TY - THES A1 - Hu, Zhongyang T1 - Earth Observation for the Assessment of Long-Term Snow Dynamics in European Mountains - Analysing 35-Year Snowline Dynamics in Europe Based on High Resolution Earth Observation Data between 1984 and 2018 T1 - Erdbeobachtung für die Beurteilung von Langzeit Schneedynamiken in Europäischen Gebirgen - Die Analyse von Scheegrenzendynamiken über 35 Jahre in Europa basierend auf hochauflösenden Erdbeobachtungsdaten zwischen 1984 und 2018 N2 - Worldwide, cold regions are undergoing significant alterations due to climate change. Snow, the most widely distributed cold region component, is highly sensitive to climate change. At the same time, snow itself profoundly impacts the Earth’s energy budget, biodiversity, and natural hazards, as well as hydropower management, freshwater management, and winter tourism/sports. Large parts of the cold regions in Europe are mountain areas, which are densely populated because of the various ecosystem services and socioeconomic well-being in mountains. At present, severe consequences caused by climate change have been observed in European mountains and their surrounding areas. Yet, large knowledge gaps hinder the development of effective regional and local adaptation strategies. Long-term and evidence-based regional studies are urgently needed to enhance the comprehension of regional responses to climate change. Earth Observation (EO) provides long-term consistent records of the Earth’s surface. It is a great alternative and/or supplement to conventional in-situ measurements which are usually time-consuming, cost-intensive and logistically demanding, particularly for the poor accessibility of cold regions. With the assistance of EO, land surface dynamics in cold regions can be observed in an objective, repeated, synoptic and consistent way. Thanks to free and open data policies, long-term archives such as Landsat Archive and Sentinel Archive can be accessed free-of-charge. The high- to medium-resolution remote sensing imagery from these freely accessible archives gives EO-based time series datasets the capability to depict snow dynamics in European mountains from the 1980s to the present. In order to compile such a dataset, it is necessary to investigate the spatiotemporal availability of EO data, and develop a spatiotemporally transferable framework from which one can investigate snow dynamics. Among the available EO image archives, the Landsat Archive has the longest uninterrupted records of the Earth’s land surface. Furthermore, its 30 m spatial resolution fulfils the requirements for snow monitoring in complex terrains. Landsat data can yield a time series of snow dynamics in mountainous areas from 1984 to the present. However, severe Landsat data gaps have occurred across certain regions of Europe. Moreover, the Landsat Level 1 Precision and Terrain (L1TP) data is scarcer (up to 50% less) in high-latitude mountainous areas than in low-latitude mountainous areas. Given the abovementioned facts, the Regional Snowline Elevation (RSE) is selected to characterize the snow dynamics in mountainous areas, as it can handle cloud obstructions in the optical images. In this thesis, I present a five-step framework to derive and densify RSE time series in European mountains, i.e. (1) pre-processing, (2) snow detection, (3) RSE retrieval, (4) time series densification, and (5) Regional Snowline Retreat Curve (RSRC) production. The results of the intra-annual RSE variations show a uniquely high variation in the beginning of the ablation seasons in the Alpine catchment Tagliamento, mainly toward higher elevation. As for inter-annual variations of RSE, median RSE increases in all selected catchments, with an average speed of around 4.66 m ∙ a−1 (median) and 5.87 m ∙ a−1 (at the beginning of the ablation season). The fastest significant retreat is observed in the catchment Drac (10.66 m ∙ a−1, at the beginning of the ablation season), and the slowest significant retreat is observed in the catchment Uzh (1.74 m ∙ a−1, at the beginning of the ablation season). The increase of RSEs at the beginning of the ablation season is faster than the median RSEs, whose average difference is nearly 1.21 m ∙ a−1, particularly in the catchment Drac (3.72 m ∙ a−1). The results of the RSRCs show a significant rise in RSEs at the beginning of the ablation season, except for the Alpine catchment Alpenrhein and Var, and the Pyrenean catchment Ariege. It indicates that 11.8 and 3.97 degrees Celsius less per year are needed for the regional snowlines to reach the middle point of the RSRC in the Tagliamento and Tysa, respectively. The variation of air temperature is regarded as an example of a potential climate driver in this thesis. The retrieved monthly mean RSEs are highly correlated (mean correlation coefficient "R" ̅ = 0.7) with the monthly temperature anomalies, which are more significant in months with extremely low/high temperature. Another case study that investigates the correlation between river discharges and RSEs is carried out to demonstrate the potential consequences of the derived snowline dynamics. The correlation analysis shows a good correlation between river discharges and RSEs (correlation coefficient, R=0.52). In this thesis, the developed framework signifies a better understanding of the snow dynamics in mountain areas, as well as their potential triggers and consequences. Nonetheless, an urgent need persists for: (1) validation data to assess long-term snow-related observations based on high-resolution EO data; (2) further studies to reveal interactions between snow and its ambient environment; and (3) regional and local adaptation-strategies coping with climate change. Further studies exploring the above-mentioned research gaps are urgently needed in the future. N2 - Weltweit erleben kalte Regionen signifikante Veränderungen durch den Klimawandel. In kalten Regionen ist der Schnee die am weitesten verbreitete Komponente, welche sehr sensibel auf Klimaänderungen reagiert. Zugleich beeinflusst der Schnee selbst das Energiebudget der Erde, die Biodiversität, Naturgefahren sowie Wasserenergiegewinnung, Süßwassergewinnung, Wintertourismus und Wintersport. Große Teile der kalten Regionen in Europa sind Gebirgsregionen. Diese sind dicht besiedelt, da Gebirgsregionen verschiedenste Ökosystemservices bereitstellen und sozioökonomisches Wohlbefinden ermöglichen. Heute kann man schwerwiegende Konsequenzen in Europäischen Gebirgen und deren angrenzenden Gebieten wahrnehmen. Dennoch verhindern große Wissenslücken die Entwicklung effektiver und regionaler/lokaler Anpassungsstrategien. Um regionaler Auswirkungen durch den Klimawandel besser verstehen zu können, ist es enorm wichtig Langzeitstudien und beweisorientierte regionale Studien durchzuführen. Erdbeobachtung (EO) bietet durchgängige Langzeitaufzeichnungen der Erdoberfläche. Dies ist eine großartige Alternative und/oder Ergänzung zu konventionellen in-situ Messungen, welche meist zeitaufwändig, teuer und logistisch herausfordernd sind – vor allem in kalten Regionen, die schwer zugänglich sind. Mit der Hilfe von Erdbeobachtung können Oberflächendynamiken objektiv, wiederholt, synoptisch und kontinuierlich aufgenommen werden. Dank freier und offener Datenpolitik, Langzeitmissionen wie Landsat und Sentinel sind diese Daten inzwischen ohne zusätzliche Kosten zugänglich. Durch die oben genannten Rahmenbedingungen, besteht die Möglichkeit aus hoch bis mittel aufgelöste Satellitenbilder erdbeobachtungsbasierte Zeitreihen zu erstellen, die die Schneedynamiken in Europäischen Gebirgen abbilden. Um dieses Ziel zu erreichen, muss die räumliche und zeitliche Verfügbarkeit von Erdbeobachtungsdaten überprüft werden und ein Rahmenwerk geschaffen werden (übertragbar in Zeit und Raum), um Schneedynamiken aus Erdbeobachtungsdaten großflächig ableiten zu können. Unter den verfügbaren Erdbeobachtungsarchiven bietet das Landsat Archiv die längsten und kontinuierlichsten Aufzeichnungen der Landoberfläche. Zudem erfüllt die räumliche Auflösung von 30 m die Anforderungen, Schnee in komplexem Terrain zu monitoren. Basierend auf Landsat L1TP Daten (z.B. terrainkorrigiert) ist es möglich, Zeitreihen von Schneedynamiken in Gebirgsregionen zwischen 1984 und 1991/1999 zu erstellen. Des Weiteren ist Landsat L1TP in hohen Breitengraden seltener verfügbar (bis zu 50 % weniger) als in Gebirgsregionen der gemäßigten Breiten. Basierend auf den oben genannten Fakten wurde die Regionale Höhe der Schneefallgrenze (RSE) ausgewählt um Schneedynamiken in Gebirgsregionen zu charakterisieren, da diese Wolken in optischen Szenen bewältigen kann. In dieser Arbeit wurde ein fünf-Stufen Rahmenwerk geschaffen, um RSE-Zeitserien in Europäischen Gebirgen abzuleiten und zu verdichten. Die Prozessierungskette besteht aus (1) Vorprozessierung, (2) Schneedetektion, (3) RSE-Ableitung, (4) Zeitreihenverdichtung und (5) Erstellung einer regionalen Schneegrenzen-Rückgangsfunktion (RSRC). Die Ergebnisse der intra-annuellen RSE Variationen zeigen eine einzigartige hohe Variation im Beginn der Abschmelzsaison im alpinen Einzugsgebiet Tagliamento, meist in höheren Gebieten. Wie für die inter-annuellen Variationen des RSE, steigt auch der Median des RSE in allen ausgewählten Einzugsgebieten mit einer durchschnittlichen Geschwindigkeit von 4.66 m ∙ a−1 (median) und 5.87 m ∙ a−1 (zum Beginn der Schmelze). Der schnellste signifikante Rückgang kann im Einzugsgebiet Drac (10.66 m ∙ a−1, Beginn der Schmelze) beobachtet werden, der langsamste Rückgang im Einzugsgebiet Uzh (1.74 m ∙ a−1, Beginn der Schmelze). Der Anstieg des RSE zu Beginn der Schmelzsaison ist schneller als der Median des RSE, dessen mittlere Differenz 1.21 m ∙ a−1 beträgt. Insbesondere für das Drac Einzugsgebiet (3.72 m ∙ a−1). Die Ergebnisse des RSRC zeigen signifikante Anstiege des RSE zu Beginn des Schmelzsaison, ausgenommen davon sind die alpinen Einzugsgebiete Alpenrhein und Var und das Einzugsgebiet Ariege in den Pyrenäen. Dies lässt darauf zurückschließen, dass 11.8 °C und 3.97 °C Grad weniger pro Jahr nötig sind für Tagliamento und Tysa, damit die regionalen Schneegrenzen den Mittelpunkt des RSRC erreicht. Zudem wird die Variation der Lufttemperatur als beispielhafter Treiber des Klimas in dieser Thesis gesehen. Die monatlich abgeleiteten mittleren RSE korrelieren stark (mittlerer korrelations Coeffizient R ̅ = 0.7) mit monatlichen Temperaturanomalien. In Monaten mit extrem hohen/tiefen Temperaturen ist die Korrelation am stärksten. Ein anderes Fallbeispiel untersucht die Korrelation zwischen Abfluss in Flüssen und RSE, um die potenziellen Konsequenzen der abgeleiteten Schneefallgrenzendynamiken zu ermitteln. Die Korrelationsanalyse weist eine gute Korrelation auf (R=0.52). Das in dieser Arbeit entwickelte Rahmenwerk ist nur ein Beginn, um das Wissen über Schneedynamiken in Gebirgsregionen zu verbessern und potentiell auslösende Faktoren und Konsequenzen zu verstehen. Dennoch wird folgendes dringend benötigt: (1) Validierungsdaten für schneebasierte Langzeitbeobachtungen aus hochaufgelösten Erdbeobachtungsdaten; (2) weitere Studien zu Interaktionen zwischen Schnee und der umgebende Umwelt; und (3) regionale und lokale Anpassungsstrategien, um Auswirkungen des Klimawandels zu meistern. Weitere Studien in den oben genannten Punkten werden in der Zukunft stark frequentiert sein, damit Wissens- und Forschungslücken geschlossen werden können. KW - Fernerkundung KW - Schnee KW - Europa KW - Remote Sensing KW - Snow KW - Europe Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200441 ER - TY - JOUR A1 - Koehler, Jonas A1 - Kuenzer, Claudia T1 - Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review JF - Remote Sensing N2 - Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing. KW - forecast KW - Earth Observation KW - land surface KW - land use KW - land cover KW - time series KW - machine learning KW - Markov chains KW - modeling Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216285 SN - 2072-4292 VL - 12 IS - 21 ER - TY - JOUR A1 - Ullmann, Tobias A1 - Nill, Leon A1 - Schiestl, Robert A1 - Trappe, Julian A1 - Lange-Athinodorou, Eva A1 - Baumhauer, Roland A1 - Meister, Julia T1 - Mapping buried paleogeographical features of the Nile Delta (Egypt) using the Landsat archive JF - E&G Quartnerny Science Journal N2 - The contribution highlights the use of Landsat spectral-temporal metrics (STMs) for the detection of surface anomalies that are potentially related to buried near-surface paleogeomorphological deposits in the Nile Delta (Egypt), in particular for a buried river branch close to Buto. The processing was completed in the Google Earth Engine (GEE) for the entire Nile Delta and for selected seasons of the year (summer/winter) using Landsat data from 1985 to 2019. We derived the STMs of the tasseled cap transformation (TC), the Normalized Difference Wetness Index (NDWI), and the Normalized Difference Vegetation Index (NDVI). These features were compared to historical topographic maps of the Survey of Egypt, CORONA imagery, the digital elevation model of the TanDEM-X mission, and modern high-resolution satellite imagery. The results suggest that the extent of channels is best revealed when differencing the median NDWI between summer (July/August) and winter (January/February) seasons (ΔNDWI). The observed difference is likely due to lower soil/plant moisture during summer, which is potentially caused by coarser-grained deposits and the morphology of the former levee. Similar anomalies were found in the immediate surroundings of several Pleistocene sand hills (“geziras”) and settlement mounds (“tells”) of the eastern delta, which allowed some mapping of the potential near-surface continuation. Such anomalies were not observed for the surroundings of tells of the western Nile Delta. Additional linear and meandering ΔNDWI anomalies were found in the eastern Nile Delta in the immediate surroundings of the ancient site of Bubastis (Tell Basta), as well as several kilometers north of Zagazig. These anomalies might indicate former courses of Nile river branches. However, the ΔNDWI does not provide an unambiguous delineation. N2 - Die Rekonstruktion der Paläotopographie und -hydrographie des Nildeltas spielt für landschaftsarchäologische Fragestellungen eine zentrale Rolle, da die antike Siedlungsaktivität stark von der Dynamik des antiken Flussnetzes beeinflusst war. Für viele Bereiche des Deltas ist die Lage antiker Flussarme jedoch unbekannt, da diese im Laufe der Zeit verlandet und heute nicht mehr eindeutig im Landschaftsbild erkennbar sind. In diesem Kontext erlauben moderne Fernerkundungsdaten eine flächendeckende Untersuchung und ermöglichen Anomalien der Landbedeckung und Diskontinuitäten der Oberflächenmorphologie zu identifizieren, wodurch wertvolle Hinweise zur paläogeomorphologischen Situation gewonnen werden können. Zur Detektion solcher Anomalien wird in diesem Beitrag das Landsat Archiv genutzt, wobei verschiedene spektrale und zeitlich-räumliche Metriken für das gesamte Nildelta (Ägypten) für den Zeitraum 1985 bis 2019 in der Google Earth Engine berechnet wurden. Die Merkmale der Merkmale der Tasseled Cap Transformation (TC), des Normalized Difference Wetness Index (NDWI) und des Normalized Difference Vegetation Index (NDVI) wurden analysiert und mit historischen topographischen Karten des Survey of Egypt, CORONA-Bildern, dem digitalen Höhenmodell der TanDEM-X-Mission und modernen Satellitenbildern verglichen. Die Ergebnisse der Zeitserienanalyse zeigen die Lage eines verlandeten Flussarms in der Nähe von Buto, der durch den Vergleich der Medianwerte des NDWI zwischen Sommer- (Juli/August) und Wintersaison (Januar/Februar) (ΔNDWI) deutlich zu erkennen ist. Der beobachtete Unterschied ist wahrscheinlich auf eine geringere Boden- und/oder Pflanzenfeuchtigkeit während des Sommers zurückzuführen, welche möglicherweise durch grobkörnige Ablagerungen im Untergrund bedingt wird. Ähnliche Anomalien wurden in der unmittelbaren Umgebung mehrerer pleistozäner Sandhügel (Geziras) und Siedlungshügel (Tells) des östlichen Nildeltas gefunden, was die Kartierungen der potentiellen oberflächennahen Fortsetzung ermöglichte. Weitere lineare und mäandrierende ΔNDWI Anomalien wurden im östlichen Nildelta in der unmittelbaren Umgebung der antiken Stätte von Bubastis (Tell Basta) sowie einige Kilometer nördlich der Stadt Zagazig gefunden. Diese Anomalien weisen vermutlich auf frühere Verläufe von Flussarmen des Nils in diesem Bereich des Deltas hin. KW - Nile Delta KW - paleogeography Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-230349 VL - 69 IS - 2 ER - TY - JOUR A1 - Schwalb‐Willmann, Jakob A1 - Remelgado, Ruben A1 - Safi, Kamran A1 - Wegmann, Martin T1 - moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R JF - Methods in Ecology and Evolution N2 - Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two‐dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an R package, which provides tools to animate movement trajectories, overlaying simultaneous uni‐ or multi‐temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame‐wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates. KW - animal tracking KW - animation KW - data visualization KW - movement data KW - movement ecology KW - spatio‐temporal data Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-214856 VL - 11 IS - 5 SP - 664 EP - 669 ER - TY - JOUR A1 - Geyer, Gerd A1 - Pais, Miguel Caldeira A1 - Wotte, Thomas T1 - Unexpectedly curved spines in a Cambrian trilobite: considerations on the spinosity in Kingaspidoides spinirecurvatus sp. nov. from the Anti-Atlas, Morocco, and related Cambrian ellipsocephaloids JF - PalZ N2 - The new ellipsocephaloid trilobite species Kingaspidoides spinirecurvatus has a spectacular morphology because of a unique set of two long and anteriorly recurved spines on the occipital ring and the axial ring of thoracic segment 8. Together with the long genal spines this whimsical dorsally directed spine arrangement is thought to act as a non-standard protective device against predators. This is illustrated by the body posture during different stages of enrolment, contrasting with the more sophisticated spinosities seen in later trilobites, which are discussed in brief. Kingaspidoides spinirecurvatus from the lower–middle Cambrian boundary interval of the eastern Anti-Atlas in Morocco has been known for about two decades, with specimens handled as precious objects on the fossil market. Similar, but far less spectacular, spine arrangements on the thoracic axial rings are known from other ellipsocephaloid trilobites from the Anti-Atlas of Morocco and the Franconian Forest region of Germany. This suggests that an experimental phase of spine development took place within the Kingaspi-doides clade during the early–middle Cambrian boundary interval. KW - Cambrian KW - Trilobita KW - Systematics KW - Biostratigraphy KW - West Gondwana KW - Morocco Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-231873 SN - 0031-0220 VL - 94 ER - TY - JOUR A1 - Reinermann, Sophie A1 - Asam, Sarah A1 - Kuenzer, Claudia T1 - Remote Sensing of Grassland Production and Management - A Review JF - Remote Sensing N2 - Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far. KW - pasture KW - use intensity KW - grazing KW - mowing KW - productivity KW - biomass KW - yield KW - satellite data KW - optical KW - SAR Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207799 SN - 2072-4292 VL - 12 IS - 12 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kuebert-Flock, Carina A1 - Borg, Erik A1 - Conrad, Christopher A1 - Ullmann, Tobias T1 - Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany JF - Remote Sensing N2 - This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m\(^2\)) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m\(^2\)). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat). KW - crop growth models KW - Landsat KW - MODIS KW - data fusion KW - STARFM KW - climate parameters KW - winter wheat Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207845 SN - 2072-4292 VL - 12 IS - 11 ER - TY - JOUR A1 - Thonfeld, Frank A1 - Steinbach, Stefanie A1 - Muro, Javier A1 - Kirimi, Fridah T1 - Long-term land use/land cover change assessment of the Kilombero catchment in Tanzania using random forest classification and robust change vector analysis JF - Remote Sensing N2 - Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment. KW - land-use/land-cover change KW - robust change vector analysis KW - Kilombero KW - wetland KW - food production KW - random forest KW - multitemporal metrics KW - Landsat KW - post-classification comparison Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203513 SN - 2072-4292 VL - 12 IS - 7 ER - TY - JOUR A1 - Akhundzadah, Noor Ahmad A1 - Soltani, Salim A1 - Aich, Valentin T1 - Impacts of climate change on the water resources of the Kunduz River Basin, Afghanistan JF - Climate N2 - The Kunduz River is one of the main tributaries of the Amu Darya Basin in North Afghanistan. Many communities live in the Kunduz River Basin (KRB), and its water resources have been the basis of their livelihoods for many generations. This study investigates climate change impacts on the KRB catchment. Rare station data are, for the first time, used to analyze systematic trends in temperature, precipitation, and river discharge over the past few decades, while using Mann–Kendall and Theil–Sen trend statistics. The trends show that the hydrology of the basin changed significantly over the last decades. A comparison of landcover data of the river basin from 1992 and 2019 shows significant changes that have additional impact on the basin hydrology, which are used to interpret the trend analysis. There is considerable uncertainty due to the data scarcity and gaps in the data, but all results indicate a strong tendency towards drier conditions. An extreme warming trend, partly above 2 °C since the 1960s in combination with a dramatic precipitation decrease by more than −30% lead to a strong decrease in river discharge. The increasing glacier melt compensates the decreases and leads to an increase in runoff only in the highland parts of the upper catchment. The reduction of water availability and the additional stress on the land leads to a strong increase of barren land and a reduction of vegetation cover. The detected trends and changes in the basin hydrology demand an active management of the already scarce water resources in order to sustain water supply for agriculture and ecosystems in the KRB. KW - climate change KW - Kunduz River Basin KW - trend analysis KW - river discharge KW - landcover changes Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-213199 SN - 2225-1154 VL - 8 IS - 10 ER - TY - JOUR A1 - Ulloa-Torrealba, Yrneh A1 - Stahlmann, Reinhold A1 - Wegmann, Martin A1 - Koellner, Thomas T1 - Over 150 years of change: object-oriented analysis of historical land cover in the Main river catchment, Bavaria/Germany JF - Remote Sensing N2 - The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services. KW - historical KW - land cover change KW - object-based classification KW - eCognition Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220029 SN - 2072-4292 VL - 12 IS - 24 ER -