Created
August 15, 2023 02:12
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analyze NDVI
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| # Extract Year and Month | |
| fireNDVI['Year'] = fireNDVI.Date.str.split(pat='-',expand=True)[0] | |
| fireNDVI['Month'] = fireNDVI.Date.str.split(pat='-',expand=True)[1] | |
| # filter to June, July, August | |
| fireNDVI = fireNDVI[fireNDVI.Month.isin(['06','07','08'])] | |
| # find average of 3 months for 2021 for object id | |
| # then find average of 3 months for 2022 for object id | |
| # subtract 2022-2021 | |
| # low numbers indicate loss of vegetation | |
| # find bottom five lowest | |
| # calculate average NDVI for each ObjectID for 2021 | |
| fireNDVI2021 = fireNDVI[fireNDVI.Year=='2021'] | |
| fireNDVI2021_mean = fireNDVI2021.groupby('OBJECTID')['NDVI'].agg('mean') | |
| # calculate average NDVI for each ObjectID for 2022 | |
| fireNDVI2022 = fireNDVI[fireNDVI.Year=='2022'] | |
| fireNDVI2022_mean = fireNDVI2022.groupby('OBJECTID')['NDVI'].agg('mean') | |
| # re-combine the 2021 and 2022 dataframes horizontally | |
| fire_mean_2021_2022 = pd.concat([fireNDVI2021_mean,fireNDVI2022_mean],axis=1) | |
| fire_mean_2021_2022.columns.values[0] = 'NDVI 2021' | |
| fire_mean_2021_2022.columns.values[1] = 'NDVI 2022' | |
| # subtract 2022-2021 to find difference | |
| fire_mean_2021_2022['difference'] = fire_mean_2021_2022['NDVI 2022'] - fire_mean_2021_2022['NDVI 2021'] | |
| fire_mean_2021_2022 = fire_mean_2021_2022.reset_index() | |
| # Merge the WFIGS dataframe (gdf) with the NDVI data (fire_mean_2021_2022) | |
| ndvi_wfigs_fires = gdf.merge(fire_mean_2021_2022, on='OBJECTID') | |
| # filter to fires over 1000 acres in size | |
| fires_over_1000_acres = ndvi_wfigs_fires[ndvi_wfigs_fires['poly_GISAcres']> 1000] | |
| # sort by difference ascending to find smallest values (largest decreas in vegetation) | |
| fires_over_1000_acres = fires_over_1000_acres.sort_values(by='difference', ascending=True) |
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