Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield.

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R2 value of 0.5 against ground LAI with RMSE of 0.8 m2/m2. Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R2 value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.

A Web-Based Application to Monitor and Inform about the COVID-19 Outbreak in Italy: The {COVID-19ita} Initiative.

The pandemic outbreak of COVID-19 has posed several questions about public health emergency risk communication. Due to the effort required for the population to adopt appropriate behaviors in response to the emergency, it is essential to inform the public of the epidemic situation with transparent data sources. The COVID-19ita project aimed to develop a public open-source tool to provide timely, updated information on the pandemic's evolution in Italy. It is a web-based application, the front end for the eponymously named R package freely available on GitHub, deployed both in English and Italian. The web application pulls the data from the official repository of the Italian COVID-19 outbreak at the national, regional, and provincial levels. The app allows the user to select information to visualize data in an interactive environment and compare epidemic situations over time and across different Italian regions. At the same time, it provides insights about the outbreak that are explained and commented upon to yield reasoned, focused, timely, and updated information about the outbreak evolution.

Innovations in Photogrammetry and Remote Sensing: Modern Sensors, New Processing Strategies and Frontiers in Applications.

The recent development and rapid evolution of modern sensors and new processing strategies of collected data have paved the way for innovations in photogrammetry and remote sensing [...].

Drivers of farmers' adoption and continuation of climate-smart agricultural practices. A study from northeastern Italy.

The EU rural development policy has addressed challenges related to climate change in agriculture by introducing public voluntary schemes, which financially support the adoption of climate-smart agricultural practices. Several factors, most of which are non-financial ones, drive adoption and continuation of these schemes by farmers. Despite the importance of these factors, only a few studies explore their role in the European context. This paper contributes to filling this gap from a twofold perspective. First, it investigates the role of the farming factors, technology accessibility, environmental features, policy design and social expertise at the territorial level on early adoption. Second, it sheds light on farmers' attitudes and motivations and on social pressure on their decision to continue or discontinue the practices, by surveying a sample of early adopters. Three schemes for the Veneto region rural development programme are considered: no-tillage, fertiliser reduction, and water and fertiliser reduction. The results highlight that non-financial factors should be considered in order to design more effective schemes to prompt farmers to adopt and continue such practices over the long run. The paper also stresses the need to complement financial support with proactive information-based instruments.

A standardization method to disentangle environmental information from axial trends of xylem anatomical traits.

Anatomical traits such as xylem conduit diameter and vessel connectivity are fundamental characteristics of the hydraulic architecture of vascular plants. Stem xylem conduits are narrow at the stem apex, and this confers resistance to embolisms that might otherwise be induced by large, negative water potentials at the top of tall trees. Below the apex, conduits progressively widen and this characteristic minimizes effects of path length on total hydraulic resistance. While interconnections among xylem vessels have been noted for decades, their role(s) are not fully clarified. For example, we do not know if they allow water to bypass embolized vessels, or increase the risk of spread of embolisms, or how their arrangement varies within a tree. Here we demonstrate the benefit of removing the independent effect of stem length on assessment of effects of external (e.g., climatic) factors on such xylem traits. We measured the hydraulic diameter (Dh) and vessel conductivity index (VCI) along the stem of 21 shrubs/trees of similar height (1.19 < H < 5.45 m) belonging to seven Acacia species, across a wide aridity gradient in Australia. All trees showed similar scaling exponents of Dh (b = 0.33) and VCI (b = 0.53) vs axial distance from the apex (L), thus conforming with general patterns in woody plants. After de-trending for L, neither Dh (P = 0.21) nor VCI (P = 0.109) differed across the aridity gradient. We found that across a wide gradient of aridity, climate had no effect on xylem anatomy of Acacia spp, which was instead dictated by axial distances from stem apices. We argue that the use of standardization procedures to filter out intrinsic patterns of vascular traits is an essential step in assessing climate-driven modifications of xylem architecture.

Using lidar data to analyse sinkhole characteristics relevant for understory vegetation under forest cover-case study of a high karst area in the dinaric mountains.

In this article, we investigate the potential for detection and characterization of sinkholes under dense forest cover by using airborne laser scanning data. Laser pulse returns from the ground provide important data for the estimation of digital elevation model (DEM), which can be used for further processing. The main objectives of this study were to map and determine the geomorphometric characteristics of a large number of sinkholes and to investigate the correlations between geomorphology and vegetation in areas with such characteristics. The selected study area has very low anthropogenic influences and is particularly suitable for studying undisturbed karst sinkholes. The information extracted from this study regarding the shapes and depths of sinkholes show significant directionality for both orientation of sinkholes and their distribution over the area. Furthermore, significant differences in vegetation diversity and composition occur inside and outside the sinkholes, which indicates their presence has important ecological impacts.

Low-Cost MEMS sensors and vision system for motion and position estimation of a scooter.

The possibility to identify with significant accuracy the position of a vehicle in a mapping reference frame for driving directions and best-route analysis is a topic which is attracting a lot of interest from the research and development sector. To reach the objective of accurate vehicle positioning and integrate response events, it is necessary to estimate position, orientation and velocity of the system with high measurement rates. In this work we test a system which uses low-cost sensors, based on Micro Electro-Mechanical Systems (MEMS) technology, coupled with information derived from a video camera placed on a two-wheel motor vehicle (scooter). In comparison to a four-wheel vehicle; the dynamics of a two-wheel vehicle feature a higher level of complexity given that more degrees of freedom must be taken into account. For example a motorcycle can twist sideways; thus generating a roll angle. A slight pitch angle has to be considered as well; since wheel suspensions have a higher degree of motion compared to four-wheel motor vehicles. In this paper we present a method for the accurate reconstruction of the trajectory of a "Vespa" scooter; which can be used as alternative to the "classical" approach based on GPS/INS sensor integration. Position and orientation of the scooter are obtained by integrating MEMS-based orientation sensor data with digital images through a cascade of a Kalman filter and a Bayesian particle filter.