Dataset of time-lagged fractions of burned area associated with land-se transitions and the number of years since transition 

This dataset supports the findings of the following publication
Article title: 'The time since land-use transition drives changes in fire activity in the Amazon-Cerrado region'
Journal: Communications Earth & Environment
Authors:
Andreia F. S. Ribeiro (andreia.ribeiro@ufz.de)*
Lucas Santos (lucas.santos@mayaenergy.com.br)
James T. Randerson (jranders@uci.edu)
Maria R. Uribe (maria.uribe@yale.edu)
Ane Alencar (ane@ipam.org.br)
Marcia Macedo (mmacedo@woodwellclimate.org)
Douglas C. Morton (douglas.morton@nasa.gov)
Jakob Zscheischler (jakob.zscheischler@ufz.de)
Rafaella A. Silvestrini (rafaella.silvestrini@ipam.org.br)
Ludmila Rattis (lrattis@woodwellclimate.org)
Sonia I. Seneviratne (sonia.seneviratne@ethz.ch)
Paulo M. Brando (paulo.brando@yale.edu)

*For more information, please contact the corresponding author of the publication.

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Each column is:

Grid. = 2.5 x 2.5 degree for model regionalization
landuse_year. = Land use transition year
fire_year. = fire occurrence year
class = Land-use transition class*
pp = Land-use change occurs pre and post fire occurrence
biome = Biome (Amazonia or Cerrado)
period = fire occurrence - post 2000s or pre 2000s.
year = difference between fire_year to landuse_year
Decade = fire_year decade
BA_area = Burned area in hectares
LUC_area = Land-use change area in hectares
area_biome = Biome area in hectares
ba_prop = BA_area divided by LUC_area
vpd_median = vapor pressure deficit (VPD) median of the grid in (kPa*0.01)/year
mcwd = maximum cumulative water deficit (MCWD) (min cwd (Jan-Dez) and media of the grid) in mm/year
xy = coordinates


*class from MapBiomas 6.0:
F-P = Forest (3) to Pasture (15)
G-P = Grassland (12) to Pasture (15)
S-P = Savanna (4) to Pasture (15)
F - Farm = Forest (3) to Farming (14,19,39,20,40,41,36,46,47,48)
G - Farm = Grassland (12) to Farming (14,19,39,20,40,41,36,46,47,48)
S - Farm = Savanna (4) to Farming (14,19,39,20,40,41,36,46,47,48)
P - Farm = Pasture (15) to Farming (14,19,39,20,40,41,36,46,47,48)

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Description:

	To model how fire activity changes with the evolution of land use in the Amazon-Cerrado agricultural frontier, we proceeded as follows: (1) generate land-use transition maps; (2) calculate the fraction of burned area associated with each land-use transition area; and (3) estimate the time interval in years since a land conversion and a given burned area (here referred to as the age of the frontier). First land-use transition maps at 500m resolution were generated by tracking yearly changes from forest to pasture (F-P), savanna to pasture (S-P), grassland to pasture (G-P), forest to cropland (F-Crop), savanna to cropland (S-Crop), grassland to cropland (G-Crop) and pasture to cropland (P-Crop). These land-use transitions represent the dominant pathways of anthropogenic activity across the Amazon-Cerrado agricultural frontier (Souza et al. 2020). Next, we overlayed the maps of burned area with the land-use transitions to estimate the fraction of burned pixels occurring in each type of transition. For model regionalization, we created a 2.5-degree spatial surface grid, which we used to calculate the fraction of burned area (at 500m resolution) associated with a given land use transition (at 500m resolution). To obtain time-lagged fractions of burned area associated with transitions we overlapped each map of burned area with all concurrent and time-shifted land-use transition maps. For each 2.5-degree grid cell, we calculated fire probability, defined as the burned fraction of the total area of converted land. Larger burned areas correspond to higher fire probabilities (fire probability of 1 means that the area of converted land equals the detected burned area). Finally, for all land-use transitions we estimated the number of years between a given burned area and the initial moment of conversion. The time interval between land conversion and area burned provides a proxy of how frontier age relates to fire activity. The modelling approach (described above) used to generate this dataset was implemented in DinamicaEGO version 5.2.1. 

To better illustrate the generation of this dataset, let s consider an example of a random 2.5-degree cell of land-cover information at 500m resolution, where forests, savanna, and grasslands were present in the year 1994. First, pixels that changed from native vegetation (forest, savanna, and grasslands) to pasture in 1995 were tracked, and a transition map was generated. Second, we overlapped the transition areas with burned areas, identifying the types of transitions that had fire occurrence and the respective fraction of burned area for each land-use transition area. The year 1995 is considered the year 0 in this case, i.e., age of 0 years, and a fire probability associated with the moment of the clearing is determined. One year later (1996), the burned areas of 1996 are overlapped with the transitions that are 1-year-old (i.e., from 1995) and the probability of burning associated with these 1-year-old transitions is determined. In a similar fashion, the fraction of burned area associated with past transitions is also tracked. One year before (1994), the burned areas of 1994 are overlapped with the transitions that are -1-year-old, and the associated probability of burning is determined. In summary, the analysis consists in tracking different types of transitions and assigning probabilities of fire occurrence to each type of transition at different ages, based on the time interval between a land conversion and a burned area. This database provides insights into the dynamics of fire occurrence and the role of land-cover transitions in shaping these dynamics. To avoid high fire probabilities of 100% for very small clearing areas and very small extents of burned area, we have filtered small areas of land use transition (below the 15th percentile). The time since transition will be henceforth referred to as the age of the frontier, ranging from -35 years to 34 years (spanning 35 years from 1986 to 2020), with the transition occurring at age 0 years. This approach allowed us to understand how the age of the different types of conversion impacts ignition sources and thus, how it influences fire probability.

MCWD and VPD:

We explored the joint impact of land-use change and climate drivers in fire activity in terms of estimated vapor pressure deficit (VPD) and maximum cumulative water deficit (MCWD), two common measures of flammability and drought impact. MCWD was estimated as the minimum annual value of the cumulative deficit between monthly precipitation and potential evapotranspiration (PET) within a calendar year, representing a simple drought metric based on the climatic water balance:

MCWD=min(sum(P-PET),0)
	
Precipitation data was derived from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) by Funk et al. (2015) and PET and VPD from the Terra-Climate dataset by Abatzoglou et al. (2018). MCWD and VPD were resampled to the 2.5degree spatial surface grid using a median reducer in GEE. Median values of VPD within a calendar year were also estimated using a median reducer in GEE. We restricted the analysis of the influence of drought and air dryness in terms of fire probability induced by conversion of forest to pasture (F-P) and pasture to cropland (P-Crop).
 

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Raw data:

The raw data analyzed in this dataset is openly available in the Google Earth Engine repository (Gorelick et al. 2017), including annual land cover maps from MapBiomas 6.0 (Souza et al. 2020), annual burned areas from MapBiomas Fire 1.0 (Alencar et al. 2022), precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (Funk et al. 2015), potential evapotranspiration and vapor pressure deficit from the Terra-Climate dataset (Abatzoglou et al. 2018). 

References:
Gorelick N, Hancher M, Dixon M, et al (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18 27. https://doi.org/10.1016/j.rse.2017.06.031

Souza CM, Shimbo JZ, Rosa MR, et al (2020) Reconstructing three decades of land use and land cover changes in brazilian biomes with landsat archive and earth engine. Remote Sens 12:. https://doi.org/10.3390/RS12172735

Alencar AAC, Arruda VLS, da Silva WV, et al (2022) Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning. Remote Sens 14:. https://doi.org/10.3390/rs14112510

Funk C, Peterson P, Landsfeld M, et al (2015) The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Sci Data 2:1 21. https://doi.org/10.1038/sdata.2015.66

Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018) TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci Data 5:1 12. https://doi.org/10.1038/sdata.2017.191


