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36 results for “moisture content”
Leaf Litter Moisture Content at Harvard Forest HEM and LPH Towers 2006
Leaf litter was collected at the time of soil respiration measurements and its moisture content was measured in order to determine the contribution of leaf litter decomposition to measurements of total soil respiration including the litter layer. Leaf litter moisture has been shown to strongly affect CO2 release from organic soil layers (Borken et al. 2003).
Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC)
<p><strong>Related paper:</strong> <a href="https://hess.copernicus.org/preprints/hess-2022-121/">Forkel et al. (2022)</a></p> <p>The VOD2LFMC dataset contains estimates of leaf moisture content as defined as live-fuel moisture content (LFMC) derived from passive microwave satellite observation of vegetation optical depth (VOD). LFMC is defined as the fresh mass of a leaf over the dry mass and is expressed in %:</p> <p><span class="math-tex">\(LFMC = {m_{fresh}-m_{dry}\over m_{dry}}*100\%\)</span></p> <p>LFMC was estimated from the <a href="https://doi.org/10.5281/zenodo.2575599">VODCA version 1</a> dataset of Ku-band VOD using the model approach “B” as described in Forkel et al. (2022).</p> <p>The file VOD2LFMC-B_v01_2000-2017.zip contains (unzipped ~ 57 GB):</p> <ul> <li>daily global data per month netCDF files</li> <li>a README file</li> <li>Ancillary file VOD2LFMC-B_v01_support-by-obs.nc</li> </ul> <p>Grid, time and variable definitions:</p> <ul> <li> <p>Grid-name: Geographic Lat/Lon</p> </li> <li> <p>Pixel-size: 1/4 degrees</p> </li> <li> <p>Size-x: 1440</p> </li> <li> <p>Size-y: 557</p> </li> <li> <p>Time period: February 2000 – July 2017</p> </li> <li> <p>Temporal resolution: daily</p> </li> <li> <p>Variable: Live-fuel moisture content (LFMC) in %</p> </li> <li> <p>Valid-range: 0-400%</p> </li> </ul> <p> </p>
Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data
<p>Live fuel moisture content (LFMC), defined as the mass of water in the foliage and small twigs relative to its total dry mass, is a key factor affecting fire potential and determining wildfire danger and activity. Fuel moisture is directly related to the amount of energy needed to evaporate water before ignition. Consequently, high moisture values reduce, or even inhibit, ignitability and subsequent fire spread.</p> <p>To cover the absence of a specific model to estimate LFMC for the Mediterranean Basin at the sub-continental scale, we built an empirical model based on Random Forests (LFMC<sub>RF</sub>) and combining MODIS spectral bands, vegetation indices, land surface temperature, and the day of year as predictors. The details on the modeling and validation methods, and the accuracy of the estimates are in the related publication <strong><a href="https://doi.org/10.3390/rs14133162">Cunill Camprubí et al., 2022</a></strong>.</p> <p>This dataset contains a collection of weekly LFMC maps from February 2000 to December 2021. The maps cover the Mediterranean and part of the Temperate biomes of the Mediterranean Basin. File <em>mapping_area_LFMC-RF_W-1.0.png</em> shows the target mapping areas.</p> <p>Metadata:</p> <ul> <li>Spectral Information: MODIS MCD43A4 C.6</li> <li>Land Surface Temperature: MODIS MOD11A2 C.6</li> <li>Land Cover Mask: MODIS MCD12Q1 C.6</li> <li>Coordinate Reference System: Native MODIS Sinusoidal</li> <li>Temporal Resolution: Weekly (W)</li> <li>Spatial Resolution: ~500 m</li> <li>File Format: NetCDF v.4</li> <li>Scale Factor: 0.01</li> </ul> <p>Fundings:</p> <p>The study was funded by the MICINN (RTI2018-094691-B-C31), European Union’s Horizon 2020-Research and Innovation Framework Programme under grant agreement no. 101003890 project FirEUrisk, the National Natural Science Foundation of China (U20A20179, 31850410483), and the talent proposals in Sichuan Province (2020JDRC0065) from Southwest University of Science and Technology (18ZX7131).</p>
Portuguese Live Fuel Moisture Content product
<p>This product contains a 2500m LFMC 8-day LFMC between 2017 and 2024 over the continental Portugal.<br><br>Metadata:</p> <p>Number of rows: 180<br>Number of columns: 240<br>Cell size: 0.025<br>File Format: netCDF4<br>Coordinate Reference System: GCS_WGS_1984 - EPSG 4326</p> <p>Files:</p> <ul> <li>LFMC_count - number of valid images for the entire period.</li> <li>LFMC_value - LFMC values product </li> </ul> <p>Fundings:</p> <p>Filippe Santos was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2022.11960.BD).<br>This research was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Refs. PCIF/MPG/0175/2019), ICT project (Refs. UIDB/04683/2020 and UIDP/04683/2020). This research was co-funded by the European Union through the European Regional Development Fund (FEDER) in the framework of the Interreg VI-A España-Portugal (POCTEP) 2021-2027, FIREPOCTEP+ (0139_FIREPOCTEP_MAS_6_E).</p> <p> </p>
South Portugal Live Fuel Moisture Content (LFMC) dataset
<p>This dataset contains a collection of biweekly LFMC field samples collected between April 2022 and July 2023 over the Alentejo region, South Portugal.<br><br>Metadata:</p> <p>Coordinate Reference System: ETRS_1989_Portugal_TM06 - EPSG 3763<br>File Format: ESRI shapefile<br>Column Fields:</p> <ul> <li>FID - Internal ID</li> <li>Ponto - control point for backup purposes.</li> <li>LFMC - Live fuel moisture content (LFMC) in percentage.</li> <li>DATE - Sample date. AREA - in-situ field name.</li> <li>POINT_X - Longitude.</li> <li>POINT_Y - Latitude.</li> <li>POINT_Z - Altitude.</li> </ul> <p>Fundings:</p> <p>Filippe Santos was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2022.11960.BD).<br>This research was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Refs. PCIF/MPG/0175/2019), ICT project (Refs. UIDB/04683/2020 and UIDP/04683/2020).</p>
Project 2021/05/X/ST8/00114: Introduction of moisture content as a parameter of the breakage probability function of granular biomass - Dataset
<p>This dataset contains the results of research conducted at Purdue University, Agricultural&Biological Engineering during research stay in the period 15.11.2021 - 14.02.2022. The aim of the research was to investigate and analyze the breakage probability of different types of grains for various moisture content levels: 10%, 14%, 18%, 22% and 26 % and particle sizes. The experimental part involves compression tests of selected grains (rice, corn) and specific breakage energy determination. In the result the breakage probability equations in dependance of moisture content and particle size were developed.</p> <p>This dataset contains:</p> <ol> <li>the results of initial moisture content measurement,</li> <li>the results of moisture content measurement after wetting,</li> <li>The results of bulk density measurement,</li> <li>the results of compression tests for low compression rate (1.25 mm/min)</li> <li>the results of compression tests for high compression rate (125 mm/min)</li> </ol>
Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean since 1996
<p>Here is a dataset of live fuel moisture content (LFMC, computed as the water mass over dry mass of living shoots) time series, collected in the French Mediterranean area by the French National Forest Organization (“Office National des Forêts”) for operational fire prevention. A network of 53 sites (called the "Reseau Hydrique" network) were sampled, among which 35 are geolocalized for a maximum period extending from 1996 to 2016. For each site and year available, LFMC is measured during the summer season on shrub species (between one and three species per site) at a weekly to biweekly frequency depending on site and year.</p> <p>The dataset can be used to validate or calibrate fire danger model, assess remote sensing drought indices and understand the physiological and climatic determinants of LFMC. There are 584 site*year data (a total of 22787 individual data) for several shrub species of the French Mediterranean area.</p> <p>From the raw dataset, researchers from the Ecology of Mediterranean Forest Unit at INRA (French National Institute for Research in Agronomy) of Avignon (France) have produced an improved dataset that includes corrections, outlier identifications and error estimations. Preliminary validation assessments of the data quality were also produced.</p> <p>A data paper describing in detail the method and all the modifications, error estimations and evaluations of the raw dataset that were performed is under review in Annals of Forest Science. Both the raw and improved datasets are made available on Zenodo (Cabane et al 2017, DOI 10.5281/zenodo.162978).</p> <p>The attached dataset consists of four tables:</p> <ol> <li>The first table (<em>LFMC_final_Table.csv</em>) contains the live fuel moisture content (LFMC) on a dry weight basis (see Supplementary S1 for details). These are the robust estimates of LFMC and their associated standard errors which were both estimated from raw data with the method fully described above described in a data paper under revision (Martin-StPaul <em>et al</em>., under review in Annals of Forest Science). Each row in the table describe the LFMC at a given date, for a given species and a given site. The table has eleven columns. The first eight columns indicate the site identifier (SiteCode and SiteName), the species (Species), a unique identifier for a given species at a given site (SitexSpecies), the date (Date, Year, Month, Day of Year). The last three columns are respectively the robust LFMC (labelled RobustLFMC), the standard error <em>SE</em> (labelled RobustStandErrLFMC) and the number of valid measurements that were not identified as outliers (labelled RobustNval). RobustStandErrLFMC, and that can be used to estimate confidence limits depending on the desired confidence rate.</li> </ol> <p> </p> <ol> <li>The second table (<em>RainTable.csv</em>) contains rainfall measurements. The site identifiers are given (SiteCode and SiteName) and the rainfall amount (rainfall) corresponding to rainfall occurring between the day of year of the previous measurement (PreviousDoy) and the day of year when the measurement was performed (Doy). The last column enables to identify the doubtful measurements (RainFlag = 1), when the discharge of the gauge during the previous measurement was uncertain.</li> </ol> <p> </p> <p> </p> <ol> <li>The fourth table (<em>InfoSite_ReseauHydrique.csv</em>) contains a basic description of each site. It includes the identifier of the site (SiteCode and SiteName), the coordinates of the site in WGS84 (Longitude and Latitude), a flag indicating whether the site is still active (1= active, 0= inactive), the names of measured species (SpeciesName1 and up to SpeciesName3), the first and last year of measurement, as well as the number of measurement year available, for each species (StartYear, EndYear, NbYears).</li> </ol> <p> </p> <ol> <li>The third table contains raw data as produced by the French National Forest Organization (<em>LFMC_raw_Table.csv</em>). The first twelve columns indicate site name, species name, and date, as in the first table. The six following columns indicate individual LFMC values (LFMC1 to LFMC5), and the mean LFMC value (FFSLFMC) released by the French Forest Service. The last six columns correspond to flags identifying outliers (LFMC1Flag to LFMC5Flag). Flags were attributed either manually or automatically (see Martin-StPaul <em>et al</em>., under review in Annals of Forest Science). Missing values (e.g. following an unforcasted rain event, see Methods) were represented by the symbol “NA“ (Not Available).</li> </ol> <p>Note that the initiative was funded by a French organization dedicated the protection of the Mediterranean forest (The "Délégation à la Protection de la Forêt Méditerranéenne") and the raw dataset is available on a French website (http://www.reseau-hydrique.org/). However the raw dataset is not fully adapted to scientific purposes for several reasons. The dataset is not referenced (<em>i.e.</em> does not have a DOI) and its description is in French. In addition, the labels of sampling sites have evolved over time and some species were given a vernacular name. Finally, raw data are expressed on fresh mass basis (instead of dry mass as generally done in scientific studies) and present some outliers, duplications and inconsistencies. Additionally, uncertainties were not provided in the raw datasets. This is why INRA researchers recommend the usage of the improved dataset. In the forthcoming month, additional data regarding the environmental description, the ecology and history of the sites will be provided.</p> <p> </p> <p>Martin-StPaul, N; Pimont, F; Dupuy JL; Rigolot E; Ruffault J; Fargeon H; Cabane E; Duché Y; Savazzi R; Toutchkov M. Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean area since 1996. Under revision in Annals of Forest Science.</p> <p> </p>
Datasets for the Results of Scratch Tests of Green Wood and Results of Scratch Tests of Timber Components (D2.1) and Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk (D2.2) of 5G-TIMBER EU Project
<p>7 June 2024: added D2.2_data_statistics.zip and D2.2_analysis_results.zip, which are the datasets for D2.2 "<span>Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</span></p> <p>D2.2 presents the Hygro-Thermo-Mechanical (HTM) models and the finite element (FE) analyses of selected wooden components that use the material properties of wood presented in deliverable D2.1 "Input database for selected wood parts and timber components: material properties, representative environmental conditions,<br>and loads" (see below). </p> <p>------</p> <p>Figures_22_23_24_25.xlsx : Results of Scratch Tests of Green Wood</p> <p>corrected_Figures_26_27_28_29_30.xlsx : Results of results of Scratch Tests of Timber Components (new version, uploaded on 26 October 2023)</p> <p>This dataset consists of 2 Excel files that correspond to the scratch test results reported in the deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</p> <p>The purpose of D2.1, to which this dataset is related, is to present the input data needed for the Hygro-Thermo-Mechanical (HTM) models and the related finite element (FE) analyses planned for a follow-up deliverable, i.e., the D2.2. (Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains And Stresses And Crack Risk). The data include the material properties for green wood and selected wooden components, as well as the plans to collect environmental conditions and loads to be considered in the analyses for prediction of the crack risk of timber components under moisture variations. In additions, new results of scratch tests of wood and wooden components, supported by computed tomography (CT) investigations, are collected to define a model for shear failure risk to be added to the HTM computational models.</p> <p>In D2.1, scratch tests carried out at VTT are described and their results are collected to provide information about the moisture effects of wood logs during cutting operations in sawmills, as well as on relevant fracture and shear properties for wooden components in sawing centres before using them to produce wooden elements of modular buildings in the production. The scratch tests are supported by CT tomography investigations and these results are also reported in the deliverable.</p> <p>D2.1 is available here: <a title="Deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" " href="../records/10577505" target="_blank" rel="noopener">https://zenodo.org/records/10577505</a> </p>
Sensitivity of fire weather indices, fuel sticks and satellite observations to fuel moisture content in Central European forests - Data
<p>This data repository contains datasets for destructively measured fuels of different types (FMC_insitu), meteorological data including 10-hour fuel stick measurements (FWS_30min) and calculated fire weather index components (FWS_FWI_24h) for four different sites in the Tharandt forest and Saxon Switzerland National Park in the Free State of Saxony (Germany) during the years 2022 (only DE-Tha) and 2023 (all four sites).</p> <p>The provided folders contain .csv files for each study site. Meteorological data in 30min for DE-Tha can be derived from the ICOS data portal (https://data.icos-cp.eu/portal/). For the remaining three sites (DE-BLB, DE-BWB, DE-SHW), past and recent data can be viewed via EMS Brno (e.g., http://www.emsbrno.cz/p.axd/en/Beech__Landberg.TU__DRESDEN.html). Upon request, the authors can share the data. </p> <p><strong>FMC_insitu</strong>: Destructively sampled fuel moisture content of different fuel types.</p> <p><strong>FWS_30min</strong>: Original measurements from the fire weather stations in 30 min time steps</p> <p><strong>FWS_FWI_24h</strong>: Measurements from fire weather stations in 24h time steps and the calculated fire weather index and its components. As requested for calculation of the FWI, meteorological variables are used at 13:00 (UTC), while PREC and PBC are the 24h sum prior to 13:00. </p> <p><strong>readme.txt</strong>: Description of repository content and the variables provided within the .csv files.</p> <p><strong>stations.csv</strong>: Contains the coordinates and a short description of the study sites. </p>
Data, plotting scripts, and figures for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures"
<p>This bundle of files contains all the data and plotting scripts for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures", as well as the figures themselves.</p> <p>These results are part of the paper:</p> <p>Tejas Chandrashekhar Mulky and Kyle E. Niemeyer. "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures," 2018. Accepted for publication in <em>Proceedings of the Combustion Institute</em>, available via <a href="https://arxiv.org/abs/1806.08396">https://arxiv.org/abs/1806.08396</a></p>
Observations of groundwater fluctuations and surface moisture content on a medium-grained, planar beach (Sand Engine, the Netherlands)
<p>These data are groundwater and beach surface moisture values collected during the MegaPex campaign between October 11 and 20, 2014 at the Sand Engine, The Hague, the Netherlands by MSc students and staff of the Coastal Research Group at Utrecht University, the Netherlands. The data were obtained at 8 locations in a cross-shore array on the intertidal and upper beach. During the measurements the beach was planar (1:30) and the median grain size was 0.365 mm. The data are supplemented with bed profiles along the instrument array. For further information and meta-data, please consult the readme.txt and the header of the individual text files in the zip-file.</p>
Moisture content and total aflatoxin content of the freshly harvested maize samples
<p>Moisture content and total aflatoxin content of the freshly harvested maize samples. </p> <p>Moisture content of the samples were determined on-site in triplicate using Superpoint handheld moisture analyzer (Supertech Agroline, Hestchaven 5, DK-5400 Bogense, Denmark; ±0.5% accuracy) following the manufacturer’s instructions.</p> <p>Total AF in the samples were quantified by a single step lateral flow immunoassay utilizing the developed Reveal Q+ test strip for Aflatoxin (Neogen Item 8085) read on a calibrated AccuSan Gold reader (Neogen Corporation, 620 Lesher Place, Lansing, MI 48912 USA) (Neogen item 9595) at 18-22<sup>o</sup>C</p>
Fig. 3 in Biological responses of Hypothenemus hampei (Coleoptera: Curculionidae) on Cenibroca artificial diet at different moisture content levels and relative humidities
Fig. 3. Coffee berry borer external feeding and reproduction behavior on Cenibroca artificial diet with 60% moisture content at 75% relative humidity. Notice how the external reproduction offers a simple separation of the coffee berry borer immature and adults from the diet for coffee berry borer parasitoid production. (A) Diet pellet 10 to 15 d afer infestation showing external feeding and development of first-generation offspring, arrows showing eggs of first generation. (B) Diet pellet 25 to 30 d afer infestation showing external feeding and development of first-generation offspring. Stages suitable for the African ectoparasitoids (Cephalonomia stephanoderis and Prorops nasuta) reproduction. (C) Diet pellet 35 to 40 d afer infestation showing external feeding and development of first-generation offspring. Notice the presence of mature and teneral females, arrows showing oviposition of second generation. (D) Diet pellet> 50 d afer infestation showing female production, suitable for the reproduction of the African ectoparasitoid P. nasuta.
Fig. 2 in Biological responses of Hypothenemus hampei (Coleoptera: Curculionidae) on Cenibroca artificial diet at different moisture content levels and relative humidities
Fig. 2. Loss of moisture content level percentage on a Cenibroca diet pellet with a 50, 60, and 70% moisture content level maintained at 65, 75, and 85% relative humidity. (95% confidence limits of the mean; n = 7 per evaluation time per treatment).
Fig. 1 in Biological responses of Hypothenemus hampei (Coleoptera: Curculionidae) on Cenibroca artificial diet at different moisture content levels and relative humidities
Fig. 1. Mean brood production of coffee berry borer per Cenibroca diet pellet with 50, 60, and 70% moisture content level maintained at 65, 75, and 85% relative humidity at different time periods. (95% confidence limits of the mean; n = 7 per evaluation time per treatment).
Field measurements of moisture content of dead leaves of Pinus pinaster (2014-2023)
<p>The Association for the Development of Industrial Aerodynamics - Forest Fire Research Centre (ADAI-CEIF) has developed a daily measurement program for the moisture content of a set of representative fine fuels in forests of Central Portugal since 1987. The sampling site is located is in Lousã, and samples are collected daily during the main fire season (15<sup>th</sup> of May - 15<sup>th</sup> of October) and twice a week for the rest of the year. These samples are then analyzed at the Laboratory for Forest Fire Studies (LEIF), located less than 1km from the sampling plot.</p> <p>As part of a study named "The role of field measurements of fine dead fuels moisture content in the Canadian Fire Weather Index System – a case study in the Central Region of Portugal" (https://doi.org/10.3390/f15081429) we present the field measurements of surface litter (<em>Pinus pinaster</em>) carried out from 2014 to 2023. Based on this dataset, the study proposes a correction for the moisture factor (<em>m<sub>f</sub></em>) in the Fine Fuel Moisture Code (FFMC) of the Canadian Fire Weather Index System (CFWIS). This moisture correction was used to directly determine the Initial Spread Index (ISI) and, subsequently, the Fire Weather Index (FWI).</p> <p>Acknowledgments:</p> <p>We gratefully acknowledge the researchers and laboratory technicians of the ADAI-CEIF team, who dedicated part of their time to the field collection of samples to determine the moisture content of forest fuels in Lousã (Coimbra, Portugal). In particular, we would like to thank the MCFIRE project (PCIF/MPG/0108/2017, https://mcfire.adai.pt/) for coordinating and supporting these measurements from 2019 to 2023. We also gratefully acknowledge the ongoing efforts of Nuno Luís and João Carvalho in ensuring the collection and processing of samples in the laboratory.</p>
Sample of Reanalysis Dead Fuel Moisture Content Dataset of California (2000-2020)
Open the record for dataset details and reuse information.
Maximum moisture content of contemporary birch bark.
<p>Data accompanying the article published on the Icom-CC proceedings 2017 Copenhagen:</p> <p> https://www.icom-cc-publications-online.org/PublicationDetail.aspx?cid=92b3e81e-ba73-46d2-875a-0fe7b3b4ffb6</p>
Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests
<p>48-year modelled dataset of below canopy fuel moisture content (FMC) for field sites established in wet temperate eucalypt forests in south-eastern Australia (allsites.csv). Raw hourly data for input to the model (hrlyallsitesgit.csv). </p>
Water table depth dynamics and surface soil moisture content from three Scottish peatland areas (2021-2022)
<p>This compilation of datasets from three monitoring sites on peatland in Scotland includes water table depth dynamics and surface soil moisture content and covers the period 2021-2022. Further data will be added on an annual basis. This is version 2 of the dataset, which corrects a small number of data QC issues (see README).</p>
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International Brain Laboratory public data
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OpenNeuro
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