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304 results for “burned area”
Raw microclimate data from plots at burned areas from the 2020 Holiday Farm fire in the Andrews Experimental Forest and Hagan Block, 2022-2024
This dataset includes a suite of microclimate sensor data from areas burned by the 2020 Holiday Farm fire within the McKenzie River basin. A total of 42 microclimate sensor suites were installed in July and August of 2022 distributed across RS01, RS08, RS15, WS02, WS09, WS01, HGBK. All sensor locations are within the Holiday Farm Fire footprint and within Permanent Sample Plots (PSPs). An additional sensor was placed within the Primary meteorological station (PRIMET) of the HJ Andrews Experimental Forest for comparison and calibration between open-air measurements. Sites are stratified across three treatment variables including 1) fire severity: high or low; 2) management: managed or unmanaged; 3) water balance: moister or drier topographic positions. This resulted in eight treatment blocks, each with 5 sensor suite replicates. Each microclimate site includes a suite of measurements: Hobo temperature and relative humidity sensor installed at 1.5m within a gill shield (recording at 30 minute interval), one TOMST TMS-4 temperature and soil moisture sensor was located within 1 m of plot center (recording at 15 minute interval). The TOMST sensors include air temperature sensors at 15cm, 2cm, and soil temperature at -6cm in soil near-surface. Soil moisture is measured across the ~10cm near surface zone.
State of Wildfires 2024-25: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions
<p>This dataset supports the State of Wildfires 2024-25 report under review at <em>Earth System Science Data</em> (Kelley et al., <em>under review)</em>. It is an update of the State of Wildfires 2023-24 report (Jones et al. 2024). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2024-February 2025), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Kelley et al. (<em>under review</em>).</p> <h3>Citation</h3> <p>Work utilising our regional summaries should <strong>cite both Kelley et al. (under review) AND the primary reference for the variable(s) of interest</strong> as follows:</p> <ul> <li>Giglio et al. (2018) for MODIS MCD64A1 BA.</li> <li>van der Werf et al. (2017) for GFED4.1s fire C emissions.</li> <li>Kaiser er al. (2012) for GFAS fire C emissions.</li> <li>van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.</li> <li>Andela et al. (2019) for the Global Fire Atlas.</li> <li>Giglio et al. (2016) for the Fire Radiative Power (FRP) observations.</li> <li>Chuvieco et al. (2024) for FireCCIS311 BA.</li> <li>Giglio et al. (2024) for VIIRS VNP64A1 BA.</li> </ul> <h3>Input Data</h3> <p><strong>Burned Area (BA)</strong></p> <ul> <li>BA data from NASA’s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available from <a href="https://lpdaac.usgs.gov/products/mcd64a1v061/">Giglio et al. (2021)</a>. <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 500m, daily</li> </ul> </li> <li>BA data from ESA's Climate Change Initiative BA product (FireCCIS311) are extended from Lizundia-Loiola et al. (2022) and are available from <a href="Chuvieco,%20E.;%20Pettinari,%20M.L.;%20Lizundia-Loiola,%20J.;%20Khairoun,%20A.;%20Danne,%20O.;%20Boettcher,%20M.;%20Storm,%20T.%20(2024):%20ESA%20Fire%20Climate%20Change%20Initiative%20(Fire_cci):%20Sentinel-3%20SYN%20Burned%20Area%20Grid%20product,%20version%201.1.%20NERC%20EDS%20Centre%20for%20Environmental%20Data%20Analysis,%2029%20February%202024.%20https://catalogue.ceda.ac.uk/uuid/da8e669a74334c82a56e0b470bc4ef04">Chuvieco et al. (2024)</a>. <ul> <li>Period: 2019-February 2025</li> <li>Resolution: 300m, daily</li> </ul> </li> <li>BA data from NASA’s VIIRS BA product (VNP64A1) are available from <a href="https://lpdaac.usgs.gov/products/vnp64a1v002/">Giglio et al. (2024)</a>. <ul> <li>Period: 2012-February 2025 (only the data after 2019 are used for consistency in the comparisons between MCD64A1, FireCCIS311, and VNP64A1).</li> <li>Resolution: 500m, daily</li> </ul> </li> </ul> <p><strong>Fire Carbon (C) Emissions</strong></p> <ul> <li>GFED4.1s fire C emissions data are extended from van der Werf and are available at <a href="https://globalfiredata.org/">https://globalfiredata.org/</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.25 degree, daily</li> </ul> </li> </ul> <ul> <li>GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available from the <a href="https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation">ECMWF Confluence Server</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.1 degree, daily</li> </ul> </li> </ul> <p><strong>Global Fire Atlas (Individual Fire Properties)</strong></p> <ul> <li>Global Fire Atlas data are extended from Andela et al. (2019) and are available from the repository maintained by <a href="https://doi.org/10.5281/zenodo.11400062">Andela and Jones (2025)</a>. <br> <ul> <li>Period: 2002-February 2025</li> <li>Driven by 500m MODIS BA data (collection 6.1)</li> </ul> </li> </ul> <p><strong>Fire Intensities</strong></p> <ul> <li>FRP data are extended from MOD14A1 and MYD14A1 (Giglio et al., 2016) and are available at <a href="https://lpdaac.usgs.gov/products/mod14a1v061/">Giglio and Justice (2021)</a>.<br> <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 1km, daily</li> </ul> </li> </ul> <h3>Regional Analysis</h3> <p>We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer). </p> <p>The statistics derived from cookie-cutting are listed below. Full details in Kelley et al. (2025).</p> <div> <table> <tbody> <tr> <td> <p>Layer</p> </td> <td> <p>Short Form </p> </td> <td> <p>Source</p> </td> </tr> <tr> <td> <p>Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Ecoregions</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Continents</p> </td> <td> <p>NA</p> </td> <td> <p>ArcGIS Hub (2024)</p> </td> </tr> <tr> <td> <p>Continental Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>See above</p> </td> </tr> <tr> <td> <p>Countries</p> </td> <td> <p>NA</p> </td> <td> <p>EU Eurostat (2020)</p> </td> </tr> <tr> <td> <p>UC Davis Global Administrative Areas (GADM) Level 1</p> </td> <td> <p>GADM-L1</p> </td> <td> <p>UC Davis (2022)</p> <br><br></td> </tr> <tr> <td> <p>Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions </p> </td> <td> <p>IPCC AR6 WGI Regions</p> </td> <td> <p>Iturbide et al. (2020)</p> </td> </tr> <tr> <td> <p>Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions</p> </td> <td> <p>RECCAP2 Regions</p> </td> <td> <p>Ciais et al. (2022)</p> </td> </tr> <tr> <td> <p>Global Fire Emissions Database (GFED) Basis Regions</p> </td> <td> <p>GFED4.1s Regions</p> </td> <td> <p>van der Werf et al. (2006)</p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3>Regional Statistics and Anomalies</h3> <ul> <li><strong>Burned Area (BA)</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> </ul> </li> </ul> <ul> <li><strong>Carbon Emissions</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2003).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> <li>Statistics available for GFAS, GFED, and their mean.</li> </ul> </li> </ul> <ul> <li><strong>Individual Fire Properties</strong> <ul> <li>Based on values of individual fire size and rate of growth ignition from the ignition point vectors of the Global Fire Atlas.</li> <li>Calculated regional count.</li> <li>Calculated regional maxima and 95th percentiles of fire size and rate of growth for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul> <ul> <li><strong>Fire Intensity</strong> <ul> <li>Based on active fire observations of FRP, which are pooled within each fire of the Global Fire Atlas.</li> <li>For each fire, the 95th percentile value of all FRP observations is the assigned intensity value (i.e. a "peak fire intensity" omitting any spurious high-end values).</li> <li>Regionally, the peak fire intensity values are averaged across individual fires.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul>
Global GFED-based monthly burned area time series (1996-2016) at 1 km and ESA CCI MODIS-based long-term monthly P90 burned area occurrence at 500 m
<p>Contains two separate datasets:</p> <ol> <li>Global <a href="https://www.globalfiredata.org/data.html">GFED-based monthly burned area</a> (in ha) <a href="https://youtu.be/kBJcP8mL2Qs">time series (1996-2016)</a> at 1 km (downscaled using cubic-splines from 25 km);</li> <li>Global burned area long term (2000-2012) P90 (quantile probability = 0.9) based on the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI burned area accumulated weekly product</a>;</li> </ol> <p>Original GFED monthly data is provided as HDF4 files (ftp.fuoco.geog.umd.edu/data/GFED/GFED4). Dataset is described in detail in <a href="https://doi.org/10.1002/jgrg.20042">Giglio et al. (2013)</a>. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/GFED"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a> or watch <a href="https://youtu.be/kBJcP8mL2Qs"><strong>this video</strong></a>.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>nhz = theme: natural hazards,</li> <li>monthly.burned.ha = variable: estimated monthly burned area in ha,</li> <li>gfed = data source GFED data,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000.02 = time reference aggregated: month Feb of year 2000,</li> <li>v4 = version number: GFEDv4,</li> </ul>
Burned Area Maps based on MODIS Surface Reflectance
<p>Burned area (BA) was classified using in-house algorithms, described in detail by Woźniak and Aleksandrowicz (2019). This method utilizes images acquired before and after fire events. All MODIS surface reflectance products MOD09A1 (tiles 24_03 and 25_03) for the period 2002 – 2021 were investigated. Since the study area is obscured by clouds or covered with snow for most of the year, only images from the time window that maximized the number of available frames across most years were selected. Hence, only images acquired between the 145th and 241st day of each year (corresponding to the spring-summer period) were retained for further processing. </p>
Pre and post-fire composition, density, basal area, and biomass for 212 sites that burned between 2004 and 2015 in Interior Alaska
We collated data on pre-fire and post-fire stand composition from 212 sites across interior Alaska that burned between the years 2004 and 2014.
Dry matter databases derived by crossing burned areas databases with above ground estimations from SMOS sensor
<p>Estimates of the the fuel consumed during a fire (dry-matter, DM) is derived by combining burned areas (from two different fire inventories) with above ground biomass (derived from SMOS L Band Vegetation Optical Depth). Six new datasets are provided on a 25 km grid for the years 2010-2017.</p> <p>The portion of vegetation that is consumed during biomass burning is expressed as:<br> <strong><span class="math-tex">\(DM= AGB * BA*\beta\)</span></strong></p> <p>where BA is the burned area; AGB is the fuel load, the amount of biomass or organic matter an ecosystem contains per unit area; <span class="math-tex">\(\beta\)</span> is the combustion completeness or burning efficiency, which is the fraction of fuel actually consumed during the fire.</p> <p>Monthly maps of AGB are converted from SMOS L-band vegetation optical depth (VOD) through an algorithm described in [1] and obtained looking at the relationship between three static AGB benchmark maps from the works of [2],[3] and [4]. Each static map provide three L-VOD to AGB conversion curves fitting the 5th, 50th and 95th percentiles of the data using a logistic regression([1]) In the following, the three different databases are named AGB-BA (Baccini), AGB-SA (Saatchi) and AGB-AV (Avitabile) </p> <p>Monthly total of burned areas are available from two sources. The first one, called hereinafter BA-GFED, is available though GEFD4.1s [5] and is based on MODIS MCD64A1 product. It has a 500m pixel resolution and is also available on a regular grid of 0.25 deg. The second BA product, named hereinafter BA-CCI, is a multi-sensors product provided by the European Space Agency Climate Change Initiative (ESA-CCI) [6] We use version FireCCI5.1 which is calculated using a two-phase algorithm, where MODIS active fire locations are used to identify seed pixels corresponding to high confidence burned areas. These areas are then grown using Medium Resolution Imaging Spectrometer (MERIS) vegetation input data.</p> <p>Combustion completeness, is a taken from table 4 of [7]. </p> <p>By selectively combining any AGB estimations with the two available BA datasets, a total of 6 products are generated.</p> <p> </p> <p><strong>References </strong></p> <p>[1] https://doi.org/10.5194/bg-15-4627-2018</p> <p>[2] <a href="https://doi.org/10.1126/science.aam5962">DOI: 10.1126/science.aam5962</a></p> <p>[3]<a href="https://cce.nasa.gov/veg3dbiomass/saatchi_tgrs07.pdf">https://cce.nasa.gov/veg3dbiomass/saatchi_tgrs07.pdf</a></p> <p>[4] <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.13139">https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.13139</a></p> <p>[5] <a href="https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html">https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4_R1.html</a></p> <p>[6] <a href="https://geogra.uah.es/fire_cci/firecci51.php">https://geogra.uah.es/fire_cci/firecci51.php</a></p> <p>[7] <a href="https://acp.copernicus.org/articles/6/3423/2006/acp-6-3423-2006.pdf">https://acp.copernicus.org/articles/6/3423/2006/acp-6-3423-2006.pdf</a></p> <p> </p>
Global Fire Emissions Database (GFED5) Burned Area
<p>The monthly GFED5 burned area data produced in this study are available in netCDF files. For years between 2001 and 2020, five layers of burned area (Norm: normal type, Crop: cropland burning, Defo: deforestation burning, Peat: peatland burning, Total: the sum of all burning) are provided at 0.25°×0.25° resolution. The ‘Norm’ layer contains burned areas in each grid cell (in km<sup>2</sup>) separated by 17 major land cover types. For the pre-MODIS era (1997-2000), only the ‘Total’ burned area layer with reduced spatial resolution (1°×1°) is provided. We also provide two global maps of burnable area (with water and snow/ice cover excluded) in each grid cell (0.25°×0.25° for the MODIS era and 1°×1° for the pre-MODIS era). Please refer to readme.html or readme.pdf for more detail about the dataset.</p>
ONFIRE Dataset: Monthly Gridded Burned Area data
<p>The ONFIRE Dataset presents a 1° x 1° lat-long gridded database detailing monthly burned areas (BA) stemming from national fire data across five regions: Australia, Canada, Chile, Europe, and the United States. Each grid cell encapsulates the center's latitude and longitude, coupled with the total square meters burned within the month. The dataset spans varying periods per region, starting from 1950 in Australia, 1959 in Canada, 1985 in Chile, 1980 in Europe, and 1984 in the US, extending up to 2021.The ONFIRE DATASET is available in netCDF4, RData, and ASCII formats for accessibility and ease of integration.</p>
Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2020 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2020, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2019 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2019, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
30 m resolution global forest burned area dataset 2018
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025<sup>°</sup>(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p>
30 m resolution global forest burned area dataset 2016
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025<span>°</span> (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p>
30 m resolution global forest burned area dataset 2014
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts : zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
30 m resolution global forest burned area dataset 2020
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025°(approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts : zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
Post-fire succession in Delta Junction burns: Measurements of pre-fire stand basal area in 1987, 1990, 1994 and 1999 burns
This dataset contains measurements of pre-fire stand basal area taken during summer 2008 in 4 burns located near Delta Junction (1987, 1994, 1999) and Tok (1990). These data can be found in Shenoy et al. 2011.
Post-fire succession in 1994 Hajdukovich Creek Burn: measurements of average basal area increment for the years 2000-2010 for aspen and spruce
This dataset contains measurements of average basal area increment from 2000-2010 in aspen and spruce individuals regenerating in the 1994 Hajdukovich Creek burn.
Tree regeneration after fire: Delta 1994 burn surveys, pre-fire stem counts and basal areas, for species other than black spruce
Data for this study were collected in 2001 and 2002 by Jill Johnstone (University of Alaska Fairbanks) and Eric Kasischke (University of Maryland). Sites were located within the perimeter of the 1994 burn southeast of Delta Junction Alaska, USA, bordering the Alaska Highway to the North and the Gerstle River to the West. Sites were selected from satellite classifications prepared by Eric Kasischke to represent different levels of burn severity and post-fire vegetation canopy greenness (NDVI). Site selection was constrained by road access, and only areas where all trees had been killed by the fire were selected. At each site, a central point was located in an area of visually homogeneous vegetation. Five parallel transects, each 50 m long, were laid out as follows: 1) the first transect started at the central point and followed a randomly-selected compass direction, 2) two additional transects were established parallel to the first, but at a random distance from the central transect up to 25 m distant. Vegetation was sampled in a 2-m wide belt centered on each transect, and soil samples were made at intervals along the transect line. Vegetation measurements included: a) basal diameters of all pre-fire trees greater than 1.3 m in height, b) counts of all post-fire tree seedlings, and c) basal diameters of tree seedlings and willows, measured in a randomly chosen 5x2 m portion of each transect. General notes were made on visual percent cover of different vegetation growth forms at the site. Destructive measurements of tree seedlings and willows made in 2001 were used to develop allometric equations to predict dry biomass from basal diameter. Measurements of soil organic layer depth were made at 5 m intervals with the use of a spade to excavate small chunks of sod. At one randomly-selected sample point per transect, a 10x10 cm sample of the organic layer was collected for bulk density measurements. Bulk density samples were dried in a 60degC oven for 48 hours and then w
Tree regeneration after fire: Delta 1994 burn surveys, pre-fire stem counts and basal areas, for black spruce
Data for this study were collected in 2001 and 2002 by Jill Johnstone (University of Alaska Fairbanks) and Eric Kasischke (University of Maryland). Sites were located within the perimeter of the 1994 burn southeast of Delta Junction Alaska, USA, bordering the Alaska Highway to the North and the Gerstle River to the West. Sites were selected from satellite classifications prepared by Eric Kasischke to represent different levels of burn severity and post-fire vegetation canopy greenness (NDVI). Site selection was constrained by road access, and only areas where all trees had been killed by the fire were selected. At each site, a central point was located in an area of visually homogeneous vegetation. Five parallel transects, each 50 m long, were laid out as follows: 1) the first transect started at the central point and followed a randomly-selected compass direction, 2) two additional transects were established parallel to the first, but at a random distance from the central transect up to 25 m distant. Vegetation was sampled in a 2-m wide belt centered on each transect, and soil samples were made at intervals along the transect line. Vegetation measurements included: a) basal diameters of all pre-fire trees greater than 1.3 m in height, b) counts of all post-fire tree seedlings, and c) basal diameters of tree seedlings and willows, measured in a randomly chosen 5x2 m portion of each transect. General notes were made on visual percent cover of different vegetation growth forms at the site. Destructive measurements of tree seedlings and willows made in 2001 were used to develop allometric equations to predict dry biomass from basal diameter. Measurements of soil organic layer depth were made at 5 m intervals with the use of a spade to excavate small chunks of sod. At one randomly-selected sample point per transect, a 10x10 cm sample of the organic layer was collected for bulk density measurements. Bulk density samples were dried in a 60degC oven for 48 hours and then w
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