Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

datasets available to search

ShareScore release 0.9.0

Dataset results

73 results for “Fire severity”

Learn how ShareScore rates datasets ↗

dryad40/100

Abiotic factors modify ponderosa pine regeneration outcomes after high-severity fire

Open the record for dataset details and reuse information.

publicMar 2024View details →

dryad40/100

Data from: Extreme fire severity interacts with seed traits to moderate post-fire species assemblages

Open the record for dataset details and reuse information.

publicMar 2025View details →

dryad40/100

Severity outweighs pyrodiversity in shaping avian and bat species distributions following an oak woodland mega-fire

Open the record for dataset details and reuse information.

publicMar 2025View details →

edi40/100

Fire Severity Estimates: 2001 Survey Line Burn

Field-based estimates of fire severity for 85 plots located near the Tanana River in the Survey Line Burn. Composite Burn Index estimates were taken during June-August 2002. The locations of each plot are stored as differentially corrected GPS files and as an ArcGIS point shapefile. Digital aerial photography of the post-fire area from August and September 2002 are in the data base. Database also includes remotely sensed fire severity indices of the entire Survey Line Burn including the Normalized Burn Ratio from a 21-July-2002 Landsat ETM+ image and the differenced Normalized Burn Ratio from a 6-June-2001 pre-fire Landsat ETM+ image and the post-fire 21-July-2002 Landsat ETM+ image.

openOpenDec 2007View details →

edi40/100

Thresholds and alternative states in neotropical dry forest in response to fire severity, 2005-2018

Neotropical xerophytic forest ecosystems evolved with fires that shaped their resilience to disturbance events. We asked if there is evidence for a fire severity threshold causing an abrupt transition from a forest to an alternative shrub thicket state in the presence of typical post-fire management. We studied a heterogeneous wildfire event to assess medium-term effects (11 years) of varying fire severity in a xerophytic Caldén forest (Prosopis caldenia) in La Pampa province, central Argentina. We conducted field vegetation surveys in patches that were exposed to low (LFS), medium (MFS), and high (HFS) fire severities, but had similar pre-fire woody canopy cover. Satellite images were used to quantify fire severity using a delta Normalized Burning Ratio (dNBR) and to map pre-fire canopy cover. The structure and composition of woody and herbaceous vegetation was measured during the 2017-18 growing season (September to March) in areas affected by different severities of the 2006 wildfire.

openCC (other)Jul 2021View details →

dryad36/100

After the 'Black Summer' fires: faunal responses to megafire depend on fire severity, proportional area burnt, and vegetation type

<ol> <li>Climate change and human activities have disrupted historical fire regimes, leading to complex and far-reaching impacts on global ecosystems. Despite extensive research in fire ecology, studies exploring vertebrate responses to megafires, and to nuanced fire characteristics, remain limited.</li> <li>We collected camera trap data 3–27 months following Australia's 2019–20 'Black Summer' megafires from 30 burnt sites and 10 unburnt sites. Our data included 14 animal species/groups, encompassing mammalian predators, small and medium-sized mammals, large herbivores, and birds. We used generalised additive mixed models to assess the influence of time-since-the-fires, burn status, fire severity, proportional area burnt, and vegetation type on species' activity.</li> <li>Models that included fire variables were well-supported for all species. The proportional cover of low-moderate or high-extreme severity fire had substantial support for five species, particularly herbivores, which generally showed a preference for burnt sites but at differing fire severities. The proportional area burnt, disregarding severity, was well supported for four species. At highly burned sites, fox activity peaked shortly after the fires, while small to medium-sized mammal activity increased more gradually. Vegetation type strongly influenced the response of four species to fire; in particular, wet forest birds preferred unburnt areas.</li> <li> Policy implications. We document variable short- to medium-term responses of a range of species to fire which could help guide management interventions. We demonstrate that animal species' responses to fire are diverse and better captured using broader landscape-scale fire variables. We found that species were strongly influenced by proportional area burnt, fire severity, and vegetation type. Introduced foxes were attracted to recently burnt areas, so timely predator control may benefit vulnerable prey species. Wet forest species were sensitive to fires and could benefit from preservation and restoration of these habitats. Some species exploited low-moderate severity burnt areas, while others preferred high-severity burns. This suggests that species will face diverse challenges and opportunities in future extreme fire events. We emphasise the importance of using multi-faceted approaches to account for the complex responses of co-occurring species to fire events.</li> </ol>

opencc-zeroNov 2023View details →

zenodo36/100

The effect of climate change on forest fire danger and severity in the Canadian boreal forests for the period 1976-2100

There are 5 files uploaded. (1) fwi26.txt.gz, RCP26(2) fwi45.txt.gz, RCP45(3) fwi85.txt.gz, RCP85(4) msk20231028.txt(5) Shape_Canadian_Boreal_Forests.7z mask20231028.txt is an ascii file covering the Canadian Boreal Forests.This file can be imported into GisMAP to generate a mask raster. Shape_Canadian_Boreal_Forests.7z is a compressed shape file cover theCanadian Boreal Forests. fwi26.txt.gz, fwi45.txt.gz, and fwi85.txt.gz are the zipped file of FFMCand DSR daily surfaces. There are 6 fields in the files: Field 1: row number in the mask file of mask20231028.txt.Field 2: column number.Field 3: Years; 1, 2, 3, ... 95 correspoind to 2006, 2007, 2008, ..., 2100.Field 4: Days; 1, 2, 3, ..., 365 in a year.Field 5: Daily FFMC values.Field 6: Daily DSR values.

opencc-by-4.0Nov 2023View details →

dryad36/100

Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019

1. PUBLICATION CORRESPONDING TO THESE DATA Sloan, Sean*; Locatelli, Bruno; Andela, Niels; Cattau, Megan E.; Gaveau, David; Tacconi, Luca. 2022 'Declining Severe Fire Activity on Managed Lands in Equatorial Asia'. Communications Earth & Environment. DOI: 10.1038/s43247-022-00522-6. *Corresponding author email: sean.sloan@viu.ca 2. ABSTRACT OF THE DATA 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. 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). 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. See Figure 4 in the publication by Sloan et al. for an illustration of the geography of fire events and their ignition AFDs. 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. EarthData. MODIS Collection 6 Active-Fire Detections standard scientific data (MCD14ML), NASA EarthData, https://earthdata.nasa.gov/firms (2019). Giglio, L., Schroeder, W. & Justice, C. O. The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment 178, 31-41, (2016). 3. DATA FILES 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. 3.1 GIS VERSION 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. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_joins_sp_LC 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. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_igs_sp_LC 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. 3.2 CSV TEXT VERSION 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. Attribute field names are included as the first row of values in a CSV file. 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,… . Note two points of caution for working with these CSV data: i) 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. ii) 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. 4. DATA STRUCTURE / GEOGRAPHY 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 geographically 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. Similarly, for the CSV-formatted data files, each record/row of values corresponds to a single AFD. There are 1,232,377 records for the data file 'nfire4_all_spatial_fire_2002_2019_joins_sp_LC'. There are 720795 records for the data file 'nfire4_all_spatial_fire_2002_2019_igs_sp_LC'. 5. ATTRIBUTE FIELDS 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. 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]. Note that many attributes below are as originally defined/measured by the input MCD 14ML data, or are derived directly thereof. 5.1 DATASET nfire4_all_spatial_fire_2002_2019_joins_sp_LC <table class="MsoTableGrid"> <tbody> <tr> <td> Field Name </td> <td> Geography of Attribute Value </td> <td> Definition </td> </tr> <tr> <td> OID </td> <td> AFD </td> <td> 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. </td> </tr> <tr> <td> Peat </td> <td> AFD </td> <td> Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. </td> </tr> <tr> <td> MIN_CONFIDE </td> <td> Fire Event </td> <td> The minimum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> MAX_CONFIDE </td> <td> Fire Event </td> <td> The maximum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> MEAN_CONFIDE </td> <td> Fire Event </td> <td> The mean detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> STD_CONFIDE </td> <td> Fire Event </td> <td> The standard deviation of detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> SUM_FRP </td> <td> Fire Event </td> <td> The sum total of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MEAN_FRP </td> <td> Fire Event </td> <td> The mean of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MIN_FRP </td> <td> Fire Event </td> <td> The minimum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MAX_FRP </td> <td> Fire Event </td> <td> The maximum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MEAN_LATITUD </td> <td> Fire Event </td> <td> The mean latitude of all AFDs in the fire event. </td> </tr> <tr> <td> MEAN_LONGITU </td> <td> Fire Event </td> <td> The mean longitude of all AFDs in the fire event. </td> </tr> <tr> <td> MIN_ACQ_DAT </td> <td> Fire Event </td> <td> The minimum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </td> </tr> <tr> <td> MAX_ACQ_DAT </td> <td> Fire Event </td> <td> The maximum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </td> </tr> <tr> <td> MIN_acq_yer </td> <td> Fire Event </td> <td> The year in which the earliest AFD of the fire event was detected. </td> </tr> <tr> <td> MIN_acq_mnt </td> <td> Fire Event </td> <td> 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. </td> </tr> <tr> <td> MAX_acq_mnt </td> <td> Fire Event </td> <td> The month in which the latest AFD of the fire event was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> MIN_yday </td> <td> Fire Event </td> <td> 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. </td> </tr> <tr> <td> MAX_yday </td> <td> Fire Event </td> <td> 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. </td> </tr> <tr> <td> RANGE_yday </td> <td> Fire Event </td> <td> The number of days of duration of the fire event, defined as [MAX_yday]-[MIN_yday] </td> </tr> <tr> <td> FIRST_subst_r </td> <td> AFD </td> <td> Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua. </td> </tr> <tr> <td> AF_Count </td> <td> Fire Event </td> <td> Number of AFDs in the fire event. </td> </tr> <tr> <td> nfireID2 </td> <td> Fire Event </td> <td> The unique ID of the fire event to which the AFD belongs. </td> </tr> <tr> <td> Island </td> <td> AFD </td> <td> Numerical values coding for major Indonesian islands/region: 1000000=Sumatra; 2000000=Kalimantan; 3000000=Sulawesi; 4000000=Papua. </td> </tr> <tr> <td> FRP_Days </td> <td> Fire Event </td> <td> The severity of the fire event, as defined by Sloan et al., equal to [Sum_FRP] * ([Range_yday]+1). </td> </tr> <tr> <td> IG_Count </td> <td> Fire Event </td> <td> Number of ignition AFDs in the fire event </td> </tr> <tr> <td> CCI_LC2002 </td> <td> </td> <td> 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. </td> </tr> <tr> <td> CCI_LC2003 </td> <td> </td> </tr> <tr> <td> CCI_LC2004 </td> <td> </td> </tr> <tr> <td> CCI_LC2005 </td> <td> </td> </tr> <tr> <td> CCI_LC2006 </td> <td> </td> </tr> <tr> <td> CCI_LC2007 </td> <td> </td> </tr> <tr> <td> CCI_LC2008 </td> <td> </td> </tr> <tr> <td> CCI_LC2009 </td> <td> </td> </tr> <tr> <td> CCI_LC2010 </td> <td> </td> </tr> <tr> <td> CCI_LC2011 </td> <td> </td> </tr> <tr> <td> CCI_LC2012 </td> <td> </td> </tr> <tr> <td> CCI_LC2013 </td> <td> </td> </tr> <tr> <td> CCI_LC2014 </td> <td> </td> </tr> <tr> <td> CCI_LC2015 </td> <td> </td> </tr> <tr> <td> CCI_LC2016 </td> <td> </td> </tr> <tr> <td> CCI_LC2017 </td> <td> </td> </tr> <tr> <td> CCI_LC2018 </td> <td> </td> </tr> <tr> <td> CCI_LC2019 </td> <td> </td> </tr> <tr> <td> Region </td> <td> AFD </td> <td> Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </td> </tr> <tr> <td> POINT_X </td> <td> AFD </td> <td> Longitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Y </td> <td> AFD </td> <td> Latitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Z </td> <td> -- </td> <td> Ignore. </td> </tr> <tr> <td> POINT_M </td> <td> -- </td> <td> Ignore. </td> </tr> </tbody> </table> 5.2 DATASET nfire4_all_spatial_fire_2002_2019_igs_sp_LC <table class="MsoTableGrid"> <tbody> <tr> <td> Field Name </td> <td> Geography of Attribute Value </td> <td> Definition </td> </tr> <tr> <td> OID </td> <td> AFD </td> <td> 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. </td> </tr> <tr> <td> IG_Count </td> <td> Fire Event </td> <td> Number of ignition AFDs in the fire event. </td> </tr> <tr> <td> MIN_acq_yer </td> <td> Fire Event </td> <td> The year in which the earliest AFD of the fire event was detected. </td> </tr> <tr> <td> MIN_acq_mnt </td> <td> Fire Event </td> <td> 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. </td> </tr> <tr> <td> FIRST_subst_r </td> <td> AFD </td> <td> Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua. </td> </tr> <tr> <td> MIN_yday </td> <td> Fire Event </td> <td> 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. </td> </tr> <tr> <td> nfireID2 </td> <td> Fire Event </td> <td> The unique ID of the fire event to which the AFD belongs. </td> </tr> <tr> <td> CCI_LC2002 </td> <td> AFD </td> <td> 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. </td> </tr> <tr> <td> CCI_LC2003 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2004 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2005 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2006 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2007 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2008 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2009 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2010 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2011 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2012 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2013 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2014 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2015 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2016 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2017 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2018 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2019 </td> <td> AFD </td> </tr> <tr> <td> Region </td> <td> AFD </td> <td> Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </td> </tr> <tr> <td> Peat </td> <td> AFD </td> <td> Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. </td> </tr> <tr> <td> POINT_X </td> <td> AFD </td> <td> Longitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Y </td> <td> AFD </td> <td> Latitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Z </td> <td> -- </td> <td> Ignore. </td> </tr> <tr> <td> POINT_M </td> <td> -- </td> <td> Ignore. </td> </tr> </tbody> </table> 5.3. LAND-COVER ATTRIBUTE DATA 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: 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), <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview</a>; <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download.php">http://maps.elie.ucl.ac.be/CCI/viewer/download.php</a>; <a href="http://www.esa-landcover-cci.org/">http://www.esa-landcover-cci.org/</a> (2020). Pérez-Hoyos, A., Rembold, F., Kerdiles, H. & Gallego, J. Comparison of global land cover datasets for cropland monitoring. Remote Sensing 9, (2017). 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. <table class="MsoNormalTable"> <tbody> <tr> <td> 1. CCI-LC Class Value </td> <td> 2. CCI-LC Class Label </td> <td> 3. New Class Value for Sloan et al. </td> <td> 4. New Class Label for Sloan et al. </td> </tr> <tr> <td> 0 </td> <td> No Data </td> <td> 0 </td> <td> No Data </td> </tr> <tr> <td> 10 </td> <td> Cropland, rainfed </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 11 </td> <td> Herbaceous cover </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 12 </td> <td> Tree or shrub cover </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 20 </td> <td> Cropland, irrigated or post‐flooding </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 30 </td> <td> Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%) </td> <td> 2 </td> <td> Mosaic Cropland </td> </tr> <tr> <td> 40 </td> <td> Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) </td> <td> 3 </td> <td> Mosaic Natural Veg </td> </tr> <tr> <td> 50 </td> <td> Tree cover, broadleaved, evergreen, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 60 </td> <td> Tree cover, broadleaved, deciduous, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 61 </td> <td> Tree cover, broadleaved, deciduous, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 62 </td> <td> Tree cover, broadleaved, deciduous, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 70 </td> <td> Tree cover, needleleaved, evergreen, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 71 </td> <td> Tree cover, needleleaved, evergreen, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 72 </td> <td> Tree cover, needleleaved, evergreen, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 80 </td> <td> Tree cover, needleleaved, deciduous, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 81 </td> <td> Tree cover, needleleaved, deciduous, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 82 </td> <td> Tree cover, needleleaved, deciduous, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 90 </td> <td> Tree cover, mixed leaf type (broadleaved and needleleaved) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 100 </td> <td> Mosaic tree and shrub (>50%) / herbaceous cover (<50%) </td> <td> 5 </td> <td> Mosaic Shrubland </td> </tr> <tr> <td> 110 </td> <td> Mosaic herbaceous cover (>50%) / tree and shrub (<50%) </td> <td> 5 </td> <td> Mosaic Shrubland </td> </tr> <tr> <td> 120 </td> <td> Shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 121 </td> <td> Evergreen shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 122 </td> <td> Deciduous shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 130 </td> <td> Grassland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 140 </td> <td> Lichens and mosses </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 150 </td> <td> Sparse vegetation (tree, shrub, herbaceous cover) (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 151 </td> <td> Sparse tree (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 152 </td> <td> Sparse shrub (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 153 </td> <td> Sparse herbaceous cover (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 160 </td> <td> Tree cover, flooded, fresh or brakish water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 170 </td> <td> Tree cover, flooded, saline water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 180 </td> <td> Shrub or herbaceous cover, flooded, fresh/saline/brakish water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 190 </td> <td> Urban areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 200 </td> <td> Bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 201 </td> <td> Consolidated bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 202 </td> <td> Unconsolidated bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 210 </td> <td> Water bodies </td> <td> 8 </td> <td> Other </td> </tr> </tbody> </table>

opencc-zeroAug 2022View details →

dryad36/100

Resilience of a tropical montane pine forest to fire and severe droughts

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. 2. This study focused on long-term dynamics of montane Pinus occidentalis forests in the Cordillera Central, Dominican Republic, after a ~1000 km2 fire in 2005, the largest since 1965. 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 0.05 ha plots and 35 large 0.1 ha plots) established in 1999 to model overstorey and understorey vegetation dynamics. 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. Lower elevations, leeward slopes, and stands with a higher proportion of smaller pine trees all burned at higher severities. 4. Growth rates of trees that survived the fire remained lower than pre-fire rates 13 years after the fire. The highest mortality rates were soon after the fire and in the census immediately after the post-fire droughts. 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 13 years after the fire. Even though high-severity patches had persistently low pine regeneration, 100% of small 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. 5. Synthesis. Climate change has increased the likelihood of wildfires in tropical montane pine forests, with long-lasting effects on vegetation dynamics. However, this biome may prove resilient to increasingly severe fires in the near future, given the ongoing recovery of Pinus occidentalis forests in Hispaniola 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.

opencc-zeroOct 2022View details →

dryad36/100

Global responses of plant abundance, diversity, and fitness to increased fire severity or frequency

Database of responses of plant abundance, diversity, and fitness to increased fire frequency or severity, collected from published scientific articles or reports. The database includes 394 studies published worldwide between 1962 and 2023. Information on the following variables are also provided: fire regime component (fire frequency or severity), time since the last fire, fire type (wildfire or prescribed fire), historical fire regime type (surface or crown fire), plant life form (woody plant, herb, or bryophyte), habitat type, and climate. The database underpins the meta-analysis 'Global plant responses to intensified fire regimes', published in Global Ecology and Biogeography.

opencc-zeroApr 2024View details →

dryad36/100

Data from: Tamm Review: a meta-analysis of thinning, prescribed fire, and wildfire effects on subsequent wildfire severity in conifer dominated forests of the Western US

Increased understanding of how active forest management (i.e., mechanical thinning, prescribed burning, and managed wildfire) affects subsequent wildfire severity is urgently needed as people and forests face a growing wildfire crisis. In response, we reviewed scientific literature for the US West and completed a meta-analysis that answered three questions: (1) How much do treatments reduce wildfire severity within treated areas? (2) How do the effects vary with treatment type, treatment age, and forest type? (3) How does fire weather moderate the effects of treatments? We found overwhelming evidence that mechanical thinning with prescribed burning, mechanical thinning with pile burning, and prescribed burning only are effective at reducing subsequent wildfire severity, resulting in reductions in severity from 62% to 72% relative to untreated areas. In comparison, thinning only was less effective – underscoring the importance of treating surface fuels when mitigating wildfire severity is the management goal. The efficacy of these treatments did not vary among forest types assessed in this study and was high across a range of fire weather conditions. Prior wildfire had more complex impacts on subsequent wildfire severity, which varied with forest type and initial wildfire severity. Across treatment types, we found that effectiveness of treatments declined over time, with the mean reduction in wildfire severity decreasing nearly threefold when wildfire occurred greater than 10 years after initial treatment. Our meta-analysis provides up-to-date information on the extent to which active forest management reduces wildfire severity and facilitates better outcomes for people and forests during future wildfire events.

opencc-zeroMay 2024View details →

dryad36/100

Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States

The combination of increasing fire-caused tree mortality and warmer, drier post-fire conditions is making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a novel dataset of post-fire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Post-fire regeneration is sensitive to high-severity fire, which limits seed availability, and post-fire climate, which influences seedling establishment and survival. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected impacts of climate change for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in post-fire regeneration. Across 40–42% of the study area, we project post-fire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031–2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981–2000 to 26–31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support post-fire conifer regeneration.

opencc-zeroMar 2023View details →

dryad36/100

Fire severity as a key determinant of aboveground and belowground biological community recovery in managed even-aged boreal forests

Changes in fire regime of boreal forests in response to climate warming are expected to impact post-fire recovery. However, quantitative data on how managed forests sustain and recover from recent fire disturbance are limited. Two years after a large wildfire in managed even-aged boreal forests in Sweden, we investigated how recovery of aboveground and belowground communities, i.e., understory vegetation and soil microbial and faunal communities, responded to variation in the severity of soil (i.e., consumption of soil organic matter) and canopy fires (i.e., tree mortality). While fire overall enhanced diversity of understory vegetation through colonization of fire adapted plant species, it reduced the abundance and diversity of soil biota. We observed contrasting effects of tree- and soil-related fire severity on survival and recovery of understory vegetation and soil biological communities. Severe fires that killed overstory Pinus sylvestris promoted a successional stage dominated by the mosses Ceratodon purpureus and Polytrichum juniperum, but reduced regeneration of tree seedlings and disfavoured the ericaceous dwarf-shrub Vaccinium vitis-idaea and the grass Deschampsia flexuosa. Moreover, high tree mortality from fire reduced fungal biomass and changed fungal community composition, in particular that of ectomycorrhizal fungi, and reduced the fungivorous soil Oribatida. In contrast, soil-related fire severity had little impact on vegetation composition, fungal communities and soil animals. Bacterial communities responded to both tree- and soil-related fire severity. Synthesis: Our results two years post-fire suggest that a change in fire regime from a historically low-severity ground-fire regime, with fires that mainly burns into the soil organic layer, to a stand-replacing fire regime with a high degree of tree mortality, as may be expected with climate change, is likely to impact the short-term recovery of stand structure and above- and belowground species composition of even-aged P. sylvestris boreal forests.

opencc-zeroMay 2023View details →

dryad36/100

Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States

Open the record for dataset details and reuse information.

publicMar 2023View details →

dryad36/100

Global responses of plant abundance, diversity, and fitness to increased fire severity or frequency

Open the record for dataset details and reuse information.

publicApr 2024View details →

dryad36/100

Fire severity as a key determinant of aboveground and belowground biological community recovery in managed even-aged boreal forests

Open the record for dataset details and reuse information.

publicMay 2023View details →

dryad36/100

After the ‘Black Summer’ fires: faunal responses to megafire depend on fire severity, proportional area burnt, and vegetation type

Open the record for dataset details and reuse information.

publicNov 2023View details →

dryad36/100

Data from: Contemporary fires are less frequent but more severe in dry conifer forests of the southwestern United States

Open the record for dataset details and reuse information.

publicSep 2024View details →

dryad36/100

Long-term benefits of burns for large mammal habitat undermined by large, severe fires in the American West

Open the record for dataset details and reuse information.

publicNov 2025View details →

dryad36/100

Data from: Repeated high severity fire effects on soil biogeochemistry in the Sierra Nevada, California

Open the record for dataset details and reuse information.

publicDec 2025View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

Abiotic factors modify ponderosa pine regeneration outcomes after high-severity fire

Data from: Extreme fire severity interacts with seed traits to moderate post-fire species assemblages

Severity outweighs pyrodiversity in shaping avian and bat species distributions following an oak woodland mega-fire

Fire Severity Estimates: 2001 Survey Line Burn

Thresholds and alternative states in neotropical dry forest in response to fire severity, 2005-2018

After the 'Black Summer' fires: faunal responses to megafire depend on fire severity, proportional area burnt, and vegetation type

The effect of climate change on forest fire danger and severity in the Canadian boreal forests for the period 1976-2100

Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019

Resilience of a tropical montane pine forest to fire and severe droughts

Global responses of plant abundance, diversity, and fitness to increased fire severity or frequency

Data from: Tamm Review: a meta-analysis of thinning, prescribed fire, and wildfire effects on subsequent wildfire severity in conifer dominated forests of the Western US

Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States

Fire severity as a key determinant of aboveground and belowground biological community recovery in managed even-aged boreal forests

Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States

Global responses of plant abundance, diversity, and fitness to increased fire severity or frequency

Fire severity as a key determinant of aboveground and belowground biological community recovery in managed even-aged boreal forests

After the ‘Black Summer’ fires: faunal responses to megafire depend on fire severity, proportional area burnt, and vegetation type

Data from: Contemporary fires are less frequent but more severe in dry conifer forests of the southwestern United States

Long-term benefits of burns for large mammal habitat undermined by large, severe fires in the American West

Data from: Repeated high severity fire effects on soil biogeochemistry in the Sierra Nevada, California

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch