Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

This article belongs to the Special Issue New Remote Sensing Technologies in Forest Fire Analysis, Prevention and Mitigation[EN] Prescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimesSIFIRESEVES (AGL2017-86075-C2-1-R) project funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund, and SEFIRECYL (LE001P17

Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Producción CientíficaPrescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (project AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17

FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

[EN] The formulation and planning of integrated fire management strategies must be strengthened by decision support systems about fire-induced ecological impacts and ecosystem recovery processes, particularly in the context of extreme wildfire events that challenge land management initiatives. Wildfire data collection and analysis through remote sensing earth observations is of utmost importance for this purpose. However, the needs of land managers are not always met because the exploitation of the full potential of remote sensing techniques requires a high level of technical expertise. In addition, data acquisition and storage, database management, networking, and computing requirements may present technical difficulties. Here, we present FIREMAP software, which leverages the potential of Google Earth Engine (GEE) cloud-based platform, an intuitive graphical user interface (GUI), and the European Forest Fire Information System (EFFIS) wildfire database for wildfire analyses through remote sensing techniques and data collections. FIREMAP software allows automatic computing of (i) machine learning-based burned area (BA) detection algorithms to facilitate the mapping of (historical) fire perimeters, (ii) fire severity spectral indices, and (iii) post-fire recovery trajectories through the inversion of physically-based radiative transfer models. We introduce (i) the FIREMAP platform architecture and the GUI, (ii) the implementation of well-established algorithms for wildfire science and management in GEE, (iii) the validation of the algorithm implementation in fifteen case-study wildfires across the western Mediterranean Basin, and (iv) the near-future and long-term planned expansion of FIREMAP featuresSIThis study was financially supported by the Spanish Ministry of Science and Innovation in the framework of LANDSUSFIRE project (PID2022-139156OB-C21) within the National Program for the Promotion of Scientific-Technical Research (2021-2023), and with Next-Generation Funds of the European Union (EU) in the framework of the FIREMAP project (TED2021-130925B-I00); and by the Regional Government of Castile and León in the framework of the IA-FIREXTCyL project (LE081P23). Víctor Fernández-García was supported by a Margarita Salas post-doctoral fellowship from the Ministry of Universities of Spain, financed with European Union-NextGenerationEU and Ministerio de Universidades Fund

Using Unmanned Aerial Vehicles (UAV) for forest damage monitoring in south-western Europe

P. 1-8Prescribed burns are being considered as a management tool for the prevention of forest fires in many countries that have important firefighting problems. Knowledge of fire intensity and eliminated vegetation fuel are of great interest to evaluate their effectiveness. Both parameters are directly related to burn severity, so their evaluation is fundamental to predict the post-fire evolution of burned area. In this study we evaluated two prescribed burnings carried out in North of Spain during October 2017 by using multispectral data from an Unmanned Aerial Vehicle (UAV). In particular, four surface reflectance images were obtained in green (550 nm), red (660 nm), red-edge (735 nm) and near infrared (790 nm) at very high spatial resolution (GSD 20 cm) from which different spectral indexes were computed. Additionally, vegetation and soil burn severity was measured in 153 field plots and an analysis of variance (ANOVA) between each spectral index and burn severity (both in vegetation and soil) was performed. A Fisher’s least significant difference test determined that three vegetation burn severity levels and two soil burn severity levels could be statistically distinguished. The identification of such burn severity levels is sufficient and useful to forest managers. We conclude that multispectral data from UAVs may be considered as a valuable indicator of burn severity for prescribed burnings.S

Experimental demonstration of advanced service management in SDN/NFV Fronthaul Networks deploying ARoF and PoF

We demonstrate two advanced services deployed in a novel SDN/NFV optical fronthaul network combining analog radio over fiber (ARoF) and power over fiber (PoF); vertical service management for virtual content delivery networks (vCDNs), and user mobility and remote optical power management for femto cells

Recurrent presence of the PLCG1 S345F mutation in nodal peripheral T-cell lymphomas

This work was supported by grants from Asociación Española contra el Cancer (AECC), Ministerio de Economía y Competitividad (MINECO) (SAF2013-47416-R), Instituto Salud Carlos III (ISCIII) – Fondos FEDER, MINECO-AES(RD012/0036/0060, PI10/00621, CP11/00018). RM is supported by the Fundación Conchita Rábago de la Fundación Jiménez Díaz, Madrid (Spain). JG-R is supported by a predoctoral grant from the Fundacion Investigacion Biomedica Puerta de Hierro. Salary support to SG is provided by ISCIII-FEDER (CP11/00018). MS-B is supported by a Miguel Servet contract from ISCIII-FEDER (CP11/00018). The Instituto de Investigación Marqués de Valdecilla (IDIVAL) is partly funded by the Sociedad para el Desarrollo Regional de Cantabria (SODERCAN)

Peripheral T-cell lymphoma: Molecular profiling recognizes subclasses and identifies prognostic markers

Peripheral T-cell lymphoma (PTCL) is a clinically aggressive disease, with a poor response to therapy and a low overall survival rate of approximately 30% after 5 years. We have analyzed a series of 105 cases with a diagnosis of PTCL using a customized NanoString platform (NanoString Technologies, Seattle, WA) that includes 208 genes associated with T-cell differentiation, oncogenes and tumor suppressor genes, deregulated pathways, and stromal cell subpopulations. A comparative analysis of the various histological types of PTCL (angioimmunoblastic T-cell lymphoma [AITL]; PTCL with T follicular helper [TFH] phenotype; PTCL not otherwise specified [NOS]) showed that specific sets of genes were associated with each of the diagnoses. These included TFH markers, cytotoxic markers, and genes whose expression was a surrogate for specific cellular subpopulations, including follicular dendritic cells, mast cells, and genes belonging to precise survival (NF-κB) and other pathways. Furthermore, the mutational profile was analyzed using a custom panel that targeted 62 genes in 76 cases distributed in AITL, PTCL-TFH, and PTCL-NOS. The main differences among the 3 nodal PTCL classes involved the RHOAG17V mutations (P < .0001), which were approximately twice as frequent in AITL (34.09%) as in PTCL-TFH (16.66%) cases but were not detected in PTCL-NOS. A multivariate analysis identified gene sets that allowed the series of cases to be stratified into different risk groups. This study supports and validates the current division of PTCL into these 3 categories, identifies sets of markers that can be used for a more precise diagnosis, and recognizes the expression of B-cell genes as an IPI-independent prognostic factor for AITL

Enhancing the First-Pass Effect in Acute Stroke: The Impact of Stent Retriever Characteristics

Introduction: Although stentrievers (SRs) have been a mainstay of mechanical thrombectomy (MT), and current guidelines recommend the use of SRs in the treatment of large vessel occlusion stroke (LVO), there is a paucity of studies in the literature comparing SRs directly against each other in terms of mechanical and functional properties. Timely access to endovascular therapy and the ability to restore intracranial flow in a safe, efficient, and efficacious manner have been critical to the success of MT. This study aimed to investigate the impact of contemporary SR characteristics, including model, brand, size, and length, on the first-pass effect (FPE) in patients with acute ischemic stroke. Methods: Consecutive patients with M1 occlusion treated with a single SR+BGC were recruited from the ROSSETTI registry. The primary outcome was the FPE that was defined as modified (mFPE) or true (tFPE) for the achievement of modified thrombolysis in cerebral infarction (mTICI) grades 2b-3 or 3 after a single device pass, respectively. We compared patients who achieved mFPE with those who achieved tFPE according to SR characteristics. Results: We included 610 patients (52.3% female and 47.7% male, mean age 75.1 +/- 13.62 years). mFPE was achieved in 357 patients (58.5%), whereas tFPE was achieved in 264 (43.3%). There was no significant association between SR characteristics and mFPE or tFPE. Specifically, the SR size did not show a statistically significant relationship with improvement in FPE. Similarly, the length of the SR did not yield significant differences in the mFPE and tFPE, even when the data were grouped. Conclusions: Our data indicate that contemporary SR-mediated thrombectomy characteristics, including model, brand, size, and length, do not significantly affect the FPE

Ingeniería Forestal y ambiental en medios insulares

Las Islas Canarias a pesar de su reducida extensión y del relativo poco peso específico a nivel mundial, no es ajena a los problemas globales detectados en la conservación de bosques y en la importancia que éstos tienen para obtener beneficios económicos, socioculturales y ambientales. La gestión forestal sostenible es en este sentido esencial para asegurar y compatibilizar los diversos beneficios del bosque. El papel específico de los bosques y su gestión son sin embargo temas aún por conocer en nuestras islas, por lo que el Año Internacional de los Bosques ha representado una oportunidad única para dar a conocer el mundo forestal y acercarlo a nuestra sociedad. El presente libro consta de 25 capítulos donde se ha contemplado la mayoría de los aspectos a tener en cuenta en la planificación y gestión del medio forestal y natural. Desde la historia forestal del archipiélago, hasta el uso y técnicas de manejo de los recursos naturales, incluyendo el agua, la energía en forma de biomasa y la selvicultura

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio