TY - THES A1 - Majewski, Lisa T1 - Input-Output-Analyse zur Ermittlung der regionalökonomischen Effekte des Tourismus in Schutzgebieten : Eine Adaption der Methodik an internationale Standards am Fallbeispiel Biosphärengebiet Schwarzwald T1 - Input-output analysis to estimate the regional economic effects of tourism in protected areas. An adaptation of the methodology towards international standards using the case of Black Forest Biosphere Reserve N2 - Schutzgebiete gelten laut der Convention on Biological Diversity als Flächeninstrument zum Schutz der Biodiversität. Menschen profitieren davon unter anderem durch die Nutzung als touristische Attraktion. Schätzungen zufolge werden weltweit etwa acht Mrd. Besuche zur Wahrnehmung des Naturerlebnisangebots der Schutzgebiete erreicht, woraus direkte Besucherausgaben in Höhe von 600 Mrd. US-$ resultieren. Schutzgebiete sind damit auch bedeutende Wirtschaftsmotoren der regionalen Ökonomien. Deutschlands Nationalparks zählen jährlich etwa 53 Mio. Besuchstage, deren tägliche Ausgaben vor Ort einen Bruttoumsatz in Höhe von 2,78 Mrd. € generieren. Die touristische Wertschöpfung beträgt 1,45 Mrd. €. Die weiteren 65 Mio. Besuchstage in deutschen Biosphärenreservaten erwirtschaften einen Bruttoumsatz in Höhe von 2,94 Mrd. €. Das Einkommen von 172.000 Personen ist vom Tourismus in deutschen Nationalparken und Biosphärenreservaten abhängig. Im Rahmen einer ersten Studie zu den regionalökonomischen Effekten des Nationalparks Berchtesgaden im Jahr 2002 wurde die touristische Wertschöpfungsanalyse als Standardmethode der deutschen Schutzgebietsforschung etabliert. Im Laufe der Jahre wurde sie dahingehend modifiziert, ein vergleichbares, weil standardisiertes Vorgehen anwenden zu können. Die internationale Forschung manifestiert mit der Herausgabe eines Leitfadens einen anderen Standard zur regionalökonomischen Wirkungsanalyse des Tourismus in Schutzgebieten: die Input-Output-Analyse. Schutzgebietsverwaltungen in den USA, Kanada, Brasilien, Namibia, Südafrika und Finnland führen für ihr Besuchermonitoring Input-Output-Analysen durch. Diese sind im Vergleich zur Wertschöpfungsanalyse als der validiere Ansatz einzustufen, weil damit ein Rechenwerk gegeben ist, womit indirekte Vorleistungs- und induzierte Konsumwirkungen zuverlässig quantifiziert werden können. Die Wertschöpfungsanalyse arbeitet hingegen mit pauschalen Wertschöpfungsquoten für alle touristischen Wirtschaftszweige und auf jeder Maßstabsebene. Aufgrund der fehlenden Datenverfügbarkeit konnte die Input-Output-Analyse in Deutschland bisher nicht angewandt werden. Eine potenzielle Datenquelle eröffnete sich durch das US-amerikanische Modellierungsunternehmen IMPLAN, welches Input-Output-Tabellen für die regionale Ebene der EU anbot. IMPLAN-Daten werden auch vom US-amerikanischen National Park Service verwendet. Die vorliegende Arbeit versteht sich als methodische Weiterentwicklung regionalökonomischer Wirkungsanalysen in Deutschlands Schutzgebieten zur Adaption an internationale Standards. Dazu erfolgt die Applikation der Input-Output-Analyse für das Fallbeispiel des Biosphärengebiets Schwarzwald, dessen Regionalökonomie einen überschaubaren Analyserahmen bietet. Der Nationalpark Schwarzwald wurde als Vergleichsregion zur Validierung der Ergebnisse untersucht. Für eine erweiterte Einordnung der touristischen Multiplikatorwirkung in der Schwarzwaldregion wurde eine multiregionale Input-Output-Analyse durchgeführt, die sich auf die Gebietsabgrenzung der beiden Naturparke Schwarzwald Mitte/Nord und Südschwarzwald bezieht. Zur Quantifizierung der direkten Wirkungsebene wurden touristische Kenngrößen der amtlichen Statistik entnommen. Die Berechnung von direkten Wertschöpfungsquoten erfolgte gemäß ihrer Definition als die in der Region verbleibende Wertschöpfung am touristischen Produktionswert. Mittels der Input-Output-Analyse wurden die indirekten und induzierten Effekte des Tourismus im Biosphärengebiet Schwarzwald ermittelt. Aus den regionalen Input-Output-Tabellen wurden inverse Koeffizienten abgeleitet, welche die regionalökonomischen Multiplikatoren anzeigen. Zwei Multiplikatortypen wurden für die touristischen Kenngrößen Output, Wertschöpfung und Beschäftigung berechnet: Typ I-Multiplikatoren bemessen die indirekten Vorleistungseffekte touristischer Ausgaben. Typ II-Multiplikatoren inkludieren auch die induzierten Konsumeffekte. In einem mehrstufigen Prozess der Analyse von weiteren Fallbeispielen können regionalökonomische Multiplikatoren für verschiedene Gebietseinheiten validiert und so ganzheitlich abgestimmt für das deutsche Schutzgebietssystem adaptiert werden. Dadurch könnte die Input-Output-Analyse als neue Standardmethode für ein dauerhaftes regionalökonomisches Monitoring in deutschen Schutzgebieten etabliert werden. N2 - According to the Convention on Biological Diversity, protected areas are a spatial instrument for the protection of biodiversity. People benefit from protected areas, among other things, by using it as a tourist attraction. An estimated eight billion visits per year benefit from the nature experience offered by protected areas, resulting in a direct spending of US-$ 600 billion worldwide. Protected areas are thus important economic drivers of regional economies. In Germany, annually 53 million visitor days are registered in the countries national parks. Their daily expenditures generate an estimated gross sales of € 2.78 billion. The tourism value added amounts to € 1.45 billion. Another 65 million visitor days to German biosphere reserves generate a gross sales of € 2.94 billion. The income of 172,000 people depends on tourism in German national parks and biosphere reserves. In a first study on the regional economic effects of the Berchtesgaden National Park in 2002, the tourism value added analysis was established as a standard method in German protected area research. Over the years, it was modified to be able to apply a comparable, standardized procedure. International research manifests another standard for regional economic impact analysis of tourism in protected areas using economic input-output analysis. Protected area administrations in the USA, Canada, Brazil, Namibia, South Africa, and Finland conduct input-output analyses for their visitor monitoring. Compared to the value added analysis, the input-output approach can be considered the more valid approach because it provides a calculation framework with which indirect, intermediate, and induced consumption effects can be reliably quantified. The value added analysis, on the other hand, works with value added ratios that are generalized across all tourism economic sectors and for every geographic scale. Due to the lack of available data, the input-output approach has not been applied in Germany so far. A potential data source was opened by the US modelling company IMPLAN, which offered input-output tables for the regional level of the EU. IMPLAN data is also used by the US National Park Service. The present study is intended to further develop Germany’s protected areas regional economic impact analysis methodologies for adaptation to international standards. For this purpose, the input-output analysis is applied to the case study of the Black Forest Biosphere Reserve, whose regional economy offers a manageable analytical framework. The Black Forest National Park was examined as a comparative region to validate the results. For an extended classification of the tourism multiplier effect in the Black Forest region, a multi-regional input-output analysis was carried out, which refers to the area delineations of the two Nature Parks Black Forest Central/North and Southern Black Forest. To quantify the direct effects, tourism measures were taken from official statistics. Direct value added ratios were calculated according to their definition as spending remaining in the region as value added. The indirect and induced effects of tourism in the Black Forest Biosphere Reserve region were determined by the input-output analysis. Inverse coefficients were derived from the regional input-output tables, which indicate the regional economic multipliers. Two types of multipliers (Type I and Type II) were derived for the tourism parameters output, value added and employment: Type I multipliers measure the indirect effects of tourism expenditure; Type II multipliers also include the induced effects. In a multi-stage process of applying the input-output method to further case studies, it is possible to validate the multipliers for different spatial levels and thus establish it as a new standard method for the permanent regional economic monitoring in German protected areas. N2 - Die einzigartigen Natur- und Kulturlandschaften von Schutzgebieten sind weltweit bedeutende Destinationen für Tages- und Übernachtungsgäste. Die Ausgaben von Besuchern erzeugen ökonomische Effekte und sichern so regionale Wertschöpfung und Beschäftigung. Zur Analyse dieser regionalökonomischen Effekte des Tourismus in Schutzgebieten stehen heute verschiedene Methoden zur Verfügung. International ist die Input-Output-Analyse das etablierte Standardverfahren in mehreren Monitoringsystemen. Die Schutzgebietsforschung in Deutschland hat sich hingegen auf die Wertschöpfungsanalyse spezialisiert und geht dabei von generellen Annahmen der touristischen Multiplikatorwirkung aus. Vor dem Hintergrund einer Adaption an internationale Standards wird erstmals eine Input-Output-Analyse der regionalökonomischen Effekte des Tourismus in deutschen Schutzgebieten durchgeführt. Berechnungen auf Grundlage eines Input-Output-Modells liefern für das Fallbeispiel Biosphärengebiet Schwarzwald regionale und branchenspezifsche Multiplikatoren. Die Ergebnisse werden zum einen mit einer Input-Output-Analyse des Nationalparks Schwarzwald und zum anderen mit einer klassischen Wertschöpfungsanalyse verglichen. Darüber hinaus ermöglicht die Anwendung eines multiregionalen Ansatzes die Analyse der touristischen Multiplikatorwirkung in der gesamten Naturparkregion Schwarzwald Mitte/Nord und Südschwarzwald. T3 - Würzburger Geographische Arbeiten - 126 KW - Schutzgebiete KW - Tourismus KW - Regionalökonomie KW - Input-Output-Analyse KW - Wirkungsanalyse Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-316545 SN - 978-3-95826-216-4 SN - 978-3-95826-217-1 SN - 0510-9833 SN - 2194-3656 N1 - Parallel erschienen als Druckausgabe bei Würzburg University Press, ISBN 978-3-95826-216-4, 37,80 Euro PB - Würzburg University Press CY - Würzburg ER - TY - THES A1 - Reinermann, Sophie T1 - Earth Observation Time Series for Grassland Management Analyses – Development and large-scale Application of a Framework to detect Grassland Mowing Events in Germany T1 - Erdbeobachtungszeitserien zur Analyse der Grünlandbewirtschaftung – Entwicklung und großflächige Anwendung einer Prozessierungsarchitektur zur automatisierten Detektion von Grünlandmahden N2 - Grasslands shape many landscapes of the earth as they cover about one-third of its surface. They are home and provide livelihood for billions of people and are mainly used as source of forage for animals. However, grasslands fulfill many additional ecosystem functions next to fodder production, such as storage of carbon, water filtration, provision of habitats and cultural values. They play a role in climate change (mitigation) and in preserving biodiversity and ecosystem functions on a global scale. The degree to what these ecosystem functions are present within grassland ecosystems is largely determined by the management. Individual management practices and the use intensity influence the species composition as well as functions, like carbon storage, while higher use intensities (e.g. high mowing frequencies) usually show a negative impact. Especially in Central European countries, like in Germany, the determining influence of grassland management on its physiognomy and ecosystem functions leads to a large variability and small-scale alternations of grassland parcels. Large-scale information on the management and use intensity of grasslands is not available. Consequently, estimations of grassland ecosystem functions are challenging which, however, would be required for large-scale assessments of the status of grassland ecosystems and optimized management plans for the future. The topic of this thesis tackles this gap by investigating the major grassland management practice in Germany, which is mowing, for multiple years, in high spatial resolution and on a national scale. Earth Observation (EO) has the advantage of providing information of the earth’s surface on multi-temporal time steps. An extensive literature review on the use of EO for grassland management and production analyses, which was part of this thesis, showed that in particular research on grasslands consisting of small parcels with a large variety of management and use intensity, like common in Central Europe, is underrepresented. Especially the launch of the Sentinel satellites in the recent past now enables the analyses of such grasslands due to their high spatial and temporal resolution. The literature review specifically on the investigation of grassland mowing events revealed that most previous studies focused on small study areas, were exploratory, only used one sensor type and/or lacked a reference data set with a complete range of management options. Within this thesis a novel framework to detect grassland mowing events over large areas is presented which was applied and validated for the entire area of Germany for multiple years (2018–2021). The potential of both sensor types, optical (Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel-1) was investigated regarding grassland mowing event detection. Eight EO parameters were investigated, namely the Enhanced Vegetation Index (EVI), the backscatter intensity and the interferometric (InSAR) temporal coherence for both available polarization modes (VV and VH), and the polarimetric (PolSAR) decomposition parameters Entropy, K0 and K1. An extensive reference data set was generated based on daily images of webcams distributed in Germany which resulted in mowing information for grasslands with the entire possible range of mowing frequencies – from one to six in Germany – and in 1475 reference mowing events for the four years of interest. For the first time a observation-driven mowing detection approach including data from Sentinel-2 and Sentinel-1 and combining the two was developed, applied and validated on large scale. Based on a subset of the reference data (13 grassland parcels with 44 mowing events) from 2019 the EO parameters were investigated and the detection algorithm developed and parameterized. This analysis showed that a threshold-based change detection approach based on EVI captured grassland mowing events best, which only failed during periods of clouds. All SAR-based parameters showed a less consistent behavior to mowing events, with PolSAR Entropy and InSAR Coherence VH, however, revealing the highest potential among them. A second, combined approach based on EVI and a SARbased parameter was developed and tested for PolSAR Entropy and InSAR VH. To avoid additional false positive detections during periods in which mowing events are anyhow reliably detected using optical data, the SAR-based mowing detection was only initiated during long gaps within the optical time series (< 25 days). Application and validation of these approaches in a focus region revealed that only using EVI leads to the highest accuracies (F1-Score = 0.65) as combining this approach with SAR-based detection led to a strong increase in falsely detected mowing events resulting in a decrease of accuracies (EVI + PolSAR ENT F1-Score = 0.61; EVI + InSAR COH F1-Score = 0.61). The mowing detection algorithm based on EVI was applied for the entire area of Germany for the years 2018-2021. It was revealed that the largest share of grasslands with high mowing frequencies (at least four mowing events) can be found in southern/south-eastern Germany. Extensively used grassland (mown up to two times) is distributed within the entire country with larger shares in the center and north-eastern parts of Germany. These patterns stay constant in general, but small fluctuations between the years are visible. Early mown grasslands can be found in southern/south-eastern Germany – in line with high mowing frequency areas – but also in central-western parts. The years 2019 and 2020 revealed higher accuracies based on the 1475 mowing events of the multi-annual validation data set (F1-Scores of 0.64 and 0.63), 2018 and 2021 lower ones (F1-Score of 0.52 and 0.50). Based on this new, unprecedented data set, potential influencing factors on the mowing dynamics were investigated. Therefore, climate, topography, soil data and information on conservation schemes were related to mowing dynamics for the year 2020, which showed a high number of valid observations and detection accuracy. It was revealed that there are no strong linear relationships between the mowing frequency or the timing of the first mowing event and the investigated variables. However, it was found that for intensive grassland usage certain climatic and topographic conditions have to be fulfilled, while extensive grasslands appear on the entire spectrum of these variables. Further, higher mowing frequencies occur on soils with influence of ground water and lower mowing frequencies in protected areas. These results show the complex interplay between grassland mowing dynamics and external influences and highlight the challenges of policies aiming to protect grassland ecosystem functions and their need to be adapted to regional circumstances. N2 - Grünland prägt viele Landschaften der Erde, da es etwa ein Drittel der Erdoberfläche bedeckt. Es ist Heimat und Lebensgrundlage für Milliarden von Menschen und wird hauptsächlich als Futterquelle für die Viehhaltung genutzt. Neben der Futterproduktion erfüllen Grünlandflächen jedoch viele weitere Ökosystemfunktionen, wie die Speicherung von Kohlenstoff, die Wasserfilterung, die Bereitstellung von Lebensräumen, als auch kulturelle Werte. Sie spielen eine Rolle bei der Abschwächung des Klimawandels und bei der Erhaltung der biologischen Vielfalt und der Ökosystemfunktionen auf globaler Ebene. Das Ausmaß, in dem diese Ökosystemfunktionen in Grünlandökosystemen vorhanden sind, wird weitgehend durch die Bewirtschaftung bestimmt. Einzelne Bewirtschaftungspraktiken und die Nutzungsintensität beeinflussen sowohl die Artenzusammensetzung als auch Funktionen wie die Kohlenstoffspeicherung, wobei höhere Nutzungsintensitäten (z. B. hohe Mähfrequenzen) in der Regel einen negativen Einfluss haben. Insbesondere in mitteleuropäischen Ländern wie Deutschland, führt der bestimmende Einfluss der Grünlandbewirtschaftung auf die Physiognomie und die Ökosystemfunktionen zu einer großen Variabilität und kleinräumigen Differenziertheit einzelner Grünlandflächen. Großräumige Informationen über die Bewirtschaftungs- und Nutzungsintensität von Grünland sind nicht verfügbar. Folglich sind Schätzungen der Ökosystemfunktionen von Grünland eine Herausforderung, die jedoch für großräumige Bewertungen des Zustands von Grünlandökosystemen und optimierte Bewirtschaftungspläne für die Zukunft erforderlich wären. Das Thema dieser Arbeit greift diese Lücke auf, indem es die wichtigste Grünlandbewirtschaftungsmethode in Deutschland, die Mahd, über mehrere Jahre, mit hoher räumlicher Auflösung und auf nationaler Ebene untersucht. Die Erdbeobachtung hat den Vorteil, Informationen über die Erdoberfläche in multitemporalen Zeitschritten zu liefern. Eine umfangreiche Literaturrecherche zur Nutzung von Erdbeobachtung für Grünlandmanagement und Produktion, welche Teil dieser Arbeit war, hat gezeigt, dass insbesondere die Forschung zu kleinparzelligem Grünland mit einer großen Vielfalt an Bewirtschaftungs- und Nutzungsintensitäten, wie in Mitteleuropa gängig, unterrepräsentiert ist. Insbesondere die vor wenigen Jahren erfolgte Start der Sentinel-Satellitenmissionen ermöglicht nun auch die Analyse solcher Grünlandflächen aufgrund der hohen räumlichen und zeitlichen Auflösung ihrer Aufnahmen. Die Literaturrecherche speziell zur Untersuchung von Mähereignissen auf Grünland ergab, dass die meisten bisherigen Studien sich auf kleine Untersuchungsgebiete konzentrierten, explorativ waren, nur einen Sensortyp verwendeten und/oder keinen Referenzdatensatz mit einer vollständigen Palette von Managementoptionen enthielten. Im Rahmen dieser Arbeit wird eine neuartige Methodik zur Erkennung von Grünlandmahdereignissen vorgestellt, welches über mehrere Jahre (2018-2021) flächendeckend in Deutschland angewendet und validiert wurde. Beide Sensortypen – optisch (Sentinel-2) und SAR (Sentinel-1) – wurden hinsichtlich ihres Potentials zur Detektion von Grünlandmahdereignissen ausgewertet. Acht EO-Parameter wurden untersucht, nämlich der Enhanced Vegetation Index (EVI), die Rückstreuintensität und die interferometrische zeitliche Kohärenz (InSAR) für beide verfügbaren Polarimetrien (VV und VH), sowie die polarimetrischen (PolSAR) Zerlegungsparameter Entropie, K0 und K1. Ein umfangreicher Referenzdatensatz wurde auf der Basis täglicher Bilder von Webcams generiert, welche über Deutschland verteilt sind. Dieser enthält Mahdinformationen für Grünland mit dem gesamten möglichen Spektrum an Mähfrequenzen – von eins bis sechs Mahden – und 1475 Referenz-Mähereignisse für die Untersuchungsjahre. Zum ersten Mal wurde ein Ansatz basierend auf tatsächlichen Beobachtungen zur Erkennung der Mahd entwickelt, angewandt und großflächig validiert, der Daten von Sentinel - 2 und Sentinel - 1 verwendet und beide miteinander kombiniert. Anhand eines Subset der Referenzdaten (13 Grünlandparzellen) wurden die EO-Parameter untersucht und der Algorithmus zur Mahddetektion entwickelt und parametrisiert. Die Analyse hat gezeigt, dass ein schwellenwertbasierter Ansatz zur Erkennung von Veränderungen auf der Grundlage des EVI die Ereignisse der Grünlandmahd am besten erfasst, und nur während Bewölkungsperioden Mahden nicht erfolgreich detektiert. Alle SAR-basierten Parameter zeigten ein inkonsistenteres Verhalten gegenüber Mähaktivitäten als EVI, wobei PolSAR Entropie und InSAR Kohärenz VH noch das höchste Potenzial aufwiesen. Ein zweiter, kombinierter Ansatz, der auf EVI und einem SAR Parameter basiert, wurde entwickelt und für PolSAR Entropie und InSAR VH getestet. Aufgrund vieler zusätzlicher Veränderungen, die in den Zeitreihen erkennbar sind, wurde die SAR-basierte Mahddetektion nur während langer Lücken in den optischen Zeitreihen (< 25 Tage) initiiert. Die Anwendung und Validierung dieser Ansätze in einer Fokusregion ergab, dass die Verwendung des EVI-Ansatzes zu den höchsten Genauigkeiten führt (F1-Score = 0.65), da die Kombination dieses Ansatzes mit der SAR-basierten Detektion zu einem starken Anstieg der falsch erkannten Mähereignisse und damit zu einer Abnahme der Genauigkeiten führte (EVI + PolSAR ENT F1-Score=0.61; EVI + InSAR COH F1-Score = 0.61). Der auf EVI basierende Mahddetektionsalgorithmus wurde für die gesamte Fläche Deutschlands für die Jahre 2018–2021 angewendet. Es zeigte sich, dass der größte Anteil an Grünland mit hoher Mähfrequenz (mindestens vier Mähereignisse) im Süden/Südosten Deutschlands zu finden ist. Extensiv genutztes Grünland (bis zu zweimal gemäht) ist über das gesamte Bundesgebiet verteilt, mit größeren Anteilen in der Mitte und im Nordosten Deutschlands. Diese Muster bleiben im Allgemeinen konstant, aber es sind kleine Schwankungen zwischen den Jahren erkennbar. Früh gemähtes Grünland findet sich in Süd-/Südostdeutschland - entsprechend den Gebieten mit hoher Mähfrequenz -, aber auch in Mittel- und Westdeutschland. Die Jahre 2019 und 2020 zeigen höhere Genauigkeiten (F1- Scores von 0.64 und 0.63), 2018 und 2021 niedrigere (F1-Score von 0.52 und 0.50). Darüber hinaus wurden mögliche Einflussfaktoren auf die Mahddynamik untersucht. So wurden Klima, Topografie, Bodendaten und Informationen über Schutzmaßnahmen mit der Mahddynamik für das Jahr 2020 in Verbindung gebracht, für welches eine hohe Anzahl gültiger Beobachtungen und eine hohe Erfassungsgenauigkeit erzielt werden konnten. Es zeigte sich, dass es keine starken linearen Beziehungen zwischen der Mahdhäufigkeit oder dem Zeitpunkt der ersten Mahd und den untersuchten Variablen gibt. Es wurde jedoch festgestellt, dass für eine intensive Grünlandnutzung bestimmte klimatische und topografische Bedingungen erfüllt sein müssen, wohingegen extensive Grünlandflächen im gesamten Spektrum dieser Variablen auftreten. Außerdem treten auf Böden mit Grundwassereinfluss höhere und in Schutzgebieten niedrigere Mahdhäufigkeiten auf. Diese Ergebnisse zeigen das komplexe Zusammenspiel zwischen der Dynamik der Grünlandmahd und äußeren Einflüssen und verdeutlichen die Herausforderungen in der gezielten Erstellung von Maßnahmen zum Schutz von Grünland-Ökosystemfunktionen und die Notwendigkeit diese regional anzupassen. KW - Grünland KW - Erdbeobachtung KW - Fernerkundung KW - Mähen KW - Grünlandnutzung KW - Zeitreihe KW - Erde KW - Sentinel-1 KW - Sentinel-2 KW - Enhanced Vegetation Index KW - PolSAR KW - InSAR Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322737 ER - TY - JOUR A1 - Ha, Tuyen V. A1 - Huth, Juliane A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - A review of Earth observation-based drought studies in Southeast Asia JF - Remote Sensing N2 - Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region. KW - drought KW - drought impact KW - agricultural drought KW - hydrological drought KW - meteorological drought KW - earth observation KW - remote sensing KW - Southeast Asia KW - Mekong Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286258 SN - 2072-4292 VL - 14 IS - 15 ER - TY - JOUR A1 - Koehler, Jonas A1 - Bauer, André A1 - Dietz, Andreas J. A1 - Kuenzer, Claudia T1 - Towards forecasting future snow cover dynamics in the European Alps — the potential of long optical remote-sensing time series JF - Remote Sensing N2 - Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach. KW - forecast KW - Earth Observation KW - time series KW - Snow Line Elevation KW - Alps KW - mountains KW - environmental modeling KW - machine learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288338 SN - 2072-4292 VL - 14 IS - 18 ER - TY - JOUR A1 - Job, Hubert A1 - Meyer, Constantin A1 - Coronado, Oriana A1 - Koblar, Simon A1 - Laner, Peter A1 - Omizzolo, Andrea A1 - Plassmann, Guido A1 - Riedler, Walter A1 - Vesely, Philipp A1 - Schindelegger, Arthur T1 - Open spaces in the European Alps — GIS-based analysis and implications for spatial planning from a transnational perspective JF - Land N2 - This article presents an open space concept of areas that are kept permanently free from buildings, technical infrastructure, and soil sealing. In the European Alps, space is scarce because of the topography; conflicts often arise between competing land uses such as permanent settlements and commercial activity. However, the presence of open spaces is important for carbon sequestration and the prevention of natural hazards, especially given climate change. A GIS-based analysis was conducted to identify an alpine-wide inventory of large-scale near-natural areas, or simply stated, open spaces. The method used identified the degree of infrastructure development for natural landscape units. Within the Alpine Convention perimeter, near-natural areas (with a degree of infrastructural development of up to 20%) account for a share of 51.5%. Only 14.5% of those areas are highly protected and are mostly located in high altitudes of over 1500 m or 2000 m above sea level. We advocate that the remaining Alpine open spaces must be preserved through the delimitation of more effective protection mechanisms, and green corridors should be safeguarded through spatial planning. To enhance the ecological connectivity of open spaces, there is the need for tailored spatial and sectoral planning strategies to prevent further landscape fragmentation and to coordinate new forms of land use for renewable energy production. KW - Alps KW - conservation KW - connectivity KW - fragmentation KW - GIS-analysis KW - land use KW - open spaces KW - protected areas KW - sectoral planning KW - spatial planning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288207 SN - 2073-445X VL - 11 IS - 9 ER - TY - THES A1 - Kraff, Nicolas Johannes T1 - Analyse raumzeitlicher Veränderungen und ontologische Kategorisierung morphologischer Armutserscheinungen - Eine globale Betrachtung mithilfe von Satellitenbildern und manueller Bildinterpretation T1 - Analysis of spatiotemporal changes and ontological categorization of morphological manifestations of poverty - A global view using satellite imagery and manual image interpretation N2 - Die städtische Umwelt ist in steter Veränderung, vor allem durch den Bau, aber auch durch die Zerstörung von städtischen Elementen. Die formelle Entwicklung ist ein Prozess mit langen Planungszeiträumen und die bebaute Landschaft wirkt daher statisch. Dagegen unterliegen informelle oder spontane Siedlungen aufgrund ihrer stets unvollendeten städtischen Form einer hohen Dynamik – so wird in der Literatur berichtet. Allerdings sind Dynamik und die morphologischen Merkmale der physischen Transformation in solchen Siedlungen, die städtische Armut morphologisch repräsentieren, auf globaler Ebene bisher kaum mit einer konsistenten Datengrundlage empirisch untersucht worden. Hier setzt die vorliegende Arbeit an. Unter der Annahme, dass die erforschte zeitliche Dynamik in Europa geringer ausfällt, stellt sich die generelle Frage nach einer katalogisierten Erfassung physischer Wohnformen von Armut speziell in Europa. Denn Wohnformen der Armut werden oft ausschließlich mit dem ‚Globalen Süden‘ assoziiert, insbesondere durch die Darstellung von Slums. Tatsächlich ist Europa sogar die Wiege der Begriffe ‚Slum‘ und ‚Ghetto‘, die vor Jahrhunderten zur Beschreibung von Missständen und Unterdrückung auftauchten. Bis heute weist dieser facettenreiche Kontinent eine enorme Vielfalt an physischen Wohnformen der Armut auf, die ihre Wurzeln in unterschiedlichen Politiken, Kulturen, Geschichten und Lebensstilen haben. Um über diese genannten Aspekte Aufschluss zu erlangen, bedarf es u.a. der Bildanalyse durch Satellitenbilder. Diese Arbeit wird daher mittels Fernerkundung bzw. Erdbeobachtung (EO) sowie zusätzlicher Literaturrecherchen und einer empirischen Erhebung erstellt. Um Unsicherheiten konzeptionell und in der Erfassung offenzulegen, ist die Methode der manuellen Bildinterpretation von Armutsgebieten kritisch zu hinterfragen. Das übergeordnete Ziel dieser Arbeit ist eine bessere Wissensbasis über Armut zu schaffen, um Maßnahmen zur Reduzierung von Armut entwickeln zu können. Die Arbeit dient dabei als eine Antwort auf die Nachhaltigkeitsziele der Vereinten Nationen. Es wird Grundlagenforschung betrieben, indem Wissenslücken in der Erdbeobachtung zu physisch-baulichen bzw. morphologischen Erscheinungen von Armut auf Gebäude-Ebene explorativ analysiert werden. Die Arbeit wird in drei Forschungsthemen bzw. Studienteile untergliedert: Ziel des ersten Studienteils ist die globale raumzeitliche Erfassung von Dynamiken durch Anknüpfung an bisherige Kategorisierungen von Armutsgebieten. Die bisherige Wissenslücke soll gefüllt werden, indem über einen Zeitraum von etwa sieben Jahren in 16 dokumentierten Manifestationen städtischer Armut anhand von Erdbeobachtungsdaten eine zeitliche Analyse der bebauten Umwelt durchgeführt wird. Neben einer global verteilten Gebietsauswahl wird die visuelle Bildinterpretation (MVII) unter Verwendung von hochauflösenden optischen Satellitendaten genutzt. Dies geschieht in Kombination mit in-situ- und Google Street View-Bildern zur Ableitung von 3D-Stadtmodellen. Es werden physische Raumstrukturen anhand von sechs räumlichen morphologischen Variablen gemessen: Anzahl, Größe, Höhe, Ausrichtung und Dichte der Gebäude sowie Heterogenität der Bebauung. Diese ‚temporale Analyse‘ zeigt zunächst sowohl inter- als auch intra-urbane Unterschiede. Es lassen sich unterschiedliche, aber generell hohe morphologische Dynamiken zwischen den Untersuchungsgebieten finden. Dies drückt sich in vielfältiger Weise aus: von abgerissenen und rekonstruierten Gebieten bis hin zu solchen, wo Veränderungen innerhalb der gegebenen Strukturen auftreten. Geographisch gesehen resultiert in der Stichprobe eine fortgeschrittene Dynamik, insbesondere in Gebieten des Globalen Südens. Gleichzeitig lässt sich eine hohe räumliche Variabilität der morphologischen Transformationen innerhalb der untersuchten Gebiete beobachten. Trotz dieser teilweise hohen morphologischen Dynamik sind die räumlichen Muster von Gebäudefluchten, Straßen und Freiflächen überwiegend konstant. Diese ersten Ergebnisse deuten auf einen geringen Wandel in Europa hin, weshalb diese europäischen Armutsgebiete im folgenden Studienteil von Grund auf erhoben und kategorisiert werden. Ziel des zweiten Studienteils ist die Erschaffung einer neuen Kategorisierung, speziell für das in der Wissenschaft unterrepräsentierte Europa. Die verschiedenen Formen nicht indizierter Wohnungsmorphologien werden erforscht und kategorisiert, um das bisherige globale wissenschaftliche ontologische Portfolio für Europa zu erweitern. Hinsichtlich dieses zweiten Studienteils bietet eine Literaturrecherche mit mehr als 1.000 gesichteten Artikeln die weitere Grundlage für den folgenden Fokus auf Europa. Auf der Recherche basierend werden mittels der manuellen visuellen Bildinterpretation (engl.: MVII) erneut Satellitendaten zur Erfassung der physischen Morphologien von Wohnformen genutzt. Weiterhin kommen selbst definierte geographische Indikatoren zu Lage, Struktur und formellem Status zum Einsatz. Darüber hinaus werden gesellschaftliche Hintergründe, die durch Begriffe wie ‚Ghetto‘, ‚Wohnwagenpark‘, ‚ethnische Enklave‘ oder ‚Flüchtlingslager‘ beschrieben werden, recherchiert und implementiert. Sie sollen als Erklärungsansatz für Armutsviertel in Europa dienen. Die Stichprobe der europäischen, insgesamt aber unbekannten Grundgesamtheit verdeutlicht eine große Vielfalt an physischen Formen: Es wird für Europa eine neue Kategorisierung von sechs Hauptklassen entwickelt, die von ‚einfachsten Wohnstätten‘ (z. B. Zelten) über ‚behelfsmäßige Unterkünfte ‘ (z. B. Baracken, Container) bis hin zu ‚mehrstöckigen Bauten‘ - als allgemeine Taxonomie der Wohnungsnot in Europa - reicht. Die Untersuchung zeigt verschiedene Wohnformen wie z. B. unterirdische oder mobile Typen, verfallene Wohnungen oder große Wohnsiedlungen, die die Armut im Europa des 21. Jahrhunderts widerspiegeln. Über die Wohnungsmorphologie hinaus werden diese Klassen durch die Struktur und ihren rechtlichen Status beschrieben - entweder als geplante oder als organisch-gewachsene bzw. weiterhin als formelle, informelle oder hybride (halblegale) Formen. Geographisch lassen sich diese ärmlichen Wohnformen sowohl in städtischen als auch in ländlichen Gebieten finden, mit einer Konzentration in Südeuropa. Der Hintergrund bei der Mehrheit der Morphologien betrifft Flüchtlinge, ethnische Minderheiten und sozioökonomisch benachteiligte Menschen - die ‚Unterprivilegierten‘. Ziel des dritten Studienteils ist eine kritische Analyse der Methode. Zur Erfassung all dieser Siedlungen werden heutzutage Satellitenbilder aufgrund der Fortschritte bei den Bildklassifizierungsmethoden meist automatisch ausgewertet. Dennoch spielt die MVII noch immer eine wichtige Rolle, z.B. um Trainingsdaten für Machine-Learning-Algorithmen zu generieren oder für Validierungszwecke. In bestimmten städtischen Umgebungen jedoch, z.B. solchen mit höchster Dichte und struktureller Komplexität, fordern spektrale und textur-basierte Verflechtungen von überlappenden Dachstrukturen den menschlichen Interpreten immer noch heraus, wenn es darum geht einzelne Gebäudestrukturen zu erfassen. Die kognitive Wahrnehmung und die Erfahrung aus der realen Welt sind nach wie vor unumgänglich. Vor diesem Hintergrund zielt die Arbeit methodisch darauf ab, Unsicherheiten speziell bei der Kartierung zu quantifizieren und zu interpretieren. Kartiert werden Dachflächen als ‚Fußabdrücke‘ solcher Gebiete. Der Fokus liegt dabei auf der Übereinstimmung zwischen mehreren Bildinterpreten und welche Aspekte der Wahrnehmung und Elemente der Bildinterpretation die Kartierung beeinflussen. Um letztlich die Methode der MVII als drittes Ziel selbstkritisch zu reflektieren, werden Experimente als sogenannte ‚Unsicherheitsanalyse‘ geschaffen. Dabei digitalisieren zehn Testpersonen bzw. Probanden/Interpreten sechs komplexe Gebiete. Hierdurch werden quantitative Informationen über räumliche Variablen von Gebäuden erzielt, um systematisch die Konsistenz und Kongruenz der Ergebnisse zu überprüfen. Ein zusätzlicher Fragebogen liefert subjektive qualitative Informationen über weitere Schwierigkeiten. Da die Grundlage der hierfür bisher genutzten Kategorisierungen auf der subjektiven Bildinterpretation durch den Menschen beruht, müssen etwaige Unsicherheiten und damit Fehleranfälligkeiten offengelegt werden. Die Experimente zu dieser Unsicherheitsanalyse erfolgen quantifiziert und qualifiziert. Es lassen sich generell große Unterschiede zwischen den Kartierungsergebnissen der Probanden, aber eine hohe Konsistenz der Ergebnisse bei ein und demselben Probanden feststellen. Steigende Abweichungen korrelieren mit einer steigenden baustrukturellen (morphologischen) Komplexität. Ein hoher Grad an Individualität bei den Probanden äußert sich in Aspekten wie z.B. Zeitaufwand beim Kartieren, in-situ Vorkenntnissen oder Vorkenntnissen beim Umgang mit Geographischen Informationssystemen (GIS). Nennenswert ist hierbei, dass die jeweilige Datenquelle das Kartierungsverfahren meist beeinflusst. Mit dieser Studie soll also auch an der Stelle der angewandten Methodik eine weitere Wissenslücke gefüllt werden. Die bisherige Forschung komplexer urbaner Areale unter Nutzung der manuellen Bildinterpretation implementiert oftmals keine Unsicherheitsanalyse oder Quantifizierung von Kartierungsfehlern. Fernerkundungsstudien sollten künftig zur Validierung nicht nur zweifelsfrei auf MVII zurückgreifen können, sondern vielmehr sind Daten und Methoden notwendig, um Unsicherheiten auszuschließen. Zusammenfassend trägt diese Arbeit zur bisher wenig erforschten morphologischen Dynamik von Armutsgebieten bei. Es werden inter- wie auch intra-urbane Unterschiede auf globaler Ebene präsentiert. Dabei sind allgemein hohe morphologische Transformationen zwischen den selektierten Gebieten festzustellen. Die Ergebnisse deuten auf einen grundlegenden Kenntnismangel in Europa hin, weshalb an dieser Stelle angeknüpft wird. Eine über Europa verteilte Stichprobe erlaubt eine neue morphologische Kategorisierung der großen Vielfalt an gefundenen physischen Formen. Die Menge an Gebieten erschließt sich in einer unbekannten Grundgesamtheit. Zur Datenaufbereitung bisheriger Analysen müssen Satellitenbilder manuell interpretiert werden. Das Verfahren birgt Unsicherheiten. Als kritische Selbstreflexion zeigt eine Reihe von Experimenten signifikante Unterschiede zwischen den Ergebnissen der Probanden auf, verdeutlicht jedoch bei ein und derselben Person Beständigkeit. N2 - Through construction as well as destruction of urban elements, the morphological manifestation of cities is in constant change. As reported in literature, there is a difference between formal and informal development: Whereas formal planning periods lead to a built landscape that appears static, unfinished informal urban forms reflect high dynamics leading to informal or spontaneous settlements. With respect to data base and scale, these kinds of settlements, which morphologically represent urban poverty, have hardly been subject to empirical studies that analyze their dynamics and morphological characteristics of physical transformation consistently. This is where the present work begins. Assuming that the temporal dynamics explored are less pronounced in Europe, the general question of indexing physical housing forms of poverty arises specifically in Europe. This is because housing forms of poverty are often exclusively associated with the 'Global South', especially through the representation of slums. In fact, Europe is even the cradle of the terms 'slum' and 'ghetto', which emerged centuries ago to describe grievances and oppression. To this day, this multifaceted continent exhibits a tremendous variety of physical housing forms of poverty that have their roots in different histories, cultures, policies and lifestyles. To gain insight into these aforementioned aspects requires, among other things, image analysis through satellite imagery. Therefore, this work is done through remote sensing or Earth Observation (EO) as well as additional literature review and an empirical survey. In order to reveal uncertainties conceptually and in the coverage, the method of manual image interpretation of poverty areas has to be critically questioned. The overall goal of this work is to create a better knowledge base about poverty in order to be able to develop measures to reduce poverty. The work serves as a response to the United Nations Sustainable Development Goals. Basic research is carried out by exploratively analyzing knowledge gaps in Earth observation on physical-structural or morphological manifestations of poverty at the building level. The work is divided into three research themes or study parts: The aim of the first part of the study is to capture global spatiotemporal dynamics by linking to established categorizations of poverty areas. The knowledge gap will be filled by conducting a temporal analysis of the built environment over a period of seven years, in 16 documented manifestations of urban poverty using earth observation data. In addition to a globally distributed area selection, visual image interpretation (MVII) and very high-resolution optical satellite data are used. In order to derive 3D city models, MVII is applied combining in-situ and Google Street View imagery. Six spatial morphological variables are applied: number, size, height, orientation and density of buildings as well as heterogeneity of the built-up pattern. In this way, physical spatial structures are measured. Inter-urban and intra-urban differences are demonstrated in the temporal analysis. Findings show different, yet generally high morphological dynamics across the study areas. The variety comprises demolished and reconstructed areas as well as such, where changes occur within the given structures. Results demonstrate increased dynamics, especially in areas of the Global South. At the intra-urban scale, morphological transformations show a high spatial variability simultaneously. However, in spite of these findings of high dynamics, the spatial patterns are mostly constant, including building alignments, streets and open spaces. These initial results indicate little change in Europe, which is why these European poverty areas are surveyed and categorized from scratch in the following part of the study. The aim of the second part of the study is to create a new categorization, specifically for Europe, which is underrepresented in science. In order to expand the existing global scientific ontological inventory for Europe, different forms of non-indexed residential morphologies are detected and categorized. Regarding this second part of the study, a literature search with more than 1,000 articles reviewed provides the further basis for the following focus on Europe. Based on the research, satellite data are again used by means of manual visual image interpretation (MVII) to obtain the physical morphologies of housing types. Furthermore, self-defined geographical indicators of location, structure and formal status are used. Additionally, social backgrounds described by terms like 'ghetto', 'trailer park', 'ethnic enclave' or 'refugee camp' are researched and implemented. They are intended to serve as an explanatory approach to poverty neighborhoods in Europe. The sample for Europe, however is an overall unknown basic population and illustrates a wide variety of physical forms: A new categorization of six main classes is developed for Europe, ranging from 'simplest dwellings' (e.g., tents) to 'makeshift shelters ' (e.g., shacks, containers) to 'multi-story structures' - as a general taxonomy of housing deprivation in Europe. The study discloses different housing types such as underground or mobile types, dilapidated dwellings or large housing estates that reflect poverty in 21st century Europe. Next to housing morphology, these classes are described by structural settlement patterns and their legal status - either as planned or organic-grown, or further as formal, informal or hybrid (semi-legal) forms. From a geographic point of view, a concentration of these poor housing forms can be found in Southern Europe and all across Europe in urban and rural areas. The societal background of the most morphologies concern the 'underprivileged' who are represented, by refugees, ethnic minorities and socioeconomically disadvantaged people. The aim of the third part of the study is a critical analysis of the method. To capture all these settlements and due to the advances in image classification methods, satellite images are typically analyzed automatically nowadays. Still, MVII is important, e.g., for the purpose of validation or to generate training data for machine learning algorithms. Thus, cognitive perception and real-world experience are still unavoidable. Nevertheless, such urban environments with highest density and structural complexity challenge the human interpreter, when it comes to detecting individual building structures because spectral and texture-based restrictions of overlapping roof structures encounter building delineation. Considering that, the aim of this work is to quantify and interpret uncertainties methodologically specifically in mapping. Roof areas are mapped as 'footprints' of such areas. One focus is the agreement between multiple image interpreters. The other focus explores influences by interpreter perception and different elements of image interpretation. Finally, to reflect self-critically on the method of MVII as a third goal, experiments are created as a so-called 'uncertainty analysis'. In these experiments, ten test persons respectively interpreters map six complex areas and produce quantitative data of spatial variables of buildings. This data allows to assess the consistency and congruence of the results in a systematical way. Additionally, a questionnaire provides subjective qualitative information about further difficulties. Since the basis of the categorizations used for this purpose so far is based on subjective image interpretation by humans, any uncertainties and thus error-proneness have to be revealed. The experiments for this uncertainty analysis are quantified and qualified. On the one hand results show remarkable differences between the mapping results of the interpreters. On the other hand, the results for one and the same interpreter reveal high consistency. Another finding demonstrates a correlation between increasing deviations among interpreters and increasing structural (morphological) complexity of the selected areas. Considering the qualitative responses, aspects such as time spent for mapping, prior in-situ knowledge, or prior knowledge of using Geographic Information Systems (GIS) reveal a high degree of individuality among the interpreters. It is noteworthy that particularly ‘data source’ usually influences the mapping procedure. Thus, this study also aims to fill another knowledge gap at the point of applied methodology. Uncertainty analyses often are neither part of research studies of complex urban areas using MVII, nor quantification of mapping errors. In future, remote sensing studies should not only be able to rely on MVII without doubt for validation, but rather data and methods are needed to rule out uncertainty. In summary, this work contributes to the hitherto little researched morphological dynamics of poverty areas. Inter- as well as intra-urban differences on a global scale are presented. Generally, high morphological transformations between the selected areas can be observed. The results indicate a fundamental lack of knowledge in Europe, which is why this work continues at this point. A sample distributed all across Europe allows a new morphological categorization of the large variety of physical forms found. The number of areas opens up in an unknown basic population. For data preparation of previous analyses, satellite images have to be interpreted manually. The procedure involves uncertainties. As a critical self-reflection, a series of experiments reveal significant differences between interpreters’ results, but illustrates consistency in the same subject. KW - Slum KW - Armutsviertel KW - Fernerkundung KW - Stadtgeographie KW - Bildinterpretation KW - physische Morphologie KW - urbane Strukturanalyse KW - raumzeitliche Dynamik KW - Manuelle visuelle Bildinterpretation KW - Wohnformen der Armut KW - Europa KW - Bildbetrachtung Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-320264 ER - TY - JOUR A1 - Dong, Ruirui A1 - Wurm, Michael A1 - Taubenböck, Hannes T1 - Seasonal and diurnal variation of land surface temperature distribution and its relation to land use/land cover patterns JF - International Journal of Environmental Research and Public Health N2 - The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime. KW - surface urban heat island (SUHI) KW - land use/cover pattern (LUCP) KW - land surface temperature (LST) KW - seasonal KW - diurnal Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-290393 SN - 1660-4601 VL - 19 IS - 19 ER - TY - JOUR A1 - Buchelt, Sebastian A1 - Blöthe, Jan Henrik A1 - Kuenzer, Claudia A1 - Schmitt, Andreas A1 - Ullmann, Tobias A1 - Philipp, Marius A1 - Kneisel, Christof T1 - Deciphering small-scale seasonal surface dynamics of rock glaciers in the Central European Alps using DInSAR time series JF - Remote Sensing N2 - The Essential Climate Variable (ECV) Permafrost is currently undergoing strong changes due to rising ground and air temperatures. Surface movement, forming characteristic landforms such as rock glaciers, is one key indicator for mountain permafrost. Monitoring this movement can indicate ongoing changes in permafrost; therefore, rock glacier velocity (RGV) has recently been added as an ECV product. Despite the increased understanding of rock glacier dynamics in recent years, most observations are either limited in terms of the spatial coverage or temporal resolution. According to recent studies, Sentinel-1 (C-band) Differential SAR Interferometry (DInSAR) has potential for monitoring RGVs at high spatial and temporal resolutions. However, the suitability of DInSAR for the detection of heterogeneous small-scale spatial patterns of rock glacier velocities was never at the center of these studies. We address this shortcoming by generating and analyzing Sentinel-1 DInSAR time series over five years to detect small-scale displacement patterns of five high alpine permafrost environments located in the Central European Alps on a weekly basis at a range of a few millimeters. Our approach is based on a semi-automated procedure using open-source programs (SNAP, pyrate) and provides East-West displacement and elevation change with a ground sampling distance of 5 m. Comparison with annual movement derived from orthophotos and unpiloted aerial vehicle (UAV) data shows that DInSAR covers about one third of the total movement, which represents the proportion of the year suited for DInSAR, and shows good spatial agreement (Pearson R: 0.42–0.74, RMSE: 4.7–11.6 cm/a) except for areas with phase unwrapping errors. Moreover, the DInSAR time series unveils spatio-temporal variations and distinct seasonal movement dynamics related to different drivers and processes as well as internal structures. Combining our approach with in situ observations could help to achieve a more holistic understanding of rock glacier dynamics and to assess the future evolution of permafrost under changing climatic conditions. KW - Sentinel-1 KW - DInSAR KW - rock glaciers KW - seasonal dynamics KW - periglacial KW - feature tracking Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-362939 SN - 2072-4292 VL - 15 IS - 12 ER - TY - JOUR A1 - Fleuchaus, Paul A1 - Blum, Philipp A1 - Wilde, Martina A1 - Terhorst, Birgit A1 - Butscher, Christoph T1 - Retrospective evaluation of landslide susceptibility maps and review of validation practice JF - Environmental Earth Sciences N2 - Despite the widespread application of landslide susceptibility analyses, there is hardly any information about whether or not the occurrence of recent landslide events was correctly predicted by the relevant susceptibility maps. Hence, the objective of this study is to evaluate four landslide susceptibility maps retrospectively in a landslide-prone area of the Swabian Alb (Germany). The predictive performance of each susceptibility map is evaluated based on a landslide event triggered by heavy rainfalls in the year 2013. The retrospective evaluation revealed significant variations in the predictive accuracy of the analyzed studies. Both completely erroneous as well as very precise predictions were observed. These differences are less attributed to the applied statistical method and more to the quality and comprehensiveness of the used input data. Furthermore, a literature review of 50 peer-reviewed articles showed that most landslide susceptibility analyses achieve very high validation scores. 73% of the analyzed studies achieved an area under curve (AUC) value of at least 80%. These high validation scores, however, do not reflect the high uncertainty in statistical susceptibility analysis. Thus, the quality assessment of landslide susceptibility maps should not only comprise an index-based, quantitative validation, but also an additional qualitative plausibility check considering local geomorphological characteristics and local landslide mechanisms. Finally, the proposed retrospective evaluation approach cannot only help to assess the quality of susceptibility maps and demonstrate the reliability of such statistical methods, but also identify issues that will enable the susceptibility maps to be improved in the future. KW - landslides KW - hazard maps KW - predictive performance KW - review KW - Swabian Alb Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-308911 SN - 1866-6280 SN - 1866-6299 VL - 80 ER - TY - JOUR A1 - Schamel, Johannes T1 - A demographic perspective on the spatial behaviour of hikers in mountain areas: the example of Berchtesgaden National Park JF - eco.mont - Journal on Protected Mountain Areas Research and Management N2 - In Germany, as in many Western societies, demographic change will lead to a higher number of senior visitors to natural recreational areas and national parks. Given the high physiological requirements of many outdoor recreation activities, especially in mountain areas, it seems likely that demographic change will affect the spatial behaviour of national park visitors, which may pose a challenge to the management of these areas. With the help of GPS tracking and a standardized questionnaire (n=481), this study empirically investigates the spatial behaviour of demographic age brackets in Berchtesgaden National Park (NP) and the potential effects of demographic change on the use of the area. Cluster analysis revealed four activity types in the study area. More than half of the groups with visitors aged 60 and older belong to the activity type of Walker. KW - geography KW - demographic change KW - spatial behaviour KW - GPS tracking KW - outdoor recreation KW - Berchtesgaden NP Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172128 SN - 2073-1558 VL - 9 IS - Special issue ER - TY - JOUR A1 - Dirscherl, Mariel A1 - Dietz, Andreas J. A1 - Kneisel, Christof A1 - Kuenzer, Claudia T1 - Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach JF - Remote Sensing N2 - Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017–2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions. KW - Antarctica KW - Antarctic ice sheet KW - supraglacial lakes KW - surface melt KW - hydrology KW - ice sheet dynamics KW - sentinel-2 KW - remote sensing KW - random forest KW - machine learning Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203735 SN - 2072-4292 VL - 12 IS - 7 ER - TY - THES A1 - Üreyen, Soner T1 - Multivariate Time Series for the Analysis of Land Surface Dynamics - Evaluating Trends and Drivers of Land Surface Variables for the Indo-Gangetic River Basins T1 - Multivariate Zeitreihen zur Analyse von Landoberflächendynamiken - Auswertung von Trends und Treibern von Landoberflächenvariablen für Flusseinzugsgebiete der Indus-Ganges Ebene N2 - The investigation of the Earth system and interplays between its components is of utmost importance to enhance the understanding of the impacts of global climate change on the Earth's land surface. In this context, Earth observation (EO) provides valuable long-term records covering an abundance of land surface variables and, thus, allowing for large-scale analyses to quantify and analyze land surface dynamics across various Earth system components. In view of this, the geographical entity of river basins was identified as particularly suitable for multivariate time series analyses of the land surface, as they naturally cover diverse spheres of the Earth. Many remote sensing missions with different characteristics are available to monitor and characterize the land surface. Yet, only a few spaceborne remote sensing missions enable the generation of spatio-temporally consistent time series with equidistant observations over large areas, such as the MODIS instrument. In order to summarize available remote sensing-based analyses of land surface dynamics in large river basins, a detailed literature review of 287 studies was performed and several research gaps were identified. In this regard, it was found that studies rarely analyzed an entire river basin, but rather focused on study areas at subbasin or regional scale. In addition, it was found that transboundary river basins remained understudied and that studies largely focused on selected riparian countries. Moreover, the analysis of environmental change was generally conducted using a single EO-based land surface variable, whereas a joint exploration of multivariate land surface variables across spheres was found to be rarely performed. To address these research gaps, a methodological framework enabling (1) the preprocessing and harmonization of multi-source time series as well as (2) the statistical analysis of a multivariate feature space was required. For development and testing of a methodological framework that is transferable in space and time, the transboundary river basins Indus, Ganges, Brahmaputra, and Meghna (IGBM) in South Asia were selected as study area, having a size equivalent to around eight times the size of Germany. These basins largely depend on water resources from monsoon rainfall and High Mountain Asia which holds the largest ice mass outside the polar regions. In total, over 1.1 billion people live in this region and in parts largely depend on these water resources which are indispensable for the world's largest connected irrigated croplands and further domestic needs as well. With highly heterogeneous geographical settings, these river basins allow for a detailed analysis of the interplays between multiple spheres, including the anthroposphere, biosphere, cryosphere, hydrosphere, lithosphere, and atmosphere. In this thesis, land surface dynamics over the last two decades (December 2002 - November 2020) were analyzed using EO time series on vegetation condition, surface water area, and snow cover area being based on MODIS imagery, the DLR Global WaterPack and JRC Global Surface Water Layer, as well as the DLR Global SnowPack, respectively. These data were evaluated in combination with further climatic, hydrological, and anthropogenic variables to estimate their influence on the three EO land surface variables. The preprocessing and harmonization of the time series was conducted using the implemented framework. The resulting harmonized feature space was used to quantify and analyze land surface dynamics by means of several statistical time series analysis techniques which were integrated into the framework. In detail, these methods involved (1) the calculation of trends using the Mann-Kendall test in association with the Theil-Sen slope estimator, (2) the estimation of changes in phenological metrics using the Timesat tool, (3) the evaluation of driving variables using the causal discovery approach Peter and Clark Momentary Conditional Independence (PCMCI), and (4) additional correlation tests to analyze the human influence on vegetation condition and surface water area. These analyses were performed at annual and seasonal temporal scale and for diverse spatial units, including grids, river basins and subbasins, land cover and land use classes, as well as elevation-dependent zones. The trend analyses of vegetation condition mostly revealed significant positive trends. Irrigated and rainfed croplands were found to contribute most to these trends. The trend magnitudes were particularly high in arid and semi-arid regions. Considering surface water area, significant positive trends were obtained at annual scale. At grid scale, regional and seasonal clusters with significant negative trends were found as well. Trends for snow cover area mostly remained stable at annual scale, but significant negative trends were observed in parts of the river basins during distinct seasons. Negative trends were also found for the elevation-dependent zones, particularly at high altitudes. Also, retreats in the seasonal duration of snow cover area were found in parts of the river basins. Furthermore, for the first time, the application of the causal discovery algorithm on a multivariate feature space at seasonal temporal scale revealed direct and indirect links between EO land surface variables and respective drivers. In general, vegetation was constrained by water availability, surface water area was largely influenced by river discharge and indirectly by precipitation, and snow cover area was largely controlled by precipitation and temperature with spatial and temporal variations. Additional analyses pointed towards positive human influences on increasing trends in vegetation greenness. The investigation of trends and interplays across spheres provided new and valuable insights into the past state and the evolution of the land surface as well as on relevant climatic and hydrological driving variables. Besides the investigated river basins in South Asia, these findings are of great value also for other river basins and geographical regions. N2 - Die Untersuchung von Erdsystemkomponenten und deren Wechselwirkungen ist von großer Relevanz, um das Prozessverständnis sowie die Auswirkungen des globalen Klimawandels auf die Landoberfläche zu verbessern. In diesem Zusammenhang liefert die Erdbeobachtung (EO) wertvolle Langzeitaufnahmen zu einer Vielzahl an Landoberflächenvariablen. Diese können als Indikator für die Erdsystemkomponenten genutzt werden und sind essenziell für großflächige Analysen. Flusseinzugsgebiete sind besonders geeignet um Landoberflächendynamiken mit multivariaten Zeitreihen zu analysieren, da diese verschiedene Sphären des Erdsystems umfassen. Zur Charakterisierung der Landoberfläche stehen zahlreiche EO-Missionen mit unterschiedlichen Eigenschaften zur Verfügung. Nur einige wenige Missionen gewährleisten jedoch die Erstellung von räumlich und zeitlich konsistenten Zeitreihen mit äquidistanten Beobachtungen über großräumige Untersuchungsgebiete, wie z.B. die MODIS Sensoren. Um bisherige EO-Analysen zu Landoberflächendynamiken in großen Flusseinzugsgebieten zu untersuchen, wurde eine Literaturrecherche durchgeführt, wobei mehrere Forschungslücken identifiziert wurden. Studien untersuchten nur selten ein ganzes Einzugsgebiet, sondern konzentrierten sich lediglich auf Teilgebietsgebiete oder regionale Untersuchungsgebiete. Darüber hinaus wurden transnationale Einzugsgebiete nur unzureichend analysiert, wobei sich die Studien größtenteils auf ausgewählte Anrainerstaaten beschränkten. Auch wurde die Analyse von Umweltveränderungen meistens anhand einer einzigen EO-Landoberflächenvariable durchgeführt, während eine synergetische Untersuchung von sphärenübergreifenden Landoberflächenvariablen kaum unternommen wurde. Um diese Forschungslücken zu adressieren, ist ein methodischer Ansatz notwendig, der (1) die Vorverarbeitung und Harmonisierung von Zeitreihen aus mehreren Quellen und (2) die statistische Analyse eines multivariaten Merkmalsraums ermöglicht. Für die Entwicklung und Anwendung eines methodischen Frameworks, das raum-zeitlich übertragbar ist, wurden die transnationalen Einzugsgebiete Indus, Ganges, Brahmaputra und Meghna (IGBM) in Südasien, deren Größe etwa der achtfachen Fläche von Deutschland entspricht, ausgewählt. Diese Einzugsgebiete hängen weitgehend von den Wasserressourcen des Monsunregens und des Hochgebirges Asiens ab. Insgesamt leben über 1,1 Milliarden Menschen in dieser Region und sind zum Teil in hohem Maße von diesen Wasserressourcen abhängig, die auch für die größten zusammenhängenden bewässerten Anbauflächen der Welt und auch für weitere inländische Bedarfe unerlässlich sind. Aufgrund ihrer sehr heterogenen geographischen Gegebenheiten ermöglichen diese Einzugsgebiete eine detaillierte sphärenübergreifende Analyse der Wechselwirkungen, einschließlich der Anthroposphäre, Biosphäre, Kryosphäre, Hydrosphäre, Lithosphäre und Atmosphäre. In dieser Dissertation wurden Landoberflächendynamiken der letzten zwei Jahrzehnte anhand von EO-Zeitreihen zum Vegetationszustand, zu Oberflächengewässern und zur Schneebedeckung analysiert. Diese basieren auf MODIS-Aufnahmen, dem DLR Global WaterPack und dem JRC Global Surface Water Layer sowie dem DLR Global SnowPack. Diese Zeitreihen wurden in Kombination mit weiteren klimatischen, hydrologischen und anthropogenen Variablen ausgewertet. Die Harmonisierung des multivariaten Merkmalsraumes ermöglichte die Analyse von Landoberflächendynamiken unter Nutzung von statistischen Methoden. Diese Methoden umfassen (1) die Berechnung von Trends mittels des Mann-Kendall und des Theil-Sen Tests, (2) die Berechnung von phänologischen Metriken anhand des Timesat-Tools, (3) die Bewertung von treibenden Variablen unter Nutzung des PCMCI Algorithmus und (4) zusätzliche Korrelationstests zur Analyse des menschlichen Einflusses auf den Vegetationszustand und die Wasseroberfläche. Diese Analysen wurden auf jährlichen und saisonalen Zeitskalen und für verschiedene räumliche Einheiten durchgeführt. Für den Vegetationszustand wurden weitgehend signifikant positive Trends ermittelt. Analysen haben gezeigt, dass landwirtschaftliche Nutzflächen am meisten zu diesen Trends beitragen haben. Besonders hoch waren die Trends in ariden Regionen. Bei Oberflächengewässern wurden auf jährlicher Ebene signifikant positive Trends festgestellt. Auf Pixelebene wurden jedoch sowohl regional als auch saisonal Cluster mit signifikant negativen Trends identifiziert. Die Trends für die Schneebedeckung blieben auf jährlicher Ebene weitgehend stabil, jedoch wurden in Teilen der Einzugsgebiete zu bestimmten Jahreszeiten signifikant negative Trends beobachtet. Die negativen Trends wurden auch für höhenabhängige Zonen festgestellt, insbesondere in hohen Lagen. Außerdem wurden in Teilen der Einzugsgebiete Rückgänge bei der saisonalen Dauer der Schneebedeckung ermittelt. Darüber hinaus ergab die Untersuchung des multivariaten Merkmalsraums auf kausale Zusammenhänge auf saisonaler Ebene erstmals Aufschluss über direkte und indirekte Relationen zwischen EO-Landoberflächenvariablen und den entsprechenden Einflussfaktoren. Zusammengefasst wurde die Vegetation durch die Wasserverfügbarkeit, die Oberflächengewässer durch den Abfluss und indirekt durch den Niederschlag sowie die Schneebedeckung durch Niederschlag und Temperatur mit räumlichen und saisonalen Unterschieden kontrolliert. Zusätzliche Analysen wiesen auf einen positiven Zusammenhang zwischen dem menschlichen Einfluss und den zunehmenden Trends in der Vegetationsfläche hin. Diese sphärenübergreifenden Untersuchungen zu Trends und Wechselwirkungen liefern neue und wertvolle Einblicke in den vergangenen Zustand von Landoberflächendynamiken sowie in die relevanten klimatischen und hydrologischen Einflussfaktoren. Neben den untersuchten Einzugsgebieten in Südasien sind diese Erkenntnisse auch für weitere Einzugsgebiete und geographische Regionen von großer Bedeutung. KW - Multivariate Analyse KW - Zeitreihe KW - Fernerkundung KW - Geographie KW - Multivariate Time Series KW - River Basins KW - Earth Observation KW - Remote Sensing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-291941 ER - TY - JOUR A1 - Näschen, Kristian A1 - Diekkrüger, Bernd A1 - Evers, Mariele A1 - Höllermann, Britta A1 - Steinbach, Stefanie A1 - Thonfeld, Frank T1 - The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios JF - Sustainability N2 - Many parts of sub-Saharan Africa (SSA) are prone to land use and land cover change (LULCC). In many cases, natural systems are converted into agricultural land to feed the growing population. However, despite climate change being a major focus nowadays, the impacts of these conversions on water resources, which are essential for agricultural production, is still often neglected, jeopardizing the sustainability of the socio-ecological system. This study investigates historic land use/land cover (LULC) patterns as well as potential future LULCC and its effect on water quantities in a complex tropical catchment in Tanzania. It then compares the results using two climate change scenarios. The Land Change Modeler (LCM) is used to analyze and to project LULC patterns until 2030 and the Soil and Water Assessment Tool (SWAT) is utilized to simulate the water balance under various LULC conditions. Results show decreasing low flows by 6–8% for the LULC scenarios, whereas high flows increase by up to 84% for the combined LULC and climate change scenarios. The effect of climate change is stronger compared to the effect of LULCC, but also contains higher uncertainties. The effects of LULCC are more distinct, although crop specific effects show diverging effects on water balance components. This study develops a methodology for quantifying the impact of land use and climate change and therefore contributes to the sustainable management of the investigated catchment, as it shows the impact of environmental change on hydrological extremes (low flow and floods) and determines hot spots, which are critical for environmental development. KW - SWAT model KW - Land Change Modeler KW - Scenario analysis KW - Extreme flows KW - Tanzania KW - Kilombero Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193825 SN - 2071-1050 VL - 11 IS - 24 ER - TY - JOUR A1 - Latifi, Hooman A1 - Valbuena, Ruben T1 - Current trends in forest ecological applications of three-dimensional remote sensing: Transition from experimental to operational solutions? JF - Forests N2 - The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide. KW - 3D remote sensing KW - composition KW - forest ecology KW - function KW - structure Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193282 SN - 1999-4907 VL - 10 IS - 10 ER - TY - JOUR A1 - Ghazaryan, Gohar A1 - Rienow, Andreas A1 - Oldenburg, Carsten A1 - Thonfeld, Frank A1 - Trampnau, Birte A1 - Sticksel, Sarah A1 - Jürgens, Carsten T1 - Monitoring of urban sprawl and densification processes in Western Germany in the light of SDG indicator 11.3.1 based on an automated retrospective classification approach JF - Remote Sensing N2 - By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning. KW - impervious surface KW - Landsat time series KW - change detection KW - SDG 11.3.1 KW - population change Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-236671 SN - 2072-4292 VL - 13 IS - 9 ER - TY - JOUR A1 - Fekri, Erfan A1 - Latifi, Hooman A1 - Amani, Meisam A1 - Zobeidinezhad, Abdolkarim T1 - A training sample migration method for wetland mapping and monitoring using Sentinel data in Google Earth Engine JF - Remote Sensing N2 - Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year. KW - wetland KW - Google Earth Engine (GEE) KW - training sample migration KW - sentinel Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248542 SN - 2072-4292 VL - 13 IS - 20 ER - TY - JOUR A1 - Schönbrodt-Stitt, Sarah A1 - Ahmadian, Nima A1 - Kurtenbach, Markus A1 - Conrad, Christopher A1 - Romano, Nunzio A1 - Bogena, Heye R. A1 - Vereecken, Harry A1 - Nasta, Paolo T1 - Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem JF - Frontiers in Water N2 - Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNov\(^{SAR}\)) or the combination of 43 SAR and 10 terrain parameters (θNov\(^{SAR+Terrain}\)). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNov\(^{SAR+Terrain}\) with 0.025 and 0.020 m3 m\(^{−3}\), and 89% compared to θNov\(^{SAR}\) with 0.028 and 0.022 m\(^3\) m\(^{−3}\, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNov\(^{SAR+Terrain}\) is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon. KW - near-surface soil moisture KW - Sentinel-1 single-look complex data KW - SAR backscatters KW - terrain parameters KW - Alento hydrological observatory KW - Mediterranean environment Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259062 VL - 3 ER - TY - JOUR A1 - Hagg, Wilfried A1 - Mayr, Elisabeth A1 - Mannig, Birgit A1 - Reyers, Mark A1 - Schubert, David A1 - Pinto, Joaquim G. A1 - Peters, Juliane A1 - Pieczonka, Tino A1 - Juen, Martin A1 - Bolch, Tobias A1 - Paeth, Heiko A1 - Mayer, Christoph T1 - Future climate change and its impact on runoff generation from the debris-covered Inylchek glaciers, Central Tian Shan, Kyrgyzstan JF - Water N2 - The heavily debris-covered Inylchek glaciers in the central Tian Shan are the largest glacier system in the Tarim catchment. It is assumed that almost 50% of the discharge of Tarim River are provided by glaciers. For this reason, climatic changes, and thus changes in glacier mass balance and glacier discharge are of high impact for the whole region. In this study, a conceptual hydrological model able to incorporate discharge from debris-covered glacier areas is presented. To simulate glacier melt and subsequent runoff in the past (1970/1971–1999/2000) and future (2070/2071–2099/2100), meteorological input data were generated based on ECHAM5/MPI-OM1 global climate model projections. The hydrological model HBV-LMU was calibrated by an automatic calibration algorithm using runoff and snow cover information as objective functions. Manual fine-tuning was performed to avoid unrealistic results for glacier mass balance. The simulations show that annual runoff sums will increase significantly under future climate conditions. A sensitivity analysis revealed that total runoff does not decrease until the glacier area is reduced by 43%. Ice melt is the major runoff source in the recent past, and its contribution will even increase in the coming decades. Seasonal changes reveal a trend towards enhanced melt in spring, but a change from a glacial-nival to a nival-pluvial runoff regime will not be reached until the end of this century. KW - glaciers KW - debris-covered glaciers KW - hydrological modelling KW - climate scenarios KW - Tian Shan Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197592 SN - 2073-4441 VL - 10 IS - 11 ER - TY - JOUR A1 - Forkuor, Gerald A1 - Hounkpatin, Ozias K.L. A1 - Welp, Gerhard A1 - Thiel, Michael T1 - High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models JF - PLOS One N2 - Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. KW - Agricultural soil science KW - Forecasting KW - Machine learning KW - Support vector machines KW - Paleopedology KW - Trees KW - Clay mineralogy KW - Remote sensing KW - South-western Burkina Faso Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-180978 VL - 12 IS - 1 ER - TY - JOUR A1 - Philipp, Marius A1 - Dietz, Andreas A1 - Ullmann, Tobias A1 - Kuenzer, Claudia T1 - Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis JF - Remote Sensing N2 - Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments. KW - permafrost KW - coastal erosion KW - deep learning KW - change vector analysis KW - Google Earth Engine KW - synthetic aperture RADAR Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281956 SN - 2072-4292 VL - 14 IS - 15 ER -