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8 results for “fire event”

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zenodo40/100

GOES-Observed Fire Event Representation (GOFER) product for 28 California wildfires from 2019-2021

The GOES-Observed Fire Event Representation (GOFER) algorithm uses geostationary satellite observations of active fires from GOES-East and GOES-West to map the hourly progression of large wildfires (over 50,000 acres or 202 sq. km). GOES observes North and South America with a spatial resolution of 2 km at the equator and at a frequency of 10-15 minutes for the full disk view. Along with the fire perimeter, we derive the active fire lines and fire spread rates. We tested the GOFER algorithm on a set of 28 wildfires in California from 2019-2021 and produced three versions of the product: GOFER-Combined, GOFER-East, and GOFER-West. GOFER-Combined uses both GOES-East and GOES-West observations, while GOFER-East and GOFER-West use only GOES-East and only GOES-West observations, respectively. We find that GOFER performs reasonably well compared to final perimeters from California's Fire and Resource Assessment Program (FRAP) and 12-hourly perimeters from the Fire Event Data Suite (FEDS), derived from 375-m active fire observations. See our <a href="https://globalfires.earthengine.app/view/gofer">GOFER Visualization</a> app on Earth Engine Apps for an overview of the dataset, alongside other datasets, such as FEDS and FRAP perimeters and 30-m burn severity from Monitoring Trends in Burn Severity (MTBS). Please refer to the corresponding <a href="https://github.com/tianjialiu/GOFER">GitHub repository</a> for the code, detailed dataset description, and version history.

opencc-by-4.0Sep 2023View details →

zenodo40/100

Data from paper: "Life cycle of bamboo in the southwestern Amazon and its relation to fire events".

This is the dataset from the paper "Life cycle of bamboo in the southwestern Amazon and its relation to fire events".   It contains: - The processed MODIS (MAIAC) time series for the southwest Amazon, already processed and ready to use for the modeling (files such as bamboo_ts_annual_2000-2017_band[...]). MODIS (MAIAC) composites for south america is not provided here because of its huge file size (contact ricds@hotmail.com). - Final bamboo die-off data from 2001-2017 using the simple bilinear model (combination from band 2 and 5, p-value < 0.001). The data used for Figure S3. File: "Theoric_death_merged_band2_band5.tif" - Bamboo die-off detected from 2001-2017 using the simple bilinear model (files Theoric_death_year_band2 and Theoric_death_year_band5, and Theoric_pvalue_band2 and Theoric_pvalue_band5). To obtain the same map as in the paper, must apply the p < 0.001 over the death year map. - Bamboo spatial distribution obtained by the die-off detection and live detection. In this map, values equal to 0, 1 and 2 correspond to non-bamboo, live bamboo and dead bamboo forests. - Bamboo die-off predictions from 2000-2028 using the empirical curves (files Empirical_death_year_band2_curves2 and Empirical_death_year_band5_curves2, and Empirical_p_value_band5_curves2 and Empirical_p_value_band5_curves2). To obtain the same map as in the paper, must apply the p < 0.001 over the death year map.   More information contact Ricardo Dalagnol (ricds@hotmail.com).

opencc-by-4.0Apr 2018View details →

zenodo36/100

Fire Events with Water Balance Shift for Western US during 2014-2020

This dataset comprises fire events in the Western US during 2014-2020, with respective fire characteristics, centroid locations, and variables necessary to calculate water balance shift during pre- and post-fire years, as described in the manuscript (Ahmad et al., 2023)

opencc-by-4.0Oct 2023View details →

zenodo36/100

Tracking and classifying Amazon fire events in near-real time

Summary Time-series (2018-2024) of the Amazon dashboard, including minor updates to the methods. The Amazon dashboard data product tracks individual fire events across most of South America (10N - 25S, 85W - 30W) in near-real time. The model classifies fires into four key fire types (deforestation, forest, small clearing and agricultural, and savanna and grassland fires) and provides estimates of individual fire carbon emissions. Methods are described in Andela et al. (2022). Near-real time estimates are provided at https://amzfire.servirglobal.net/ and here we archive historic time-series. Methods The data archived here (v1.1) include several small updates. Two updates relate to the use of VIIRS active fire detections. First, VIIRS active fire detections have been updated from collection 1 to collection 2. Second, any full day of missing data from either the VIIRS instrument onboard NOAA-20 or Suomi NPP is now replaced by data of the other instrument. This "gap" filling helps reduce the impact of periods with instrument outage, like those of Suomi NPP VIIRS during the 2024 burning season.  The other two updates relate to the emissions calculations. First, to convert dry matter burned to carbon emissions, we have introduced fire type specific emissions factors instead of the earlier assumption of 50% carbon content for all fire types. Second, as part of the Sense4Fire project (https://sense4fire.eu/), we provide daily gridded emissions estimates of Dry Matter (DM), C, CO2, CO, and NOx at 0.1 degree resolution. We used emissions factors provided by Andrea (2019) for savanna and grassland fires as well as small clearing and agricultural fires while for forest and deforestation fires we reviewed the literature to select the most relevant emissions factors (Table 1).  Table 1: Emissions factors (gram species per kg dry matter burned) used to calculate C, CO2, CO, and NOx emissions.  <table> <tbody> <tr> <td>Fire type / trace gas emissions</td> <td>C</td> <td>CO2</td> <td>CO</td> <td>NOx</td> </tr> <tr> <td>Savanna and grassland</td> <td>480</td> <td>1656</td> <td>69.2</td> <td>2.5</td> </tr> <tr> <td>Small clearing and agricultural</td> <td>430</td> <td>1431</td> <td>76.2</td> <td>2.4</td> </tr> <tr> <td>Forest</td> <td>480</td> <td>1561</td> <td>104.0</td> <td>2.0</td> </tr> <tr> <td>Deforestation</td> <td>490</td> <td>1641</td> <td>95.5</td> <td>1.7</td> </tr> </tbody> </table>   Dataset description For full detail, please see Andela et al. (2022). The tables below (Tables 2 - 4) describe the content of the fire event (polygon) and active fire detections (point) shapefiles as well as the gridded emissions product. The active fire detections and associated estimates of dry matter burned can be combined with emissions factors (Table 1) to derive daily trace gas emissions time series for species and areas of interest. Table 2: Explanation of fire event shapefile attribute table. <table> <tbody> <tr> <td>Attribute class</td> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>Fire type classification</td> <td>Fire type</td> <td>(1) savanna and grassland, (2) small clearing and agriculture, (3) forest, and (4) deforestation fires</td> </tr> <tr> <td> </td> <td>Confidence</td> <td>(1) low, (2) moderate, and (3) high</td> </tr> <tr> <td>Fire Atlas</td> <td>Size</td> <td>Fire size in km2</td> </tr> <tr> <td> </td> <td>Start day</td> <td>Day of new fire start as day of year (1-366)</td> </tr> <tr> <td> </td> <td>Duration</td> <td>Fire duration in days</td> </tr> <tr> <td> </td> <td>C Emissions</td> <td>Fire carbon emissions (ton C)</td> </tr> <tr> <td>Fire characterization</td> <td>Tree cover</td> <td>Average tree cover fraction within perimeter (%)</td> </tr> <tr> <td> </td> <td>Biomass</td> <td>Average biomass within fire perimeter (ton ha-1)</td> </tr> <tr> <td> </td> <td>Deforestation </td> <td>Fraction of 550 m grid cells with historic deforestation (five years prior to fire) within fire perimeter (%)</td> </tr> <tr> <td> </td> <td>FRP</td> <td>Average fire radiative power (FRP) for all fire detections within fire perimeter (MW)</td> </tr> <tr> <td> </td> <td>Persistence</td> <td>Average fire persistence across 550 m grid cells within fire perimeter (days)</td> </tr> <tr> <td> </td> <td>Progression</td> <td>Average fire progression fraction across 550 m grid cells within perimeter (%)</td> </tr> <tr> <td> </td> <td>Daytime</td> <td>Fraction of 1:30 pm detections (%) for all fire detections within fire perimeter</td> </tr> <tr> <td> </td> <td>Detections</td> <td>Total active fire detections within fire perimeter</td> </tr> </tbody> </table>   Table 3: Explanation of active fire detection shapefile attribute table. <table> <tbody> <tr> <td>Attribute class</td> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>VIIRS active fire detections</td> <td>FRP</td> <td>Fire radiative power (MW)</td> </tr> <tr> <td> </td> <td>DOY</td> <td>Day of year (1-366)</td> </tr> <tr> <td>Fire type classification</td> <td>Fire type</td> <td>(1) savanna and grassland, (2) small clearing and agriculture, (3) forest, and (4) deforestation fires</td> </tr> <tr> <td> </td> <td>Confidence</td> <td>(1) low, (2) moderate, and (3) high</td> </tr> <tr> <td>Emissions</td> <td>C Emissions</td> <td>Fire carbon emissions (ton C) associated with each active fire detection</td> </tr> <tr> <td> </td> <td>DM Emissions</td> <td>Dry matter burned (ton) associated with each active fire detection</td> </tr> </tbody> </table>   Table 4: Content of daily gridded (0.1 degree resolution) emissions netcdf files. The daily emissions product provides emissions estimates of dry matter, C, CO2, CO, and NOx. For DM and CO partitioned emissions are also provided by fire type, for other species these can be derived by multiplying the dry matter burned (DM) estimates with trace gas specific emissions factors (Table 1). Values of each grid cell can be multiplied by the number of seconds per day and grid cell area to calculate total emissions (convert "kg species m-2 s-1" to "kg species day-1 per grid cell"). <table> <tbody> <tr> <td>/ancill</td> <td>grid_cell_area</td> </tr> <tr> <td>/partitioned_DM_emissions</td> <td>Deforestation emissions</td> </tr> <tr> <td> </td> <td>Forest emissions</td> </tr> <tr> <td> </td> <td>Savanna and grassland emissions</td> </tr> <tr> <td> </td> <td>Small clearing and agricultural emissions</td> </tr> <tr> <td>/partitioned_CO_emissions</td> <td>Deforestation emissions</td> </tr> <tr> <td> </td> <td>Forest emissions</td> </tr> <tr> <td> </td> <td>Savanna and grassland emissions</td> </tr> <tr> <td> </td> <td>Small clearing and agricultural emissions</td> </tr> <tr> <td>/total_emissions</td> <td>DM emissions</td> </tr> <tr> <td> </td> <td>C emissions</td> </tr> <tr> <td> </td> <td>CO2 emissions</td> </tr> <tr> <td> </td> <td>CO emissions</td> </tr> <tr> <td> </td> <td>NOx emissions</td> </tr> </tbody> </table>   Results Despite the various small improvements to the dataset, the data are largely consistent with the original dataset published for 2019-2020 (Table 5).  Table 5: Comparison of model versions (original from Andela et al., 2022 and v1.1 published here) for April-December 2019 (equator-25S, 85W - 30W). Note that the current version (v1.1) is complete for 2019, but the original dataset had missing data due to incomplete active fire detections from NOAA-20 VIIRS at that time. <table> <tbody> <tr> <td>Dataset</td> <td>Fire type</td> <td>Fire detections (x1,000)</td> <td>Mean fire radiative power (MW)</td> <td>Number of events (x1,000)</td> <td>Emissions (Tg C)</td> </tr> <tr> <td>Original</td> <td>Deforestation</td> <td>756.65</td> <td>15.15</td> <td>24.24</td> <td>99.18</td> </tr> <tr> <td>Original</td> <td>Forest</td> <td>637.58</td> <td>12.73</td> <td>5.28</td> <td>85.46</td> </tr> <tr> <td>Original</td> <td>Small clearing and agricultural</td> <td>348.49</td> <td>10.91</td> <td>154.68</td> <td>10.55</td> </tr> <tr> <td>Original</td> <td>Savanna and grassland</td> <td>1935.06</td> <td>12.11</td> <td>296.42</td> <td>71.75</td> </tr> <tr> <td>v1.1</td> <td>Deforestation</td> <td>742.64</td> <td>14.81</td> <td>24.02</td> <td>97.18</td> </tr> <tr> <td>v1.1</td> <td>Forest</td> <td>626.92</td> <td>12.06</td> <td>5.16</td> <td>77.84</td> </tr> <tr> <td>v1.1</td> <td>Small clearing and agricultural</td> <td>350.7</td> <td>10.89</td> <td>155.56</td> <td>9.27</td> </tr> <tr> <td>v1.1</td> <td>Savanna and grassland</td> <td>1877.16</td> <td>11.89</td> <td>299.17</td> <td>70.55</td> </tr> </tbody> </table>   Acknowledgements The Sense4Fire project is funded by ESA under ESA Contract Number: 4000134840/21/I-NB.  References Andela, N., Morton, D.C., Schroeder, W., Chen, Y., Brando, P.M. and Randerson, J.T., 2022. Tracking and classifying Amazon fire events in near real time. Science advances, 8, eabd2713. https://doi.org/10.1126/sciadv.abd2713. Andreae, M.O., 2019. Emission of trace gases and aerosols from biomass burning–an updated assessment. Atmospheric Chemistry and Physics, 19, 8523-8546. https://doi.org/10.5194/acp-19-8523-2019.

opencc-by-4.0Jun 2022View 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

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

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publicAug 2022View details →

dryad32/100

Biodiversity and Forest structure one year following the 2016 Fort McMurray (Alberta) fire event

Exploration practices for oil sands developments in the boreal forest of western Canada create a network of thousands of kilometers of linear features, particularly seismic lines that dissect these forests posing significant environmental challenges. As wildfire is one of the prevalent stand-replacing natural disturbances in the Canadian boreal forest, it is an important driver of environmental change and stand development that may contribute to the mitigation of such linear industrial footprint. Here, we evaluate the short-term cumulative (also known as combined) effects of seismic lines and wildfire on biodiversity and site conditions. One year after the Horse River (Fort McMurray, Alberta) fire event in the spring of 2016, we compared dissected and undisturbed forests in burned and unburned boreal peatlands, assessing changes in overall stand structure and the responses of a variety of organisms. Soil moisture was significantly higher on seismic lines than in the adjacent forest, suggesting why most of the study sites within the fire perimeter showed little evidence of burning at the line in relation to the adjacent forest. Low fire severity on seismic lines seemed an important driver of local species diversity for ants, beetles, spiders, and plants in disturbed peatlands, resulting in similar species composition on seismic lines both within and outside the burned area, but different assemblages in burned and unburned adjacent forests. Our results suggest that fire did not erase seismic lines; rather, wildfire might increase the influence of this footprint on the recovering adjacent forest. Longer term monitoring will be necessary to understand how boreal treed peatlands respond to the cumulative effect of wildfire and linear disturbances.

opencc-zeroOct 2020View details →

dryad32/100

Biodiversity and Forest structure one year following the 2016 Fort McMurray (Alberta) fire event

Open the record for dataset details and reuse information.

publicOct 2020View details →

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