@article{SognoKleinKuenzer2022,
  author    = {Sogno, Patrick and Klein, Igor and Kuenzer, Claudia},
  title     = {Remote sensing of surface water dynamics in the context of global change — a review},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {10},
  issn      = {2072-4292},
  doi       = {10.3390/rs14102475},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275274},
  year      = {2022},
  abstract  = {Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource — if not overexploited — sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.},
  language  = {en}
}
@article{LappeUllmannBachofer2022,
  author    = {Lappe, Ronja and Ullmann, Tobias and Bachofer, Felix},
  title     = {State of the Vietnamese coast — assessing three decades (1986 to 2021) of coastline dynamics using the Landsat archive},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {10},
  issn      = {2072-4292},
  doi       = {10.3390/rs14102476},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275281},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{YangYaoLietal.2022,
  author    = {Yang, Xuting and Yao, Wanqiang and Li, Pengfei and Hu, Jinfei and Latifi, Hooman and Kang, Li and Wang, Ningjing and Zhang, Dingming},
  title     = {Changes of SOC content in China's Shendong coal mining area during 1990-2020 investigated using remote sensing techniques},
  series = {Sustainability},
  volume    = {14},
  journal   = {Sustainability},
  number    = {12},
  issn      = {2071-1050},
  doi       = {10.3390/su14127374},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278939},
  year      = {2022},
  abstract  = {Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990-2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R\(^2\) > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg\(^{-1}\) in 1990 to 9.23 gC·kg\(^{-1}\) in 2010 and then declined to 5.31 gC·Kg\(^{-1}\) in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.},
  language  = {en}
}
@article{HaHuthBachoferetal.2022,
  author    = {Ha, Tuyen V. and Huth, Juliane and Bachofer, Felix and Kuenzer, Claudia},
  title     = {A review of Earth observation-based drought studies in Southeast Asia},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {15},
  issn      = {2072-4292},
  doi       = {10.3390/rs14153763},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-286258},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{KoehlerBauerDietzetal.2022,
  author    = {Koehler, Jonas and Bauer, Andr{\´e} and Dietz, Andreas J. and Kuenzer, Claudia},
  title     = {Towards forecasting future snow cover dynamics in the European Alps — the potential of long optical remote-sensing time series},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {18},
  issn      = {2072-4292},
  doi       = {10.3390/rs14184461},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-288338},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{DongWurmTaubenboeck2022,
  author    = {Dong, Ruirui and Wurm, Michael and Taubenb{\"o}ck, Hannes},
  title     = {Seasonal and diurnal variation of land surface temperature distribution and its relation to land use/land cover patterns},
  series = {International Journal of Environmental Research and Public Health},
  volume    = {19},
  journal   = {International Journal of Environmental Research and Public Health},
  number    = {19},
  issn      = {1660-4601},
  doi       = {10.3390/ijerph191912738},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290393},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{AppelHardaker2022,
  author    = {Appel, Alexandra and Hardaker, Sina},
  title     = {Einzelhandel als Katalysator f{\"u}r nachhaltige urbane Radlogistik? - W{\"u}Livery, ein Fallbeispiel aus W{\"u}rzburg},
  series = {Standort},
  volume    = {46},
  journal   = {Standort},
  number    = {1},
  issn      = {1432-220X},
  doi       = {10.1007/s00548-021-00758-y},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-268437},
  pages     = {9-15},
  year      = {2022},
  abstract  = {Die Covid-19-Pandemie gilt in vielen gesellschaftlichen Teilbereichen als Beschleuniger f{\"u}r Transformationsprozesse. Auch im Bereich der Organisation urbaner Logistik und Einzelhandelslandschaften etablieren sich neue Akteur*innen und Funktionen. Logistiker*innen integrieren lokale Onlinemarktpl{\"a}tze in ihre Profile und der station{\"a}re Einzelhandel generiert Wettbewerbsf{\"a}higkeit gegen{\"u}ber großen Onlineh{\"a}ndler*innen {\"u}ber die Nutzung lokaler Radlogistiknetzwerke, mittels derer Lieferungen noch am Tag der Bestellung (Same-Day-Delivery) verteilt werden k{\"o}nnen. Damit leisten die involvierten Akteur*innen potenziell auch einen Beitrag zur Nachhaltigkeitstransformation im Bereich urbaner Logistiksysteme. Im Fokus steht das Fallbeispiel W{\"u}Livery, ein Kooperationsprojekt des Stadtmarketingvereins, der Wirtschaftsf{\"o}rderung, Radlogistiker*innen sowie Einzelh{\"a}ndler*innen in W{\"u}rzburg, welches w{\"a}hrend des zweiten coronabedingten Lockdowns im November 2020 umgesetzt wurde. Die entstehenden Dynamiken und Organisationsformen werden auf Basis von 11 Expert*inneninterviews dargestellt und analysiert. Es kann gezeigt werden, dass st{\"a}dtische Akteur*innen grundlegende Mediator*innen f{\"u}r Transformationsprozesse darstellen und Einzelh{\"a}ndler*innen und lokale Onlinemarktpl{\"a}tze als Katalysator*innen fungieren k{\"o}nnen. Das ist auch vor dem Hintergrund planerischer und politischer Kommunikationsprozesse zur Legitimation neuer Verkehrsinfrastrukturen nutzbar, da die einzelnen Akteur*innengruppen in Austausch kommen und ein gesteigertes Bewusstsein f{\"u}r die jeweiligen Bedarfe entsteht.},
  language  = {de}
}
@article{KleemannZamoraVillacisChiluisaetal.2022,
  author    = {Kleemann, Janina and Zamora, Camilo and Villacis-Chiluisa, Alexandra Belen and Cuenca, Pablo and Koo, Hongmi and Noh, Jin Kyoung and F{\"u}rst, Christine and Thiel, Michael},
  title     = {Deforestation in continental Ecuador with a focus on protected areas},
  series = {Land},
  volume    = {11},
  journal   = {Land},
  number    = {2},
  issn      = {2073-445X},
  doi       = {10.3390/land11020268},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262078},
  year      = {2022},
  abstract  = {Forest conservation is of particular concern in tropical regions where a large refuge of biodiversity is still existing. These areas are threatened by deforestation, forest degradation and fragmentation. Especially, pressures of anthropogenic activities adjacent to these areas significantly influence conservation effectiveness. Ecuador was chosen as study area since it is a globally relevant center of forest ecosystems and biodiversity. We identified hotspots of deforestation on the national level of continental Ecuador between 1990 and 2018, analyzed the most significant drivers of deforestation on national and biome level (the Coast, the Andes, The Amazon) as well as inside protected areas in Ecuador by using multiple regression analysis. We separated the national system of protected areas (SNAP) into higher and lower protection levels. Besides SNAP, we also considered Biosphere Reserves (BRs) and Ramsar sites. In addition, we investigated the rates and spatial patterns of deforestation in protected areas and buffer zones (5 km and 10 km outwards the protected area boundaries) using landscape metrics. Between 1990 and 2018, approximately 4\% of the accumulated deforestation occurred within the boundaries of SNAP, and up to 25.5\% in buffer zones. The highest rates of deforestation have been found in the 5 km buffer zone around the protected areas with the highest protection level. Protected areas and their buffer zones with higher protection status were identified as the most deforested areas among SNAP. BRs had the highest deforestation rates among all protected areas but most of these areas just became BRs after the year 2000. The most important driver of deforestation is agriculture. Other relevant drivers differ between the biomes. The results suggest that the SNAP is generally effective to prevent deforestation within their protection boundaries. However, deforestation around protected areas can undermine conservation strategies to sustain biodiversity. Actions to address such dynamics and patterns of deforestation and forest fragmentation, and developing conservation strategies of their landscape context are urgently needed especially in the buffer zones of areas with the highest protection status.},
  language  = {en}
}
@article{RoeschPlank2022,
  author    = {R{\"o}sch, Moritz and Plank, Simon},
  title     = {Detailed mapping of lava and ash deposits at Indonesian volcanoes by means of VHR PlanetScope change detection},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {5},
  issn      = {2072-4292},
  doi       = {10.3390/rs14051168},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262232},
  year      = {2022},
  abstract  = {Mapping of lava flows in unvegetated areas of active volcanoes using optical satellite data is challenging due to spectral similarities of volcanic deposits and the surrounding background. Using very high-resolution PlanetScope data, this study introduces a novel object-oriented classification approach for mapping lava flows in both vegetated and unvegetated areas during several eruptive phases of three Indonesian volcanoes (Karangetang 2018/2019, Agung 2017, Krakatau 2018/2019). For this, change detection analysis based on PlanetScope imagery for mapping loss of vegetation due to volcanic activity (e.g., lava flows) is combined with the analysis of changes in texture and brightness, with hydrological runoff modelling and with analysis of thermal anomalies derived from Sentinel-2 or Landsat-8. Qualitative comparison of the mapped lava flows showed good agreement with multispectral false color time series (Sentinel-2 and Landsat-8). Reports of the Global Volcanism Program support the findings, indicating the developed lava mapping approach produces valuable results for monitoring volcanic hazards. Despite the lack of bands in infrared wavelengths, PlanetScope proves beneficial for the assessment of risk and near-real-time monitoring of active volcanoes due to its high spatial (3 m) and temporal resolution (mapping of all subaerial volcanoes on a daily basis).},
  language  = {en}
}
@article{PhilippDietzUllmannetal.2023,
  author    = {Philipp, Marius and Dietz, Andreas and Ullmann, Tobias and Kuenzer, Claudia},
  title     = {A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {3},
  issn      = {2072-4292},
  doi       = {10.3390/rs15030818},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304447},
  year      = {2023},
  abstract  = {This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June-September for the years 2017-2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.},
  language  = {en}
}