Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data

Over the last decades, climate change has triggered an increase in the frequency of sprucebark beetle (Ips typographusL.) in Central Europe. More than 50% of forests in the Czech Republic areseriously threatened by this pest, leading to high ecological and economic losses. The exponentialincrease of bark beetle infestation hinders the implementation of costly field campaigns to prevent andmitigate its effects. Remote sensing may help to overcome such limitations as it provides frequent andspatially continuous data on vegetation condition. Using Sentinel-2 images as main input, two modelshave been developed to test the ability of this data source to map bark beetle damage and severity.All models were based on a change detection approach, and required the generation of previous forestmask and dominant species maps. The first damage mapping model was developed for 2019 and2020, and it was based on bi-temporal regressions in spruce areas to estimate forest vitality and barkbeetle damage. A second model was developed for 2020 considering all forest area, but excludingclear-cuts and completely dead areas, in order to map only changes in stands dominated by alivetrees. The three products were validated with in situ data. All the maps showed high accuracies (acc>0.80). Accuracy was higher than 0.95 and F1-score was higher than 0.88 for areas with high severity,with omission errors under 0.09 in all cases. This confirmed the ability of all the models to detectbark beetle attack at the last phases. Areas with no damage or low severity showed more complexresults. The no damage category yielded greater commission errors and relative bias (CEs=0.30-0.42,relB=0.42-0.51). The similar results obtained for 2020 leaving out clear-cuts and dead trees provedthat the proposed methods could be used to help forest managers fight bark beetle pests. These bioticdamage products based on Sentinel-2 can be set up for any location to derive regular forest vitalitymaps and inform of early damage.O

Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: Influence of local factors

One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real time ground observations are not available. These include several international river locations in Africa: Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (Random Forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measurement sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest; with a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; Leaf Area Index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. The work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.JRC.H.7-Climate Risk Managemen

The benefit of continental flood early warning systems to reduce the impact of flood disasters

Flooding is a natural phenomenon, an intrinsic part of the natural cycle that serves important ecological functions. However, in highly anthropogenic-developed landscapes they cause serious consequences for human lives, societies in general, and their economy. Therefore comprehensive disaster risk reduction policies have been promoted in the last decade including actions on the development of early warning systems at local as well as regional scale. This report provides a brief global overview on the occurrences and damages resulting from riverine floods over the past decades. The first part of the report then summarises European policies put in place to deal with flooding in the different phases of the disaster management cycle addressing the prevention, preparedness, response, and recovery phase. This is followed by a description of the development of flood early warning capabilities at European scale, how such a system fits into the responsibility chain between national services and EU civil protection and what the potential financial benefit of flood early warning systems in Europe amounts to. The second part of the report addresses the gaps in flood early warning systems in Africa and presents a description of the African Flood Forecasting System (AFFS), which has been built in analogy to EFAS, but which is still in experimental stageJRC.H.7-Climate Risk Managemen

On the use of global flood forecasts and satellite-derived inundation maps for flood monitoring in data-sparse regions

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012-2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: 1) general agreement was found between the GFDS and MODIS flood detection systems, 2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and 3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, the satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large scale flood monitoring tools.JRC.H.7-Climate Risk Managemen

Improving urban flood mapping by merging Synthetic Aperture Radar-derived flood footprints with flood hazard maps

Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High resolution Synthetic Aperture Radar (SAR) sensors have the potential to detect flood extents in urban areas through cloud during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpass improve the SAR estimate of the urban flood extent. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to the urban ones. It was compared to an existing method using SAR returns in both the rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas, and was more accurate than the existing method

Effectiveness of an intervention for improving drug prescription in primary care patients with multimorbidity and polypharmacy:Study protocol of a cluster randomized clinical trial (Multi-PAP project)

This study was funded by the Fondo de Investigaciones Sanitarias ISCIII (Grant Numbers PI15/00276, PI15/00572, PI15/00996), REDISSEC (Project Numbers RD12/0001/0012, RD16/0001/0005), and the European Regional Development Fund ("A way to build Europe").Background: Multimorbidity is associated with negative effects both on people's health and on healthcare systems. A key problem linked to multimorbidity is polypharmacy, which in turn is associated with increased risk of partly preventable adverse effects, including mortality. The Ariadne principles describe a model of care based on a thorough assessment of diseases, treatments (and potential interactions), clinical status, context and preferences of patients with multimorbidity, with the aim of prioritizing and sharing realistic treatment goals that guide an individualized management. The aim of this study is to evaluate the effectiveness of a complex intervention that implements the Ariadne principles in a population of young-old patients with multimorbidity and polypharmacy. The intervention seeks to improve the appropriateness of prescribing in primary care (PC), as measured by the medication appropriateness index (MAI) score at 6 and 12months, as compared with usual care. Methods/Design: Design:pragmatic cluster randomized clinical trial. Unit of randomization: family physician (FP). Unit of analysis: patient. Scope: PC health centres in three autonomous communities: Aragon, Madrid, and Andalusia (Spain). Population: patients aged 65-74years with multimorbidity (≥3 chronic diseases) and polypharmacy (≥5 drugs prescribed in ≥3months). Sample size: n=400 (200 per study arm). Intervention: complex intervention based on the implementation of the Ariadne principles with two components: (1) FP training and (2) FP-patient interview. Outcomes: MAI score, health services use, quality of life (Euroqol 5D-5L), pharmacotherapy and adherence to treatment (Morisky-Green, Haynes-Sackett), and clinical and socio-demographic variables. Statistical analysis: primary outcome is the difference in MAI score between T0 and T1 and corresponding 95% confidence interval. Adjustment for confounding factors will be performed by multilevel analysis. All analyses will be carried out in accordance with the intention-to-treat principle. Discussion: It is essential to provide evidence concerning interventions on PC patients with polypharmacy and multimorbidity, conducted in the context of routine clinical practice, and involving young-old patients with significant potential for preventing negative health outcomes. Trial registration: Clinicaltrials.gov, NCT02866799Publisher PDFPeer reviewe

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe

The spatial and temporal dynamics of the forest cover can be captured using remote sensing data. Forest masks are a valuable tool to monitor forest characteristics, such as biomass, deforestation, health condition and disturbances. This study was carried out under the umbrella of the EC H2020 MySustainableForest (MSF) project. A key achievement has been the development of supervised classification methods for delineating forest cover. The forest masks presented here are binary forest/non-forest classification maps obtained using Sentinel-2 data for 16 study areas across Europe with different forest types. Performance metrics can be selected to measure accuracy of forest mask. However, large-scale reference datasets are scarce and typically cannot be considered as ground truth. In this study, we implemented a stratified random sampling system and the generation of a reference dataset based on visual interpretation of satellite images. This dataset was used for validation of the forest masks, MSF and two other similar products: HRL by Copernicus and FNF by the DLR. MSF forest masks showed a good performance (OAMSF = 96.3%; DCMSF = 96.5), with high overall accuracy (88.7–99.5%) across all the areas, and omission and commission errors were low and balanced (OEMSF = 2.4%; CEMSF = 4.5%; relBMSF = 2%), while the other products showed on average lower accuracies (OAHRL = 89.2%; OAFNF = 76%). However, for all three products, the Mediterranean areas were challenging to model, where the complexity of forest structure led to relatively high omission errors (OEMSF = 9.5%; OEHRL = 59.5%; OEFNF = 71.4%). Comparing these results with the vision from external local stakeholders highlighted the need of establishing clear large-scale validation datasets and protocols for remote sensing-based forest products. Future research will be done to test the MSF mask in forest types not present in Europe and compare new outputs to available reference datasets

Filling the gaps : calibrating a rainfall-runoff model using satellite-derived surface water extent

Calibration is a crucial step in the application of hydrological models and is typically performed using in situ streamflow data. However, many rivers on the globe are ungauged or poorly gauged, or the gauged data are not readily available. In this study, we used remotely-sensed surface water extent from the Global Flood Detection System(GFDS) as a proxy for streamflow, and tested its value for calibration of the distributed rainfall-runoff routing model LISFLOOD. In a first step, we identified 30 streamflow gauging sites with a high likelihood of reliable GFDS data. Next, for each of these 30 sites, themodel parameters related to groundwater and routing were independently calibrated against in situ and GFDS-derived streamflow time series, and against the rawGFDS surfacewater extent time series. We compared the performance of the three calibrated and the uncalibrated model simulations in terms of reproducing the in situ streamflow time series. Furthermore, we calculated the gain achieved by each scenario that used satellite-derived information relative to the reference uncalibrated scenario and the one that used in situ data. Results showthat using the rawGFDS data as a proxy for streamflowfor calibration improved the skill of the simulated streamflow (in particular the high flows) for 21 of the 30 sites using correlation as a metric. Furthermore, we discuss a calibration strategy using a combination of in situ and satellite data for global hydrological models.JRC.H.7-Climate Risk Managemen

On the Use of Global Flood Forecasts and Satellite-Derived Inundation Maps for Flood Monitoring in Data-Sparse Regions

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012–2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: (1) general agreement was found between the GFDS and MODIS flood detection systems, (2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and (3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large-scale flood monitoring tools