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 - Hardaker, Sina T1 - More Than Infrastructure Providers – Digital Platforms' Role and Power in Retail Digitalisation in Germany JF - Tijdschrift voor Economische en Sociale Geografie N2 - Digital platforms, such as Amazon, represent the major beneficiaries of the Covid‐19 crisis. This study examines the role of digital platforms and their engagement in digitalisation initiatives targeting (small) brick‐and‐mortar retailers in Germany, thereby contributing to a better understanding of how digital platforms augment, substitute or reorganise physical retail spaces. This study applies a mixed‐method approach based on qualitative interviews, participant observation as well as media analysis. First, the study illustrates the controversial role of digital platforms by positioning themselves as supporting partners of the (offline) retailers, while simultaneously shifting power towards the platforms themselves. Second, digital platforms have established themselves not only as infrastructure providers but also as actors within these infrastructures, framing digital as well as physical retail spaces, inter alia due to their role as publicly legitimised retail advisers. Third, while institutions want to help retailers to survive, they simultaneously enhance retailers' dependency on digital platforms. KW - platform economy KW - digitalisation initiative KW - e‐commerce KW - Covid‐19 KW - two‐sided markets KW - framing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-287297 VL - 113 IS - 3 SP - 310 EP - 328 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 - Ayala-Carrillo, Mariana A1 - Farfán, Michelle A1 - Cárdenas-Nielsen, Anahí A1 - Lemoine-Rodríguez, Richard T1 - Are wildfires in the wildland-urban interface increasing temperatures? A land surface temperature assessment in a semi-arid Mexican city JF - Land N2 - High rates of land conversion due to urbanization are causing fragmented and dispersed spatial patterns in the wildland-urban interface (WUI) worldwide. The occurrence of anthropogenic fires in the WUI represents an important environmental and social issue, threatening not only vegetated areas but also periurban inhabitants, as is the case in many Latin American cities. However, research has not focused on the dynamics of the local climate in the WUI. This study analyzes whether wildfires contribute to the increase in land surface temperature (LST) in the WUI of the metropolitan area of the city of Guanajuato (MACG), a semi-arid Mexican city. We estimated the pre- and post-fire LST for 2018–2021. Spatial clusters of high LST were detected using hot spot analysis and examined using ANOVA and Tukey’s post-hoc statistical tests to assess whether LST is related to the spatial distribution of wildfires during our study period. Our results indicate that the areas where the wildfires occurred, and their surroundings, show higher LST. This has negative implications for the local ecosystem and human population, which lacks adequate infrastructure and services to cope with the effects of rising temperatures. This is the first study assessing the increase in LST caused by wildfires in a WUI zone in Mexico. KW - fire KW - grassland KW - urban climate KW - burned area KW - periurban Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297308 SN - 2073-445X VL - 11 IS - 12 ER - TY - JOUR A1 - Ansah, Christabel Edena A1 - Abu, Itohan-Osa A1 - Kleemann, Janina A1 - Mahmoud, Mahmoud Ibrahim A1 - Thiel, Michael T1 - Environmental contamination of a biodiversity hotspot — action needed for nature conservation in the Niger Delta, Nigeria JF - Sustainability N2 - The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control. KW - nature conservation KW - NDVI KW - pollution KW - remote sensing KW - species KW - vegetation indices KW - oil spill Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297214 SN - 2071-1050 VL - 14 IS - 21 ER - TY - JOUR A1 - Sogno, Patrick A1 - Klein, Igor A1 - Kuenzer, Claudia T1 - Remote sensing of surface water dynamics in the context of global change — a review JF - Remote Sensing N2 - 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. KW - remote sensing KW - surface water KW - dynamics KW - global change KW - earth observation KW - hydrology KW - biosphere KW - anthroposphere KW - review Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-275274 SN - 2072-4292 VL - 14 IS - 10 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 - Yang, Xuting A1 - Yao, Wanqiang A1 - Li, Pengfei A1 - Hu, Jinfei A1 - Latifi, Hooman A1 - Kang, Li A1 - Wang, Ningjing A1 - Zhang, Dingming T1 - Changes of SOC content in China's Shendong coal mining area during 1990–2020 investigated using remote sensing techniques JF - Sustainability N2 - 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. KW - loess plateau KW - coal mining area KW - SOC content prediction KW - human disturbance KW - vegetation restoration KW - climate change Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278939 SN - 2071-1050 VL - 14 IS - 12 ER - TY - JOUR A1 - Ha, Tuyen V. A1 - Huth, Juliane A1 - Bachofer, Felix A1 - Kuenzer, Claudia T1 - A review of Earth observation-based drought studies in Southeast Asia JF - Remote Sensing N2 - Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and the environment. Given the increasing climate crisis, it is likely that extreme drought phenomena will become more frequent, and their impacts will probably be more devastating. Drought observations from space, therefore, play a key role in dissimilating timely and accurate information to support early warning drought management and mitigation planning, particularly in sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products in Southeast Asia between 2000 and 2021. The results of this review indicated that drought publications in the region are on the increase, with a majority (70%) of the studies being undertaken in Vietnam, Thailand, Malaysia and Indonesia. These countries also accounted for nearly 97% of the economic losses due to drought extremes. Vegetation indices from multispectral optical remote sensing sensors remained a primary source of data for drought monitoring in the region. Many studies (~21%) did not provide accuracy assessment on drought mapping products, while precipitation was the main data source for validation. We observed a positive association between spatial extent and spatial resolution, suggesting that nearly 81% of the articles focused on the local and national scales. Although there was an increase in drought research interest in the region, challenges remain regarding large-area and long time-series drought measurements, the combined drought approach, machine learning-based drought prediction, and the integration of multi-sensor remote sensing products (e.g., Landsat and Sentinel-2). Satellite EO data could be a substantial part of the future efforts that are necessary for mitigating drought-related challenges, ensuring food security, establishing a more sustainable economy, and the preservation of the natural environment in the region. KW - drought KW - drought impact KW - agricultural drought KW - hydrological drought KW - meteorological drought KW - earth observation KW - remote sensing KW - Southeast Asia KW - Mekong Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286258 SN - 2072-4292 VL - 14 IS - 15 ER - TY - JOUR A1 - Koehler, Jonas A1 - Bauer, André A1 - Dietz, Andreas J. A1 - Kuenzer, Claudia T1 - Towards forecasting future snow cover dynamics in the European Alps — the potential of long optical remote-sensing time series JF - Remote Sensing N2 - Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach. KW - forecast KW - Earth Observation KW - time series KW - Snow Line Elevation KW - Alps KW - mountains KW - environmental modeling KW - machine learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288338 SN - 2072-4292 VL - 14 IS - 18 ER -