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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.
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.
Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
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.
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.
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.
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).
The command area of the Rakh branch canal grows wheat, sugarcane, and rice crops in abundance. The canal water, which is trivial for irrigating these crops, is conveyed to the farms through the network of canals and distributaries. For the maintenance of this vast infrastructure; the end users are charged on a seasonal basis. The present water charges are severely criticized for not being adequate to properly manage the entire infrastructure. We use the residual value to determine the value of the irrigation water and then based on the quantity of irrigation water supplied to farm land coupled with the infrastructure maintenance cost, full cost recovery figures are executed for the study area, and policy recommendations are made for the implementation of the full cost recovery system. The approach is unique in the sense that the pricings are based on the actual quantity of water conveyed to the field for irrigating crops. The results of our analysis showed that the canal water is severely under charged in the culturable command area of selected distributaries, thus negating the plan of having a self-sustainable irrigation system.
Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000-2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.
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.