Estimating Fuel Loads and Structural Characteristics of Shrub Communities by Using Terrestrial Laser Scanning

Forest fuel loads and structural characteristics strongly affect fire behavior, regulating the rate of spread, fireline intensity, and flame length. Accurate fuel characterization, including disaggregation of the fuel load by size classes, is therefore essential to obtain reliable predictions from fire behavior simulators and to support decision-making in fuel management and fire hazard prediction. A total of 55 sample plots of four of the main non-tree covered shrub communities in NW Spain were non-destructively sampled to estimate litter depth and shrub cover and height for species. Fuel loads were estimated from species-specific equations. Moreover, a single terrestrial laser scanning (TLS) scan was collected in each sample plot and features related to the vertical and horizontal distribution of the cloud points were calculated. Two alternative approaches for estimating size-disaggregated fuel loads and live/dead fractions from TLS data were compared: (i) a two-steps indirect estimation approach (IE) based on fitting three equations to estimate shrub height and cover and litter depth from TLS data and then use those estimates as inputs of the existing species-specific fuel load equations by size fractions based on these three variables; and (ii) a direct estimation approach (DE), consisting of fitting seven equations, one for each fuel fraction, to relate the fuel load estimates to TLS data. Overall, the direct approach produced more balanced goodness-of-fit statistics for the seven fractions considered jointly, suggesting that it performed better than the indirect approach, with equations explaining more than 80% of the observed variability for all species and fractions, except the litter loadsThis research was funded by the projects GEPRIF (RTA2014-00011-c06-04) and VIS4FIRE (RTA 2017-0042-C05-05) of the Spanish Ministry of Economy, Industry, and CompetitivenessS

Modelling aboveground biomass and fuel load components at stand level in shrub communities in NW Spain

Shrub-dominated ecosystems cover large areas globally and play essential roles in ecological processes. Aboveground biomass expressed on an area basis (AGB) is central to many of the ecological processes and services provided by shrublands and is important as the main fuel source for wildfires. Hence, its accurate estimation in shrublands is crucial for ecologists and land managers. This is especially relevant in fire-prone regions such as NW Spain, where shrublands are an important part of the landscape, providing multiple services, but are severely impacted by wildfires. Although biomass models are available for numerous shrub species at the individual plant level, operational models based directly on easily measured shrub stand attributes are scarce. In this study, equations for estimating AGB and loads of different fuel components by size and condition (live and dead) from stand biometric variables were developed for the nine most prevalent shrub communities in NW Spain. Non-linear iterative seemingly unrelated regression was used to fit compatible systems of equations for estimating fuel loads, with shrub stand height and cover and litter depth as predictors for individual shrub communities and all data combined. In general, the goodness-of-fit statistics indicated that the estimates were reasonably accurate for all communities (grouped and ungrouped). The best results were obtained for AGB and total fuel load, including litter, whereas the poorest results were obtained for standing live and dead fine fuel load. Model performance was reduced when height was the only independent variable, although the reduction was small for most fuel categories, except litter load for which the variability was adequately explained by the litter depth. These results illustrate the feasibility of the stand level approach for constructing operational models of shrub fuel load that are accurate for most of fuel components, while also highlighting the ongoing challenges in live and dead fine fuel modelling. The equations developed represent an appreciable advance in shrubland biomass assessment in the region and areas with similar characteristics and may be instrumental in generating fuel maps, fire management improvement and better C storage assessment by vegetation, among other many usesS

Modelling fuel loads of understorey vegetation and forest floor components in pine stands in NW Spain

[EN] In this study, 310 destructively sampled plots were used to develop two equation systems for the three main pine species in NW Spain (P. pinaster; P. radiata and P. sylvestris): one for estimating loads of understorey fuel components by size and condition (live and dead) and another one for forest floor fuels. Additive systems of equations were simultaneously fitted for estimating fuel loads using overstorey, understorey and forest floor variables as regressors. The systems of equations included both the effect of pine species and the effect of understorey compositions dominated by ferns-brambles or by woody species, due to their obvious structural and physiological differences. In general, the goodness-of-fit statistics indicated that the estimates were reasonably robust and accurate for all of the fuel fractions. The best results were obtained for total understorey vegetation, total forest floor and raw humus fuel loads, with more than 76% of the observed variability explained, whereas the poorest results were obtained for coarse fuel loads of understory vegetation with a 53% of observed variability explained. To reduce the overall costs associated with the field inventories necessary for operational use of the models, the additive systems were fitted again using only overstorey variables as potential regressors. Only relationships for fine (<6 ​mm) and total understorey vegetation and total forest floor fuel loads were obtained, indicating the complexity of the forest overstorey-understorey and overstorey-forest floor relationships. Nevertheless, these models explained around 52% of the observed variability. Finally, equations estimating the total understorey vegetation and the total forest floor fuel loads based only on canopy cover were fitted. These models explained only 26%–32% of the observed variability; however, their main advantage is that although understorey vegetation in forested landscapes is largely invisible to remote sensing, canopy cover can be estimated with moderate accuracy, allowing for landscape-scale estimates of total fuel loads. The equations represent an appreciable advance in understorey and forest floor fuel load assessment in the region and areas with similar characteristics and may be instrumental in generating fuel maps, fire management improvement and better C storage assessment by vegetation type, among many other usesSIThis work was funded by following projects: INIA p5608, INIA p7613, INIA p8038, INIA 9130 and INIA SC96-034 of the Sectorial Research Program of the INIA (Spanish National Institute of Agrarian Research, Ministry of Agriculture), INIA-RTA 2009-00153-C03 (INFOCOPAS), INIA-RTA 2014-00011-C06 (GEPRIF) and INIA-RTA2017-00042-C05 (VIS4FIRE) of the Spanish National Program of Research, Development and Innovation co-funded by the ERDF Program of the European Union; also by project CTYO-0087 of the Science and Technology for Environmental Protection Program and projects ENV5V-CT94-0473, ENV4-CT98-0701 (SALTUS), ENV-CT97-0715 (FIRE TORCH), EVG1-CT2001-00041 (FIRESTAR), EVR1-CT-2002-4002 (EUFIRELAB) and CTFP6-018505 (FIRE PARADOX), funded by the Environment Program of the Directorate-General for Research and Innovation, of the European Commission of the European Union. Finally, also by project PGIDITOSRF-050202PR of the Xunta de Galici

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scalesinfo:eu-repo/semantics/publishedVersio

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

[EN] In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand-and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing crossvalidation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scalesSIThis research was funded by the projects GEPRIF (RTA2014-00011-C06-04) and VIS4FIRE (RTA2017-00042-C05-05) of the Spanish Ministry of Economy, Industry, and Competitiveness and a pre-doctoral grant of the first author funded by the “Consejería de Educación, Universidad y Formación Profesional” and the “Consejería de Economía, Empleo e Industria” of the Galician Government and the EU operational program “FSE Galicia 2014–2020”

Recurrent dissemination of SARS-CoV-2 through the Uruguayan–Brazilian border

Uruguay is one of the few countries in the Americas that successfully contained the coronavirus disease 19 (COVID-19) epidemic during the first half of 2020. Nevertheless, the intensive human mobility across the dry border with Brazil is a major challenge for public health authorities. We aimed to investigate the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains detected in Uruguayan localities bordering Brazil as well as to measure the viral flux across this ∼1,100 km uninterrupted dry frontier. Using complete SARS-CoV-2 genomes from the Uruguayan–Brazilian bordering region and phylogeographic analyses, we inferred the virus dissemination frequency between Brazil and Uruguay and characterized local outbreak dynamics during the first months (May–July) of the pandemic. Phylogenetic analyses revealed multiple introductions of SARS-CoV-2 Brazilian lineages B.1.1.28 and B.1.1.33 into Uruguayan localities at the bordering region. The most probable sources of viral strains introduced to Uruguay were the Southeast Brazilian region and the state of Rio Grande do Sul. Some of the viral strains introduced in Uruguayan border localities between early May and mid-July were able to locally spread and originated the first outbreaks detected outside the metropolitan region. The viral lineages responsible for Uruguayan urban outbreaks were defined by a set of between four and 11 mutations (synonymous and non-synonymous) with respect to the ancestral B.1.1.28 and B.1.1.33 viruses that arose in Brazil, supporting the notion of a rapid genetic differentiation between SARS-CoV-2 subpopulations spreading in South America. Although Uruguayan borders have remained essentially closed to non-Uruguayan citizens, the inevitable flow of people across the dry border with Brazil allowed the repeated entry of the virus into Uruguay and the subsequent emergence of local outbreaks in Uruguayan border localities. Implementation of coordinated bi-national surveillance systems is crucial to achieve an efficient control of the SARS-CoV-2 spread across this kind of highly permeable borderland regions around the world

Real-Time genomic surveillance for SARS-CoV-2 variants of concern, Uruguay

We developed a genomic surveillance program for realtime monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) in Uruguay. We report on a PCR method for SARSCoV- 2 VOCs, the surveillance workflow, and multiple independent introductions and community transmission of the SARS-CoV-2 P.1 VOC in Uruguay

Emergence and spread of a B.1.1.28-derived P.6 lineage with Q675H and Q677H spike mutations in Uruguay

Uruguay controlled the viral dissemination during the first nine months of the SARS-CoV-2 pandemic. Unfortunately, towards the end of 2020, the number of daily new cases exponentially increased. Herein, we analyzed the country-wide genetic diversity of SARS-CoV-2 between November 2020 and April 2021. We identified that the most prevalent viral variant during the first epidemic wave in Uruguay (December 2020–February 2021) was a B.1.1.28 sublineage carrying Spike mutations Q675H + Q677H, now designated as P.6, followed by lineages P.2 and P.7. P.6 probably arose around November 2020, in Montevideo, Uruguay’s capital department, and rapidly spread to other departments, with evidence of further local transmission clusters; it also spread sporadically to the USA and Spain. The more efficient dissemination of lineage P.6 with respect to P.2 and P.7 and the presence of mutations (Q675H and Q677H) in the proximity of the key cleavage site at the S1/S2 boundary suggest that P.6 may be more transmissible than other lineages co-circulating in Uruguay. Although P.6 was replaced by the variant of concern (VOC) P.1 as the predominant lineage in Uruguay since April 2021, the monitoring of the concurrent emergence of Q675H + Q677H in VOCs should be of worldwide interest

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality

Estimación de biomasa y características estructurales del combustible en comunidades de matorral desarbolado y pinares en Galicia a partir de escáner láser terrestre, láser aéreo e inventario de campo

En este documento de tesis se presentan los resultados de un estudio sobre la utilización de diferentes metodologías para la estimación de cargas y estructura vertical de los combustibles forestales incluyendo matorral desarbolado y sotobosque, combustibles de escalera y del dosel de copas en pinares. Los modelos obtenidos se basan en datos LiDAR aéreo (ALS) y terrestre (TLS) e inventarios de campo tradicionales