TY - JOUR A1 - Uereyen, Soner A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - A framework for multivariate analysis of land surface dynamics and driving variables — a case study for Indo-Gangetic river basins JF - Remote Sensing N2 - The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI. KW - time series analysis KW - trends KW - seasonality KW - partial correlation KW - causal networks KW - NDVI KW - snow cover area KW - surface water area KW - Indus-Ganges-Brahmaputra-Meghna KW - Himalaya Karakoram Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-255295 SN - 2072-4292 VL - 14 IS - 1 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 - Rai, P. A1 - Ziegler, K. A1 - Abel, D. A1 - Pollinger, F. A1 - Paeth, H. T1 - Performance of a regional climate model with interactive vegetation (REMO-iMOVE) over Central Asia JF - Theoretical and Applied Climatology N2 - The current study evaluates the regional climate model REMO (v2015) and its new version REMO-iMOVE, including interactive vegetation and plant functional types (PFTs), over two Central Asian domains for the period of 2000–2015 at two different horizontal resolutions (0.44° and 0.11°). Various statistical metrices along with mean bias patterns for precipitation, temperature, and leaf area index have been used for the model evaluation. A better representation of the spatial pattern of precipitation is found at 0.11° resolution over most of Central Asia. Regarding the mean temperature, both model versions show a high level of agreement with the validation data, especially at the higher resolution. This also reduces the biases in maximum and minimum temperature. Generally, REMO-iMOVE shows an improvement regarding the temperature bias but produces a larger precipitation bias compared to the REMO conventional version with interannually static vegetation. Since the coupled version is capable to simulate the mean climate of Central Asia like its parent version, both can be used for impact studies and future projections. However, regarding the new vegetation scheme and its spatiotemporal representation exemplified by the leaf area index, REMO-iMOVE shows a clear advantage over REMO. This better simulation is caused by the implementation of more realistic and interactive vegetation and related atmospheric processes which consequently add value to the regional climate model. KW - regional climate model (RCM) KW - interactive vegetation KW - REMO-iMOVE KW - Central Asia KW - evaluation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324155 SN - 0177-798X VL - 150 IS - 3-4 ER - TY - JOUR A1 - Frimmel, Hartwig E. A1 - Chakravarti, Rajarshi A1 - Basei, Miguel A. S. T1 - Detrital zircon ages from Archaean conglomerates in the Singhbhum Craton, eastern India: implications on economic Au-U potential JF - Mineralium Deposita N2 - New U–Pb age and Hf isotope data obtained on detrital zircon grains from Au- and U-bearing Archaean quartz-pebble conglomerates in the Singhbhum Craton, eastern India, specifically the Upper Iron Ore Group in the Badampahar Greenstone Belt and the Phuljhari Formation below the Dhanjori Group provide insights into the zircon provenance and maximum age of sediment deposition. The most concordant, least disturbed \(^{207}\)Pb/\(^{206}\)Pb ages cover the entire range of known magmatic and higher grade metamorphic events in the craton from 3.48 to 3.06 Ga and show a broad maximum between 3.38 and 3.18 Ga. This overlap is also mimicked by Lu–Hf isotope analyses, which returned a wide range in \(_{εHf}\)(t) values from + 6 to − 5, in agreement with the range known from zircon grains in igneous and metamorphic rocks in the Singhbhum Craton. A smaller but distinct age peak centred at 3.06 Ga corresponds to the age of the last major magmatic intrusive event, the emplacement of the Mayurbhanj Granite and associated gabbro, picrite and anorthosite. Thus, these intrusive rocks must form a basement rather than being intrusive into the studied conglomerates as previously interpreted. The corresponding detrital zircon grains all have a subchondritic Hf isotopic composition. The youngest reliable zircon ages of 3.03 Ga in the case of the basal Upper Iron Ore Group in the east of the craton and 3.00 Ga for the Phuljhari Formation set an upper limit on the age of conglomerate sedimentation. Previously published detrital zircon age data from similarly Au-bearing conglomerates in the Mahagiri Quartzite in the Upper Iron Ore Group in the south of the craton gave a somewhat younger maximum age of sedimentation of 2.91 Ga. There, the lower limit on sedimentation is given by an intrusive relationship with a c. 2.8 Ga granite. The time window thus defined for conglomerate deposition on the Singhbhum Craton is almost identical to the age span established for the, in places, Au- and U-rich conglomerates in the Kaapvaal Craton of South Africa: the 2.98–2.78 Ga Dominion Group and Witwatersrand Supergroup in South Africa. Since the recognition of first major concentration of gold on Earth’s surface by microbial activity having taken place at around 2.9 Ga, independent of the nature of the hinterland, the above similarity in age substantially increases the potential for discovering Witwatersrand-type gold and/or uranium deposits on the Singhbhum Craton. Further age constraints are needed there, however, to distinguish between supposedly less fertile (with respect to Au) > 2.9 Ga and more fertile < 2.9 Ga successions. KW - quartz-pebble conglomerate KW - gold KW - Mesoarchaean KW - Singhbhum Craton KW - zircon geochronology Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324084 SN - 0026-4598 VL - 57 IS - 8 ER - TY - JOUR A1 - Fäth, Julian A1 - Kunz, Julius A1 - Kneisel, Christof T1 - Monitoring spatiotemporal soil moisture changes in the subsurface of forest sites using electrical resistivity tomography (ERT) JF - Journal of Forestry Research N2 - The effects of drought on tree mortality at forest stands are not completely understood. For assessing their water supply, knowledge of the small-scale distribution of soil moisture as well as its temporal changes is a key issue in an era of climate change. However, traditional methods like taking soil samples or installing data loggers solely collect parameters of a single point or of a small soil volume. Electrical resistivity tomography (ERT) is a suitable method for monitoring soil moisture changes and has rarely been used in forests. This method was applied at two forest sites in Bavaria, Germany to obtain high-resolution data of temporal soil moisture variations. Geoelectrical measurements (2D and 3D) were conducted at both sites over several years (2015–2018/2020) and compared with soil moisture data (matric potential or volumetric water content) for the monitoring plots. The greatest variations in resistivity values that highly correlate with soil moisture data were found in the main rooting zone. Using the ERT data, temporal trends could be tracked in several dimensions, such as the interannual increase in the depth of influence from drought events and their duration, as well as rising resistivity values going along with decreasing soil moisture. The results reveal that resistivity changes are a good proxy for seasonal and interannual soil moisture variations. Therefore, 2D- and 3D-ERT are recommended as comparatively non-laborious methods for small-spatial scale monitoring of soil moisture changes in the main rooting zone and the underlying subsurface of forested sites. Higher spatial and temporal resolution allows a better understanding of the water supply for trees, especially in times of drought. KW - geoelectrical monitoring KW - forest ecology KW - hydrology KW - soil water content Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324073 SN - 1007-662X VL - 33 IS - 5 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 - 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 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kübert-Flock, Carina A1 - Steffan-Dewenter, Ingolf A1 - Zhang, Jie A1 - Ullmann, Tobias T1 - Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria JF - Remote Sensing N2 - The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. KW - Landsat KW - Sentinel-2 KW - NDVI KW - fusion KW - agriculture KW - grassland KW - forest KW - urban KW - water Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323471 SN - 2072-4292 VL - 14 IS - 3 ER - TY - JOUR A1 - Ghasemi, Marziye A1 - Latifi, Hooman A1 - Pourhashemi, Mehdi T1 - A novel method for detecting and delineating coppice trees in UAV images to monitor tree decline JF - Remote Sensing N2 - Monitoring tree decline in arid and semi-arid zones requires methods that can provide up-to-date and accurate information on the health status of the trees at single-tree and sample plot levels. Unmanned Aerial Vehicles (UAVs) are considered as cost-effective and efficient tools to study tree structure and health at small scale, on which detecting and delineating tree crowns is the first step to extracting varied subsequent information. However, one of the major challenges in broadleaved tree cover is still detecting and delineating tree crowns in images. The frequent dominance of coppice structure in degraded semi-arid vegetation exacerbates this problem. Here, we present a new method based on edge detection for delineating tree crowns based on the features of oak trees in semi-arid coppice structures. The decline severity in individual stands can be analyzed by extracting relevant information such as texture from the crown area. Although the method presented in this study is not fully automated, it returned high performances including an F-score = 0.91. Associating the texture indices calculated in the canopy area with the phenotypic decline index suggested higher correlations of the GLCM texture indices with tree decline at the tree level and hence a high potential to be used for subsequent remote-sensing-assisted tree decline studies. KW - UAV KW - crown delineation KW - coppice KW - Zagros oak forests KW - edge detection KW - decline KW - texture analysis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297258 SN - 2072-4292 VL - 14 IS - 23 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 -