Harmonization of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from Sea-Viewing Wide Field-of-View Sensor SeaWiFS) and Medium Resolution Imaging Spectrometer Instrument (MERIS)

This paper describes the combination of terrestrial vegetation observations from two sensors, providing a historical dataset used for an in-depth analysis of the corresponding spatio-temporal patterns. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is an important variable suitable for regional to large-scale monitoring of climate impacts on vegetation. In this work, we create an extensive dataset of FAPAR using a 10-day product at $\sim$1 km resolution from September, 1997, to April, 2012, combining information from two sensors: the NASA/Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the European Space Agency (ESA)/Medium Resolution Imaging Spectrometer Instrument (MERIS). The proposed methodology reduces the noise, fills the gaps and corrects for the spurious trends in the data, providing a time-consistent coverage of FAPAR. We develop a fast merging method and evaluate its performance over Europe and the Horn of Africa.JRC.H.7-Climate Risk Managemen

On The Response Of The European Vegetation Phenology To Hydroclimatic Anomalies

Climate change is expected to alter vegetation and carbon cycle processes, with implications for ecosystems and feedback to regional and global climate. Notably, understanding the sensitivity of vegetation to the anomalies of precipitation and temperature over different land cover classes and the corresponding temporal response is essential for improved climate prediction. In this paper, we analyse vegetation response to hydroclimatic forcings using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) derived from SeaWiFS (1998-2002) and MERIS (2003-2011) sensors at 1 km resolution. Based on land cover and pixel-wise analysis, we quantify the extent of the dependence between the FAPAR, and ultimately the phenology, and the anomalies of precipitation and temperature over Europe. Statistical tests are performed to establish where this correlation may be regarded as statistically dependent. Further, we assess a statistical link between the climate variables and a set of phenological metrics defined from FAPAR measurement. Variation in the phenological response to the unusual values of precipitation and temperature can be interpreted as the result of balanced opposite effects of water and temperature on vegetation processes. Results suggest very different responses on different land cover classes and timing seasons. The degree of observed coupled behaviour also indicates that European phenology may be quite sensitive to the perturbations in precipitation and temperature regimes such as those induced by the climate change.JRC.H.7-Climate Risk Managemen

Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020

The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves

High-resolution precipitation datasets in South America and West Africa based on satellite-derived rainfall, Enhanced Vegetation Index and Digital Elevation Model

Mean Annual Precipitation is one of the most important variables used in water resource management. However, quantifying Mean Annual Precipitation at high spatial resolution, needed for advanced hydrological analysis, is challenging in developing countries which often present a sparse gauge network and a highly variable climate. In this work, we present a methodology to quantify Mean Annual Precipitation at 1 km spatial resolution using different precipitation products from satellite estimates and gauge observations at coarse spatial resolution (i.e., ranging from 4 km to 25 km). Examples of this methodology are given for South America and West Africa. We develop a downscaling method that exploits the relationship among satellite-derived rainfall, Digital Elevation Model and Enhanced Vegetation Index. At last, we validate its performance using rain gauge measurements: comparable annual precipitation estimates for both South America and West Africa are retrieved. Validation indicates that high resolution Mean Annual Precipitation downscaled from CHIRP (Climate Hazards Group Infrared Precipitation) and GPCC (Global Precipitation Climatology Centre) datasets present the best ensemble of performance statistics for both South America and West Africa. Results also highlight the potential of the presented technique to downscale satellite-derived rainfall worldwide.JRC.H.1-Water Resource

Potential improvement for forest cover and forest degradation mapping with the forthcoming sentinel-2 program

The forthcoming European Space Agency’s Sentinel-2 mission promises to provide high (10 m) resolution optical data at higher temporal frequencies (5 day revisit with two operational satellites) than previously available. CNES, the French national space agency, launched a program in 2013, ‘SPOT4 take 5’, to simulate such a dataflow using the SPOT HRV sensor, which has similar spectral characteristics to the Sentinel sensor, but lower (20m) spatial resolution. Such data flow enables the analysis of the satellite images using temporal analysis, an approach previously restricted to lower spatial resolution sensors. We acquired 23 such images over Tanzania for the period from February to June 2013. The data were analysed with aim of discriminating between different forest cover percentages for landscape units of 0.5 ha over a site characterised by deciduous intact and degraded forests. The SPOT data were processed by one extracting temporal vegetation indices. We assessed the impact of the high acquisition rate with respect to the current rate of one image every 16 days. Validation data, giving the percentage of forest canopy cover in each land unit were provided by very high resolution satellite data. Results show that using the full temporal series it is possible to discriminate between forest units with differences of more than 40% tree cover or more. Classification errors fell exclusively into the adjacent forest canopy cover class of 20% or less. The analyses show that forestation mapping and degradation monitoring will be substantially improved with the Sentinel-2 programJRC.H.3-Forest Resources and Climat

Vulnerability of European forests to natural disturbances

European forests provide a set of fundamental services that contribute to climate change mitigation and human well-being. At the same time, forests are vulnerable systems because the long life-span of trees limits the possibility of rapid adaptation to drastic environmental changes. Climate-driven disturbances in forests, such as fires, windstorms and insect outbreaks, are expected to rise drastically under global warming. As a result, key forest services, such as carbon sequestration and supply of wood materials, could be seriously affected in the near future. Despite the relevance and urgency of the issue, little is known about the vulnerability of European forests to multiple climate-related hazards and the adaptation benefits of alternative forest management strategies. To fill this knowledge gap we investigated the susceptibility of European forests when exposed to a given natural disturbance under different forest management scenarios. For this purpose, we assessed forest vulnerability by integrating in a data-driven framework satellite observations, national forest inventories, land surface climatic data and records of disturbances over the 2000-2017 period. The integration of these data streams is meant to capture the key drivers of vulnerability and to quantify, for the first time, the vulnerability of European forests to fires, windstorms and insect outbreaks in a systematic and spatially explicit manner. We point out that, the term vulnerability is used in this study to express to what degree a forest ecosystem is affected when exposed to a given disturbance. In order to derive risk estimates, vulnerability estimates should be integrated with hazard and exposure components, according to typical impact assessment frameworks. Results of this analyses show that in average at Europe level forest vulnerability to windstorms appears the disturbance with larger biomass loss both in relative and absolute terms (~38%, ~17 t ha-1) compared to fires (~24%, ~12.5 t ha-1) and insect outbreaks (~21%, ~9 t ha-1). Substantial spatial variations in vulnerability emerge and depict generally higher values in norther and Mediterranean regions. Overall, forest structural properties play a larger control on the vulnerability of European forests to natural disturbances compared to climate and landscape features. However, increases in temperature and changes in precipitation patterns occurred over the last two decades, have contributed substantially to make European forests more vulnerable to natural disturbances. We found that these changes in climate led to a limited increase in vulnerability at Europe for fires and windstorms and to a strong increase for insect outbreaks. However, contrasting regional trends emerging over Europe mask relevant temporal changes in vulnerability occurring at local scale. When analyses of single disturbances are combined together, results show that large part of the European forests are substantially vulnerable to at least one natural disturbance and that many of the areas more vulnerable have been subject to an amplification of vulnerability over the observational period due to changes in climate. Reducing tree age and tree density appear effective forest management strategies to reduce the vulnerability of European forests to climate-driven disturbances. The magnitude of the potential benefits appears strongly dependent on local environmental conditions. Previous assessments of future climate risks to European forests, based on catalogues of disturbances collected at country level, have showed that damage from fires, windstorms and insect outbreaks is likely to increase further in coming decades. Such intensification could offset the impact of land-based strategies aiming to increase the forest carbon sink. However, the country scale approach used in such studies do not allow to explore in detail the underlying physical processes and to elaborate adaptation strategies at appropriate local scales. It is therefore fundamental to elaborate new modelling approaches that address in explicit manner the high spatial and temporal variability of forest disturbances. In this respect, machine learning approaches and the increasing availability of multi-platform satellite observations of land surface in combination with high regional climate model simulations, represent valuable opportunities to appraise the impact of forest disturbances at a spatial and temporal resolution relevant for forest management strategies. This explorative study represents a first step towards such integrated framework.JRC.D.1-Bio-econom

Emergent vulnerability to climate-driven disturbances in European forests

Forest disturbance regimes are expected to intensify as Earth's climate changes. Quantifying forest vulnerability to disturbances and understanding the underlying mechanisms is crucial to develop mitigation and adaptation strategies. However, observational evidence is largely missing at regional to continental scales. Here, we quantify the vulnerability of European forests to fires, windthrows and insect outbreaks during the period 1979-2018 by integrating machine learning with disturbance data and satellite products. We show that about 33.4 billion tonnes of forest biomass could be seriously affected by these disturbances, with higher relative losses when exposed to windthrows (40%) and fires (34%) compared to insect outbreaks (26%). The spatial pattern in vulnerability is strongly controlled by the interplay between forest characteristics and background climate. Hotspot regions for vulnerability are located at the borders of the climate envelope, in both southern and northern Europe. There is a clear trend in overall forest vulnerability that is driven by a warming-induced reduction in plant defence mechanisms to insect outbreaks, especially at high latitudes. Natural disturbances imperil healthy and productive forests, but quantifying their effects at large scales is challenging. Here the authors apply machine learning to disturbance records and satellite data to quantify and map European forest vulnerability to fires, windthrows, and insect outbreaks through 1979-2018.Peer reviewe

Changes in land use and management led to a decline in Eastern Europe’s terrestrial carbon sink

Land-based mitigation is essential in reducing net carbon emissions. Yet, the attribution of carbon fluxes remains highly uncertain, in particular for the forest-rich region of Eastern Europe (incl. Western Russia). Here we integrate various data sources to show that Eastern Europe accounted for an above-ground biomass carbon sink of ~0.41 gigatons of carbon per year over the period 2010–2019, that is 78% of the entire European carbon sink. We find that this carbon sink is declining, mainly driven by changes in land use and land management, but also by increasing natural disturbances. Based on a random forest model, we show that land use and management changes are main drivers of the declining carbon sink in Eastern Europe, although soil moisture variability is also important. Specifically, the saturation effect of tree regrowth in abandoned agricultural areas, combined with increasing wood harvest removals, particularly in European Russia, contributed to the decrease in the Eastern European carbon sink

The fourth phase of the radiative transfer model intercomparison (RAMI) exercise : Actual canopy scenarios and conformity testing

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RI model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. (C) 2015 The Authors. Published by Elsevier Inc.Peer reviewe

Climate change impacts and adaptation in Europe

The JRC PESETA IV study shows that ecosystems, people and economies in the EU will face major impacts from climate change if we do not urgently mitigate greenhouse gas emissions or adapt to climate change. The burden of climate change shows a clear north-south divide, with southern regions in Europe much more impacted, through the effects of extreme heat, water scarcity, drought, forest fires and agriculture losses. Limiting global warming to well below 2°C would considerably reduce climate change impacts in Europe. Adaptation to climate change would further minimize unavoidable impacts in a cost-effective manner, with considerable co-benefits from nature-based solutions.JRC.C.6-Economics of Climate Change, Energy and Transpor