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263 results for “forest fire”
Bastrop (TX, USA) forest fire cross-sensor change detection images
<p><strong>Abstract. </strong></p> <p>This dataset is composed of a set of four images acquired by different sensors over the Bastrop County, Texas (USA). On September 4, 2011, the region has been struck by ‘‘the most destructive wildland-urban interface wildfire in Texas history’’ which caused 2 casualties, more than 1300 destroyed buildings and almost burned entirely the Bastrop county state park.</p> <p>This dataset is composed of a pair of pre- and post-event images from the same sensor, the Landsat 5 TM (L5T1 and L5T2), which are completed by a post-event of another sensor, the Advanced Land Imager (ALI) from the Earth Observing (EO-1) mission, acquired very shortly after the L5T2 (denoted ALIT2). These three scenes are very similar to each other and no apparent changes between L5T2 and ALIT2 are visible, since images were acquired within 1 day. Differences between these pre- and post-event pairs are only due to burned forest since they were acquired at a 16 days interval during summer. We also dispose of a fourth image of the same area acquired one year and 9 months after the forest fire by the Landsat 8 Operational Land Imager (OLI, L8T2 hereafter). The differences between L5T1 and L8T2 are significant, due both to sun/sensor angles and the long temporal interval between acquisitions. A whole new series of building has been constructed in the burn scar and many cultivated crops are at a different stage of growth.</p> <p>Table : Dataset description</p> <pre><code class="language-markdown">| | Pre-event | Post-event 1 | Post-event 2 | Post-event 3 | |--------- |--------------|--------------|--------------|---------------| | Filename | t1_L5 | t2_L5 | t2_ALI | t2_L8 | | Sensor | Landast 5 TM | Landsat 5TM | EO-1 ALI | Landsat 8 OLI | | Channels | 7 | 7 | 9a | 7b | | GSD | 30, 120 | 30, 120 | 30 | 30 | | Sp. Range | [0.45–2.35, | [0.45–2.35, | [0.4–2.4] | [0.43–2.25] | | [\mu m] | 10.40–12.50] | 10.40–12.50] | | |</code></pre> <p>We prepared the ground truth for pairs of change detection problems by photo-interpretation and relying on the maps provided on the emergency response website [1]. In the ground truth involving the L5T1-L8T2 problem we also included changes related to vegetation density and vegetation/bare soil transitions, since these changes are of the same spectral class as those related to the burned scar.</p> <p>This data has been collected from the NASA LP DAAC Program [2], we are free to redistribute the data. For this reason we provide the Bastrop data and the ground truth we defined and used in these experiments (subset of original crops and ground truth).</p> <p><strong>Data Files</strong></p> <p>The archive contains the following files:</p> <pre><code>data ├── t1_L5.tif ├── t2_L5.tif ├── t2_ALI.tif ├── t2_L8.tif ├── ROI_1.tif ├── ROI_2.tif ├── Cross-sensor-Bastrop-data.mat </code></pre> <p>Where:</p> <ul> <li>`t1_L5.tif` is the pre-event image acquired by Landsat 5 TM</li> <li>`t2_L5.tif` is the post-event image acquired by Landsat 5 TM - `t2_ALI.tif` is the post-event image acquired by EO-1 ALI</li> <li>`t2_L8.tif` is the post-event image acquired by Landsat 8 OLI</li> <li>`ROI_1.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L5.tif`</li> <li>`ROI_2.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L8.tif`</li> <li>`Cross-sensor-Bastrop-data.mat` is a Matlab file containing the data in a more convenient format for Matlab users</li> </ul> <p><strong>Citation</strong></p> <p>If you are using this dataset, please cite the following paper:</p> <pre><code>@article{volpi2015jisprs, author = {Michele Volpi and Gustau Camps-Valls and Devis Tuia}, title = {Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {107}, pages = {50-63}, year = {2015}, doi = {https://doi.org/10.1016/j.isprsjprs.2015.02.005}, url = {https://www.sciencedirect.com/science/article/pii/S0924271615000404}, issn = {0924-2716}, }</code></pre> <p>Volpi, M., Camps-Valls, G., Tuia, D. (2015). Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 107, 2015, Pages 50-63. https://doi.org/10.1016/j.isprsjprs.2015.02.005</p>
Forest fire assessement training dataset (2022-07-18 fire at Maclas - France)
<p>This dataset has been created to train Univ. Eiffel personnels on raster data handling with QGIS.</p><p>It provides the following elements:</p><ul><li>Geopackage database with the following layers:<ul><li>QGIS project</li><li>Extract from the SENTINEL-2 2022-06-11 B8A band</li><li>Extract from the SENTINEL-2 2022-06-11 B12 band</li><li>Extract from the SENTINEL-2 2022-07-21 B8A band</li><li>Extract from the SENTINEL-2 2022-07-21 B12 band</li><li>Reclassified delta NBR raster layer</li><li>Delta NBR vector layer</li><li>Studied area bounding box</li></ul></li><li>Intermediate results:<ul><li>pre-event NBR raster file</li><li>post-event NBR raster file</li><li>Delta NBR raster file</li><li>Delta NBR raster file multiplied by 1000 (for easier reclassification)</li></ul></li></ul><p>Data sources IDs from opensearch-theia.cnes.fr-sentinel2-l2a catalogue :</p><ul><li>SENTINEL2B_20220721-104826-811_L2A_T31TFL_D</li><li>SENTINEL2B_20220611-104824-395_L2A_T31TFL_D</li></ul><p> </p>
Young forests and fire: Using lidar-imagery fusion to explore fuels and burn severity in a subalpine forest reburn, Grand Teton National Park, Wyoming.
Anticipating fire behavior as climate change and fire activity accelerate is an increasingly pressing management challenge in fire-prone landscapes. In subalpine forests adapted to infrequent, stand-replacing fire, self-limitation of burn severity in short-interval fire is incompletely understood. Spatially explicit fuels data can support assessments of landscape-scale fire risk and fuels feedbacks on burn severity. For a about 1,450 km2 largely forested landscape in the US Northern Rocky Mountains, we used airborne lidar and imagery to predict and map canopy and surface fuels. In a fire that burned mature ( greater than 125-year-old) and also reburned young (~30-year-old) subalpine forest, we then asked: (1) How do pre-fire fuels and burn severity compare between young and mature forests that burned under similar fire weather conditions? (2) How well do pre-fire fuels and forest structure predict burn severity under extreme versus moderate fire weather? Lidar-imagery fusion predicted fuel characteristics with high accuracy across forest and shrubland vegetation (R2 from 0.65-0.95). Young post-fire forests had abundant, densely packed canopy fuels, and both young and mature forests had similar canopy fuel loads and coarse wood biomass. Under similar weather conditions, young and mature forests burned at similar severity. Overall, fuels were weak predictors of burn severity and, surprisingly, better predicted severity under extreme (R2LMM(m) = 0.27) rather than moderate (R2LMM(m) = 0.15) fire weather. Our findings are relevant for subalpine landscapes increasingly dominated by young lodgepole pine (Pinus contorta var. latifolia) forests vulnerable to short-interval fire and provide a benchmark to assess how fuels influence burn severity in future fires. Fire managers should continually reassess fuels and update expectations about fire behavior as landscapes change. Although recovering post-fire forests can limit fire spread and severity for a period of time, our resu
Less fuel for the next fire? Short-interval fire delays forest recovery and interacting drivers amplify effects, Greater Yellowstone Ecosystem, Montana and Wyoming, USA
As 21st-century climate and disturbance dynamics depart from historical baselines, ecosystem resilience is uncertain. Multiple drivers are changing simultaneously, and interactions among drivers could amplify ecosystem vulnerability to change. We explored how interacting drivers affected post-fire recovery of subalpine forests, which Subalpine forests in Greater Yellowstone (Northern Rocky Mountains, USA) were historically resilient to infrequent (100-300 year), severe fire., in Greater Yellowstone (Northern Rocky Mountains, USA). We sampled paired short- (< 30 year) and long- (> 125 year) interval post-fire plots most recently last burned between 1988 and 2018 to address two questions: (1) How do short-interval fire, climate, topography, and distance to unburned live forest edge and other factors (topography, distance to live edge) interact to affect post-fire forest recoveryregeneration? (2) How do forest biomass and fuels vary following short- versus long-interval severe fires? Mean post-fire live stem density was an order of magnitude lower following short- versus long-interval fires (3,240 versus 28,741 stems ha-1, respectively). Differences between paired plots increased with greater climate water deficit normal (ρ = 0.67) and were amplified at longer distances to live forest edge. Surprisingly, warmer-drier climate was associated with higher seedling densities even after short-interval fire, likely relating to regional variation in serotiny of lodgepole pine (Pinus contorta var. latifolia). Unlike conifers, density of aspen (Populus tremuloides), a deciduous resprouter, increased with short- versus long-interval fire (mean 384 versus 62 stems ha-1, respectively). Live biomass and canopy fuels remained low nearly 30 years after short-interval fire, in contrast to rapid recovery after long-interval fire, suggesting that future burn severity may be reduced for several decades following reburns. Short-interval plots also had half as much dead woody biomass compar
Peeking under the canopy: anomalously short fire-return intervals alter subalpine forest understory plant communities
Changing climate and fire regimes are profoundly affecting temperate coniferous forests, driving greatly reduced tree cover postfire. However, whether similar changes are present in the understory of these forests remains less well-understood. We sampled understory plant communities in 20 plot pairs across Greater Yellowstone (Wyoming, USA) in July and August 2021, with each including one plot burned at short (<30 year) fire-return interval and one plot burned in the same most recent fire but not burned previously for >125 years. We also included 11 plot pairs meeting our definition of short- and long-interval fire that were sampled 12 years after the 1988 Yellowstone fires in summer 2000. We also used previously collected published and unpublished data to compare understory communities following recent (2016) short-interval fires to those following the previous long-interval fire in the same general area. In each plot, percent cover of understory plant species was estimated in 0.25-m2 quadrats, and species richness determined via a whole-plot sweep. Understory plant community cover, richness, and diversity did not differ by interval class, but species able to persist in drier conditions and in lower vegetation zones became more abundant following shot interval fire. Further, previously distinct understory communities following long-interval fire in two regions of Greater Yellowstone became slightly more similar following recent short-interval fire. Dissimilarity between plot pairs increased with greater historical snowfall and decreased with time since fire and postfire winter snowfall. These changes to understory plant communities may continue with ongoing shifts in climate and fire across temperate and boreal forests.
Snag-fall patterns following stand-replacing fire vary with stem characteristics and topography in subalpine forests of Greater Yellowstone
We assessed the stem- and landscape-level drivers of snag persistence and snag-fall mode within the area burned as stand-replacing fire in the 1988 Yellowstone Fires in Yellowstone National Park, Wyoming, USA. Snags were sampled 14-15 years postfire (n = 131) and again in a separate set of plots 34 years postfire (n = 55). Stem characteristics such as species identity (e.g., lodgepole pine, whitebark pine, Engelmann spruce, subalpine fir, and Douglas-fir), diameter at breast height, whether the tree was alive or dead at the time of fire, and the mode of snag-fall (snapping or uprooting) were measured and used to explain patterns of snag persistence and modes of snag-fall. In addition, plot-level environmental variables (e.g., slope, aspect, elevation, stand density) were measured and related to the proportion of stems still standing as snags at 14-15 and 34 years postfire. Data collection is complete and is part of a forthcoming manuscript in revision at Forest Ecology and Management.
Data for: Sparse subalpine forest recovery pathways, plant communities, and carbon stocks 34 years after stand-replacing fire (Greater Yellowstone Ecosystem, Wyoming, USA; 2022)
We assessed postfire forest recovery pathways, stem densities, understory plant communities, and carbon stocks across 55 plots in areas exhibiting sparse and reduced forest recovery 34 years after the 1988 Yellowstone Fires in the Greater Yellowstone Ecosystem, Wyoming, USA. Recovery pathways were identified using plot-level frequency distributions of tree ages and correlated with potentially important biotic and abiotic variables (e.g., elevation, seed source distance). Species- and age-specific stem densities were similarly regressed across environmental factors to determine variability in forest recovery across the sampled landscape. Understory plant communities were sampled in 0.25m-square quadrats and environmental drivers of individual species occurrence and whole compositional shifts were determined. Finally, carbon stock sizes were derived from field measures of tree characteristics, understory cover, and soil combined with regionally derived allometric equations. Data collection is complete and is part of a forthcoming manuscript at Ecological Monographs.
Data for: Reburning before recovery: Effects of short-interval fire on subalpine forest nitrogen stocks and fluxes
In forests adapted to infrequent (>100-yr) stand-replacing fires, novel short-interval (<30-yr) fires have started to burn young forests before they recover from previous burns. Postfire tree regeneration is reduced, plant communities shift, soils are hotter and drier, but effects on biogeochemical cycling are unresolved. This study focused on how postfire nitrogen (N) stocks, N availability and N fixation varied in lodgepole pine (Pinus contorta var. latifolia) forests burned at long and short intervals in Grand Teton National Park (Wyoming, USA). This data package includes our field data from 2021 and 2022, along with laboratory analyses of foliar and litter chemistry, resin-sorbed N, and field measurements of N fixation. The data included here were also used to compute aboveground N stocks. Our study found that short-interval fires reduced and repartitioned aboveground N stocks, but soil N stocks were unaffected. Results indicate that these shifts in N pools and fluxes suggest reburns can markedly alter N cycling in subalpine forests. The citation for the publication associated with these data is: Turner, M. G., R. E. Heumann, N. G. Kiel, J. A. Warren, and C. C. Cleveland. Reburning before recovery: Effects of short-interval fire on subalpine forest nitrogen stocks and fluxes. Ecosystems (In press)
Dendrochronology study of fire history, Andrews Experimental Forest and central western Cascades, Oregon, 1482-1952
Fire history is documented for an 11,000 hectare (27,110 acre) area in the western Oregon Cascades , including H. J. Andrews Experimental Forest. Fire scar and tree origin data were collected mainly from stumps at 359 sites. Thirty-five fire events are mapped from 1482 to 1952. Mean fire return intervals are derived from data at individual sites, about 5 hectares in size, rather than from the corrected master fire chronology.
Spot fire locations (1991), Andrews Experimental Forest
1991 Spot Fire Locations for the HJ Andrews Experimental Forest. This data documents the locations of fires during the 1991 fire season. There is little additional information about the fires and suppression efforts.
Fire history reconstruction (1482 - 1952), Andrews Experimental Forest and vicinity
Fire History - H J Andrews and Vicinity (1482 - 1952) individual fires and fire frequency. Fire history studies by Peter Teensma provide the base information for this layer. Individual fire episodes were manuscripted onto HJA base maps and digitized. Fire episodes were maintained in thirty one separate polygon coverages, until Arc/Info Version 7 provided the region feature class to accomodate overlapping polygons. REGIONPOLY was used to create regions for each fire episode and then the 31 episode regions were combined using UNION. The field "AGE" = 0 means that a fire occured in the polygon. AGE = 1 means that fire was absent
Age structure, developmental pathways, and fire regime characterization of Douglas-fir/western hemlock forests in the central western Cascades of Oregon
These data are the raw forest stand- and age-structure data from 124 stands in the central western Cascades of Oregon used to construct a conceptual model of stand development under the mixed-severity fire regime that has operated extensively in this region.
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
Data from: A changing climate is snuffing out post-fire recovery in montane forests
Aim: <p>Climate warming is increasing fire activity in many of Earth's forested ecosystems. Because fire is an important catalyst for change, investigation of post-fire vegetation response is crucial for understanding the potential for future conversions from forest to non-forest vegetation types. To better understand effects of wildfire and climate warming on forest recovery, we assessed the extent to which climate and terrain influence spatiotemporal variation in past and future post-fire tree regeneration.</p> Location: <p>Montane forests, Rocky Mountains, USA</p> Time Period: <p>1981-2099</p> Taxa Studied: <p><i>Pinus ponderosa</i>; <i>Pseudotsuga menziesii</i></p> Methods: <p>We developed a network of dendrochronological samples (n = 717) and field plots (n = 1301) from post-fire environments spanning a range of topographic and climatic settings. We then used boosted regression trees to predict annual suitability for post-fire seedling establishment and generalized linear mixed models to predict total post-fire seedling abundances, reconstructing recent trends in post-fire recovery and projecting future dynamics using three general circulation models (GCMs) under moderate and extreme emission scenarios.</p> Results: <p>Though 1981-2015 declines in growing season (April-September) precipitation were associated with declining suitability for seedling establishment, 2021-2099 trends in precipitation were widely variable among GCMs, leading to mixed projections of future establishment suitability. In contrast, climatic water deficit (CWD), strongly tied to warming temperature and increased evaporative demand, was projected to increase throughout our study area. Our projections strongly suggest that future increases in CWD and an increased frequency of extreme drought will reduce post-fire seedling abundances.</p> Main Conclusions: <p>Our findings highlight the key roles of warming and drying in declines in forest resilience to wildfire. The striking differences in projections of post-fire recovery between moderate and extreme emissions scenarios suggest that the most extreme impacts on forest resilience in the latter part of the 21<sup>st</sup> century may be mitigated with aggressive emissions reductions in the next two decades.</p>
Loss and fragmentation of fire-resistent primary forest cover in Sumatra and Kalimantan
<p>Here we share primary forest loss and fire occurrence in Sumatra and Kalimantan covering 2001 through 2019 period. The datasets include primary forest cover fraction and active fire detection counts at 1km spatial resolution and annual time step.</p> <p>For details on the datasets see included README file and the following open access publication:</p> <p>Nikonovas <em>et al</em>., Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan,<em> Communs Earth and Environ., <strong>1</strong>, (2020).</em></p> <p>Usage Notes</p> <p>Contact Tadas Nikonovas (tadas.nik@gmail.com) for questions on usage or additional details.</p> <p> </p> <p>Acknowledgements</p> <p>This study forms part of the Towards a Fire Early Warning System for Indonesia (ToFEWSI) project (Oct. 2017- Oct. 2021), which is funded through the UK’s National Environment Research Council – Newton Fund on behalf of UK Research & Innovation (NE/P014801/1), Indonesia Endowment Fund for Education and the Indonesian Science Fund (Principal Investigators: Allan Spessa (UK) and Muhammad Ali Imron (Indonesia)). The ToFEWSI project is developing a suite of climate, hydrological- and agent-based models to predict the incidence of peat forest fires in Indonesia, plus new evidence-based proposals for managing fires in Indonesia.</p> <p> </p>
Data from: Thinning and prescribed burning increase shade-tolerant conifer regeneration in a fire excluded mixed-conifer forest
<p>Fire exclusion and past management have altered the composition, structure, and function of frequent-fire forests throughout western North America. In mixed-conifer forests of the California Sierra Nevada, fire exclusion has exacerbated the effects of drought and endemic bark beetles, resulting in extensive mortality of fire-adapted pine species. Thinning and prescribed fire are widely used in these forests to reduce fuels, moderate fire behavior, and restore ecosystems. Tree regeneration influences future forest composition and structure, and therefore future resilience to disturbances, but long-term effects of thinning and prescribed burning on tree regeneration after prolonged fire exclusion are poorly understood. We measured tree regeneration one year prior to, and periodically for 16 years following thinning and prescribed burning in a mixed-conifer forest in the Sierra Nevada, California, USA. We asked three questions. How did the composition and density of tree regeneration change after thinning and prescribed burning? Did pretreatment vegetation types influence conifer regeneration density after treatments? Did planting after overstory thinning increase regeneration density of native pine species?</p> <p>Sixteen years after treatments, combined natural regeneration of shade-tolerant white fir (Abies concolor) and incense-cedar (<em>Calocedrus</em> <em>decurrens</em>) averaged 2,032 trees per hectare (tph) after understory thinning, and 7,745 tph after understory thinning combined with prescribed burning, increases of 37% and 146% from pretreatment densities. In contrast, combined natural regeneration of white fir and incense-cedar averaged 497 tph after overstory thinning, 780 tph after overstory thinning with prescribed burning, 113 tph after prescribed burning alone, and 807 tph in untreated controls, all of which were declines from pretreatment densities. Natural regeneration of white fir and incense-cedar was consistently an order of magnitude greater than Jeffrey pine (<em>Pinus</em> <em>jeffreyi</em>) and sugar pine (<em>Pinus</em> <em>lambertiana</em>), whose combined densities 16 years after treatments averaged 37 tph across treatments and did not significantly respond to thinning and/or prescribed burning. Natural conifer regeneration after treatments varied by pre-treatment vegetation type (closed canopy, <em>Ceanothus</em> <em>cordulatus</em> shrub-dominated, and open sparse), with large increases of natural regeneration after understory thinning in closed canopy and <em>Ceanothus</em> shrub vegetation types. Planting increased sugar pine regeneration density after overstory thinning, marginally increased Jeffrey pine regeneration after overstory thinning combined with prescribed burning, and increased white fir regeneration after overstory thinning with and without burning. No treatments reduced white fir and incense-cedar natural regeneration while simultaneously increasing natural pine regeneration, suggesting new thinning, burning, and planting approaches may be required to meet regeneration restoration objectives.</p>
Structure of the Canadian Forest Fire Weather Index System: the model and its components
<p>This material is part of:</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br><strong>Structure of the Canadian Forest Fire Weather Index system: the model and its components</strong> — The <a href="../record/10806780#preview-iframe">figure below</a> (formats: <a href="../record/10806780/files/FWI-sys_simple_diagram.png?download=1">PNG</a> or <a href="../record/10806780/files/FWI-sys_simple_diagram.pdf?download=1">PDF</a>) shows the logical subdivision of the Canadian Forest Fire Weather Index system (FWI-sys) in components.</p> <p> </p> <p>The Canadian FWI-sys (De Groot,1987; Van Wagner,1987) is an index of fire danger by weather designed to consider the effects on vegetation fuels of the sequence of weather conditions. It is designed to estimate a uniform numerical rating for the relative fire potential accounting for the local sequence of temperature, wind speed, relative humidity, and precipitation, for the day in which the rating is estimated but also modelling the dynamics of the previous days. In addition, the variable amount of possible drying due to the varying solar irradiation in different seasons is taken into account by adjusting the parameters per each month of the year.<br><br>The system is standardised to consider the behaviour of a reference typology of vegetation fuel (mature pine stand) regardless of other non-weather factors which may locally influence the fire danger, such as the specific topography or the pattern, composition, and structure of vegetation assemblages. Therefore, FWI-sys is suitable to support the harmonised comparison among variable weather conditions, either spatially (comparing different spatial regions) or temporally (comparing the same region over time).<br><br>The FWI-sys components are organised in three layers, processing at the daily frequency weather information (either from observations, reanalysis, forecast, or climate scenarios) and estimating from it a final standard aggregated numerical rating of fire intensity.<br><br>The required input variables are</p> <ul> <li>Temperature T (nominally, FWI-sys requires T at noon)</li> <li>Wind speed W (nominally, FWI-sys requires T at noon)</li> <li>Relative humidity</li> <li>Precipitation (24-hour rainfall)</li> <li>Month of the year</li> </ul> <p>The FWI-sys was originally designed to fit the Candian conditions. Following its success, adaptations of the system were studied for different areas of the globe. This implies that the parameters used inside the FWI-sys globally also depend on the latitude (Alexander, 2008).</p> <p>The first layer of components (the <em>fuel moisture codes</em>: Fine Fuel Moisture Content, FFMC; Duff Moisture Code, DMC; Drought Code, DC) is composed by dynamic variables. This means that the value of each component for a given day depends also on the value of the same component the day before. The dynamic components with longer memory of their past history also approximate the seasonal changes in solar radiation, by considering the month of the year (see Figure, bottom left).</p> <ul> <li><strong>Fine Fuel Moisture Code (FFMC)</strong> : provides a numerical rating of the moisture content of the top litter and other cured fine fuels, indicating the relative ease of ignition and flammability of fine fuel.</li> <li><strong>Duff Moisture Code (DMC)</strong> : models a standard moisture content of loosely-compacted organic layers of moderate depth (duff layers and medium-sized woody material). This component of the FWI-sys represents wooden fuels of intermediate thickness.</li> <li><strong>Drought Code (DC)</strong> : models a standard moisture content of deeper, compact, organic layers. This component of the FWI-sys is able to track seasonal drought effects on coarse wooden fuels.</li> </ul> <p> </p> <p>The second layer of components (the<em> fire behaviour indices</em>: Initial Spread Index, ISI; Buildup Index, BUI; Fire Weather Index, FWI) mathematically is composed by stateless D-TM components. This means that these components do not have an internal memory of the past conditions, while instead they rely on the combined information offered by the different temporal inertia of the fuel moisture codes, which they process as input information.</p> <ul> <li><strong>Initial Spread Index (ISI)</strong> : represents the expected rate of fire spread. It considers the combined effects of wind and the FFMC on the rate of spread. However, it excludes the influence of fuel moisture and availabity for the coarser wooden fuels.</li> <li><strong>Buildup Index (BUI)</strong> : combines DMC and DC to model the total amount of fuel available for combustion to the spreading fire.</li> <li><strong>Fire Weather Index (FWI)</strong> : offers a standard aggregated numerical rating of fire intensity which combines ISI and BUI.</li> </ul> <p><br>Given its structure, the model can also be interpreted as a recurrent neural network (RNN) where the input variables are transformed into the final aggregated numerical rating (FWI) by means of two hidden layers: the <em>fuel moisture codes</em> (three nodes/neurons); and the <em>fire behaviour indices</em> (two nodes/neurons).</p> <p>Note that this structure is not a simple feedforward network, as the first hidden layer is made by dynamic components (FFMC, DMC, DC, see highlighted feedack loops in <a href="../record/10806780/files/FWI-sys_simple_diagram_recurrent.png?download=1">PNG</a> format). The activation functions are complex, and the D-TM components (either dynamic or stateless) generally mix physically-based and empirical aspects. A consequence of the complexity of the FWI-sys activation functions is that a neural network with standard (e.g. sigmoidal) activation functions would need to exploit disproportionally many more additional neurons for the same FWI-sys D-TM complexity to be reasonably approximated.</p> <p> </p> <p>An additional FWI-sys component is a simple transfromation of the aggregated FWI values to better account for the nonlinear increase of fire control effort with increasing FWI values (Van Wagner, 1987):</p> <ul> <li><strong>Daily Severity Rating (DSR)</strong>: this transformation of FWI is meant to provide a measure of control difficulty:<br> DSR = 0.0272 ⋅ FWI <sup>1.77</sup><br>which easily invertible:<br> FWI = ( DSR / 0.0272 ) <sup>1 / 1.77</sup></li> </ul> <p><br><br>To cite the Figure, please refer to:<br><br>de Rigo, 2016. <strong>Structure of the Canadian Forest Fire Weather Index System: the model and its components</strong>. https://doi.org/10.5281/zenodo.6558576</p> <p>which is part of</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br> </p> <p> </p> <p><strong>References</strong></p> <p>De Groot, W.J., 1987. <strong>Interpreting the Canadian Forest Fire Weather Index (FWI) System</strong>. In: <em>Fourth Central Regional Fire Weather Committee Scientific and Technical Seminar, Proceedings</em>. Winnipeg, Manitoba, Canada, pp. 3-14. <a href="https://purl.org/INRMM-MiD/c-14176512">https://purl.org/INRMM-MiD/c-14176512</a> </p> <p>Van Wagner, C.E., 1987. <strong>Development and structure of the Canadian Forest Fire Weather Index System</strong>. <em>Forestry Technical Report</em>. Canadian Forestry Service, Ottawa, Canada. <a href="https://purl.org/INRMM-MiD/c-14168337">https://purl.org/INRMM-MiD/c-14168337</a> </p> <p>Alexander, M.E., 2008. <strong>Latitude considerations in adapting the Canadian Forest Fire Weather Index System for use in other countries</strong>. In: Lawson, B.D., Armitage, O.B. (Eds.), <em>Weather Guide for the Canadian Forest Fire Danger Rating System</em>. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada, pp. 67–73. ISBN:978-1-100-11565-8 <a href="https://purl.org/INRMM-MiD/z-MBDA6A6I">https://purl.org/INRMM-MiD/z-MBDA6A6I</a></p> <p> </p>
Supporting data for managing fire-prone forests in a time of decreasing carbon carrying capacity
<p>These data and code include surface fuels and prescribed fire emissions data from the Teakettle Experimental Forest in the Sierra Nevada, California, USA. These data include transect data of surface fuels and the emissions from a 2017 prescribed burn. Emissions from the prescribed burn were calculated using a stock change approach by subtracting pre-burn surface fuels from post-burn surface fuels. We used these data and a Monte Carlo simulation approach to estimate the frequency of prescribed burning required to reduce surface fuels following a widespread overstory tree mortality event.</p>
Burnt forest area and CO2 emissions from fires in Russian forests by fire protection zones in 2010-2020
<p>The dataset is Supplementary Materials for the article ''<em>Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2010-2020</em>'' in the Carbon Balance and Management journal. It contains files with burnt forest area and carbon dioxide emissions from fires data in Russia 2010-2020.<br> Article Supplementary materials are stored in the file Supplementary Tables and contains:</p> <p>- Table 1. Burnt forest area from NIR and MODIS (MCD64A1) in 2010-2020;</p> <p>- Table 2. Burnt forest area in the ground, aviation and the control (no fire protection) zones in 2010-2020 using MCD64A1;</p> <p>- Table 3. Carbon emissions from forest fires from National Inventory Report (NIR) and Copernicus Atmosphere Monitoring Service (CAMS) in 2010-2020</p> <p>Also, there is Supplementary Figure 1 with the Federal Districts of Russia schematic map.</p> <p>There are 22 files in GeoTIFF format for every year: </p> <p>1. Burnt forest area obtained using MODIS product MCD64A1 (250 m pixel, ESRI:102025). Coverage: -4064059.5401764437556267,1967242.6686790268868208 : 3658440.4598235562443733, 6012242.6686790268868208</p> <p>2. CO2 emissions using Copernicus Atmosphere Monitoring System (CAMS) (0.1 degrees, VGS 84). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594<br> <br> In addition, we share Shapefiles:</p> <p>1. Russian borders (necessary to cut Russia from CO2 GeoTIFFs), EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992;</p> <p>2. Forest Fire Protection zoning in 2019: ground zone, aviation zone, the so-called control zone (no fire protection), EPSG:4326. Coverage: 27.4019779002987676,41.3483353426717599 : 173.8255532772949721,72.6575707670955353.</p>
Dataset for: Fire and forest loss in the Dominican Republic during the 21st Century
<p>This dataset is required to reproduce the code for the manuscript entitled 'Fire and forest loss in the Dominican Republic during the 21st Century'</p> <p>Preprint: https://doi.org/10.1101/2021.06.15.448604</p> <p>Code: https://github.com/geofis/forest-loss-fire-reproducible</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.