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Forest fuels are the core element of fire management; each fuel component plays an important role in fire behavior. Therefore, accurate determination of their characteristics and spatial distribution is crucial. This paper introduces a novel method for mapping the spatial distribution of litter and duff fuel loads using data collected by unmanned aerial vehicles. The approach leverages a very high-resolution multispectral data analysis within a machine learning framework to achieve precise and detailed results. A set of vegetation indices and texture metrics derived from the multispectral data, optimized by a "Variable Selection Using Random Forests" (VSURF) algorithm, were used to train random forest (RF) models, enabling the modeling of high-resolution maps of litter and duff fuel loads. A field campaign to measure fuel loads was conducted in the mixed forest of the natural protected area of "Sierra de Quila", Jalisco, Mexico, to measure fuel loads and obtain field reference data for calibration and validation purposes. The results revealed moderate determination coefficients between observed and predicted fuel loads with R2 = 0.32, RMSE = 0.53 Mg/ha for litter and R2 = 0.38, RMSE = 13.14 Mg/ha for duff fuel loads, both with significant p-values of 0.018 and 0.015 for litter and duff fuel loads, respectively. Moreover, the relative root mean squared errors were 33.75% for litter and 27.71% for duff fuel loads, with a relative bias of less than 5% for litter and less than 20% for duff fuel loads. The spatial distribution of the litter and duff fuel loads was coherent with the structure of the vegetation, despite the high complexity of the study area. Our modeling approach allows us to estimate the continuous high-resolution spatial distribution of litter and duff fuel loads, aligned with their ecological context, which dictates their dynamics and spatial variability. The method achieved acceptable accuracy in monitoring litter and duff fuel loads, providing researchers and forest managers with timely data to expedite decision-making in fire and forest fuel management. [ABSTRACT FROM AUTHOR]

Introduction The EVALUA GPS project aims to evaluate the impact of the implementation of the National Institute for Health Care and Excellence (NICE) guideline ‘Community engagement: improving health and well-being and reducing health inequalities’ adapted to the Spanish context.Methods and analysis Phase I: A tool will be designed to evaluate the impact of implementing the recommendations of the adapted NICE guideline. The tool will be developed through a review of the literature on implementation of public health guidelines between 2000 and 2021 and an expert’s panel consensus. Phase II: The developed tool will be implemented in 16 community-based programmes, acting as intervention sites, and 4 controls through a quasi-experimental pre–post study. Phase III: A final online web tool, based on all previously collected information, will be developed to support the implementation of the adapted NICE guidelines recommendations in other contexts and programmes.Data collection and analysis Data will be collected through surveys and semistructured interviews. Quantitative and qualitative data will be analysed to identify implementation scenarios, changes in community engagement approaches, and barriers and facilitators to the implementation of the recommendations. All this information will be further synthesised to develop the online tool.Ethics and dissemination The proposed research has been approved by the Clinical Research Ethics Committee of Aragon. Results will be presented at national and international conferences and published in peer-reviewed open access journals. The interactive online tool (phase III) will include examples of its application from the fieldwork.

Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg· ha-1, 0.09, and 0.02 kg·m-3 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels.

RESUMEN: Partiendo del Tratado breve de la cultivación y cura de las colmenas (1586), de Luis Méndez de Torres, primer texto donde se documenta hasta ahora, se analiza la historia de la voz des(a)hitadera en el ámbito de la tratadística apícola española. De igual forma, se estudia su presencia en otros testimonios no especializados, fundamentalmente inventarios de bienes, y en algunos repertorios lexicográficos que le dieron cobijo, así como en ciertos atlas lingüísticos que han avalado su uso y su supervivencia hasta el siglo XX. Tras la identificación del referente, una herramienta propia de la colmenería tradicional, y la descripción de su utilidad, se realiza una propuesta etimológica que explica las diferentes variantes con las que se ha registrado. [ABSTRACT FROM AUTHOR]

There is a well-established need within the remote sensing community for improved estimation and understanding of canopy structure and its influence on the retrieval of leaf biochemical properties. The main goal of this research was to assess the potential of optical spectral information from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types. In the first phase, we assessed the relationships between optical metrics and canopy structural parameters obtained from LiDAR in terms of different canopy structural attributes (biomass (i.e., area under Vegetation Vertical Profile, VVPint), canopy height and vegetation complexity). Secondly, we identified and classified different “canopy structural types” by integrating several structural traits using Random Forests (RF). The study area is a heterogeneous forest in Sierra National Forest in California (USA). AVIRIS optical properties were analyzed by means of several sets of variables, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, optimized normalized difference indices and Principal Component Analysis (PCA) components. Our results demonstrate that optical data contain structural information that can be retrieved. The first principal component, used as a proxy for albedo, was the most strongly correlated optical metric with vegetation complexity, and it also correlated well with biomass (VVPint) and height. In conifer forests, the shade fraction was especially correlated to vegetation complexity, while water-sensitive optical metrics had high correlations with biomass (VVPint). Single spectral band analysis results showed that correlations differ in magnitude and in direction, across the spectrum and by vegetation type and structural variable. This research illustrates the potential of AVIRIS to analyze canopy structure and to distinguish several structural types in a heterogeneous forest. Furthermore, RF using optical metrics derived from AVIRIS proved to be a powerful technique to generate maps of structural attributes. The results emphasize the importance of using the whole optical spectrum, since all spectral regions contributed to canopy structure assessment.