Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems

Abstract: The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities

Experimental investigation on CO2methanation process for solar energy storage compared to CO2-based methanol synthesis

The utilization of the captured CO2 as a carbon source for the production of energy storage media offers a technological solution for overcoming crucial issues in current energy systems. Solar energy production generally does not match with energy demand because of its intermittent and non-programmable nature, entailing the adoption of storage technologies. Hydrogen constitutes a chemical storage for renewable electricity if it is produced by water electrolysis and is also the key reactant for CO2 methanation (Sabatier reaction). The utilization of CO2 as a feedstock for producing methane contributes to alleviate global climate changes and sequestration related problems. The produced methane is a carbon neutral gas that fits into existing infrastructure and allows issues related to the aforementioned intermittency and non-programmability of solar energy to be overcome. In this paper, an experimental apparatus, composed of an electrolyzer and a tubular fixed bed reactor, is built and used to produce methane via Sabatier reaction. The objective of the experimental campaign is the evaluation of the process performance and a comparison with other CO2 valorization paths such as methanol production. The investigated pressure range was 2–20 bar, obtaining a methane volume fraction in outlet gaseous mixture of 64.75% at 8 bar and 97.24% at 20 bar, with conversion efficiencies of, respectively, 84.64% and 99.06%. The methanol and methane processes were compared on the basis of an energy parameter defined as the spent energy/stored energy. It is higher for the methanol process (0.45), with respect to the methane production process (0.41–0.43), which has a higher energy storage capability

Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process

Transmission dynamics of the ongoing chikungunya outbreak in Central Italy. From coastal areas to the metropolitan city of Rome, summer 2017

A large chikungunya outbreak is ongoing in Italy, with a main cluster in the Anzio coastal municipality. With preliminary epidemiological data, and a transmission model using mosquito abundance and biting rates, we estimated the basic reproduction number R0 at 2.07 (95% credible interval: 1.47–2.59) and the first case importation between 21 May and 18 June 2017. Outbreak risk was higher in coastal/rural sites than urban ones. Novel transmission foci could occur up to mid-November

An Innovative Configuration for CO2 Capture by High Temperature Fuel Cells

Many technological solutions have been proposed for CO 2 capture in the last few years. Most of them are characterized by high costs in terms of energy consumption and, consequently, higher fossil fuel use and higher economic costs. High temperature fuel cells are technological solutions currently developed for energy production with low environmental impact. In CIRIAF—University of Perugia labs, cylindrical geometry, small-sized molten carbonate fuel cell (MCFC) prototypes were built and tested with good energy production and lifetime performances. In the present work, an innovative application for MCFCs is proposed, and an innovative configuration for CO 2 capture/separation is investigated. The plant scheme is based on a reformer and a cylindrical MCFC. MCFCs are the most suitable solutions, because CO 2 is used in their operating cycle. An analysis in terms of energy consumption/kgCO 2 captured is made by coupling the proposed configuration with a gas turbine plant. The proposed configuration is characterized by a theoretical energy consumption of about 500 kJ/kgCO 2 , which is quite lower than actual sequestration technologies. An experimental campaign will be scheduled to verify the theoretical findings

A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellite spectrometers can be a highly valuable proxy for photosynthesis. The SIF signal is very small and notoriously difficult to measure, requiring sub-nanometre spectral-resolution measurements, which to date are only available from atmospheric spectrometers sampling at low spatial resolution. For example, the widely used SIF dataset derived from the GOME-2 mission is typically provided in 0.5∘ composites. This paper presents a new SIF dataset based on GOME-2 satellite observations with an enhanced spatial resolution of 0.05∘ and an 8 d time step covering the period 2007–2018. It leverages on a proven methodology that relies on using a light-use efficiency (LUE) modelling approach to establish a semi-empirical relationship between SIF and various explanatory variables derived from remote sensing at higher spatial resolution. An optimal set of explanatory variables is selected based on an independent validation with OCO-2 SIF observations, which are only sparsely available but have a high accuracy and spatial resolution. After bias correction, the resulting downscaled SIF data show high spatio-temporal agreement with the first SIF retrievals from the new TROPOMI mission, opening the path towards establishing a surrogate archive for this promising new dataset. We foresee this new SIF dataset becoming a valuable asset for Earth system science in general and for monitoring vegetation productivity in particular. The dataset is available at https://doi.org/10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1 (Duveiller et al., 2019)

Resilient Drone Mission Management and Route Optimization in Drone Delivery Context

The last two decades were characterized by a rapidly increasing of innovative solutions in the microelectronic field, having therefore a significant impact on a huge set of applicative scenarios. This aspect allows the development and improvement of new solutions, giving the possibility of growth and development of new markets, such as the drones ones. Actually, in the unmanned field we have seen an exponential growth of the market, given not only from the increased computing capabilities, but also by a more efficient developed hardware, thus leading to the definition of innovative uses, service paradigms and applications. The latter span in several different areas, from agriculture monitoring to society's services including the Package Delivery which immediately plays a strategic role in the modern society. These types of applications took place mainly in an urban environment, highlighting therefore new rules, needs and management system in order to accommodate the mission's achievement guaranteeing at the same time a high degree of resilience, citizen safety and risks minimization. Furthermore, to assist these types of operations, T-DROMES, a RPAS (Remotely Piloted Aerial Systems) fleet and mission management solution, was developed allowing to scale-up the use of drones in complex operations from a geographical and mission point of view, in different applicative scenarios. The paper aims therefore to presents the tools capabilities and how the developed architecture is able to manage the entire mission for any context scenario and how the developed platforms and tools can be a valid framework for developing new operative working models

Non-Parametric Statistical Approaches for Leaf Area Index Estimation from Sentinel-2 Data: A Multi-Crop Assessment

The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes related to vegetation dynamics and crop growth. Despite the large number of retrieved LAI products and the efforts to develop new and updated algorithms for LAI estimation, the available products are not yet capable of capturing site-specific variability, as requested in many agricultural applications. The objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach. For this purpose, we built a large database combining a multispectral satellite data set and ground LAI measurements collected over two growing seasons (2018 and 2019), including three crops (i.e., winter wheat, maize, and alfalfa) characterized by different growing cycles and canopy structures, and considering different agronomic conditions (i.e., at three farms in three different sites). The accuracy of parametric and non-parametric methods for LAI estimation was assessed by cross-validation (CV) at both the pixel and field levels over mixed-crop (MC) and crop-specific (CS) data sets. Overall, the non-parametric approach showed a higher accuracy of prediction at pixel level than parametric methods, and it was also observed that Gaussian Process Regression (GPR) did not provide any significant difference (p-value > 0.05) between the predicted values of LAI in the MC and CS data sets, regardless of the crop. Indeed, GPR at the field level showed a cross-validated coefficient of determination (R2CV) higher than 0.80 for all three crops

Global vulnerability of soil ecosystems to erosion

International audienceContextSoil erosion is one of the main threats driving soil degradation across the globe with important impacts on crop yields, soil biota, biogeochemical cycles, and ultimately human nutrition.ObjectivesHere, using an empirical model, we present a global and temporally explicit assessment of soil erosion risk according to recent (2001–2013) dynamics of rainfall and vegetation cover change to identify vulnerable areas for soils and soil biodiversity.MethodsWe used an adaptation of the Universal Soil Loss Equation together with state of the art remote sensing models to create a spatially and temporally explicit global model of soil erosion and soil protection. Finally, we overlaid global maps of soil biodiversity to assess the potential vulnerability of these soil communities to soil erosion.ResultsWe show a consistent decline in soil erosion protection over time across terrestrial biomes, which resulted in a global increase of 11.7% in soil erosion rates. Notably, soil erosion risk systematically increased between 2006 and 2013 in relation to the baseline year (2001). Although vegetation cover is central to soil protection, this increase was mostly driven by changes in rainfall erosivity. Globally, soil erosion is expected not only to have an impact on the vulnerability of soil conditions but also on soil biodiversity with 6.4% (for soil macrofauna) and 7.6% (for soil fungi) of these vulnerable areas coinciding with regions with high soil biodiversity.ConclusionsOur results indicate that an increasing proportion of soils are degraded globally, affecting not only livelihoods but also potentially degrading local and regional landscapes. Similarly, many degraded regions coincide with and may have impacted high levels of soil biodiversity