DOI: 10.5281/zenodo.4943354
title: A Datacube for the analysis of wildfires in Greece

description: <strong>dataset_greece.nc</strong> <strong>This dataset is meant to be used to develop models for next-day fire hazard forecasting in Greece. It contains data from 2009 to 2020 at a 1km x 1km x 1 daily grid.</strong> Check our Jupyter notebook for an example showing how to access the dataset. ==================================================================================== <strong>Dynamic Variables </strong> <strong>IMPORTANT NOTE: The Fire, Meteorological Variables and Fire Weather Index have been shifted one day back to ease the development of the models.</strong> This is to ease the development of our models, because operationally Meteorological variables and the Fire Weather Index are available as forecast and the Fire Variables are what we want our models to forecast given all the other variables. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ It includes the following dynamic variables<strong> resampled at daily temporal resolution and 1km spatial resolution</strong>: <strong>1. Previous day Leaf Area Index - MOD15A2H Variables (</strong>https://lpdaac.usgs.gov/products/mod15a2hv006/) Fpar_500m<br> Lai_500m<br> FparLai_QC<br> FparExtra_QC<br> FparStdDev_500m<br> LaiStdDev_500m <strong>2. Previous day MOD13A2 Variables </strong>(https://lpdaac.usgs.gov/products/mod13a2v006/)<br> 1 km 16 days NDVI<br> 1 km 16 days EVI<br> 1 km 16 days VI Quality <strong>3. Previous daty Evapotranspiration. MOD16A2 Variables </strong>(https://lpdaac.usgs.gov/products/mod16a2v006/)<br> ET_500m<br> LE_500m<br> PET_500m<br> PLE_500m<br> ET_QC_500m <strong>4. Previous day Land Surface Temperature. MOD11A1 variables </strong>(https://lpdaac.usgs.gov/products/mod11a1v006/)<br> LST_Day_1km<br> QC_Day<br> LST_Night_1km<br> QC_Night <strong>5. Meteorological data. ERA5-Land variables </strong>(https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview)<br> era5_max_u10<br> era5_max_v10<br> era5_max_t2m<br> era5_max_tp<br> era5_min_u10<br> era5_min_v10<br> era5_min_t2m<br> era5_min_tp <strong>6. Fire variables</strong><br> ignition_points Ignition points derived from the association of burned areas product from EFFIS (effis.jrc.ec.europa.eu/) with FIRMS active fire product.<br> burned_areas: Burned areas from EFFIS (effis.jrc.ec.europa.eu/), associated with FIRMS active fire product to find ignition date<br> number_of_fires: Count of fire events for the given day. 7. <strong>Fire Weather Index </strong>(https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview)<br> fwi ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ <strong>Static Variables</strong> It includes the following static variables <strong>resampled at 1km spatial resolution</strong>: <strong>1. clc_YYYY </strong>for years 2006,, 2012, 2018: Corine Land Cover. (https://land.copernicus.eu/) <strong>2. roads_density_2020</strong>: raster derived from OpenStreetMaps polygons for 2020. (https://www.openstreetmap.org/)<br> <strong>3. population_density_YYYY </strong>for years 2009-2020: population density at 1km spatial resolution. Source - https://www.worldpop.org/ <strong>4. Topography layers </strong>derived from EU-DEM. (https://land.copernicus.eu/) <strong>dem_{agg}</strong><strong>, aspect_{agg}. slope_{agg}</strong>, where agg is <em>mean </em>(mean value), <em>std </em>(standard deviation), <em>max </em>(maximum value), <em>min </em>(minimun value) and specifies the applied aggregation for the resampling to 1km.<br>
description: Check our notebook to see how to access the dataset https://github.com/DeepCube-org/uc3-public-notebooks/blob/main/1_UC3_Datacube_Access_and_Plotting.ipynb .