Agnessa14/Wildfires-Angola

Code for the project "Largest Wildfires in Angola: Correlation of vegetation and meteorological variables with wildfire intensity" by Karapetian et al., conducted during the Climatematch Summer School (2023). # Wildfires-Angola Code for the project "Largest Wildfires in Angola: Correlation of vegetation and meteorological variables with wildfire intensity", led by Karapetian et al. during the Climatematch Summer School (2023). Written by Agnessa Karapetian, Kamil Vlček, Caroline Temporão, Hatice Büşra and Sedem Buabassah. # NDVI_DNBR_Micropublication.ipynb This code uses the Globfire (Artés et al., 2019) and MODIS Surface Reflectance 8-Day product (Vermote, 2021) data to calculate the NDVI and dNBR for the largest fires of Angola in 2020. 0. Data acquisition Prior to running this code, the following datasets should be downloaded: 1) Globfire (Artés et al., 2019), available here: gwis.jrc.ec.europa.eu *Note: the dataset used here is a subset of the Globfire dataset, created by Brittany Engle, containing only fires of category F (1000 acres) or greater. This dataset was graciously provided to us by the Climatematch organizers, saved here: "~/shared/Data/Projects/Wildfires/ClimateAction_countries.shp". 2) MODIS Surface Reflectance 8-Day product (Vermote, 2021) data (more information here lpdaac.usgs.gov), available here: search.earthdata.nasa.gov][v]=t&pg[1][dnf]=DAY&pg[1][id]=MOD09A1*h19v09*&pg[1][ecd]=2020-01-01T00%3A00%3A00.000Z%2C2020-12-31T23%3A59%3A59.999Z&pg[1][gsk]=-start_date&pg[1][m]=download&pg[1][cd]=f&fi=MODIS&tl=1704799757!3!! (need to register to have access to data) - we selected the entire 2020 year and tiles (19,09), (20,09), (19,10) and (20,10), which cover Angola, by searching for filenames "MOD09A1*h19v09*", "MOD09A1*h19v10*", "MOD09A1*h20v09*" "and MOD09A1*h20v10*" and all dates in the 2020 year - this resulted in 187 .hdf files (46 or 47 per tile) - these images are saved under ~/shared-public/Jintasaurus_Skip_Energico/modis_images 1. Using the data in the Globfire dataset (Artés et al., 2019), select …