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Detection of grassland mowing events for Germany by combining Sentinel-1 and Sentinel-2 time series
(2022)
Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany.
Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product's quality with respect to its use for modeling and the geoscientific community.
Regardless of political boundaries, river basins are a functional unit of the Earth’s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream–downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.
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.
Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-
temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total
number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented.
New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.)and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet)will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.
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.
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.
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.
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.
A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts
(2023)
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.