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1,989 results for “Fires”
Investigations on Fire-Gilding
<p>Fire-gilding is a historic technique for the application of golden layers on a number of different base materials, identifiable by the Hg content of these layers. Considering recent findings, the previously accepted lower limit for Hg content comes into scrutiny, motivating the development of a new, destruction-free identification method independent of the absolute Hg content. During the course of development, we identified a characteristic Hg profile in fire-gildings on Ag substrates and gathered evidence to further corroborate the accept lower limit.</p>
VIIRS-based Fire Emission Inventory (data)
<p>The VIIRS-based Fire Emission Inventory provides daily open biomass burning emission fluxes for 46 species of aerosols and gases at ~500 m resolution (globally). The data starts on early 2012 because it uses the VIIRS I-band active fire product.</p>
VIIRS-based Fire Emission Inventory (data)
<p>The VIIRS-based Fire Emission Inventory provides daily open biomass burning emission fluxes for 46 species of aerosols and gases at ~500 m resolution (globally). The data starts on early 2012 because it uses the VIIRS I-band active fire product.</p>
Fire and forage quality: post-fire regrowth quality and pyric herbivory in subtropical grasslands of Nepal
<p>Indiscriminate fire is rampant throughout subtropical South and Southeast Asian grasslands. However, very little is known about the role of fire and pyric herbivory on the functioning of highly productive subtropical monsoon grasslands lying within Cwa-climatic region. We collected grass samples from 60 m x 60 m plots and determined vegetation physical and chemical properties at regular 30-day intervals from April to July 2020, starting from 30 days after fire to assess post-fire regrowth forage quality. We counted pellet groups for the same four months from 2 m x 2 m quadrats that were permanently marked with pegs along the diagonal of each 60 m x 60 m plot to estimate grazing intensity to the progression of post-fire regrowth. We observed strong and significant reductions in crude protein (mean value 9.1 to 4.1 [55% decrease]) and phosphorus (mean value 0.2 to 0.11 [45% decrease]) in forage collected during different time intervals i.e., from 30 days to 120 days after fire. Mesofaunal deer utilised the burned areas extensively for a short period, <i>i.e</i>., up to two months after fire when the burned areas contained short grasses with a higher level of crude protein and phosphorus. Grazing intensity of chital (<i>Axis axis</i>) to post-fire regrowth differed significantly over time since fire, with higher intensity of use at 30 days after fire. Grazing intensity of swamp deer (<i>Rucervus duvaucelii</i>) did not differ significantly until 90 days after fire, however, decreased significantly after 90 days since fire. Large-scale indiscriminate single event fires thus may not fulfil nutritional requirements of all species in mesofaunal deer community in these subtropical monsoon grasslands. We recommend for a spatio-temporal manipulation of fire to reinforce grazing feedback and to yield for the longest possible period a reasonably good food supply for the conservation of mesofaunal deer.</p>
Direct and indirect effects of fire on microbial communities in a pyrodiverse dry-sclerophyll forest
<p>Fire is one of the predominant drivers of the structural and functional dynamics of forest ecosystems. In recent years, novel fire regimes have posed a major challenge to the management of pyrodiverse forests. While previous research efforts have focused on quantifying the impacts of fire on above-ground forest biodiversity, how microbial communities respond to fire is less understood, despite their functional significance.</p> <p>Here, we describe the effects of time since fire, fire frequency and their interaction on soil and leaf litter fungal and bacterial communities from the pyrodiverse, <em>Eucalyptus pilularis</em> forests of south-eastern Australia. Using structural equation models, we also elucidate how fire can influence these communities both directly and indirectly through biotic-abiotic interactions.</p> <p>Our results demonstrate that fire is a key driver of litter and soil bacterial and fungal communities, with effects most pronounced for soil fungal communities. Notably, recently burnt forest hosted lower abundances of symbiotic ectomycorrhizal fungi and Acidobacteria in the soil, and basidiomycetous fungi and Actinobacteriota in the litter. Compared with low fire frequencies, high fire frequency increased soil fungal plant pathogens, but reduced Actinobacteriota. The majority of fire effects on microbial communities were mediated by fire-induced changes in litter and soil abiotic properties. For instance, recent and more frequent fire was associated with reduced soil sulphur, which led to an increase in soil fungal plant pathogens and saprotrophic fungi in these sites. Pathogenic fungi also increased in recently burnt forests that had a low fire frequency, mediated by a decline in litter carbon and an increase in soil pH in these sites.</p> <p><em>Synthesis</em>. Our findings indicate that predicted increases in the frequency of fire <span>may select for specific microbial communities directly and indirectly through ecological interactions, which may have functional implications</span> for plants (increase in pathogens, decrease in symbionts), decomposition rates (declines in Actinobacteriota and Acidobacteriota) and carbon storage (decrease in ectomycorrhizal fungi). In the face of predicted shifts in wildfire regimes, which may exacerbate fire-induced changes in microbial communities, adaptive fire-management and monitoring is required to address the potential functional implications of fire-altered microbial communities<span>.</span></p>
Data: Drought and fire determine juvenile and adult woody diversity and dominance in a semi-arid African savanna
<p><em>Aim</em>: To understand how communities of adult and juvenile (seedlings and saplings) woody plants were impacted by fire and the 2014 – 2016 El Niño drought in Kruger National Park, South Africa.</p> <p><em>Methods</em>: We used a landscape scale fire experiment spanning 2013-2019 in a semi-arid savanna in the central west of Kruger National Park (mean annual precipitation, 543 mm). Adult and juvenile woody species composition were recorded during and after the drought in 40 plots that experienced a mix of no fire, moderate fire and frequent fire treatments. Using multivariate modelling, we related community composition in juvenile and adult woody plants to year of sampling and the experimental fire treatments.</p> <p><em>Results</em>: Post-drought, there was significant adult woody plant top-kill, especially in dominant species <em>Dichrostachys cinerea</em> (81% reduction in abundance), <em>Acacia nigrescens</em> (30%), and <em>Combretum apiculatum</em> (19%), but no significant change in adult species richness. Two years post-drought, abundance of all juveniles decreased by 35%, and species richness increased in juveniles in both the frequent fire (7%) and no fire treatments (32%).</p> <p><em>Conclusion</em>: Counter-intuitively, the El Niño drought increased species richness of the woody plant community due to the recruitment of new species as juveniles, a potential lasting impact on diversity, and where different fire regimes were associated with differences in community composition. Drought events in semi-arid savannas could drive temporal dynamics in species richness and composition in previously unrecognised ways.</p>
Fire regime parameters and potential biophysical fire regime drivers - NUTS2 Centro, Portugal
<p>The dataset, presented as an SPSS data file, includes three components. All were calculated for the civil parishes comprised in the NUTS2 Centro territorial unit in Central Portugal, identified by name in the column <em>Parish_name</em>.</p> <p>These components are:</p> <p>1) three fire regime descriptors (expressed in original units and as z-scores)</p> <p>2) twelve potential biophysical fire regime drivers (expressed in original units and as z-scores).</p> <p>3) the cluster associated to each parish in both 3-cluster and 4-cluster solutions, obtained by running cluster analyses using the three fire regime descriptors as input variables.</p> <p>All fire regime descriptors and potential fire regime drivers are described in:</p> <p>Bergonse, R., Oliveira, S., Zêzere, J. L., Moreira, F., Ribeiro, P. F., Leal, M., & Lima e Santos, J. M. (2022). Biophysical controls over fire regime properties in Central Portugal. Science of The Total Environment, 810, 152314. <a href="https://doi.org/10.1016/j.scitotenv.2021.152314">https://doi.org/10.1016/j.scitotenv.2021.152314</a>.</p> <p>They are also identified in the ReadMe file included in the Dataset.</p>
Can fire-age mosaics really deal with conflicting needs of species? A study using population hotspots of multiple threatened birds
<p> Locations that support high densities of a species ("population hotspots") have a disproportionate influence on species' persistence. In fire-prone ecosystems, managers attempting to promote population hotspots of multiple species must understand how hotspot locations might shift with post-fire succession and how much overlap exists in the locations of population hotspots for multiple species. Mangers are then tasked with resolving fire-management conflicts in overlapping locations.</p> <p>We studied three co-occurring threatened bird species in a fire-prone 'mallee' region of south-eastern Australia. We undertook field surveys for each species (1508 surveys; 540 sites; 9-ha each). We used N-mixture models to determine (a) what factors affect species' density (including post-fire succession); (b) species' population sizes; (c) locations of species' current population hotspots and locations that may become population hotspots in the future as the post-fire successional state changes and (d) the degree of overlap in the current and possible future hotspots of species.</p> <p>We found substantial variation in the densities of the three species across the study area, with roughly half of each species' population occurring in only 20 percent of potential habitat (i.e. population hotspots). All species shared a preference for subtle depressions in the landscape, resulting in substantial overlap in their population hotspots. Two species had contrasting responses to post-fire succession in the subtle depressions. As a result, there was only a narrow post-fire period that supported population hotspots of both species, creating a challenge for fire managers in these shared locations.</p> <p><em>Synthesis and Applications.</em> Many studies make vague recommendations for fire-age mosaics that do not provide managers with the detail they need to implement appropriate fire-age mosaics. By contrast, we explicitly quantify, then balance the conflicting post-fire needs of species in locations that support population hotspots of multiple species. Using this approach, we develop principles to guide the implementation of fire-age mosaics in such locations. This approach represents a step towards applying fire-age mosaic theory to support effective species conservation.</p>
The effects of a half century of warming and fire exclusion on montane forests of the Klamath Mountains, California, USA
<p>These files are the raw data used in the manuscript "Effects of a half century of warming and fire exclusion on montane forests of the Klamath Mountains, California, USA"</p> <p>Manuscript authors: Erik S. Jules, Melissa H. DeSiervo, Matthew J. Reilly, Drew S. Bost, and Ramona J. Butz</p> <p>Climate warming and altered disturbance regimes are changing forest composition and structure worldwide. Given that species often exhibit individualistic responses to change, making predictions about the cumulative effects of multiple stressors across environmental gradients is challenging, especially in diverse communities. For example, warming temperatures are predicted to drive species upslope, while fire exclusion promotes expansion of species at lower elevations where fire was historically frequent.</p> <p>We resampled 148 vegetation plots to assess 46-years (1969 to 2015) of species and community-level response to warming and fire exclusion in a topographically complex landscape in the Klamath Mountains, California (USA), a diverse region that served as a climate refugia throughout the Holocene. We compared cover and assessed change in the elevational distributions of 12 conifer species at different life stages (i.e., seedlings, saplings, canopy).</p> <p>We observed consistent but non-significant shifts upward in elevation for eight species, and a significant shift upward for one species, all of which were far less than expectations based on recent warming. Six species declined in total cover and another five declined in at least one life stage, while the drought- and fire-intolerant <em>Abies concolor</em> increased by 30.7%. The largest declines were at lower elevations in drought-tolerant, early seral species (<em>Pinus lambertiana</em> and <em>Pinus ponderosa</em>) and at higher elevations for the shade-tolerant <em>Abies magnifica</em> var. <em>shastensis</em> and the regionally rare <em>Abies lasiocarpa</em>. Regionally rare (<em>Picea engelmannii</em>) and endemic (<em>Picea breweriana</em>) species had reductions in early life stages, portending future declines. Multivariate analyses revealed a high degree of inertia with a minor but significant shift in composition and a slight decrease in species turnover along the elevation gradient driven by expansion of <em>A. concolor</em>. Our results indicate that most species are declining, especially at lower- and mid-elevations where fire exclusion increased cover of shade-tolerant species and reduced recruitment for fire-adapted species. Collectively, declines in most species, insufficient upward movement to track warming, reductions in drought- and fire-tolerant early seral species, and an increase in a single, shade-tolerant species will leave these communities maladapted to projected climate scenarios and questions the potential for future climate refugia in this region.</p>
Data from: Quantifying the environmental limits to fire spread in grassy ecosystems
<p>Modeling fire spread as an infection process is intuitive: an ignition lights a patch of fuel, which infects its neighbor, and so on. Infection models produce non-linear thresholds, whereby fire spreads only when fuel connectivity and infection probability are sufficiently high. These thresholds are fundamental both to managing fire and to theoretical models of fire spread, whereas applied fire models more often apply quasi-empirical approaches. Here, we resolve this tension by quantifying thresholds in fire spread locally, using field data from individual fires (n=1131) in grassy ecosystems across a precipitation gradient (496-1442mm mean annual precipitation), and evaluating how these scaled regionally (across 533 sites) and across time (1989-2012, 2016-2018) using data from Kruger National Park in South Africa. An infection model captured observed patterns in individual fire spread better than competing models. The proportion of the landscape that burned was well described by measurements of grass biomass, fuel moisture, and vapor pressure deficit. Regionally, averaging across variability resulted in quasi-linear patterns. Altogether, results suggest that models aiming to capture fire responses to global change should incorporate non-linear fire spread thresholds, but that linear approximations may sufficiently capture medium-term trends under a stationary climate.</p> <p><span> </span></p>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
Transient Freeze-Thaw Responses to the 2018 and 2019 Fires near Batagaika Megaslump, Northeast Siberia
<p>This repository contains the dataset for the Transient Freeze-Thaw Responses to the 2018 and 2019 Fires near Batagaika Megaslump, Northeast Siberia. All files are formatted as CSV. </p> <p>TD_Burn14_site1.csv contains thaw depth data at the site1 in the 2014 fire scar. </p> <p>TD_Burn14_site2.csv contains thaw depth data at the site2 in the 2014 fire scar.</p> <p>TD_Burn18.csv contains thaw depth data in the 2018 fire scar.</p> <p>TD_Unburn-Burn19.csv contains thaw depth data along the transect across the 2019 fire scar and unburned control site.</p> <p>TD_Unburn18.csv contains thaw depth data in the unburned control site for the 2018 fire scar.</p> <p>Temperature_TidbiT_190924-210916.csv contains hourly air and surface temperature data from 24 September 2019 to 16 September 2016. All data were taken by the TidbiT logger. </p> <p> </p> <p> </p>
Data from: Pyrogeography across the western Palearctic: A diversity of fire regimes
<p>We characterised fire regimes and estimated fire regime parameters (area burnt, size, intensity, season, patchiness, pyrodiversity) at broad spatial scales using remotely sensed individual-fire data. Specifically, we focused on the western part of the Palearctic realm, i.e., Europe, North Africa, and the Near East. We first divided the study area into eight large ecoregions based on their environment and vegetation (ecoregions): Mediterranean, Arid, Atlantic, Mountains, Boreal, Steppes, Continental, and Tundra. Then we intersected each ecoregion with individual-fire data obtained from remote sensing hotspots to estimate fire regime parameters for each environment. This allowed us to compute annual area burnt, fire size, fire intensity, fire season, fire patchiness, fire recurrence, and pyrodiversity for each ecoregion. We then related those fire parameters with the ecoregions' climate and analysed the temporal trends in fire size. The results suggest that fire regime parameters vary across different environments (ecoregions). The Mediterranean had the largest, most intense, and most recurrent fires, but the Steppes had the largest burnt area. Arid ecosystems had the most extended fire season, Tundra had the patchiest fires, and Boreal forests had the earliest fires of the year. The spatial variability in fire regimes was largely explained by the variability of climate and vegetation, with a tendency for greater fire activity in the warmer ecoregions. There was also a temporal tendency for fires to become larger during the last two decades, especially in Arid and Continental environments. In conclusion, fire regime characteristics of each ecoregion are unique, with a tendency for greater fire activity in warmer environments, and for increasingly large fires in recent decades.</p>
Molecular Level Estimation of the Composition and Fate of Dissolved Organic Matter in Fire-Impacted Temperate and Subtropical Peatlands
<p>Calculated values of optical (UV-visible and fluorescence) spectroscopy parameters and molecular formulas determined by ultrahigh resolution mass spectrometry for dissolved organic matter (DOM) in porewaters from a temperate and sub-tropical peatland. These data were used to generate figures in a manuscript submitted to the Journal of Geophysical Research - Biogeosciences.</p>
Identification of starch granules on ground stone tools exposed to fire
<p>Intense wildfires destroy everything in their path, including archaeological sites. Prehistorically, archaeological sites were regularly and intentionally burned. In what ways does burning affect those sites? With increased wildfire activity, research has begun to describe the effects of fire on archaeological materials through post-fire and experimental treatment, yet, little is known about the effects of fire on microbotanical remains, such as starch granules. Although some studies address the impact of fire on starch-rich foods, there is virtually no research on the fire effects of starch granules embedded in ground stone tools. The current study examines changes in the morphology of starch granules embedded in ground stone tools before and after exposure to flame combustion. A measurable amount of intact and identifiable starch granules was recovered from all of the treated samples. However, significantly fewer intact, identifiable granules were found as tools were exposed to higher temperatures for longer periods of time.</p>
Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019
<p class="MsoNormal"><strong><span>1. PUBLICATION CORRESPONDING TO THESE DATA</span></strong></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Sloan, Sean*; Locatelli, Bruno; Andela, Niels; Cattau, Megan E.; Gaveau, David; Tacconi, Luca. 2022 'Declining Severe Fire Activity on Managed Lands in Equatorial Asia'. <em>Communications Earth & Environment</em>. DOI: 10.1038/s43247-022-00522-6.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>*Corresponding author email: sean.sloan@viu.ca</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>2. ABSTRACT OF THE DATA</span></strong></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>The GIS data and corresponding attribute data described here pertain to discrete fire events, their severity, and their ignitions, as derived on the basis of daily MODIS Collection 6 MCD14ML active-fire detections (AFDs). Data on fire events and their ignitions are provided separately, as two data files. These data files on fire events and ignitions may however be linked to each other by the data user. Fire-event severity is quantified per fire event and reported in the data file for fire events.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>A fire event is a cluster of MODIS Collection 6 MCD14ML active-fire detections (AFDs) wherein each AFD has a spatial (<=1-km) and temporal (<=4-day) proximity to another AFD in the same fire event, inferring thus a relational co-occurrence amongst AFDs in time and space. In other words, a fire event is considered a likely occasion of burning wherein all constituent AFDs are related to each other in time and space, either directly (as for proximate AFDs) or indirectly (as in the case of a large area of fire activity that spread progressively over time and space from an initial source). </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Each fire event has a designated ignition AFD, being the AFD of the fire event with the earliest detection date. A given fire event can have more than one ignition AFD if the ignitions all share same earliest detection date. The ignition AFD(s) is the nominal initial source of the burning described by the corresponding fire event. All other, non-ignition AFDs of a fire event are deemed its 'propagation' AFDs, since these AFDs follow from the ignitions, temporally and spatially.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>See Figure 4 in the publication by Sloan et al. for an illustration of the geography of fire events and their ignition AFDs. </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Fire events and their ignitions were derived from standard science-quality MODIS Collection 6 MCD 14ML AFD data, commonly referred to as fire 'hotspot' data. Data were detected by both the Terra and Aqua satellite sensors daily for Indonesia between July 2002 and December 2019. Information on these input data are provide by the two citations below. The publication of Sloan et al. provides methodological details on how the MODIS Collection 6 MCD 14ML AFD data were processed into discrete fire events and ignitions. </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>EarthData. MODIS Collection 6 Active-Fire Detections standard scientific data (MCD14ML), NASA EarthData, https://earthdata.nasa.gov/firms (2019).</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Giglio, L., Schroeder, W. & Justice, C. O. The Collection 6 MODIS active fire detection algorithm and fire products. <em>Remote Sensing of Environment</em> 178, 31-41, (2016).</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>3. DATA FILES</span></strong></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Two data files are distributed here – one for discrete fire events, and another for the ignition AFDs of each fire event. The data files are provided in a GIS-compatible format, and also as a generic text format, as described below.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>3.1 GIS VERSION</span></strong></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Data files in GIS-compatible format are provided as 'feature classes' within an ArcGIS file geodatabase 'Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb'. These data files can be viewed and manipulated using either ArcGIS Desktop or ArcGIS Pro software. There is one feature class for fire events, and another file for ignitions.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><u><span>Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_joins_sp_LC</span></u></p> <p class="MsoNormal"><span>This file pertains to fire events. All AFDs of a given fire event are included, without differentiation as to whether the AFDs are ignition AFDs or other (propagation) AFDs. Fire events are assigned unique ID values and basic attribute data.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><u><span>Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_igs_sp_LC</span></u></p> <p class="MsoNormal"><span>This file pertains to ignitions. Only ignition AFDs are included for a given fire event. Fire events corresponding to the ignitions are assigned unique ID values and basic attribute data.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>3.2 CSV TEXT VERSION</span></strong></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Both data files are also supplied as comma-separated value (CSV) text files for viewing and manipulation in non-GIS software, such as Excel, text editors, or any statistical software. The text files can also be read into various GIS software. CSV-formatted files have the same file name and attribute fields as the corresponding GIS-formatted data files. These CSV-formatted data files (as well as the GIS-formatted data files) include attribute data on the latitude and the longitude of each AFD. </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Attribute field names are included as the first row of values in a CSV file. </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>No 'text qualifiers' like quotations (" ") or inverted commas ('') are used to designate text/string values within the CSV file. Text values appear directly between commas in the CSV data file, e.g., …,Kalimantan_Southern,… .</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Note two points of caution for working with these CSV data:</span></p> <p class="MsoListParagraphCxSpFirst"><span> </span></p> <p class="MsoListParagraphCxSpMiddle"><span>i)<span> </span></span><span>Microsoft Excel may be used for a partial view of the data file nfire4_all_spatial_fire_2002_2019_joins_sp_LC.csv, but it is not recommended for working with this data file. This is because the number of records/rows in this csv file slightly exceeds that maximum that may be read by Excel, which is just over 1 million. This limitation does not apply to the other csv file, however.</span></p> <p class="MsoListParagraphCxSpMiddle"><span> </span></p> <p class="MsoListParagraphCxSpLast"><span>ii)<span> </span></span><span>The GIS-formatted data files employ 'null values' in their attribute tables, and so the corresponding 'values' in the CSV-formatted data files are similarly null. For null values, no value whatsoever is ascribed, not even 0. In the syntax of a CSV file (apparent upon opening the file in any text editor like Microsoft Notepad), a null value is denoted by two consecutive commas without any value, text, or space between them. If a CSV file were opened in Excel, a cell assigned a null value would be blank, not 0 or otherwise. This denotes the correct transcription of the GIS-formatted data. This feature will not impede the correct reading of these CSV data by whatever software. Users are made aware of this feature merely to ensure the proper input of these data into whatever software.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>4. DATA STRUCTURE / GEOGRAPHY</span></strong></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>The GIS-formatted data files are 'point data', i.e., they map the geography of AFDs as individual 'points', in keeping with how these MODIS MCD14ML AFD data were originally structured. For the GIS-formatted data files, each record/row in its corresponding attribute tables corresponds <em>geographically</em> to single AFD 'point', regardless of whether that AFD belongs to a fire event comprised of many AFDs. In the parlance of GIS files, the files depict 'single-part' point features. The unique ID field [nfireID2] serves to denote the fire event to which a given AFD belongs.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Similarly, for the CSV-formatted data files, each record/row of values corresponds to a single AFD.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>There are 1,232,377 records for the data file 'nfire4_all_spatial_fire_2002_2019_joins_sp_LC'.</span></p> <p class="MsoNormal"><span>There are 720795 records for the data file 'nfire4_all_spatial_fire_2002_2019_igs_sp_LC'.</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>5. ATTRIBUTE FIELDS</span></strong></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>In the data files, while some attribute fields pertain to the individual AFD as the unit of observation (e.g., the land-cover class coincident with the AFD), other attribute fields correspond to the larger 'fire event' to which the individual AFD belongs (e.g., the total duration of fire activity for the fire event). Accordingly, for certain attribute fields pertaining to the fire event as a whole, their values will appear 'duplicated' in the data file amongst those individual AFDs (records) that constitute the fire event in question. Whether a given attribute field pertains to the individual AFD or to its constituent fire event is denoted below for each field. </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Each AFD is assigned a unique ID field denoting its constituent fire event, [nfireID2]. This field is consistent between both data files, so that attribute data for a given fire event may be 'matched' to attribute data for its corresponding ignition AFD(s), and vice versa, on the basis of the common value of the field [nfireID2].</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Note that many attributes below are as originally defined/measured by the input MCD 14ML data, or are derived directly thereof. </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>5.1 DATASET nfire4_all_spatial_fire_2002_2019_joins_sp_LC</span></strong></p> <p class="MsoNormal"><span> </span></p> <table class="MsoTableGrid"> <tbody> <tr> <td> <p class="MsoNormal"><strong><span>Field Name</span></strong></p> </td> <td> <p class="MsoNormal"><strong><span>Geography of Attribute Value</span></strong></p> </td> <td> <p class="MsoNormal"><strong><span>Definition</span></strong></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>OID</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>Peat</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_CONFIDE</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The minimum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MAX_CONFIDE</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The maximum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MEAN_CONFIDE</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The mean detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>STD_CONFIDE</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The standard deviation of detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>SUM_FRP</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The sum total of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MEAN_FRP</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The mean of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_FRP</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The minimum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MAX_FRP</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The maximum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MEAN_LATITUD</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The mean latitude of all AFDs in the fire event. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MEAN_LONGITU</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The mean longitude of all AFDs in the fire event. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_ACQ_DAT</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The minimum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MAX_ACQ_DAT</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The maximum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_acq_yer</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The year in which the earliest AFD of the fire event was detected. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_acq_mnt</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The month in which the earliest AFD of the fire event (i.e., ignition AFD) was detected. Months are coded numerically, e.g., 1=January, 12=December.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MAX_acq_mnt</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The month in which the latest AFD of the fire event was detected. Months are coded numerically, e.g., 1=January, 12=December.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_yday</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The detection day of year of the earliest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MAX_yday</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The detection day of year of the latest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>RANGE_yday</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The number of days of duration of the fire event, defined as [MAX_yday]-[MIN_yday]</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>FIRST_subst_r</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>AF_Count</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>Number of AFDs in the fire event.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>nfireID2</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The unique ID of the fire event to which the AFD belongs.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>Island</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Numerical values coding for major Indonesian islands/region: 1000000=Sumatra; 2000000=Kalimantan; 3000000=Sulawesi; 4000000=Papua.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>FRP_Days</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The severity of the fire event, as defined by Sloan et al., equal to [Sum_FRP] * ([Range_yday]+1).</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>IG_Count</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>Number of ignition AFDs in the fire event</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2002</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> <td> <p class="MsoNormal"><span>The land-cover class coincident with the AFD. The class is coded by a numerical value as per the left-most column in the table in Section 5.3. The class is that observed for the calendar year in which the AFD occurred, where the year #### is denoted in the field name 'CCI_LC####'. The land-cover data source is as described in Section 5.3.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2003</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2004</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2005</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2006</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2007</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2008</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2009</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2010</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2011</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2012</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2013</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2014</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2015</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2016</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2017</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2018</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2019</span></p> </td> <td> <p class="MsoNormal"><span> </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>Region</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>POINT_X</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Longitude, given as decimal degrees with WGS84 datum.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>POINT_Y</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Latitude, given as decimal degrees with WGS84 datum.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>POINT_Z</span></p> </td> <td> <p class="MsoNormal"><span>--</span></p> </td> <td> <p class="MsoNormal"><span>Ignore.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>POINT_M</span></p> </td> <td> <p class="MsoNormal"><span>--</span></p> </td> <td> <p class="MsoNormal"><span>Ignore.</span></p> </td> </tr> </tbody> </table> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>5.2 DATASET nfire4_all_spatial_fire_2002_2019_igs_sp_LC</span></strong></p> <p class="MsoNormal"><span> </span></p> <table class="MsoTableGrid"> <tbody> <tr> <td> <p class="MsoNormal"><strong><span>Field Name</span></strong></p> </td> <td> <p class="MsoNormal"><strong><span>Geography of Attribute Value</span></strong></p> </td> <td> <p class="MsoNormal"><strong><span>Definition</span></strong></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>OID</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>IG_Count</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>Number of ignition AFDs in the fire event.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_acq_yer</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The year in which the earliest AFD of the fire event was detected. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_acq_mnt</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The month in which the earliest AFD of the fire event (i.e., ignition AFD) was detected. Months are coded numerically, e.g., 1=January, 12=December.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>FIRST_subst_r</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>MIN_yday</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The detection day of year of the earliest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>nfireID2</span></p> </td> <td> <p class="MsoNormal"><span>Fire Event</span></p> </td> <td> <p class="MsoNormal"><span>The unique ID of the fire event to which the AFD belongs.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2002</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>The land-cover class coincident with the AFD. The class is coded by a numerical value as per the left-most column in the table in Section 5.3. The class is that observed for the calendar year in which the AFD occurred, where the year #### is denoted in the field name 'CCI_LC####'. The land-cover data source is as described in Section 5.3.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2003</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2004</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2005</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2006</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2007</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2008</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2009</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2010</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2011</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2012</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2013</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2014</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2015</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2016</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2017</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2018</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>CCI_LC2019</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>Region</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>Peat</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>POINT_X</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Longitude, given as decimal degrees with WGS84 datum.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>POINT_Y</span></p> </td> <td> <p class="MsoNormal"><span>AFD</span></p> </td> <td> <p class="MsoNormal"><span>Latitude, given as decimal degrees with WGS84 datum.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>POINT_Z</span></p> </td> <td> <p class="MsoNormal"><span>--</span></p> </td> <td> <p class="MsoNormal"><span>Ignore.</span></p> </td> </tr> <tr> <td> <p class="MsoNormal"><span>POINT_M</span></p> </td> <td> <p class="MsoNormal"><span>--</span></p> </td> <td> <p class="MsoNormal"><span>Ignore.</span></p> </td> </tr> </tbody> </table> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><strong><span>5.3. LAND-COVER ATTRIBUTE DATA</span></strong></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>As noted in Section 5.1 and Section 5.2, the nominal values of the attribute fields 'CCI_LC####' correspond to column 1 of the table below. These hierarchical values, and their corresponding land-cover classes labels in column 2 of the table, pertain to the land-cover classification of the Copernicus Climate Change Initiative Land-Cover Product of the European Space Agency. This classification has an annual temporal resolution and 300-meter spatial resolution. Pertinent citations for these land-cover data are below:</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>ESA. Annual land-cover product, 1992 to 2019/present, based on MERIS 300-m and ancillary SPOT, AVHRR, Sentinel-3 and PROB-V satellite data. European Space Agency (ESA) European Centre for Medium-Range Weather Forecasts (ECMFW) Copernicus Climate Change Service (C3S) Climate Change Initiative (CCI), </span><span><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview"><span>https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview</span></a></span><span>; </span><span><a href="http://maps.elie.ucl.ac.be/CCI/viewer/download.php"><span>http://maps.elie.ucl.ac.be/CCI/viewer/download.php</span></a></span><span>; </span><span><a href="http://www.esa-landcover-cci.org/"><span>http://www.esa-landcover-cci.org/</span></a></span><span> (2020).</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Pérez-Hoyos, A., Rembold, F., Kerdiles, H. & Gallego, J. Comparison of global land cover datasets for cropland monitoring. <em>Remote Sensing</em> 9, (2017).</span></p> <p class="MsoNormal"><span> </span></p> <p class="MsoNormal"><span>Columns 3 and 4 in the table below illustrate how the original land-cover classes of the Copernicus Climate Change Initiative Land-Cover Product were reclassified for analysis in Sloan et al. </span></p> <p class="MsoNormal"><em><span> </span></em></p> <table class="MsoNormalTable"> <tbody> <tr> <td> <p class="TableParagraph"><strong><span>1. CCI-LC Class Value</span></strong></p> </td> <td> <p class="TableParagraph"><strong><span>2. CCI-LC Class Label</span></strong></p> </td> <td> <p class="TableParagraph"><strong><span>3. New Class Value for Sloan et al.</span></strong></p> </td> <td> <p class="TableParagraph"><strong><span>4. New Class Label for Sloan et al.</span></strong></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>0</span></p> </td> <td> <p class="TableParagraph"><span>No Data</span></p> </td> <td> <p class="TableParagraph"><span>0</span></p> </td> <td> <p class="TableParagraph"><span>No Data</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>10</span></p> </td> <td> <p class="TableParagraph"><span>Cropland, rainfed</span></p> </td> <td> <p class="TableParagraph"><span>1</span></p> </td> <td> <p class="TableParagraph"><span>Cleared/Cultivated</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>11</span></p> </td> <td> <p class="TableParagraph"><span>Herbaceous cover</span></p> </td> <td> <p class="TableParagraph"><span>1</span></p> </td> <td> <p class="TableParagraph"><span>Cleared/Cultivated</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>12</span></p> </td> <td> <p class="TableParagraph"><span>Tree or shrub cover</span></p> </td> <td> <p class="TableParagraph"><span>1</span></p> </td> <td> <p class="TableParagraph"><span>Cleared/Cultivated</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>20</span></p> </td> <td> <p class="TableParagraph"><span>Cropland, irrigated or post‐flooding</span></p> </td> <td> <p class="TableParagraph"><span>1</span></p> </td> <td> <p class="TableParagraph"><span>Cleared/Cultivated</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>30</span></p> </td> <td> <p class="TableParagraph"><span>Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)</span></p> </td> <td> <p class="TableParagraph"><span>2</span></p> </td> <td> <p class="TableParagraph"><span>Mosaic Cropland</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>40</span></p> </td> <td> <p class="TableParagraph"><span>Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)</span></p> </td> <td> <p class="TableParagraph"><span>3</span></p> </td> <td> <p class="TableParagraph"><span>Mosaic Natural Veg</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>50</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, broadleaved, evergreen, closed to open (>15%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>60</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, broadleaved, deciduous, closed to open (>15%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>61</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, broadleaved, deciduous, closed (>40%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>62</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, broadleaved, deciduous, open (15‐40%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>70</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, needleleaved, evergreen, closed to open (>15%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>71</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, needleleaved, evergreen, closed (>40%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>72</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, needleleaved, evergreen, open (15‐40%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>80</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, needleleaved, deciduous, closed to open (>15%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>81</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, needleleaved, deciduous, closed (>40%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>82</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, needleleaved, deciduous, open (15‐40%)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>90</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, mixed leaf type (broadleaved and needleleaved)</span></p> </td> <td> <p class="TableParagraph"><span>4</span></p> </td> <td> <p class="TableParagraph"><span>Forest</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>100</span></p> </td> <td> <p class="TableParagraph"><span>Mosaic tree and shrub (>50%) / herbaceous cover (<50%)</span></p> </td> <td> <p class="TableParagraph"><span>5</span></p> </td> <td> <p class="TableParagraph"><span>Mosaic Shrubland</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>110</span></p> </td> <td> <p class="TableParagraph"><span>Mosaic herbaceous cover (>50%) / tree and shrub (<50%)</span></p> </td> <td> <p class="TableParagraph"><span>5</span></p> </td> <td> <p class="TableParagraph"><span>Mosaic Shrubland</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>120</span></p> </td> <td> <p class="TableParagraph"><span>Shrubland</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>121</span></p> </td> <td> <p class="TableParagraph"><span>Evergreen shrubland</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>122</span></p> </td> <td> <p class="TableParagraph"><span>Deciduous shrubland</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>130</span></p> </td> <td> <p class="TableParagraph"><span>Grassland</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>140</span></p> </td> <td> <p class="TableParagraph"><span>Lichens and mosses</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>150</span></p> </td> <td> <p class="TableParagraph"><span>Sparse vegetation (tree, shrub, herbaceous cover) (<15%)</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>151</span></p> </td> <td> <p class="TableParagraph"><span>Sparse tree (<15%)</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>152</span></p> </td> <td> <p class="TableParagraph"><span>Sparse shrub (<15%)</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>153</span></p> </td> <td> <p class="TableParagraph"><span>Sparse herbaceous cover (<15%)</span></p> </td> <td> <p class="TableParagraph"><span>6</span></p> </td> <td> <p class="TableParagraph"><span>Low/Sparse Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>160</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, flooded, fresh or brakish water</span></p> </td> <td> <p class="TableParagraph"><span>7</span></p> </td> <td> <p class="TableParagraph"><span>Flooded Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>170</span></p> </td> <td> <p class="TableParagraph"><span>Tree cover, flooded, saline water</span></p> </td> <td> <p class="TableParagraph"><span>7</span></p> </td> <td> <p class="TableParagraph"><span>Flooded Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>180</span></p> </td> <td> <p class="TableParagraph"><span>Shrub or herbaceous cover, flooded, fresh/saline/brakish water</span></p> </td> <td> <p class="TableParagraph"><span>7</span></p> </td> <td> <p class="TableParagraph"><span>Flooded Vegetation</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>190</span></p> </td> <td> <p class="TableParagraph"><span>Urban areas</span></p> </td> <td> <p class="TableParagraph"><span>8</span></p> </td> <td> <p class="TableParagraph"><span>Other</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>200</span></p> </td> <td> <p class="TableParagraph"><span>Bare areas</span></p> </td> <td> <p class="TableParagraph"><span>8</span></p> </td> <td> <p class="TableParagraph"><span>Other</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>201</span></p> </td> <td> <p class="TableParagraph"><span>Consolidated bare areas</span></p> </td> <td> <p class="TableParagraph"><span>8</span></p> </td> <td> <p class="TableParagraph"><span>Other</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>202</span></p> </td> <td> <p class="TableParagraph"><span>Unconsolidated bare areas</span></p> </td> <td> <p class="TableParagraph"><span>8</span></p> </td> <td> <p class="TableParagraph"><span>Other</span></p> </td> </tr> <tr> <td> <p class="TableParagraph"><span>210</span></p> </td> <td> <p class="TableParagraph"><span>Water bodies</span></p> </td> <td> <p class="TableParagraph"><span>8</span></p> </td> <td> <p class="TableParagraph"><span>Other</span></p> </td> </tr> </tbody> </table>
The emergence of a cryptic lineage and cytonuclear discordance through past hybridization in the Japanese fire-bellied newt, Cynops pyrrhogaster (Amphibia: Urodela)
<p> Discrepancies in geographic variation patterns between nuclear DNA and mitochondrial DNA (mtDNA) are the result of the complicated differentiation processes in organisms and the key to understanding their true evolutionary process. The genetic differentiation of the northern and southern Izu lineages of the Japanese newt <em>Cynops pyrrhogaster</em> was investigated <span>through their single nucleotide polymorphism</span> variations by multiplexed ISSR genotyping by sequencing (MIG-seq). We found three genetic groups (Tohoku, N-Kanto, and S-Kanto) those not detected by mtDNA in the northern lineage. N-Kanto has intermediate genetic characteristics between Tohoku and S-Kanto. The western populations of N-Kanto are close to S-Kanto, whereas the eastern populations of N-Kanto are close to Tohoku. Tohoku, N-Kanto, and S-Kanto are now moderately isolated from each other and have unique genetic characteristics. An estimation of the evolutionary history by t<span>he </span><span>Approximate Bayesian Computation </span>approach suggested that Tohoku diverged from the common ancestor of S-Kanto and S-Izu. Then, S-Kanto and S-Izu split and the recent hybridization between Tohoku and S-Kanto gave rise to N-Kanto. The origin of N-Kanto through the hybridization is relatively young and seems to be related to changes in the distributions of Tohoku and S-Kanto as a result of the climatic oscillation in the Pleistocene. We concluded that the mitochondrial genome of S-Kanto was captured into Tohoku and the past original mitochondrial genome of Tohoku was entirely swept out from Tohoku through the hybridization. </p>
Fire favors sexual precocity in a Mediterranean pine
<p><span>Wildfires are a natural disturbance in many ecosystems. Consequently, plant species have acquired traits that allow them to resist and regenerate in an environment with recurrent fires. A key trait in fire-prone ecosystems is the age at first reproduction (maturity age); populations of non-resprouting species cannot persist when fire interval is shorter than this age. Maturity age is variable among individuals, so we hypothesize that short fire intervals select for early seed production (precocity). We selected 13 plots with different fire regimes in eastern Spain, all dominated by <em>Pinus halepensis</em> (a non-resprouting serotinous species). Then, we evaluated the age at first reproduction and the size of the canopy seed bank of each individual pine. Our results show a significant effect of fire regime on the onset of reproduction in this species, suggesting a selection towards higher precocity in populations subject to shorter fire intervals. Due to this higher precocity, pines stored more cones, and therefore, increased their potential for post-fire regeneration. We provide the first field evidence that fire can act as a driver of precocity. Being precocious in fire-prone environments is adaptive because it increases the probability of having a significant seed bank when the next fire arrives.</span></p>
Resilience of a tropical montane pine forest to fire and severe droughts
<p><span>1. Higher temperatures, declining precipitation, changing cloud cover, and increased wildfires threaten tropical montane pine forests by overriding the environmental heterogeneity that typically buffers these systems from catastrophic fires. Severe fires threaten to overwhelm forest resilience and tip this biome into alternate vegetation states.</span></p> <p><span>2. This study focused on long-term dynamics of montane <em>Pinus</em> <em>occidentalis</em> forests in the Cordillera Central, Dominican Republic, after a ~1000 km<sup>2</sup> fire in 2005, the largest since 1965</span>. <span>We used long-term records to investigate climate before and after the fire and a 19-year dataset of pre- and post-fire vegetation change from a network of 55 permanent plots (20 small </span>0.05 ha<span> plots and 35 large 0.1 ha plots) established in 1999 to model overstorey and understorey vegetation dynamics. </span></p> <p><span>3. The 2005 fire was synchronized with the most extreme drought in the region in over 60 years. The fire burned from < 1600 to > 3000 m a.s.l. in elevation across windward and leeward slopes, creating a mosaic of low-, moderate-, and high-severity patches. L</span><span>ower elevations</span><span>, </span><span>leeward slopes, and stands </span><span>with a higher proportion of smaller pine trees</span><span> all burned at higher severities. </span></p> <p><span>4. Growth rates of trees that survived the fire remained lower than pre-fire rates 13 years after the fire. The </span><span>highest mortality rates were soon after the fire and in the census immediately after the post-fire droughts</span><span>. Post-fire pine seedling abundance was significantly greater in stands with higher basal area of live canopy trees and significantly reduced by increased shrub abundance in the understorey. Understorey composition recovered rapidly to pre-fire states in sites affected by low- and moderate-severity fires, but sites affected by high-severity fires remained dissimilar to pre-fire composition</span> 13 years after the fire<span>. Even though high-severity patches had persistently low pine regeneration, 100% of </span>small <span>plots and 96% of large plots had at least one pine sapling or canopy tree recruit by 2018. Shrub taxa survived the fire in higher numbers and recovered to pre-fire densities much faster than the pine, especially in high-severity burns.</span></p> <p><span>5. Synthesis</span><span>. Climate change has increased the likelihood of wildfires in tropical montane pine forests, with long-lasting effects on vegetation dynamics. However, this</span> biome may prove resilient to increasingly severe fires in the near future<span>, given the ongoing recovery of <em>Pinus occidentalis</em> forests</span> in Hispaniola <span>despite repeated severe droughts. Nevertheless, highly drought- and fire-resistant taxa (e.g. shrubs) may form alternate stable states in drier portions of tropical montane landscapes in the future as droughts and high-severity fires become more common.</span></p>
Raster-based dataset for spatio-temporal analysis of forest fires in the Amazon rainforest from 2001 to 2020
<p>Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associated study to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides three sets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.