BUSCADOR RECOLECTA

Global environmental changes are affecting both the distribution and abundance of species at an unprecedented rate. To assess these effects, species distribution models (SDMs) have been greatly developed over the last decades, while species abundance models (SAMs) have generally received less attention even though these models provide essential information for conservation management. With population abundance defined as an essential biodiversity variable (EBV), SAMs could offer spatially explicit predictions of species abundance across space and time. Satellite-derived ecosystem functioning attributes (EFAs) are known to inform on processes controlling species distribution, but they have not been tested as predictors of species abundance. In this study, we assessed the usefulness of SAMs calibrated with EFAs (as process-related variables) to predict local abundance patterns for a rare and threatened species (the narrow Iberian endemic ‘Gerês lily’ Iris boissieri; protected under the European Union Habitats Directive), and to project inter-annual fluctuations of predicted abundance. We compared the predictive accuracy of SAMs calibrated with climate (CLI), topography (DEM), land cover (LCC), EFAs, and combinations of these. Models fitted only with EFAs explained the greatest variance in species abundance, compared to models based only on CLI, DEM, or LCC variables. The combination of EFAs and topography slightly increased model performance. Predictions of the inter-annual dynamics of species abundance were related to inter-annual fluctuations in climate, which holds important implications for tracking global change effects on species abundance. This study underlines the potential of EFAs as robust predictors of biodiversity change through population size trends. The combination of EFA-based SAMs and SDMs would provide an essential toolkit for species monitoring programs.

Fires have significant impacts on soil erosion and water supply that may be exacerbated by future climate. The aims of this study were: To simulate the effects of a large fire event in the SWAT (Soil and Water Assessment Tool) hydrological model previously calibrated to a medium-sized watershed in Portugal; and to predict the hydrological impacts of large fires and future climate on water supply and soil erosion. For this, post-fire recovery was parametrized in SWAT based on satellite information, namely, the fraction of vegetation cover (FVC) calculated from the normalized difference vegetation index (NDVI). The impact of future climate was based on four regional climate models under the stabilization (RCP 4.5) and high emission (RCP 8.5) scenarios, focusing on mid-century projections (2020–2049) compared to a historical period (1970–1999). Future large fire events (>3000 ha) were predicted from a multiple linear regression model, which uses the daily severity rating (DSR) fire weather index, precipitation anomaly, and burnt area in the previous three years; and subsequently simulated in SWAT under each climate model/scenario. Results suggest that time series of satellite indices are useful to inform SWAT about vegetation growth and post-fire recovery processes. Different land cover types require different time periods for returning to the pre-fire fraction of vegetation cover, ranging from 3 years for pines, eucalypts, and shrubs, to 6 years for sparsely vegetated low scrub. Future climate conditions are expected to include an increase in temperatures and a decrease in precipitation with marked uneven seasonal distribution, and this will likely trigger the growth of burnt area and an increased frequency of large fires, even considering differences across climate models. The future seasonal pattern of precipitation will have a strong influence on river discharge, with less water in the river during spring, summer, and autumn, but more discharge in winter, the latter being exacerbated under the large fire scenario. Overall, the decrease in water supply is more influenced by climate change, whereas soil erosion increase is more dependent on fire, although with a slight increase under climate change. These results emphasize the need for adaptation measures that target the combined hydrological consequences of future climate, fires, and post-fire vegetation dynamics.

The ability of ecological niche models (ENMs) to produce robust predictions for different time frames (i.e. temporal transferability) may be hindered by a lack of ecologically relevant predictors. Model performance may also be affected by species traits, which may reflect different responses to processes controlling species distribution. In this study, we tested four primary hypotheses involving the role of species traits and environmental predictors in ENM performance and transferability. We compared the predictive accuracy of ENMs based upon (1) climate, (2) land-use/cover (LULC) and (3) ecosystem functional attributes (EFAs), and (4) the combination of these factors for 27 bird species within and beyond the time frame of model calibration. The combination of these factors significantly increased both model performance and transferability, highlighting the need to integrate climate, LULC and EFAs to improve biodiversity projections. However, the overall model transferability was low (being only acceptable for less than 25% of species), even under a hierarchical modelling approach, which calls for great caution in the use of ENMs to predict bird distributions under global change scenarios. Our findings also indicate that positive effects of species traits on predictive accuracy within model calibration are not necessarily translated into higher temporal transferability

Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by wind and insect outbreaks are crucial for guiding management decisions. To this end, past studies relied mostly on passive sensors (e.g., optical), and active sensors (i.e., radar) were rarely used. This study used L-band space-bome synthetic aperture radar (SAR) within a change-detection framework to delineate forested areas affected by wind and insect disturbances. The results showed that changes in backscatter relate to damage caused by wind and insect outbreaks. Overall accuracies of 69- 84% and 65--88% were obtained for delineation of areas affected by wind damage and insect outbreaks, respectively, depending on the acquisition date and environmental conditions. Areas susceptible to insect outbreaks or experiencing the initial outbreak phase (green) were detected with lower accuracies (64--74%). lt is expected that L-band space-borne SAR data can be applied over larger areas and ecosystem types in the temperate and boreal regions to delineate and detect damaged areas., European Commission

Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management., S.T., E.G., D.A‐S., and F.H. were supported by the project DeepSCOP‐Ayudas Fundación BBVA a Equipos de Investigación Científica en Big Data 2018. E.G. is supported by the European Research Council grant agreement 647038 (BIODESERT). S.T. is supported by the Ramón y Cajal Program of the Spanish Government (RYC‐2015‐18136). S.T., E.G., and F.H. were supported by the Spanish Ministry of Science under the project TIN2017‐89517‐P. D.A‐S., E.G., and J.C. received support from project ECOPOTENTIAL, funded by European Union Horizon 2020 Research and Innovation Program, under grant agreement No. 641762, and from European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612. D.A‐S, S.T. and E.G. received support from Programa Operativo FEDER‐Andalucía 2014‐2020 under project DETECTOR A‐RNM‐256‐UGR18. D.A‐S. received support from NASA’s Work Program on Group on Earth Observations—Biodiversity Observation Network (GEOBON) under grant 80NSSC18K0446.

Conservation biology faces the challenge of safeguarding the ecosystem functions and ecological processes (the water cycle, nutrients, energy flow, and community dynamics) that sustain the multiple facets of biodiversity. Characterization and evaluation of these processes and functions can be carried out through functional attributes or traits related to the exchanges of matter and energy between vegetation and the atmosphere. Based on this principle, satellite imagery can provide integrative spatiotemporal characterizations of ecosystem functions at local to global scales. Here, we provide a multitemporal dataset at protected-area level that characterizes the spatial patterns and temporal dynamics of ecosystem functioning in the Biosphere Reserve of the Sierra Nevada (Spain), captured through the spectral Enhanced Vegetation Index (EVI, using product MOD13Q1.006 from the MODIS sensor) from 2001 to 2018. The database contains, at the annual scale, a synthetic map of Ecosystem Functional Type (EFT) classes from three Ecosystem Functional Attributes (EFAs): (i) descriptors of annual primary production, (ii) seasonality, and (iii) phenology of carbon gains. It also includes two ecosystem functional-diversity indices derived from the above datasets: (i) EFT richness and (ii) EFT rarity. Finally, it provides interannual summaries for all previously mentioned variables, i.e., their long-term means and interannual variability. The datasets are available at two open-source sites (PANGAEA: https://doi.org/10.1594/PANGAEA.924792; Cazorla et al., 2020a; interannual summaries at http://obsnev.es/apps/efts_SN.html, last access: 17 April 2023). This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada, a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTER) network., This research has been supported by the Plan Propio PhD program of the University of Almería; projects EarthCul (reference PID2020-118041GB-I00); INDALO (grant no. LifeWatch-2019-10-UGR-01); LifeWatch-2019-10-UGR-01_WP-8, LifeWatch-2019-10-UGR-01_WP-7, and LifeWatch-2019-10-UGR-01_WP-4; ECOPOTENTIAL grant agreement no. 641762; “Ecosystem and Socio-Ecosystem Functional Types: Integrating biophysical and social functions to characterize and map the ecosystems of the Anthropocene” (CGL2014-61610-EXP); Project LIFE-ADAPTAMED (LIFE14 CCA/ES/000612); Generalitat Valenciana and the European Social Fund (APOSTD/2021/188).

Aim: Rarity—an important measure for conservation biogeography—can vary over
many orders of magnitude. However, it is unclear which regional-scale
abiotic conditions
drive processes affecting rarity of endemic species on islands. To support conservation
efforts, we (1) assess the main abiotic drivers of endemic rarity, (2) determine
how well existing protected areas (PAs) coincide with hotspots of endemic rarity and
(3) introduce and evaluate a new hypervolume-based
rarity estimator.
Location: La Palma (Canary Islands).
Methods: We recorded all present endemic vascular plant species in 1,212 plots covering
the entire island. We calculated endemic rarity (corrected range-rarity
richness
for endemics) using a rarity estimation approach based on kernel density estimations
(hypervolume approach). We performed a sensitivity analysis based on multiple linear
regressions and relative importance estimations of environmental drivers to estimate
the performance of the hypervolume-based
rarity estimation compared to standard
methods (occurrence frequency, convex hulls, alpha hulls).
Results: Climate variables (mean annual temperature, climatic rarity, precipitation variability)
best explained archipelago endemic (AE) and single-island
endemic (SIE) rarity.
Existing PAs covered the majority of AE and SIE rarity, especially national and natural
parks as well as the Natura 2000 sites. In our study system, hypervolumes performed
better than standard measures of range size.
Main conclusion: Both AE and SIE rarity on La Palma show a clear spatial pattern, with
hotspots of endemic rarity found at high elevations and in rare climates, presumably
owing to geographical and climatic constraints and possibly anthropogenic pressure
(e.g., land use, introduced herbivores, fire). Areas of high rarity estimates coincide with
the distribution and extent of PAs on La Palma, especially since the recent addition of
the Natura 2000 sites. The hypervolume approach is a promising tool to estimate species
range sizes, and can be applied on all scales where point/plot data are available., European Union, Elite Network of Bavaria

Accurate estimation of forest biomass to enable the mapping of forest C stocks over large areas is of considerable interest nowadays. Airborne laser scanning (ALS) systems bring a new perspective to forest inventories and subsequent biomass estimation. The objective of this research was to combine growth models used to update old inventory data to a reference year, low-density ALS data, and k-nearest neighbor (kNN) algorithm Random Forest to conduct biomass inventories aimed at estimating the C sequestration capacity in large Pinus plantations. We obtained a C stock in biomass (Wt-S) of 12.57 Mg·ha−1, ranging significantly from 19.93 Mg·ha−1 for P. halepensis to 49.05 Mg·ha−1 for P. nigra, and a soil organic C stock of the composite soil samples (0–40 cm) ranging from 20.41 Mg·ha−1 in P. sylvestris to 37.32 Mg·ha−1 in P. halepensis. When generalizing these data to the whole area, we obtained an overall C-stock value of 48.01 MgC·ha−1, ranging from 23.96 MgC·ha−1 for P. halepensis to 58.09 MgC·ha−1 for P. nigra. Considering the mean value of the on-site C stock, the study area sustains 1,289,604 Mg per hectare (corresponding to 4,732,869 Mg CO2), with a net increase of 4.79 Mg·ha−1·year−1. Such C cartography can help forest managers to improve forest silviculture with regard to C sequestration and, thus, climate change mitigation.

The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies.

Forest fires are a serious environmental hazard in southern Europe. Quantitative assessment of recent trends in fire statistics is important for assessing the possible shifts induced by climate and other environmental/socioeconomic changes in this area. Here we analyse recent fire trends in Portugal, Spain, southern France, Italy and Greece, building on a homogenized fire database integrating official fire statistics provided by several national/EU agencies. During the period 1985-2011, the total annual burned area (BA) displayed a general decreasing trend, with the exception of Portugal, where a heterogeneous signal was found. Considering all countries globally, we found that BA decreased by about 3020 km2 over the 27-year-long study period (i.e. about -66% of the mean historical value). These results are consistent with those obtained on longer time scales when data were available, also yielding predominantly negative trends in Spain and France (1974-2011) and a mixed trend in Portugal (1980-2011). Similar overall results were found for the annual number of fires (NF), which globally decreased by about 12600 in the study period (about -59%), except for Spain where, excluding the provinces along the Mediterranean coast, an upward trend was found for the longer period. We argue that the negative trends can be explained, at least in part, by an increased effort in fire management and prevention after the big fires of the 1980's, while positive trends may be related to recent socioeconomic transformations leading to more hazardous landscape configurations, as well as to the observed warming of recent decades. We stress the importance of fire data homogenization prior to analysis, in order to alleviate spurious effects associated with non-stationarities in the data due to temporal variations in fire detection efforts.

Summer fires frequently rage across Mediterranean Europe, often intensified by high temperatures and droughts. According to the state-of-the-art regional fire risk projections, in forthcoming decades climate effects are expected to become stronger and possibly overcome fire prevention efforts. However, significant uncertainties exist and the direct effect of climate change in regulating fuel moisture (e.g. warmer conditions increasing fuel dryness) could be counterbalanced by the indirect effects on fuel structure (e.g. warmer conditions limiting fuel amount), affecting the transition between climate-driven and fuel-limited fire regimes as temperatures increase. Here we analyse and model the impact of coincident drought and antecedent wet conditions (proxy for the climatic factor influencing total fuel and fine fuel structure) on the summer Burned Area (BA) across all eco-regions in Mediterranean Europe. This approach allows BA to be linked to the key drivers of fire in the region. We show a statistically significant relationship between fire and same-summer droughts in most regions, while antecedent climate conditions play a relatively minor role, except in few specific eco-regions. The presented models for individual eco-regions provide insights on the impacts of climate variability on BA, and appear to be promising for developing a seasonal forecast system supporting fire management strategies.

Societal exposure to large fires has been increasing in recent years. Estimating the expected fire activity a few months in advance would allow reducing environmental and socio-economic impacts through short-term adaptation and response to climate variability and change. However, seasonal prediction of climate-driven fires is still in its infancy. Here, we discuss a strategy for seasonally forecasting burned area anomalies linking seasonal climate predictions with parsimonious empirical climate-fire models using the standardized precipitation index as the climate predictor for burned area. Assuming near-perfect climate predictions, we obtained skilful predictions of fire activity over a substantial portion of the global burnable area (~60%). Using currently available operational seasonal climate predictions, the skill of fire seasonal forecasts remains high and significant in a large fraction of the burnable area (~40%). These findings reveal an untapped and useful burned area predictive ability using seasonal climate forecasts, which can play a crucial role in fire management strategies and minimise the impact of adverse climate conditions.

Spring sea ice phenology regulates the timing of the two consecutive pulses of marine autotrophs that form the base of the Arctic marine food webs. This timing has been suggested to be the single most essential driver of secondary production and the efficiency with which biomass and energy are transferred to higher trophic levels. We investigated the chronological sequence of productivity pulses and its potential cascading impacts on the reproductive performance of the High Arctic seabird community from Svalbard, Norway. We provide evidence that interannual changes in the seasonal patterns of marine productivity may impact the breeding performance of little auks and Brünnich's guillemots. These results may be of particular interest given that current global warming trends in the Barents Sea region predict one of the highest rates of sea ice loss within the circumpolar Arctic. However, local- to regional-scale heterogeneity in sea ice melting phenology may add uncertainty to predictions of climate-driven environmental impacts on seabirds. Indeed, our fine-scale analysis reveals that the inshore Brünnich's guillemots are facing a slower advancement in the timing of ice melt compared to the offshore-foraging little auks. We provide a suitable framework for analyzing the effects of climate-driven sea ice disappearance on seabird fitness.

Global warming and direct anthropogenic impacts, such as water extraction, largely affect water budgets in Mediterranean wetlands, thereby increasing wetland salinities and isolation, and decreasing water depths and hydroperiods (duration of the inundation period). These wetland features are key elements structuring waterbird communities. However, the ultimate and net consequences of these dynamic conditions on waterbird assemblages are largely unknown. We combined regular sampling of waterbird presence through one annual cycle with in-situ data on relevant environmental predictors of waterbird distribution to model habitat selection for 69 species in a typical Mediterranean wetland network in southwestern Spain. Species associations with environmental features were subsequently used to predict changes in habitat suitability for each species under three climate change scenarios (encompassing changes in environmental predictors that ranged from 10% to 50% change as predicted by regional climatic models). Waterbirds distributed themselves unevenly throughout environmental gradients and water salinity was the most important gradient structuring the distribution of the community. Environmental suitability for the guilds of diving birds and vegetation gleaners will decline in future climate scenarios, while many small wading birds will benefit from changing conditions. Resident species and those that breed in this wetland network will also be more negatively impacted than those using this area for wintering or stopover. We provide a tool that can be used in a horizon-scanning framework to identify emerging issues in waterbird conservation and to anticipate suitable management actions.

The observed trend towards warmer and drier conditions in southern Europe is projected to continue in the next decades, possibly leading to increased risk of large fires. However, an assessment of climate change impacts on fires at and above the 1.5 °C Paris target is still missing. Here, we estimate future summer burned area in Mediterranean Europe under 1.5, 2, and 3 °C global warming scenarios, accounting for possible modifications of climate-fire relationships under changed climatic conditions owing to productivity alterations. We found that such modifications could be beneficial, roughly halving the fire-intensifying signals. In any case, the burned area is robustly projected to increase. The higher the warming level is, the larger is the increase of burned area, ranging from ~40% to ~100% across the scenarios. Our results indicate that significant benefits would be obtained if warming were limited to well below 2 °C.

The mechanisms translating global circulation changes into rapid abrupt shifts in forest carbon capture in semi‐arid biomes remain poorly understood. Here, we report unprecedented multidecadal shifts in forest carbon uptake in semi‐arid Mediterranean pine forests in Spain over 1950-2012. The averaged carbon sink reduction varies between 31% and 37%, and reaches values in the range of 50% in the most affected forest stands. Regime shifts in forest carbon uptake are associated with climatic early warning signals, decreased forest regional synchrony and reduced long‐term carbon sink resilience. We identify the mechanisms linked to ocean multidecadal variability that shape regime shifts in carbon capture. First, we show that low‐frequency variations of the surface temperature of the Atlantic Ocean induce shifts in the non‐stationary effects of El Niño Southern Oscillation (ENSO) on regional forest carbon capture. Modelling evidence supports that the non‐stationary effects of ENSO can be propagated from tropical areas to semi‐arid Mediterranean biomes through atmospheric wave trains. Second, decadal changes in the Atlantic Multidecadal Oscillation (AMO) significantly alter sea-air heat exchanges, modifying in turn ocean vapour transport over land and land surface temperatures, and promoting sustained drought conditions in spring and summer that reduce forest carbon uptake. Third, we show that lagged effects of AMO on the winter North Atlantic Oscillation also contribute to the maintenance of long‐term droughts. Finally, we show that the reported strong, negative effects of ocean surface temperature (AMO) on forest carbon uptake in the last decades are unprecedented over the last 150 years. Our results provide new, unreported explanations for carbon uptake shifts in these drought‐prone forests and review the expected impacts of global warming on the profiled mechanisms.

Climate impacts on marine ecosystems may be exacerbated by other, more local stressors interacting synergistically, such as pollution and overexploitation of marine resources. The reduction of these human stressors has been proposed as an achievable way of retaining ecosystems within a "safe operating space" (SOS), where they remain resilient to ongoing climate change. However, the operability of an SOS requires a thorough understanding of the spatial distribution of these climate and human impacts. Using the Mediterranean Sea as a case study, we illustrate the spatial congruence between climate and human stressors impacting this iconic "miniature ocean" synergistically. We use long-term, spatially-explicit information on the distribution of multiple stressors to identify those highly impacted marine areas where human stressors should be prioritized for management if the resilience to climate impacts is to be maintained. Based on our spatial analysis, we exemplify how the management of an essential supporting service (seafood provision) and the conservation of a highly impacted Mediterranean sub-region (the Adriatic Sea) may benefit from the SOS framework.

This paper presents a semi-automatic procedure to discriminate seasonally flooded areas in the shallow temporary marshes of Doñana National Park (SW Spain) by using a radiommetrically normalized long time series of Landsat MSS, TM, and ETM+ images (1974–2014). Extensive field campaigns for ground truth data retrieval were carried out simultaneous to Landsat overpasses. Ground truth was used as training and testing areas to check the performance of the method. Simple thresholds on TM and ETM band 5 (1.55–1.75 μm) worked significantly better than other empirical modeling techniques and supervised classification methods to delineate flooded areas at Doñana marshes. A classification tree was applied to band 5 reflectance values to classify flooded versus non-flooded pixels for every scene. Inter-scene cross-validation identified the most accurate threshold on band 5 reflectance (ρ < 0.186) to classify flooded areas (Kappa = 0.65). A joint TM-MSS acquisition was used to find the MSS band 4 (0.8 a 1.1 μm) threshold. The TM flooded area was identical to the results from MSS 4 band threshold ρ < 0.10 despite spectral and spatial resolution differences. Band slicing was retrospectively applied to the complete time series of MSS and TM images. About 391 flood masks were used to reconstruct historical spatial and temporal patterns of Doñana marshes flooding, including hydroperiod. Hydroperiod historical trends were used as a baseline to understand Doñana’s flooding regime, test hydrodynamic models, and give an assessment of relevant management and restoration decisions. The historical trends in the hydroperiod of Doñana marshes show two opposite spatial patterns. While the north-western part of the marsh is increasing its hydroperiod, the southwestern part shows a steady decline. Anomalies in each flooding cycle allowed us to assess recent management decisions and monitor their hydrological effects., : This study was funded by the Spanish Ministry of Science and Innovation through the
research projects HYDRA (#CGL2006-02247/BOS) and HYDRA2 (CGL2009-09801/BOS), and by funding from
the European Union’s Horizon 2020 research and innovation program under grant agreement No. 641762 to
ECOPOTENTIAL project. The Espacio Natural de Doñana provided permits for fieldwork in protected areas with
restricted access and historical data from water column readings. We are grateful to many MSc students who
helped in image processing and field sampling.
We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)., Peer reviewed

We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (<i>Spartina densiflora</i> Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of <i>S. densiflora</i>. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate <i>S. densiflora</i> detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target <i>S. densiflora</i> was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread <i>S. densiflora</i> in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images., This study has been funded by the Spanish Ministry of Science and Innovation through the
research projects HYDRA (No. CGL2006-02247/BOS) and HYDRA2 (CGL2009-09801/BOS), by the National
Parks Authority (Organismo Autonomo de Parques Nacionales) of the Spanish Ministry of Environment to project
OAPN 042/2007, and by funding from the European Union (EU) Horizon 2020 research and innovation program
under grant agreement No. 641762 to ECOPOTENTIAL project. The Espacio Natural de Doñana provided
permits for field work in protected areas with restricted access. We are grateful to the Instituto Nacional de
Técnica Aeroespacial (INTA), Spain, for performing the airborne campaign and the geometric correction of the
images. J.B. has to acknowledge a sabbatical stay at Pye Laboratory of the Commonwealth Scientific and Research
Organization (CSIRO) Marine and Atmospheric Sciences, Australia, and at the Climate Change Cluster (C3)
of the University of Technology Sydney, Australia, funded by the Spanish Ministry of Education, during data
analysis and writing of this paper. This publication is a contribution from CEIMAR and also a contribution
from CEICAMBIO. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

Many of the world’s wetlands may be profoundly affected by climate change over the coming decades.
Although wetland managers may have little control over the causes of climate change, they can help to
counteract
its effects through local measures. This is because direct anthropogenic impacts, such as water
extraction and nutrient loading, work in concert with climate change to damage wetlands. Control of these
local stressors may therefore ameliorate undesired effects of climate change, such as a shift towards dominance
by invasive floating plants, increasingly frequent cyanobacteria blooms, or extinction of key species.
Using the iconic Doñana wetlands in Spain as a case study, we illustrate how the concept of creating a “safe
operating space” may be implemented to better ensure that ecosystems do not surpass thresholds for collapse
during an era of global change., WIMEK grant for a research stay at WUR. JAE DOCTORES. European Union. European Social Fund. Grant Number: ESF2007‐2013. Spanish Ministry for Economy and Competitiveness. EU FP7 project FAST. Grant Number: 607131.
European Union's Horizon 2020 research and innovation program. Grant Number: 641762, Peer reviewed

Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources—particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park’s marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001–2015 used to calibrate the models (R² = 0.65). Estimators based on a single MODIS NDVI image performed worse (R² ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems., We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)., David Aragonés, Isabel Afán, Ricardo Díaz-Delgado and Diego García Díaz (EBD-LAST)
provided support for remote-sensing and LSP analyses. Alfredo Chico, José Luis del Valle and Rocío Fernández
Zamudio (ESPN, ICTS-RBD) provided logistic support and taxonomic expertise during the field work (validation
dataset). Ernesto García and Cristina Pérez assisted with biomass harvesting and processing (calibration dataset).
Gerrit Heil provided support in the project design. This study received funding from Ministerio de Medio
Ambiente-Parque Nacional de Doñana, Consejeria de Medio Ambiente, Junta de Andalucia (1999–2000): RNM118
Junta de Andalucia (2003); the European Union’s Horizon 2020 Research and Innovation Program under grant
agreement No. 641762 to ECOPOTENTIAL project; and the Spanish Ministry of Economy, Plan Estatal de I+D+i
2013–2016, under grant agreement CGL2016-81086-R to GRAZE project.

Human activities drive environmental changes at scales that could potentially cause ecosystem collapses in the marine environment. We combined information on marine biodiversity with spatial assessments of the impacts of climate change to identify the key areas to prioritize for the conservation of global marine biodiversity. This process identified six marine regions of exceptional biodiversity based on global distributions of 1729 species of fish, 124 marine mammals, and 330 seabirds. Overall, these hot spots of marine biodiversity coincide with areas most severely affected by global warming. In particular, these marine biodiversity hot spots have undergone local to regional increasing water temperatures, slowing current circulation, and decreasing primary productivity. Furthermore, when we overlapped these hot spots with available industrial fishery data, albeit coarser than our estimates of climate impacts, they suggest a worrying coincidence whereby the world’s richest areas for marine biodiversity are also those areas mostly affected by both climate change and industrial fishing. In light of these findings, we offer an adaptable framework for determining local to regional areas of special concern for the conservation of marine biodiversity. This has exposed the need for finer-scaled fishery data to assist in the management of global fisheries if the accumulative, but potentially preventable, effect of fishing on climate change impacts is to be minimized within areas prioritized for marine biodiversity conservation., We thank the continued support of the Phillip Island Nature Parks, Penguin Ecosystem Research Centre. Grants were received from the Penguin Foundation, the Australian Research Council, and the European Union Horizon 2020 research and innovation program under grant agreement no. 641762 to the ECOPOTENTIAL project., We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)., Peer reviewed

Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics., This study is supported and funded by the European Union’s Horizon 2020 research and innovation
program under Grant Agreement No. 641762, ECOPOTENTIAL.

Long-term ecological research (LTER) sites need a periodic assessment of the state of their ecosystems and services in order to monitor trends and prevent irreversible changes. The ecological integrity (EI) framework opens the door to evaluate any ecosystem in a comparable way, by measuring indicators on ecosystem structure and processes. Such an approach also allows to gauge the sustainability of conservation management actions in the case of protected areas. Remote sensing (RS), provided by satellite, airborne, or drone-borne sensors becomes a very synoptic and valuable tool to quickly map isolated and inaccessible areas such as wetlands. However, few RS practical indicators have been proposed to relate to EI indicators for wetlands. In this work, we suggest several RS wetlands indicators to be used for EI assessment in wetlands and specially to be applied with unmanned aerial vehicles (UAVs). We also assess the applicability of multispectral images captured by UAVs over two long-term socio-ecological research (LTSER) wetland sites to provide detailed mapping of inundation levels, water turbidity and depth as well as aquatic plant cover. We followed an empirical approach to find linear relationships between UAVs spectral reflectance and the RS indicators over the Doñana LTSER platform in SW Spain. The method assessment was carried out using ground-truth data collected in transects. The resulting empirical models were implemented for Doñana marshes and can be applied for the Braila LTSER platform in Romania. The resulting maps are a very valuable input to assess habitat diversity, wetlands dynamics, and ecosystem productivity as frequently as desired by managers or scientists. Finally, we also examined the feasibility to upscale the information obtained from the collected ground-truth data to satellite images from Sentinel-2 MSI using segments from the UAV multispectral orthomosaic. We found a close multispectral relationship between Parrot Sequoia and Sentinel-2 bands which made it possible to extend ground-truth to map inundation in satellite images., The authors have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements no. 654359 (eLTER Horizon 2020 project) and no. 641762 (ECOPOTENTIAL Horizon 2020 project). The authors have also received funding from the Romanian Space Agency under grant agreement 145/20.07.2017 “Integrated platform for monitoring of aquatic and terrestrial ecosystems based on in situ and satellite measurements” (RISE)., We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI), Peer reviewed

Protected Areas are a key component of nature conservation. They can play an important role in counterbalancing the impacts of ecosystem degradation. For an optimal protection of a Protected Area it is essential to account for the variables underlying the major Ecosystem Services an area delivers, and the threats upon them. Here we show that the perception of these important variables differs markedly between scientists and managers of Protected Areas in mountains and transitional waters. Scientists emphasise variables of abiotic and biotic nature, whereas managers highlight socio-economic, cultural and anthropogenic variables. This indicates fundamental differences in perception. To be able to better protect an area it would be advisable to bring the perception of scientists and managers closer together. Intensified and harmonised communication across disciplinary and professional boundaries will be needed to implement and improve Ecosystem Service oriented management strategies in current and future Protected Areas., This study was done in the frame of the EcoPotential project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641762.

Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate.

Invasive plant species are increasingly altering species composition and the functioning of ecosystems from a local to a global scale. The grass species Pennisetum setaceum has recently raised concerns as an invader on different archipelagos worldwide. Among these affected archipelagos are the Canary Islands, which are a hotspot of endemism. Consequently, conservation managers and stakeholders are interested in the potential spreading of this species in the archipelago. We identify the current extent of the suitable habitat for P. setaceum on the island of La Palma to assess how it affects island ecosystems, protected areas (PAs), and endemic plant species richness. We recorded in situ occurrences of P. setaceum from 2010 to 2018 and compiled additional ones from databases at a 500 m × 500 m resolution. To assess the current suitable habitat and possible distribution patterns of P. setaceum on the island, we built an ensemble model. We projected habitat suitability for island ecosystems and PAs and identified risks for total as well as endemic plant species richness. The suitable habitat for P. setaceum is calculated to cover 34.7% of the surface of La Palma. In open ecosystems at low to mid elevations, where native ecosystems are already under pressure by land use and human activities, the spread of the invader will likely lead to additional threats to endemic plant species. Forest ecosystems (e.g., broadleaved evergreen and coniferous forests) are not likely to be affected by the spread of P. setaceum because of its heliophilous nature. Our projection of suitable habitat of P. setaceum within ecosystems and PAs on La Palma supports conservationists and policymakers in prioritizing management and control measures and acts as an example for the potential threat of this graminoid invader on other islands., This project has received funding from the European Union’s Horizon 2020 research and innovation program ECOPOTENTIAL under grant agreement No 641762. This publication was funded by the German Research Foundation (DFG) and the University of Bayreuth in the funding program Open Access Publishing.

Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes are important steps for management and control but are challenging with the limited resources typically available and particularly difficult to implement on oceanic islands with very steep terrain. Remote sensing may provide an excellent solution in circumstances where the invading species can be reliably detected from imagery. We here develop a method to map the distribution of the alien chestnut (Castanea sativa Mill.) on the island of La Palma (Canary Islands, Spain), using freely available satellite images. On La Palma, the chestnut invasion threatens the iconic laurel forest, which has survived since the Tertiary period in the favourable climatic conditions of mountainous islands in the trade wind zone. We detect chestnut presence by taking advantage of the distinctive phenology of this alien tree, which retains its deciduousness while the native vegetation is evergreen. Using both Landsat 8 and Sentinel-2 (parallel analyses), we obtained images in two seasons (chestnuts leafless and in-leaf, respectively) and performed image regression to detect pixels changing from leafless to in-leaf chestnuts. We then applied supervised classification using Random Forest to map the present-day occurrence of the chestnut. Finally, we performed species distribution modelling to map the habitat suitability for chestnut on La Palma, to estimate which areas are prone to further invasion. Our results indicate that chestnuts occupy 1.2% of the total area of natural ecosystems on La Palma, with a further 12–17% representing suitable habitat that is not yet occupied. This enables targeted control measures with potential to successfully manage the invasion, given the relatively long generation time of the chestnut. Our method also enables research on the spread of the species since the earliest Landsat images., This work was supported by the European H2020 Project ECOPOTENTIAL, grant agreement No. 641762. We thank Reinhold Stahlmann for his support in all questions related to spatial data., Peer reviewed

Plant primary production is a key factor in ecosystem dynamics. In environments with high climatic variability such as the Mediterranean region, plant primary production shows strong seasonal and inter-annual fluctuations, which both drive and interplay with herbivore grazing. Knowledge on the responses of different vegetation types to the variability in both rainfall and grazing pressure by wild and domestic ungulates is a necessary starting point for the sustainable management of these ecosystems. In this work we combine a 15 year series of remote sensing data on plant production (NDVI) with meteorological (daily precipitation data) and ungulate abundance (annual counts of four species of wild and domestic ungulates: red deer, fallow deer, cattle, and horses) in an iconic protected area (the Doñana National Park, SW Spain) to (i) estimate the impact of intra- and inter-annual variation in rainfall and herbivore pressure on primary production, for each of four main vegetation types; and (ii) evaluate the potential impact of different policy (i.e., herbivore management) strategies under expected climate change scenarios. Our results show that the production of different vegetation types differed strongly in their responses to phenology (a surrogate of the effect of climatology on vegetation development), water availability (rainfall accumulated until the phenological peak), and grazing pressure. Although the density of domestic ungulates shows a linear, negative effect on the primary production of three of the four vegetation types, differences in primary production and phenology among vegetation types increase ecosystem resilience to both climatological variability and grazing pressure. Such resilience may, however, be reduced under the conditions predicted by climate change models, if the moderate predicted reduction in rainfall levels combines with moderate to high densities of domestic ungulates, resulting in important reductions in primary production that may compromise plant regeneration, leading to irreversible degradation. New management strategies taking advantage of habitat heterogeneity and phenological alternation, more flexible stocking rates, and the redistribution of management units should be considered to mitigate these effects. The use of available remote sensing data and techniques in combination with statistical models represents a valuable tool for developing, monitoring, and refining such strategies., Peer reviewed

Global warming and direct anthropogenic impacts, such as water extraction, largely affect water budgets in Mediterranean wetlands, thereby increasing wetland salinities and isolation, and decreasing water depths and hydroperiods (duration of the inundation period). These wetland features are key elements structuring waterbird communities. However, the ultimate and net consequences of these dynamic conditions on waterbird assemblages are largely unknown. We combined regular sampling of waterbird presence through one annual cycle with in-situ data on relevant environmental predictors of waterbird distribution to model habitat selection for 69 species in a typical Mediterranean wetland network in southwestern Spain. Species associations with environmental features were subsequently used to predict changes in habitat suitability for each species under three climate change scenarios (encompassing changes in environmental predictors that ranged from 10% to 50% change as predicted by regional climatic models). Waterbirds distributed themselves unevenly throughout environmental gradients and water salinity was the most important gradient structuring the distribution of the community. Environmental suitability for the guilds of diving birds and vegetation gleaners will decline in future climate scenarios, while many small wading birds will benefit from changing conditions. Resident species and those that breed in this wetland network will also be more negatively impacted than those using this area for wintering or stopover. We provide a tool that can be used in a horizon-scanning framework to identify emerging issues in waterbird conservation and to anticipate suitable management actions., The study was supported by the
European Union (EU) Horizon 2020 research and
innovation program under grant agreement No.
641762 to ECOPOTENTIAL project. Field data were
collected under the project "Propuesta
metodolo´gica para diagnosticar y pronosticar las
consecuencias de las actuaciones humanas en el
estuario del Guadalquivir" financed by Autoridad
Portuaria de Sevilla. F. Ramı´rez was partially funded by ECOPOTENTIAL and “Subprograma
Juan de la Cierva-Incorporacio´n; Ministerio de
Economı´a, Industria y Competitividad” (IJCI-2015-
24531). The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript., Peer reviewed

Time Series water turbidity derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, the product and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/

Teh methodology is described in the paper: Empirical models to estimate water turbidity from reflectance data from TM or ETM+ Landsat sensors in shallow wetlands such as Doñana marshes. See the reference: Bustamante, J. et al. 2009. Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images. Journal of Environmental Management. 90:2219-2225.https://doi.org/10.1016/j.jenvman.2007.08.021, European Commission:
ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762), Peer reviewed

Time Series of NDVI derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, the product and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/, European Commission:
ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762), Peer reviewed

Time Series of annual Hydroperiods derived from Landsat MSS, TM, ETM+ & OLI in the Path 202 Row 34 (Doñana) cover the period 1974-2018., European Commission:
ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762), Peer reviewed

Time Series of flooded areas derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, these products and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/

Methodology is described in this paper: Remote Sensing 8(9):775 · September 2016. DOI: 10.3390/rs8090775, European Commission:
ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762), Peer reviewed

The synthesis, structural characterization, topological analysis, proton conductivity, and catalytic properties are reported of two Cu(II)-based compounds, namely a dinuclear Cu(II) complex [Cu(μ-VPA)(phen)(HO)]·8HO (1) (HVPA = vinylphosphonic acid, phen = 1,10-phenanthroline) and a 1D coordination polymer [Cu(μ-SO)(phen)(HO)] (2). Their structural features and H-bonding interactions were investigated in detail, showing that the metal-organic structures of 1 and 2 are extended by multiple hydrogen bonds to more complex 2D or 1D H-bonded architectures with the kgd [Shubnikov plane net (3.6.3.6)/dual] and SP 1-periodic net (4,4)(0,2) topology, respectively. These nets are primarily driven by the H-bonding interactions involving water ligands and HO molecules of crystallization; besides, the (HO)/(HO) clusters were identified in 1. Both 1 and 2 are moderate proton conductors, with proton conductivity values, σ = 3.65 × 10 and 3.94 × 10 S·cm, respectively (measured at 80 °C and 95% relative humidity). Compounds 1 and 2 are also efficient homogeneous catalysts for the mild oxidative functionalization of C-C cycloalkanes (cyclopentane, cyclohexane, cycloheptane, and cyclooctane), namely for the oxidation by HO to give cyclic alcohols and ketones and the hydrocarboxylation by CO/HO and SO
to the corresponding cycloalkanecarboxylic acids as major products. The catalytic reactions proceed under mild conditions (50-60 °C) in aqueous acetonitrile medium, resulting in up to 34% product yields based on cycloalkane substrate., Financial support was given by the European LIFE Project ADAPTAMED (LIFE14 CCA/ES/000612), (CGL2016‐80687‐R AEI/FEDER) DAMAGE, Spanish MINECO (grant JC2015‐00316 and project CGL2014‐61610‐EXP), and ERDF. The work was also partially developed as part of project ECOPOTENTIAL, which received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement 641762.

Summer fires frequently rage across Mediterranean Europe, often intensified by high temperatures and droughts. According to the state-of-the-art regional fire risk projections, in forthcoming decades climate effects are expected to become stronger and possibly overcome fire prevention efforts. However, significant uncertainties exist and the direct effect of climate change in regulating fuel moisture (e.g. warmer conditions increasing fuel dryness) could be counterbalanced by the indirect effects on fuel structure (e.g. warmer conditions limiting fuel amount), affecting the transition between climate-driven and fuel-limited fire regimes as temperatures increase. Here we analyse and model the impact of coincident drought and antecedent wet conditions (proxy for the climatic factor influencing total fuel and fine fuel structure) on the summer Burned Area (BA) across all eco-regions in Mediterranean Europe. This approach allows BA to be linked to the key drivers of fire in the region. We show a statistically significant relationship between fire and same-summer droughts in most regions, while antecedent climate conditions play a relatively minor role, except in few specific eco-regions. The presented models for individual eco-regions provide insights on the impacts of climate variability on BA, and appear to be promising for developing a seasonal forecast system supporting fire management strategies., We thank the European Forest Fire Information System-EFFIS (http://effis.jrc.ec.europa.eu) of the European
Commission Joint Research Centre for the fire data. We acknowledge the SPEI data providers (http://sac.csic.
es/spei/database.html). Special thanks to Joaquín Bedia, Esteve Canyameras, Xavier Castro and Andrej Ceglar
for helpful discussions on the study. This work was partially funded by the Project of Interest “NextData” of the Italian Ministry for Education, University and Research and by the EU H2020 Project 641762 “ECOPOTENTIAL: Improving Future Ecosystem Benefits through Earth Observations”. Ricardo Trigo was supported by IMDROFLOOD funded by Portuguese FCT (WaterJPI/0004/2014)., Peer Reviewed

Societal exposure to large fires has been increasing in recent years. Estimating the expected fire activity a few months in advance would allow reducing environmental and socio-economic impacts through short-term adaptation and response to climate variability and change. However, seasonal prediction of climate-driven fires is still in its infancy. Here, we discuss a strategy for seasonally forecasting burned area anomalies linking seasonal climate predictions with parsimonious empirical climate–fire models using the standardized precipitation index as the climate predictor for burned area. Assuming near-perfect climate predictions, we obtained skilful predictions of fire activity over a substantial portion of the global burnable area (~60%). Using currently available operational seasonal climate predictions, the skill of fire seasonal forecasts remains high and significant in a large fraction of the burnable area (~40%). These findings reveal an untapped and useful burned area predictive ability using seasonal climate forecasts, which can play a crucial role in fire management strategies and minimise the impact of adverse climate conditions., This work was partially funded by the EU H2020 Project 641762 “ECOPOTENTIAL:
Improving Future Ecosystem Benefits through Earth Observations” and the SERVFORFIRE
project of the ERA-NET for Climate Services, ERA4CS. M. Turco was supported
by the Spanish Juan de la Cierva Programme (IJCI-2015-26953). F.J. Doblas-
Reyes was supported by the H2020 IMPREX (GA 641811) and EUCP (GA 776613)
projects. A.A. was partially supported by the National Oceanic and Atmospheric
Administration (NOAA) award NA14OAR4310222, National Aeronautics and Space
Administration (NASA) award NNX15AC27G, and National Science Foundation (NSF)
INFEWS grant EAR 1639318. Special thanks to Esteve Canyameras and Xavier Castro for
helpful discussions on the study., Peer Reviewed

Protected Areas contribute to the conservation of nature with associated cultural ecosystem services (CES) and values, such as recreational and educational opportunities, wildlife observation, scenic beauty, inspiration and sense of belonging. Informed management of Protected Areas needs to consider the distinct use and preferences for CES of different types of visitors to increase opportunities for nature experience while avoiding conflicts with biodiversity conservation. Therefore, it is important to understand the linkages between visitor characteristics and their demand for specific sets of CES, particularly in fragile mountain ecosystems. Here we do so by combining information from individual on-site surveys and participatory mapping of visitors in four European mountain Protected Areas. We analysed visitors’ frequency of use of eight CES and their socio-demographic information, identifying three clusters of visitors. We also assessed the spatial distribution of CES locations used by each visitor cluster. Our results highlight strong differences between clusters both in the most frequently experienced CES and in the spatial location where those CES were experienced. We suggest that a better understanding of visitors regarding the way they experience nature is relevant for the environmental management of mountain Protected Areas and their surroundings.

The complexities of the intricate geospatial resources and formats make preservation and distribution of GIS data difficult even among experts. The proliferation of, for instance, KML, Internet map services, etc, reflects the need for sharing geodata but a comprehensive solution when having to deal with data and metadata of a certain complexity is not currently provided. Original geospatial data is usually divided into several parts to record its different aspects (spatial and thematic features, etc), plus additional files containing, metadata, symbolization specifications and tables, etc; these parts are encoded in different formats, both standard and proprietary. To simplify data access, software providers encourage the use of an additional element that we call generically "map project", and this contains links to other parts (local or remote). Consequently, in order to distribute the data and metadata refereed by the map in a complete way, or to apply the Open Archival Information System (OAIS) standard to preserve it for the future, we need to face the multipart problem. This paper proposes a package allowing the distribution of real (comprehensive although diverse and complex) GIS data over the Internet and for data preservation. This proposal, complemented with the right tools, hides but keeps the multipart structure, so providing a simpler but professional user experience. Several packaging strategies are reviewed in the paper, and a solution based on ISO 29500-2 standard is chosen. The solution also considers the adoption of the recent Open Geospatial Consortium Web Services common standard (OGC OWS) context document as map part, and as a way for also combining data files with geospatial services. Finally, and by using adequate strategies, different GIS implementations can use several parts of the package and ignore the rest: a philosophy that has proven useful (e.g. in TIFF).

In southern Europe, climate trends are expected to be characterized by an increase in temperatures and less water availability. Analyzing the role of structural factors and the influence of a changing climate provides insights into the evolution of forest ecosystems in regions with similar environmental conditions. The Mediterranean fringe of the Iberian Peninsula is of particular interest due to its diverse topo climatic conditions and the increase in drought episodes during the last decades. This work studies forest dynamics in large areas of this geographical region by analyzing nine forest transitions. Vegetation covers were classified from three Landsat scenes for the period 1987-2012, and sub-periods 1987-2002 and 2002-2012. Conditions were described by topography derived variables, human factors and drought-occurrence variables. Boosted regression trees were used to identify the most important variables and describe the relationships between the forest dynamics and key factors. Variables such as solar radiation, topographic wetness index and tolerance to drought have been shown to be key factors in forest succession and when comparisons are made between vegetation groups. Main findings: The transition rate to Mediterranean and sub-Mediterranean broadleaf forests has increased during the analyzed period, while the transition rate to coniferous forests has decreased; Transitions to Mediterranean and sub-Mediterranean broadleaf forests are positively associated with drought occurrence while transitions to conifers are negatively affected by drought; Transitions from shrublands to forest stages are more vulnerable to factors controlling water availability; Important interactions between topography derived variables and drought have been found. The study provides robust evidence that drought occurrence plays an important role in the decline of conifers and the expansion of broadleaves, which could become the dominant species in many areas of the Mediterranean if climate model forecasts are met.

Urbanization is an important driver of changes in land cover in the Mediterranean Basin and it is likely to impact the supply and demand of ecosystem services (ES). The most significant land cover changes occur in the peri-urban zone, but little is known about how these changes affect the ES supply. For eight European and four North African cities, we have quantified changes in peri-urban land cover, for periods of sixteen years (1990-2006) in the Northern African, and twenty-two years (1990-2012) in the European cities, respectively. Using an expert-based method, we derived quantitative estimates of the dynamics in the supply of twenty-seven ES. The nature of land cover changes slightly differed between European and North African Mediterranean cities, but overall it increased in urban areas and decreased in agricultural land. The capacity of the peri-urban areas of Mediterranean cities to supply ES generally reduced over the last 20-30 years. For nine ES the potential supply actually increased for all four North African cities and three out of the eight European cities. Across all cities, the ES timber, wood fuel and religious and spiritual experience increased. Given the expected increase of urban population in the Mediterranean Basin and the current knowledge of ES deficits in urban areas, the overall decrease in ES supply capacity of peri-urban areas is a risk for human well-being in the Mediterranean and poses a serious challenge for the Sustainable Development Goals in the Mediterranean basin.

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSE > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSE < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.

The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program's continuity, a goal of great interest for the environmental, scientific, and technical community.

Map browsers currently in place present maps and geospatial information using common image formats such as JPEG or PNG, usually created from a service on demand. This is a clear approach for a simple visualization map browser but prevents the browser from modifying the visualization since the content of the image file represents the intensity of colors of each pixel. In a desktop GIS, a coverage dataset is an array of values quantifying a certain property in each pixel of a subdomain of the space. The standard used to describe and distribute coverages is called web coverage service (WCS). Traditionally, encoding of coverages was too complex for map browsers implemented in JavaScript, relegating the WCS to a data download, a process that creates a file that will be later used in a desktop GIS. The combination of a coverage implementation schema in JSON, binary arrays, and HTML5 canvas makes it possible that web map browsers can be directly implemented in JavaScript.

Earth observations data cubes (EODCs) are a paradigm transforming the way users interact with large spatio-temporal Earth observation (EO) data. It enhances connections between data, applications and users facilitating management, access and use of analysis ready data (ARD). The ambition is allowing users to harness big EO data at a minimum cost and effort. This significant interest is illustrated by various implementations that exist. The novelty of the approach results in different innovative solutions and the lack of commonly agreed definition of EODC. Consequently, their interoperability has been recognized as a major challenge for the global change and Earth system science domains. The objective of this paper is preventing EODC from becoming silos of information; to present how interoperability can be enabled using widely-adopted geospatial standards; and to contribute to the debate of enhanced interoperability of EODC.We demonstrate how standards can be used, profiled and enriched to pave the way to increased interoperability of EODC and can help delivering and leveraging the power of EO data building, effcient discovery, access and processing services.

The amount of data that Sentinel fleet is generating over a territory such as Catalonia makes it virtually impossible to manually download and organize as files. The Open Data Cube (ODC) offers a solution for storing big data products in an effcient way with a modest hardware and avoiding cloud expenses. The approach will still be useful up to the next decade. Yet, ODC requires a level of expertise that most people who could benefit from the information do not have. This paper presents a web map browser that gives access to the data and goes beyond a simple visualization by combining the OGC WMS standard with modern web browser capabilities to incorporate time series analytics. This paper shows how we have applied this tool to analyze the spatial distribution of the availability of Sentinel 2 data over Catalonia and revealing differences in the number of useful scenes depending on the geographical area that ranges from one or two images per month to more than one image per week. The paper also demonstrates the usefulness of the same approach in giving access to remote sensing information to a set of protected areas around Europe participating in the H2020 ECOPotential project.

Remote sensing is providing an increasing number of crucial data about Earth. Systematic revisitation time allows the analysis of long time series as well as imagery utilization in the most interesting moments. Nevertheless, the current huge amount of data makes essential the usage of automatic methods to select the best captures, as many of them are not useful because of clouds, shadows, etc. Because of that, one of the characteristics of the more recent missions is the distribution, along with the spectral data, of a large amount of quality ancillary datasets. These datasets can act synergistically in the aim of selecting the best quality images, but the criteria they provide are not always enough. Indeed, these datasets are often used on a per pixel basis and the spatial pattern of the different spectral bands is forgotten, so ignoring the key information they can provide for our goals. With this aim, our work takes one of the most successful instruments in remote sensing, MODIS, and demonstrates, through geostatistical techniques, that the role of the spatial patterns of the spectral bands can effectively improve image selection in a complex (for climate, relief, and vegetation and crop phenology) region of 63,700  km2. The results show that band 01 (red) is the preferred one, as it achieves a 13% higher success than when only using quality bands criteria: a 94% global accuracy (66 true classifications, and only four omissions and one commission error). A second, important finding, is that the geostatistical selection improves results when using any band, except for band 02 (NIR1), which makes our proposal potentially useful for most remote sensing missions. Finally, the method can be executed in a reasonable computing time due to previously developed high-performance computing techniques.

Open-pit mine is still an unavoidable activity but can become unsustainable without the restoration of degraded sites. Monitoring the restoration after extractive activities is a legal requirement for mine companies and public administrations in many countries, involving financial provisions for environmental liabilities. The objective of this contribution is to present a rigorous, low-cost and easy-to-use application of Unmanned Aerial Systems (UAS) for supporting opencast mining and restoration monitoring, complementing the inspections with very high (<10 cm) spatial resolution multispectral imagery, and improving any restoration documentation with detailed land cover maps. The potential of UAS as a tool to control restoration works is presented in a calcareous quarry that has undergone different post-mining restoration actions in the last 20 years, representing 4 reclaimed stages. We used a small (<2 kg) drone equipped with a multispectral sensor, along with field spectroradiometer measurements that were used to radiometrically correct the UAS sensor data. Imagery was processed with photogrammetric and Remote Sensing and Geographical Information Systems software, resulting in spectral information, vegetation and soil indices, structural information and land cover maps. Spectral data and land cover classification, which were validated through ground-truth plots, aided in the detection and quantification of mine waste dumping, bare soil and other land cover extension. Moreover, plant formations and vegetation development were evaluated, allowing a quantitative, but at the same time visual and intuitive comparison with the surrounding reference systems. The protocol resulting from this research constitutes a pipeline solution intended for the implementation by public administrations and privates companies for precisely evaluating restoration dynamics in an expedient manner at a very affordable budget. Furthermore, the proposed solution prevents subjective interpretations by providing objective data, which integrate new technologies at the service of scientists, environmental managers and decision makers.

The mechanisms translating global circulation changes into rapid abrupt shifts in forest carbon capture in semi-arid biomes remain poorly understood. Here, we report unprecedented multidecadal shifts in forest carbon uptake in semi-arid Mediterranean pine forests in Spain over 1950-2012. The averaged carbon sink reduction varies between 31% and 37%, and reaches values in the range of 50% in the most affected forest stands. Regime shifts in forest carbon uptake are associated with climatic early warning signals, decreased forest regional synchrony and reduced long-term carbon sink resilience. We identify the mechanisms linked to ocean multidecadal variability that shape regime shifts in carbon capture. First, we show that low-frequency variations of the surface temperature of the Atlantic Ocean induce shifts in the non-stationary effects of El Niño Southern Oscillation (ENSO) on regional forest carbon capture. Modelling evidence supports that the non-stationary effects of ENSO can be propagated from tropical areas to semi-arid Mediterranean biomes through atmospheric wave trains. Second, decadal changes in the Atlantic Multidecadal Oscillation (AMO) significantly alter sea-air heat exchanges, modifying in turn ocean vapour transport over land and land surface temperatures, and promoting sustained drought conditions in spring and summer that reduce forest carbon uptake. Third, we show that lagged effects of AMO on the winter North Atlantic Oscillation also contribute to the maintenance of long-term droughts. Finally, we show that the reported strong, negative effects of ocean surface temperature (AMO) on forest carbon uptake in the last decades are unprecedented over the last 150 years. Our results provide new, unreported explanations for carbon uptake shifts in these drought-prone forests and review the expected impacts of global warming on the profiled mechanisms.