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Copyright of Giornale Italiano di Medicina del Lavoro ed Ergonomia is the property of Giornale Italiano di Medicina del Lavoro ed Ergonomia Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates whether a Water Deficit Index (WDI) based on imagery from Unmanned Aerial Vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and the late growing season are included to investigate whether the WDI index has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI index differs from the more commonly applied Crop Water Stress Index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature). Lightweight thermal and RGB (Red-Green-Blue) cameras were mounted on a UAV on three occasions during the growing season, 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the Normalized Green-Red Difference Index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI index is assessed. Final WDI maps had a spatial resolution of 0.25 m. It was found that the UAV-based WDI index determines accurate crop water status. Further, the WDI index is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI index accounts for areas of ripe crops that no longer have the same need of irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing seasons. [ABSTRACT FROM AUTHOR]

Nickel (Ni) and Ni compounds are widely present in the urban air. The purpose of this study is to estimate exposure of individuals to Ni and the correlation between this exposure and the values of blood counts in outdoor workers. This study focused on a sample of 101 outdoor workers (55 male and 46 female; 65 nonsmokers and 36 smokers), all employed in the municipal police in a large Italian city. The personal levels of exposure to Ni were assessed through (a) environmental monitoring of Ni present in the urban air obtained from individual samples and (b) biological monitoring of urinary and blood Ni. The blood count parameters were obtained from the hemochromocytometric tests. Pearson correlation coefficients (r) were calculated to assess the association between the blood and urinary Ni and the complete blood count. Multiple linear regression models were used to examine the associations between the complete blood count and the independent variables (age, gender, years of work for current tasks, cigarette smoking habit (current and never smoker), values of airborne Ni, and blood and urinary Ni). Multiple linear regression analysis performed on the total group of 101 subjects confirms the association among the red blood cells count, the hematocrit, and the urinary Ni (R2 = 0.520, p = 0.025 and R2 = 0.530, p = 0.030). These results should lead to further studies on the effects of Ni in working populations exposed to urban pollutants. The possibility that the associations found in our study may be partially explained by other urban pollutants (such as benzene, toluene, and other heavy metals) not taken into consideration in this study cannot be ruled out. [ABSTRACT FROM AUTHOR]

Copyright of Giornale Italiano di Medicina del Lavoro ed Ergonomia is the property of Giornale Italiano di Medicina del Lavoro ed Ergonomia Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article focuses on work-related stress such as alienation and burn-out.

Forests are increasingly subject to a number of disturbances that can adversely influence their health. Remote sensing offers an efficient alternative for assessing and monitoring forest health. A myriad of methods based upon remotely sensed data have been developed, tailored to the different definitions of forest health considered, and covering a broad range of spatial and temporal scales. The purpose of this review paper is to identify and analyse studies that addressed forest health issues applying remote sensing techniques, in addition to studying the methodological wealth present in these papers. For this matter, we applied the PRISMA protocol to seek and select studies of our interest and subsequently analyse the information contained within them. A final set of 107 journal papers published between 2015 and 2020 was selected for evaluation according to our filter criteria and 20 selected variables. Subsequently, we pair-wise exhaustively read the journal articles and extracted and analysed the information on the variables. We found that (1) the number of papers addressing this issue have consistently increased, (2) that most of the studies placed their study area in North America and Europe and (3) that satellite-borne multispectral sensors are the most commonly used technology, especially from Landsat mission. Finally, most of the studies focused on evaluating the impact of a specific stress or disturbance factor, whereas only a small number of studies approached forest health from an early warning perspective. [ABSTRACT FROM AUTHOR]

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data. [ABSTRACT FROM AUTHOR]