche durchschnittliche Klassifikationsgüte von ca. +15%. Der Zugewinn und die Möglichkeit zur Differenzierung war vor allem für die Bedeckungstypen der Feuchtgebiete (wetlands) und der niedrigen Tundrengesellschaften festzustellen. Die Analyse der digitalen Höhenmodelle zeigte ein hohes Potential der TanDEM-X Daten für die Charakterisierung der topographischen Gegebenheiten. Die aus den Daten abgeleiteten absoluten und relativen topographischen Merkmale waren für eine morphometrische Quantifizierung der Landoberflächen-Morphologie geeignet. Zudem konnten diese Merkmale auch für eine initiale Klassifikation der Landformen genutzt werden. Die Daten zeigten somit ein hohes Potential für die Unterstützung der geomorphologischen Kartierung und für die Identifizierung der aktuellen und zukünftigen Dynamik der Landoberfläche. KW - Mackenzie-River-Delta KW - Banks Islands KW - Radarfernerkundung KW - Topografie KW - Formmessung KW - Klassifikation KW - Relief KW - PolSAR KW - Synthetic Aperture Radar KW - Land Cover Classification KW - Digital Elevation Model KW - Arctic Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-115719 ER - TY - JOUR A1 - Ullmann, Tobias A1 - Schmitt, Andreas A1 - Roth, Achim A1 - Duffe, Jason A1 - Dech, Stefan A1 - Hubberten, Hans-Wolfgang A1 - Baumhauer, Roland T1 - Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada N2 - In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques. KW - radar KW - arctic KW - tundra KW - land cover KW - classification KW - polarimetry KW - PolSAR KW - SAR KW - TerraSAR-X KW - Radarsat-2 Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-113303 ER -