TY - JOUR A1 - Ibebuchi, Chibuike Chiedozie T1 - Circulation patterns linked to the positive sub-tropical Indian Ocean dipole JF - Advances in Atmospheric Sciences N2 - The positive phase of the subtropical Indian Ocean dipole (SIOD) is one of the climatic modes in the subtropical southern Indian Ocean that influences the austral summer inter-annual rainfall variability in parts of southern Africa. This paper examines austral summer rain-bearing circulation types (CTs) in Africa south of the equator that are related to the positive SIOD and the dynamics through which specific rainfall regions in southern Africa can be influenced by this relationship. Four austral summer rain-bearing CTs were obtained. Among the four CTs, the CT that featured (i) enhanced cyclonic activity in the southwest Indian Ocean; (ii) positive widespread rainfall anomaly in the southwest Indian Ocean; and (iii) low-level convergence of moisture fluxes from the tropical South Atlantic Ocean, tropical Indian Ocean, and the southwest Indian Ocean, over the south-central landmass of Africa, was found to be related to the positive SIOD climatic mode. The relationship also implies that positive SIOD can be expected to increase the amplitude and frequency of occurrence of the aforementioned CT. The linkage between the CT related to the positive SIOD and austral summer homogeneous regions of rainfall anomalies in Africa south of the equator showed that it is the principal CT that is related to the inter-annual rainfall variability of the south-central regions of Africa, where the SIOD is already known to significantly influence its rainfall variability. Hence, through the large-scale patterns of atmospheric circulation associated with the CT, the SIOD can influence the spatial distribution and intensity of rainfall over the preferred landmass through enhanced moisture convergence. KW - subtropical Indian Ocean dipole KW - circulation types KW - rainfall KW - South Indian Ocean KW - moisture convergence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324119 SN - 0256-1530 VL - 40 IS - 1 ER - TY - JOUR A1 - Ibebuchi, Chibuike Chiedozie T1 - Patterns of atmospheric circulation in Western Europe linked to heavy rainfall in Germany: preliminary analysis into the 2021 heavy rainfall episode JF - Theoretical and Applied Climatology N2 - The July 2021 heavy rainfall episode in parts of Western Europe caused devastating floods, specifically in Germany. This study examines circulation types (CTs) linked to extreme precipitation in Germany. It was investigated if the classified CTs can highlight the anomaly in synoptic patterns that contributed to the unusual July 2021 heavy rainfall in Germany. The North Atlantic Oscillation was found to be the major climatic mode related to the seasonal and inter-annual variations of most of the classified CTs. On average, wet (dry) conditions in large parts of Germany can be linked to westerly (northerly) moisture fluxes. During spring and summer seasons, the mid-latitude cyclone when located over the North Sea disrupts onshore moisture transport from the North Atlantic Ocean by westerlies driven by the North Atlantic subtropical anticyclone. The CT found to have the highest probability of being associated with above-average rainfall in large part of Germany features (i) enhancement and northward track of the cyclonic system over the Mediterranean; (ii) northward track of the North Atlantic anticyclone, further displacing poleward, the mid-latitude cyclone over the North Sea, enabling band of westerly moisture fluxes to penetrate Germany; (iii) cyclonic system over the Baltic Sea coupled with northeast fluxes of moisture to Germany; (iv) and unstable atmospheric conditions over Germany. In 2021, a spike was detected in the amplitude and frequency of occurrence of the aforementioned wet CT suggesting that in addition to the nearly stationary cut-off low over central Europe, during the July flood episode, anomalies in the CT contributed to the heavy rainfall event. KW - circulation type (CT) KW - atmospheric circulation KW - Western Europe KW - Germany KW - flood KW - heavy rainfall Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324100 SN - 0177-798X VL - 148 IS - 1-2 ER - TY - JOUR A1 - Ibebuchi, Chibuike Chiedozie A1 - Schönbein, Daniel A1 - Paeth, Heiko T1 - On the added value of statistical post-processing of regional climate models to identify homogeneous patterns of summer rainfall anomalies in Germany JF - Climate Dynamics N2 - A fuzzy classification scheme that results in physically interpretable meteorological patterns associated with rainfall generation is applied to classify homogeneous regions of boreal summer rainfall anomalies in Germany. Four leading homogeneous regions are classified, representing the western, southeastern, eastern, and northern/northwestern parts of Germany with some overlap in the central parts of Germany. Variations of the sea level pressure gradient across Europe, e.g., between the continental and maritime regions, is the major phenomenon that triggers the time development of the rainfall regions by modulating wind patterns and moisture advection. Two regional climate models (REMO and CCLM4) were used to investigate the capability of climate models to reproduce the observed summer rainfall regions. Both regional climate models (RCMs) were once driven by the ERA-Interim reanalysis and once by the MPI-ESM general circulation model (GCM). Overall, the RCMs exhibit good performance in terms of the regionalization of summer rainfall in Germany; though the goodness-of-match with the rainfall regions/patterns from observational data is low in some cases and the REMO model driven by MPI-ESM fails to reproduce the western homogeneous rainfall region. Under future climate change, virtually the same leading modes of summer rainfall occur, suggesting that the basic synoptic processes associated with the regional patterns remain the same over Germany. We have also assessed the added value of bias-correcting the MPI-ESM driven RCMs using a simple linear scaling approach. The bias correction does not significantly alter the identification of homogeneous rainfall regions and, hence, does not improve their goodness-of-match compared to the observed patterns, except for the one case where the original RCM output completely fails to reproduce the observed pattern. While the linear scaling method improves the basic statistics of precipitation, it does not improve the simulated meteorological patterns represented by the precipitation regimes. KW - summer precipitation regions KW - Germany KW - climate models KW - fuzzy classification KW - bias correction Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324122 SN - 0930-7575 VL - 59 IS - 9-10 ER - TY - JOUR A1 - Kacic, Patrick A1 - Kuenzer, Claudia T1 - Forest biodiversity monitoring based on remotely sensed spectral diversity — a review JF - Remote Sensing N2 - Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts “vegetation indices”, “spectral information content”, and “spectral species” for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future. KW - forest KW - biodiversity KW - alpha diversity KW - beta diversity KW - gamma diversity KW - spectral variation hypothesis KW - spectral diversity KW - optical diversity KW - satellite data KW - remote sensing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-290535 SN - 2072-4292 VL - 14 IS - 21 ER - TY - JOUR A1 - Kacic, Patrick A1 - Thonfeld, Frank A1 - Gessner, Ursula A1 - Kuenzer, Claudia T1 - Forest structure characterization in Germany: novel products and analysis based on GEDI, Sentinel-1 and Sentinel-2 data JF - Remote Sensing N2 - Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience. KW - forest KW - forest structure Germany KW - canopy height KW - Global Ecosystem Dynamics Investigation KW - GEDI KW - Sentinel-1 KW - Sentinel-2 KW - random forest regression Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-313727 SN - 2072-4292 VL - 15 IS - 8 ER - TY - JOUR A1 - Kanmegne Tamga, Dan A1 - Latifi, Hooman A1 - Ullmann, Tobias A1 - Baumhauer, Roland A1 - Thiel, Michael A1 - Bayala, Jules T1 - Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems JF - Agroforestry Systems N2 - Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in Côte d’Ivoire, close to the Taï National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user’s accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in Côte d’Ivoire. KW - cocoa mapping KW - geographically weighted regression KW - Sentinel-1 KW - Sentinel-2 KW - Shannon entropy KW - spatial error assessment Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324139 SN - 0167-4366 VL - 97 IS - 1 ER - TY - JOUR A1 - Khare, Siddhartha A1 - Deslauriers, Annie A1 - Morin, Hubert A1 - Latifi, Hooman A1 - Rossi, Sergio T1 - Comparing time-lapse PhenoCams with satellite observations across the boreal forest of Quebec, Canada JF - Remote Sensing N2 - Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R\(^2\) from 0.66 to 0.85) than NDVI (R\(^2\) from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather. KW - PhenoCam KW - GCC KW - NDVI KW - EVI KW - Google Earth Engine KW - coniferous species KW - Picea mariana Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252213 SN - 2072-4292 VL - 14 IS - 1 ER - TY - JOUR A1 - Khare, Suyash A1 - Latifi, Hooman A1 - Khare, Siddhartha T1 - Vegetation growth analysis of UNESCO World Heritage Hyrcanian forests using multi-sensor optical remote sensing data JF - Remote Sensing N2 - Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility. KW - Hyrcanian forest KW - NDVI KW - phenology KW - Sentinel-2 KW - TNPI KW - World Heritage Sites KW - Google Earth Engine Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248398 SN - 2072-4292 VL - 13 IS - 19 ER - TY - JOUR A1 - Klein, Igor A1 - Cocco, Arturo A1 - Uereyen, Soner A1 - Mannu, Roberto A1 - Floris, Ignazio A1 - Oppelt, Natascha A1 - Kuenzer, Claudia T1 - Outbreak of Moroccan locust in Sardinia (Italy): a remote sensing perspective JF - Remote Sensing N2 - The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats. KW - agricultural pests KW - food security KW - remote sensing KW - locust outbreak KW - abandoned land KW - Sentinel-2 KW - Dociostaurus maroccanus Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297232 SN - 2072-4292 VL - 14 IS - 23 ER - TY - JOUR A1 - Klein, Igor A1 - Oppelt, Natascha A1 - Kuenzer, Claudia T1 - Application of remote sensing data for locust research and management — a review JF - Insects N2 - Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species — the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera) — and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management. KW - locust monitoring KW - locust outbreak KW - remote sensing KW - locust habitat KW - locust pest Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-234090 SN - 2075-4450 VL - 12 IS - 3 ER - TY - JOUR A1 - Knauer, Kim A1 - Gessner, Ursula A1 - Fensholt, Rasmus A1 - Forkuor, Gerald A1 - Kuenzer, Claudia T1 - Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment JF - Remote Sensing N2 - Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91% between 2001 and 2014 to a total of 116,900 km\(^2\). While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future. KW - remote sensing KW - Africa KW - agriculture KW - Burkina Faso KW - data fusion KW - ESTARFM framework KW - irrigation KW - land surface phenology KW - Landsat KW - MODIS KW - plantation KW - protected areas KW - TIMESAT Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-171905 VL - 9 IS - 2 ER - TY - JOUR A1 - Knauer, Kim A1 - Gessner, Ursula A1 - Fensholt, Rasmus A1 - Kuenzer, Claudia T1 - An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes JF - Remote Sensing N2 - Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km\(^{2}\) in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture KW - vegetation dynamics KW - ESTARFM KW - MODIS KW - Landsat KW - phenology KW - West Africa KW - cloud gap filling KW - time series analysis Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-180712 VL - 8 IS - 5 ER - TY - THES A1 - Knöfel, Patrick T1 - Energiebilanzmodellierung zur Ableitung der Evapotranspiration – Beispielregion Khorezm T1 - Optimization of energy balance modelling in order to determine evapotranspiration by developing a physical based soil heat flux approach on the example of Khorezm region in Uzbekistan N2 - Zum Verständnis der komplexen Wechselwirkungen innerhalb des Klimasystems der Erde sind Kenntnisse über den hydrologischen Zyklus und den Energiekreislauf essentiell. Eine besondere Rolle obliegt hierbei der Evapotranspiration (ET), da sie eine wesentliche Teilkomponente beider oben erwähnter Kreisläufe ist. Die exakte Quantifizierung der regionalen, tatsächlichen Evapotranspiration innerhalb der Wasser- und Energiekreisläufe der Erdoberfläche auf unterschiedlichen zeitlichen und räumlichen Skalen ist für hydrologische, klimatologische und agronomische Fragestellungen von großer Bedeutung. Dabei ist eine realistische Abschätzung der regionalen tatsächlichen Evapotranspiration die wichtigste Herausforderung der hydrologischen Modellierung. Besonders die unterschiedlichen räumlichen und zeitlichen Auflösungen von Satelliteninformationen machen die Fernerkundung sowohl für globale als auch regionale hydrologischen Fragestellungen interessant. Zusätzlich zur Notwendigkeit des Prozessverständnisses des Wasserkreislaufs auf globaler Ebene kommt dessen regionale Bedeutung für die Landwirtschaft, insbesondere in Bewässerungssystemen arider Regionen. In ariden Klimazonen übersteigt die Menge der Verdunstung oft bei weitem die Niederschlagsmengen. Aufgrund der geringen Niederschlagsmenge muss in ariden agrarischen Regionen das zum Pflanzenwachstum benötigte Wasser mit Hilfe künstlicher Bewässerung aufgebracht werden. Der jeweilige lokale Bewässerungsbedarf hängt von der Feldfrucht und deren Wachstumsphase, den Klimabedingungen, den Bodeneigenschaften und der Ausdehnung der Wurzelzone ab. Die Evapotranspiration ist als Komponente der regionalen Wasserbilanz eine wichtige Steuerungsgröße und Effizienzindikator für das lokale Bewässerungsmanagement. Die Bewässe-rungslandwirtschaft verbraucht weltweit etwa 70 % der verfügbaren Süßwasservorkom-men. Dies wird als einer der Hauptgründe für die weltweit steigende Wasserknappheit identifiziert. Dabei liegt die Wasserentnahme des landwirtschaftlichen Sektors in den OECD Staaten im Mittel bei etwa 44 %, in den Staaten Mittelasiens bei über 90 %. Bei der Erstellung der vorliegenden Arbeit kam die Methode der residualen Bestimmung der Energiebilanz zum Einsatz. Eines der weltweit am häufigsten eingesetzten und vali-dierten fernerkundlichen Residualmodelle zur ET Ableitung ist das SEBAL-Modell (Surface Energy Balance Algorithm for Land, mit über 40 veröffentlichten Studien. SEBAL eignet sich zur Quantifizierung der Verdunstung großflächiger Gebiete und wurde bisher über-wiegend in der Bewässerungslandwirtschaft eingesetzt. Aus diesen Gründen wurde es für die Bearbeitung der Fragestellungen in dieser Arbeit ausgewählt. SEBAL verwendet physikalische und empirische Beziehungen zur Berechnung der Energiebilanzkomponenten basierend auf Fernerkundungsdaten, bei gleichzeitig minimalem Einsatz bodengestützter Daten. Als Eingangsdaten werden u.a. Informationen über Strahlung, Bodenoberflächentemperatur, NDVI, LAI und Albedo verwendet. Zusätzlich zu SEBAL wurden einige Komponenten der SEBAL Weiterentwicklung METRIC (Mapping Evapotranspiration with Internalized Calibration) verwendet, um die Modellierung der ET vorzunehmen. METRIC überwindet einige Limitierungen des SEBAL Verfahrens und kann beispielsweise auch in stärker reliefierten Regionen angewendet werden. Außerdem ermöglicht die Integration einer gebietsspezifischen Referenz-ET sowie einer Landnutzungsklassifikation eine bessere regionale Anpassung des Residualverfahrens. Unter der Annahme der Bedingungen zum Zeitpunkt der Fernerkundungsaufnahme ergibt sich die Energiebilanz an der Erdoberfläche RN = LvE + H + G. Demnach teilt sich die verfügbare Strahlungsenergie RN in die Komponenten latenter Wärme (LVE), fühlbarer Wärme (H) und Bodenwärme (G) auf. Durch Umstellen der Gleichung kann auf die latente Wärme geschlossen werden. Das wesentliche Ziel der vorliegenden Arbeit ist die Optimierung, Erweiterung und Validierung des ausgewählten SEBAL Verfahrens zur regionalen Modellierung der Energiebilanzkomponenten und der daraus abgeleiteten tatsächlichen Evapotranspiration. Die validierten Modellergebnisse der Gebietsverdunstung der Jahre 2009-2011 sollen anschließend als Grundlage dienen, das Gesamtverständnis der regionalen Prozesse des Wasserkreislaufs zu verbessern. Die Arbeit basiert auf der Datengrundlage von MODIS Daten mit 1 km räumlicher Auflösung. Während die Komponenten verfügbare Strahlungsenergie und fühlbarer Wärmestrom physikalisch basiert ermittelt werden, beruht die Berechnung des Bodenwärmestroms ausschließlich auf empirischen Abschätzungen. Ein großer Nachteil des empirischen Ansatzes ist die Vernachlässigung des zeitlichen Versatzes zwischen Strahlungsbilanz und Bodenwärmestrom in Abhängigkeit der aktuellen Bodenfeuchtesituation. Ein besonderer Schwerpunkt der vorliegenden Arbeit liegt auf der Bewertung und Verbesserung der Modellgüte des Bodenwärmestroms durch Verwendung eines neuen Ansatzes zur Integration von Bodenfeuchteinformationen. Daher wird in der Arbeit ein physikalischer Ansatz entwickelt der auf dem Ansatz der periodischen Temperaturveränderung basiert. Hierbei wurde neben dem ENVISAT ASAR SSM Produkt der TU Wien das operationelle Oberflächenbodenfeuchteprodukt ASCAT SSM als Fernerkundungseingangsdaten ausgewählt. Die mit SEBAL modellierten Energiebilanzkomponenten werden durch eine intensive Validierung mit bodengestützten Messungen bewertet, die Messungen stammen von Bodensensoren und Daten einer Eddy-Kovarianz-Station aus den Jahren 2009 bis 2011. Die Region Khorezm gilt als charakteristisch für die wasserbezogene Problematik der Bewässerungslandwirtschaft Mittelasiens und wurde als Untersuchungsgebiet für diese Arbeit ausgewählt. Die wesentlichen Probleme dieser Region entstehen durch die nach wie vor nicht nachhaltige Land- und Wassernutzung, das marode Bewässerungsnetz mit einer Verlustrate von bis zu 40 % und der Bodenversalzung aufgrund hoher Grundwasserspiegel. Im Untersuchungsgebiet wurden in den Jahren 2010 und 2011 umfangreiche Feldarbeiten zur Erhebung lokaler bodengestützter Informationen durchgeführt. Bei der Evaluierung der modellierten Einzelkomponenten ergab sich für die Strahlungsbi-lanz eine hohe Modellgüte (R² > 0,9; rRMSE < 0,2 und NSE > 0,5). Diese Komponente bildet die Grundlage bei der Bezifferung der für die Prozesse an der Erdoberfläche zur Verfügung stehenden Energie. Für die fühlbaren Wärmeströme wurden ebenfalls gute Ergebnisse erzielt, mit NSE von 0,31 und rRMSE von ca. 0,21. Für die residual bestimmte Größe der latenten Wärmeströmung konnte eine insgesamt gute Modellgüte festgestellt werden (R² > 0,6; rRMSE < 0,2 und NSE > 0,5). Dementsprechend gut wurde die tägliche Evapotranspiration modelliert. Hier ergab sich, nach der Interpolation täglicher Werte, eine insgesamt ausreichend gute Modellgüte (R² > 0,5; rRMSE < 0,2 und NSE > 0,4). Dies bestätigt die Ergebnisse vieler Energiebilanzstudien, die lediglich den für die Ableitung der Evapotranspiration maßgebenden Wärmestrom untersuchten. Die Modellergebnisse für den Bodenwärmestrom konnten durch die Entwicklung und Verwendung des neu entwickelten physikalischen Ansatzes von NSE < 0 und rRMSE von ca. 0,57 auf NSE von 0,19 und rRMSE von 0,35 verbessert werden. Dies führt zu einer insgesamt positiven Einschätzung des Verbesserungspotenzials des neu entwickelten Bodenwärmestromansatzes bei der Berechnung der Energiebilanz mit Hilfe von Fernerkundung. N2 - The understanding of the hydrological and the energy cycles are essential in order to describe the complex interactions within the climate system of the earth. Being recognized as an important component of both, the water and the energy cycle, reliable estimation of actual evapotranspiration and its spatial distribution is one outstanding challenge in this context. Detailed knowledge of land surface fluxes, especially latent and sensible heat components, is important for monitoring the climate and land surface, and for agriculture applications such as irrigation scheduling and water management. The use of remote sensing data to determine actual evapotranspiration (ET) is particularly suitable to provide area based indicators for the evaluation of the efficiency and productivity of irrigation systems as well as sustainability studies. Accurate estimation of evapotranspiration plays an important role in quantification of the water balance at watershed, basin, and regional scale for better planning and managing water resources. For instance, in irrigation systems of arid regions, artificial locations of evapotranspiration have been created. An in-depth process understanding is of paramount importance, as irrigated agriculture consumes about 70 % of the available freshwater resources worldwide, with a significant but unsatisfyingly quantified impact on the water cycle, especially on regional scale. Moreover, an exact quantification of ET inside these artificial ecosystems enables assessments of crop water consumptions and hence about water use efficiency. The withdrawal of water for agricultural use in the countries of Central Asia is more than 90 %. For this thesis the residual methods of energy budget are of interest. One of the most common models dealing with energy budget residual is the Surface Energy Balance Algorithm for Land (SEBAL). SEBAL uses physical and empirical relationships to calculate the energy partitioning with minimum of ground data and atmospheric variables are estimated from remote sensing data. The determination of wet and dry surfaces is necessary to extract threshold values. SEBAL requires remote sensing input data like radiation, surface temperature, NDVI, and albedo. For this thesis an algorithm was developed based on SEBAL, its adaptations METRIC (Mapping Evapotranspiration with Internalized Calibration) and some regional adjustments. METRIC introduces the leaf area index (LAI) and land use classification data to determine the dry and hot surfaces as well as the input of additional meteorological data in order to improve the results of the model. Estimation of latent heat flux (LvE, corresponding to evapotranspiration) with SEBAL is based on assessing the energy balance through several surface properties such as albedo, LAI, NDVI, LST etc. Considering instantaneous condition, the energy balance is written as RN = LvE + H + G. Net radiation energy (RN) is available as the sum of the atmospheric convective fluxes sensible heat flux (H), latent heat flux (LvE) and the soil heat flux (G). The main objective of this thesis is to optimize, improve, and evaluate the existing remote sensing based algorithms for the estimation of actual evapotranspiration. For this purpose the seasonal actual ET was calculated using a partly modified SEBAL. SEBAL was implemented based on MODIS time series to solve the energy balance equation. The applied model has proven practicable for this area and is accepted to fulfil the scientific demands. The SEBAL algorithm is tested and set up for the use of 1km MODIS products. Land surface temperature (LST), emissivity, albedo, Normalized Differenced Vegetation Index (NDVI), and leaf area index (LAI) were combined for modelling the actual ET. Land use classification results were aggregated to 1km MODIS scale. Furthermore, the surface soil moisture products ASCAT SSM and ASAR SSM will be used as input data for the model. In addition to remote sensing data meteorological and ground truth data are used in this study. Meteorological data are wind speed, air temperature, relative humidity, and net radiation. The data is required at time of satellite overpass (about 12 p.m.). RN depends on incoming shortwave radiation, incoming and outgoing longwave radiant fluxes, albedo, emissivity and surface temperature. H is mostly calculated using the aerodynamic resistance between the surface and the reference height in the lower atmosphere (commonly 2 m) above surface. G is usually estimated using an empirical equation. This thesis introduces a modified equation to estimate G using an adjusted form of the thermal conduction equation. This method uses microwave soil moisture products (ASAR-SSM and ASCAT-SSM) as additional input information. The SEBAL modelled energy balance components were intensively validated by field measurements with an eddy covariance system and soil sensors in 2009, 2010, and 2011. The thesis is primarily concerned with the irrigation farming of cotton ecosystems in Central Asia, in particular with the situation within Khorezm Oblast in Uzbekistan. Regional problems of Khorezm are high groundwater levels, soil salinity, and non-sustainable use of land and water. Amongst others, the determination of ground truth data driven by the above mentioned objectives are part of two extensive field campaigns in 2010 and 2011. The validation of the modelled energy balance components leads to a good quality assessment. The model shows very good performance for RN with average model efficiency (NSE) of 0,68 and small relative errors (rRMSE) of about 0,10. For turbulent heat fluxes good results can be achieved with NSE of 0,31 for H and 0,55 for LE, the rRMSE are about 0,21 (H) and 0,18 (LvE). Soil heat flux estimation could be improved using the physically based approach. While the empirical equation leads to negative NSE and rRMSE of about 0,57, the improved approach shows rRMSE of 0,35 and NSE of 0,19. Thus, the improved G estimation can be registered as a valuable contribution for the remote sensing based estimation of energy balance components. N2 - Die Bewässerungslandwirtschaft verbraucht weltweit etwa 70 % der verfügbaren Süßwasservorkommen. Dabei liegt die Wasserentnahme des landwirtschaftlichen Sektors in den Staaten Mittelasiens bei über 90 %. Wichtige Voraussetzungen für die Landwirtschaft sind der Produktionsfaktor Boden und das Klima. Der Wassergehalt und die Temperatur des Bodens bestimmen im Wesentlichen den Anteil der verfügbaren solaren Strahlungsenergie, der in den Boden geleitet wird. Existierende Fernerkundungsansätze verwenden zur Ermittlung des Bodenwärmestroms überwiegend empirische Gleichungen, da zuverlässige flächenhafte Informationen über die Bodenfeuchte bisher aufgrund räumlich unzureichender messtechnischer Bedingungen nicht ermittelt werden können. In der vorliegenden Arbeit wird ein neu entwickelter, physikalisch-basierter Ansatz vorgestellt, der erstmals räumlich hochaufgelöste Bodenfeuchteinformationen aus Radardatensätzen zur Berechnung des Bodenwärmestroms verwendet. Dieser Ansatz wird zur Lösung der Energiebilanz an der Erdoberfläche verwendet, um indirekt auf die tatsächlichen Evapotranspiration zu schließen. Denn eine realistische Quantifizierung der regionalen, tatsächlichen Evapotranspiration als Komponente der regionalen Wasserbilanz ist eine wichtige Steuerungsgröße und ein Effizienzindikator für das lokale Bewässerungsmanagement. T3 - Würzburger Geographische Arbeiten - 120 KW - Evapotranspiration KW - Energiebilanz KW - Mikrometeorologie KW - Bodenfeuchte KW - Fernerkundung KW - Eddy-Kovarianz Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-135669 SN - 978-3-95826-042-9 (Print) SN - 978-3-95826-043-6 (Online) SN - 0510-9833 SN - 2194-3656 N1 - Eingereicht mit dem Titel: Optimierung der Energiebilanzmodellierung zur Ableitung der Evapotranspiration durch Entwicklung eines physikalischen Bodenwärmestromansatzes am Beispiel der Region Khorezm (Usbekistan). N1 - Parallel erschienen als Druckausgabe in Würzburg University Press, 978-3-95826-042-9, 34,90 EUR. PB - Würzburg University Press CY - Würzburg ET - 1. Auflage 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 - Koehler, Jonas A1 - Kuenzer, Claudia T1 - Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review JF - Remote Sensing N2 - Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing. KW - forecast KW - Earth Observation KW - land surface KW - land use KW - land cover KW - time series KW - machine learning KW - Markov chains KW - modeling Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216285 SN - 2072-4292 VL - 12 IS - 21 ER - TY - JOUR A1 - Kotte, K. A1 - Löw, F. A1 - Huber, S. G. A1 - Krause, T. A1 - Mulder, I. A1 - Schöler, H. F. T1 - Organohalogen emissions from saline environments - spatial extrapolation using remote sensing as most promising tool JF - Biogeosciences N2 - Due to their negative water budget most recent semi-/arid regions are characterized by vast evaporates (salt lakes and salty soils). We recently identified those hyper-saline environments as additional sources for a multitude of volatile halogenated organohalogens (VOX). These compounds can affect the ozone layer of the stratosphere and play a key role in the production of aerosols. A remote sensing based analysis was performed in the Southern Aral Sea basin, providing information of major soil types as well as their extent and spatial and temporal evolution. VOX production has been determined in dry and moist soil samples after 24 h. Several C1- and C2 organohalogens have been found in hyper-saline topsoil profiles, including CH3Cl, CH3Br, CHBr3 and CHCl3. The range of organohalogens also includes trans-1,2-dichloroethene (DCE), which is reported here to be produced naturally for the first time. Using MODIS time series and supervised image classification a daily production rate for DCE has been calculated for the 15 000 km\(^2\) ranging research area in the southern Aralkum. The applied laboratory setup simulates a short-term change in climatic conditions, starting from dried-out saline soil that is instantly humidified during rain events or flooding. It describes the general VOX production potential, but allows only for a rough estimation of resulting emission loads. VOX emissions are expected to increase in the future since the area of salt affected soils is expanding due to the regressing Aral Sea. Opportunities, limits and requirements of satellite based rapid change detection and salt classification are discussed. KW - aral sea basin KW - methyl-bromide KW - methane emissions KW - abiotic formation KW - time series KW - salt lakes KW - land KW - Uzbekistan KW - soils/sediments KW - classifiaction Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-134265 VL - 9 IS - 3 ER - TY - JOUR A1 - Kuenzer, Claudia A1 - Klein, Igor A1 - Ullmann, Tobias A1 - Georgiou, Efi Foufoula A1 - Baumhauer, Roland A1 - Dech, Stefan T1 - Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series JF - Remote Sensing N2 - River deltas belong to the most densely settled places on earth. Although they only account for 5% of the global land surface, over 550 million people live in deltas. These preferred livelihood locations, which feature flat terrain, fertile alluvial soils, access to fluvial and marine resources, a rich wetland biodiversity and other advantages are, however, threatened by numerous internal and external processes. Socio-economic development, urbanization, climate change induced sea level rise, as well as flood pulse changes due to upstream water diversion all lead to changes in these highly dynamic systems. A thorough understanding of a river delta's general setting and intra-annual as well as long-term dynamic is therefore crucial for an informed management of natural resources. Here, remote sensing can play a key role in analyzing and monitoring these vast areas at a global scale. The goal of this study is to demonstrate the potential of intra-annual time series analyses at dense temporal, but coarse spatial resolution for inundation characterization in five river deltas located in four different countries. Based on 250 m MODIS reflectance data we analyze inundation dynamics in four densely populated Asian river deltas-namely the Yellow River Delta (China), the Mekong Delta (Vietnam), the Irrawaddy Delta (Myanmar), and the Ganges-Brahmaputra (Bangladesh, India)-as well as one very contrasting delta: the nearly uninhabited polar Mackenzie Delta Region in northwestern Canada for the complete time span of one year (2013). A complex processing chain of water surface derivation on a daily basis allows the generation of intra-annual time series, which indicate inundation duration in each of the deltas. Our analyses depict distinct inundation patterns within each of the deltas, which can be attributed to processes such as overland flooding, irrigation agriculture, aquaculture, or snowmelt and thermokarst processes. Clear differences between mid-latitude, subtropical, and polar deltas are illustrated, and the advantages and limitations of the approach for inundation derivation are discussed. KW - difference water index KW - ENVISAT ASAR WSM KW - TerraSAR-X KW - central asia KW - SAR imagery KW - synthetic aperture radar KW - mekong delta KW - mangrove ecosystems KW - flood detection KW - dynamics Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151552 VL - 7 SP - 8516 EP - 8542 ER - TY - JOUR A1 - Lappe, Ronja A1 - Ullmann, Tobias A1 - Bachofer, Felix T1 - State of the Vietnamese coast — assessing three decades (1986 to 2021) of coastline dynamics using the Landsat archive JF - Remote Sensing N2 - Vietnam's 3260 km coastline is densely populated, experiences rapid urban and economic growth, and faces at the same time a high risk of coastal hazards. Satellite archives provide a free and powerful opportunity for long-term area-wide monitoring of the coastal zone. This paper presents an automated analysis of coastline dynamics from 1986 to 2021 for Vietnam's entire coastal zone using the Landsat archive. The proposed method is implemented within the cloud-computing platform Google Earth Engine to only involve publicly and globally available datasets and tools. We generated annual coastline composites representing the mean-high water level and extracted sub-pixel coastlines. We further quantified coastline change rates along shore-perpendicular transects, revealing that half of Vietnam's coast did not experience significant change, while the remaining half is classified as erosional (27.7%) and accretional (27.1%). A hotspot analysis shows that coastal segments with the highest change rates are concentrated in the low-lying deltas of the Mekong River in the south and the Red River in the north. Hotspots with the highest accretion rates of up to +47 m/year are mainly associated with the construction of artificial coastlines, while hotspots with the highest erosion rates of −28 m/year may be related to natural sediment redistribution and human activity. KW - coastline dynamics KW - Landsat archive KW - sub-pixel coastline extraction KW - time series KW - hotspot analysis KW - Google Earth Engine Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-275281 SN - 2072-4292 VL - 14 IS - 10 ER - TY - JOUR A1 - Latifi, Hooman A1 - Heurich, Marco T1 - Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects JF - Remote Sensing N2 - Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide. KW - remote sensing KW - forest resources inventory KW - spatial scale Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197358 SN - 2072-4292 VL - 11 IS - 11 ER - TY - JOUR A1 - Lausch, Angela A1 - Borg, Erik A1 - Bumberger, Jan A1 - Dietrich, Peter A1 - Heurich, Marco A1 - Huth, Andreas A1 - Jung, András A1 - Klenke, Reinhard A1 - Knapp, Sonja A1 - Mollenhauer, Hannes A1 - Paasche, Hendrik A1 - Paulheim, Heiko A1 - Pause, Marion A1 - Schweitzer, Christian A1 - Schmulius, Christiane A1 - Settele, Josef A1 - Skidmore, Andrew K. A1 - Wegmann, Martin A1 - Zacharias, Steffen A1 - Kirsten, Toralf A1 - Schaepman, Michael E. T1 - Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches JF - Remote Sensing N2 - Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support. KW - forest health KW - in situ forest monitoring KW - remote sensing KW - data science KW - digitalization KW - big data KW - semantic web KW - linked open data KW - FAIR KW - multi-source forest health monitoring network Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197691 SN - 2072-4292 VL - 10 IS - 7 ER -