Carbon Dioxide Emissions of the City Center of Firenze, Italy: Measurement, Evaluation, and Source Partitioning

AbstractAn eddy covariance station was installed in the city center of Firenze, Italy, to measure carbon fluxes at half-hourly intervals over a mostly homogeneous urban area. Carbon dioxide (CO2) emission observations made over an initial period of 3.5 months were compared with indirect estimates of CO2 emissions based on inventory data sources of vehicle circulation and natural gas consumption for domestic heating and cooking. Such a comparison provided proper evaluation of the measurements. Using seasonal dynamics of observed fluxes, the overall CO2 source of the city center was partitioned into its major components (i.e., road traffic and domestic heating). Results were directly compared with CO2 source estimates based on inventory sources

This article is published in cooperation with the XIIIth International Terroir Congress November 17-18 2020, Adelaide, Australia. Guests editors: Cassandra Collins and Roberta De Bei

Currently, the greatest challenge for vine growers is to improve the yield and quality of grapes by minimizing costs and environmental impacts. This goal can be achieved through a better knowledge of vineyard spatial variability. Traditional platforms such as airborne, satellite and unmanned aerial vehicles (UAVs) solutions are useful investigation tools for vineyard site specific management. These remote sensing techniques are mainly exploited to get the Normalized Difference Vegetation Index (NDVI), which is useful for describing the morpho-vegetational characteristics of vineyards. This study was conducted in a vineyard in Tuscany (Italy) during the 2017, 2018 and 2019 seasons. Ground data were acquired to detect some agronomic variables such as yield (kg/vine), total soluble solids (TSS), and pruning weight (kg/vine). Remote sensed multispectral images acquired by UAV and Sentinel-2 (S2) satellite platform were used to assess the analysis of the vegetative variability. The UAV NDVI was extracted using both a mixed pixels approach (both vine and inter-row) and from pure canopy pixels. In addition to these UAV layers, the vine thickness was extracted. The aim of this study was to evaluate both classical Ordinary Least Square (OLS) and spatial statistical methods (Moran Index-MI and BILISA) to assess their performance in a multi-temporal comparison between satellite and ground data with UAV information. Good correlations were detected between S2 NDVI and UAV NDVI mixed pixels through both methods (R2 = 0.80 and MI = 0.75). Regarding ground data, UAV layers showed low and negative association with TSS (MI = - 0.34 was the lowest value) whereas better spatial autocorrelations with positive values were detected between UAV layers and both yield (MI ranged from 0.42 to 0.52) and pruning weight (MI ranged from 0.45 to 0.64). The spatial analysis made by MI and BILISA methodologies added more information to this study, clearly showing that both UAV and Sentinel-2 satellite allowed the vigour spatial variability within the vineyard to be detected correctly, overcoming the classical comparison methods by adding the spatial effect. MI and BILISA play a key role in identifying spatial patterns and could be successfully exploited by agricultural stakeholders

A Low-Cost and Unsupervised Image Recognition Methodology for Yield Estimation in a Vineyard

Yield prediction is a key factor to optimize vineyard management and achieve the desired grape quality. Classical yield estimation methods, which consist of manual sampling within the field on a limited number of plants before harvest, are time-consuming and frequently insufficient to obtain representative yield data. Non-invasive machine vision methods are therefore being investigated to assess and implement a rapid grape yield estimate tool. This study aimed at an automated estimation of yield in terms of cluster number and size from high resolution RGB images (20 MP) taken with a low-cost UAV platform in representative zones of the vigor variability within an experimental vineyard. The flight campaigns were conducted in different light conditions and canopy cover levels for 2017 and 2018 crop seasons. An unsupervised recognition algorithm was applied to derive cluster number and size, which was used for estimating yield per vine. The results related to the number of clusters detected in different conditions, and the weight estimation for each vigor zone are presented. The segmentation results in cluster detection showed a performance of over 85% in partially leaf removal and full ripe condition, and allowed grapevine yield to be estimated with more than 84% of accuracy several weeks before harvest. The application of innovative technologies in field-phenotyping such as UAV, high-resolution cameras and visual computing algorithms enabled a new methodology to assess yield, which can save time and provide an accurate estimate compared to the manual method

AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture

Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as the freely and open access policy adopted by the European Space Agency (ESA), software and tools are also available for data processing and deeper analysis. Nowadays, a bottleneck in this valuable chain is represented by the difficulty in shadow identification of Sentinel-2 data that, for precision agriculture applications, results in a tedious problem. To overcome the issue, we present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d'Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA

An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories

Abstract An integrated monitoring platform was developed for real-time monitoring of air pollution and traffic flows in urban areas. The air quality monitoring unit, integrating the "Arduino" open-source technology with low-cost and high-resolution sensors, collects concentrations of CO, NO 2 and CO 2 . The traffic monitoring device, equipped with a camera sensor and a video analysis software, collects vehicles' counts, speed and size. Air pollution and traffic readings are archived on a spatial data infrastructure composed of a central GeoDatabase, a GIS engine, and a web interface. A platform's description and the results of its installation in Florence (Italy) are presented

Use of remote sensing‑derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub‑field level

Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2 = 0.61; RMSE = 19.86 dry matter g m−2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2 = 0.47; RMSE = 75.52 dry matter g m−2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level

Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach

Abstract Surface soil water content plays an important role in driving the exchange of latent and sensible heat between the atmosphere and land surface through transpiration and evaporation processes, regulating key physiological processes affecting plants growth. Given the high impact of water scarcity on yields, and of irrigated agriculture on the overall withdrawal rate of freshwater, it is important to define models that help to improve water resources management for agricultural purposes, and to optimize rainfed crop yield. Recent advances in satellite-based remote sensing have led to valuable solutions to estimate soil water content based on microwave or optical/thermal-infrared data. This study aims at improving soil water content estimation at high spatial and temporal resolution, by means of the Optical Trapezoid Model (OPTRAM) driven by Copernicus Sentinel-2 data. Two different model variations were considered, based on linear and nonlinear parameters constraints, and validated against in situ soil water content measurements made with time domain reflectometry (TDR) on irrigated maize in central Italy and on rainfed maize and pasture in northern Italy. For the first site the non-linear model shows a better correlation between measured and estimated soil water content values (r = 0.80) compared to the linear model (r = 0.73). In both cases the modeled soil moisture tends to overestimate the measured values at medium to high water content level, while both models underestimate soil moisture at low water content level. Estimated versus measured normalized surface soil water for rainfed pasture plots from nonlinear OPTRAM parametrized based on irrigated maize parameterization (SIM1), and site-specific parametrization for rainfed pasture (SIM2), indicate that both models (SIM1 and SIM2) are comparable for rotational grazing pasture (RMSEsim1 = 0.0581 vs. RMSEsim2 = 0.0485 cm3 cm-3) and the continuous grazing pasture (RMSEsim1 = 0.0485 vs. RMSEsim2 = 0.0602 cm3 cm-3), while for the rainfed maize plots SIM1 shows lower RMSE (average for all plots RMSE = 0.0542 cm3 cm-3) compared to the site-specific calibration model (SIM2 – average for all plots RMSE = 0.0645 cm3 cm-3). Finally, OPTRAM estimations are close to in situ measurement values while Surface Soil Moisture at 1 km (SSM1 km) tends to underestimate the measurements during maize crop growing season. Soil moisture retrieval from high-resolution Sentinel-2 optical images allows water stress conditions to be effectively mapped, supporting decision making in irrigation scheduling and other crop management

Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex

Foliar symptoms of grapevine leaf stripe disease (GLSD, a disease within the esca complex) are linked to drastic alteration of photosynthetic function and activation of defense responses in affected grapevines several days before the appearance of the first visible symptoms on leaves. The present study suggests a methodology to investigate the relationships between high-resolution multispectral images (0.05 m/pixel) acquired using an Unmanned Aerial Vehicle (UAV), and GLSD foliar symptoms monitored by ground surveys. This approach showed high correlation between Normalized Differential Vegetation Index (NDVI) acquired by the UAV and GLSD symptoms, and discrimination between symptomatic from asymptomatic plants. High-resolution multispectral images were acquired during June and July of 2012 and 2013, in an experimental vineyard heavily affected by GLSD, located in Tuscany (Italy), where vines had been surveyed and mapped since 2003. Each vine was located with a global positioning system, and classified for appearance of foliar symptoms and disease severity at weekly intervals from the beginning of each season. Remote sensing and ground observation data were analyzed to promptly identify the early stages of disease, even before visual detection. This work suggests an innovative methodology for quantitative and qualitative analysis of spatial distribution of symptomatic plants. The system may also be used for exploring the physiological bases of GLSD, and predicting the onset of this disease.

Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. This special issue (SI) collects nine papers reporting research on different forestry applications using UAV imagery. The special issue covers seven Red-Green-Blue (RGB) sensor papers, three papers on multispectral imagery, and one further paper on hyperspectral data acquisition system. Several data processing and machine learning methods are presented. The special issue provides an overview regarding potential applications to provide forestry characteristics in a timely, cost-efficient way. With the fast development of sensors technology and image processing algorithms, the forestry potential applications will growing fast, but future work should consider the consistency and repeatability of these novel techniques