
import glob, os
import pandas as pd
import geopandas as gpd
import rasterio
import rasterio.plot
import rasterstats
from rasterstats import zonal_stats
import matplotlib.pyplot as plt
from pathlib import Path
import datetime
from IPython.display import display
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import plotly.offline
plotly.offline.init_notebook_mode()

print('All libraries successfully imported!')
print(f'Rasterstats : {rasterstats.__version__}')

All libraries successfully imported!
Rasterstats : 0.14.0


import warnings
warnings.filterwarnings("ignore")

print("BE CAREFULL warning are masked !!")

BE CAREFULL warning are masked !!


index_name = 'NDVI'

stat_name = 'mean'

nodata_val = -10000


format     = '%Y%m%d' # format : YYYYMMDD
begin_date = 8


computer_path = '/export/miro/ndeffense/LBRAT2104/'
grp_letter    = 'X'

# Directory for all work files
work_path = f'{computer_path}GROUP_{grp_letter}/WORK/'


# Rasters files directory
raster_file_list = sorted(glob.glob(f'{work_path}{index_name}/*{index_name}.tif'))

# Vector file
vector_file  = f'{work_path}IN_SITU/NAMUR_2020_IN_SITU_ROI.shp'

# Zonal statistics output
zonal_stat_path = f'{work_path}ZONAL_STATS/'
zonal_stat_csv  = f'{zonal_stat_path}zonal_stat_cont_{index_name}.csv'
zonal_stat_shp  = f'{zonal_stat_path}zonal_stat_cont_{index_name}.shp'

Path(zonal_stat_path).mkdir(parents=True, exist_ok=True)


gdf = gpd.read_file(vector_file)
src = rasterio.open(raster_file_list[0], "r")

crs_vector = str(gdf.crs).split(":",1)[1]
crs_raster = str(src.crs).split(":",1)[1]

if crs_vector == crs_raster:
    print(f'CRS are the same : EPSG:{crs_vector} = EPSG:{crs_raster}')
else:
    print('We must reproject vector file')

    gdf = gdf.to_crs(epsg=crs_raster)

CRS are the same : EPSG:32631 = EPSG:32631


fig, ax = plt.subplots(1, 1, figsize=(15,15))

# Plot vector
gdf.plot(facecolor='none', edgecolor='black', linewidth = 2, ax=ax)

# Plot image
color_map = plt.cm.get_cmap("RdYlGn")
reversed_color_map = color_map.reversed()

src = rasterio.open(raster_file_list[6], "r")
im_rio = rasterio.plot.show(src, cmap=reversed_color_map, vmin=0, vmax=1, ax=ax)
im_rio = im_rio.get_images()[0]

cax = fig.add_axes([ax.get_position().x1+0.01,ax.get_position().y0,0.02,ax.get_position().height])
plt.colorbar(im_rio, ax=ax, cax=cax)


plt.box(False)


dict_list = []

for im in raster_file_list:
            
        date_str = os.path.basename(im)[begin_date-1:begin_date-1+8]

        date_im = datetime.datetime.strptime(date_str, format).date()

        dict_list.append({'date': date_im,
                            'im_path': im})

im_date_path_df = pd.DataFrame.from_dict(dict_list).sort_values('date')

display(im_date_path_df)


start_date = datetime.datetime.strptime('20200101', format).date()
end_date   = datetime.datetime.strptime('20200601', format).date()

mask = (im_date_path_df['date'] > start_date) & (im_date_path_df['date'] <= end_date)

im_date_path_df = im_date_path_df.loc[mask]

display(im_date_path_df)


def mean_div_std(x):

    mean = np.mean(x)
    std  = np.std(x)

    result = mean / std

    return result


df_list = []
date_list = []

for i, row in im_date_path_df.iterrows():
    im_file         = row['im_path']
    long_date  = row['date']

    day   = str(long_date)[8:10]
    month = str(long_date)[5:7]
    year  = str(long_date)[0:4]
    
    print(long_date)

    date_list.append(long_date)

    date = day + month + year  # Year at the end because it will be truncated when saved to ESRI Shapefile

    # Open GeoTIFF file
    src = rasterio.open(im_file, 'r')
    im_arr = src.read(1)
    
    # Change NaN by "nodata_val"
    im_arr = np.nan_to_num(im_arr, nan=nodata_val)

    # Get transform from profile (metadata)
    profile = src.profile
    transform = profile['transform']
    
    src.close()

    df = pd.DataFrame(zonal_stats(gdf,
                                  im_arr,
                                  affine=transform,
                                  stats=['count','nodata','mean','std'],
                                  nodata=nodata_val,
                                  add_stats={'mean_div_std':mean_div_std}
                                  ))
    
    df = df.rename(columns={'count': f'valid_{date}','nodata': f'nan___{date}','mean': f'mean__{date}','std': f'std___{date}', 'mean_div_std':f'mDIVs_{date}'})

    df[f'nan___{date}'] = df[f'nan___{date}'].fillna(0)
    df = df.astype({f'nan___{date}': int, f'mDIVs_{date}': float})
    
    df_list.append(df)
    

dfs = pd.concat(df_list, axis=1).round(4)

display(dfs)

2020-01-16
2020-02-12
2020-03-16
2020-04-17
2020-05-20
2020-06-21
2020-07-19
2020-08-13
2020-09-14
2020-10-19
2020-11-18
2020-12-18


df = dfs.filter(regex='mean')

display(df)

df['parcel_id'] = df.index

df = df.set_index('parcel_id')

df = df.transpose()

df['date'] = date_list


df = df.melt(id_vars=['date'], var_name='parcel_id')

df = df.merge(gdf, left_on='parcel_id', right_on='id')

df = df[['date','parcel_id','value','sub_nb','sub','class_nb','class']]

display(df)


fig = px.line(df,
              x='date',
              y='value',
              color='class_nb',
              line_group='parcel_id',
              hover_data=['sub_nb','sub'],
              title= 'title')

fig.show()


gdf_with_zonal_stat = pd.concat([gdf, dfs], axis=1, join="inner")

display(gdf_with_zonal_stat)

#gdf_with_zonal_stat.columns = gdf_with_zonal_stat.columns.astype(str)


gdf_with_zonal_stat.to_file(zonal_stat_shp)

print(f'SHP file was created : {zonal_stat_shp}')

SHP file was created : /export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/ZONAL_STATS/zonal_stat_cont_NDVI.shp


gdf_with_zonal_stat = pd.concat([gdf, dfs], axis=1, join="inner")

gdf_with_zonal_stat = gdf_with_zonal_stat.drop(columns='geometry')

display(gdf_with_zonal_stat)

gdf_with_zonal_stat.to_csv(zonal_stat_csv)

print(f'CSV file was created : {zonal_stat_csv}')

CSV file was created : /export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/ZONAL_STATS/zonal_stat_cont_NDVI.csv

	mean__16012020	valid_16012020	std___16012020	nan___16012020	mDIVs_16012020	mean__12022020	valid_12022020	std___12022020	nan___12022020	mDIVs_12022020	...	mean__18112020	valid_18112020	std___18112020	nan___18112020	mDIVs_18112020	mean__18122020	valid_18122020	std___18122020	nan___18122020	mDIVs_18122020
0	0.7395	25	0.0834	0	8.8643	0.1272	3	0.1198	22	1.0619	...	0.7976	25	0.0516	0	15.4615	0.7610	25	0.0409	0	18.6177
1	0.7475	26	0.0380	0	19.6801	NaN	0	NaN	26	NaN	...	0.7914	26	0.0376	0	21.0200	0.7185	26	0.0311	0	23.0900
2	0.7392	26	0.1276	1	5.7916	NaN	0	NaN	27	NaN	...	0.6331	27	0.1587	0	3.9892	0.6759	26	0.1439	1	4.6973
3	0.6598	70	0.0600	18	11.0008	0.6801	19	0.0407	69	16.6937	...	0.6573	88	0.0958	0	6.8626	0.6309	62	0.0733	26	8.6033
4	0.7268	208	0.0634	0	11.4695	NaN	0	NaN	208	NaN	...	0.8323	206	0.0724	2	11.5005	0.8127	204	0.0597	4	13.6152
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
728	NaN	0	NaN	56	NaN	0.3371	33	0.0269	23	12.5295	...	0.6595	15	0.0514	41	12.8283	0.5909	5	0.0741	51	7.9720
729	0.5909	65	0.0865	71	6.8274	0.1317	4	0.0358	132	3.6768	...	0.6570	118	0.1176	18	5.5887	0.5920	68	0.0652	68	9.0778
730	0.6248	45	0.0683	59	9.1492	0.2534	43	0.0603	61	4.1994	...	0.6661	96	0.0597	8	11.1671	0.6007	70	0.0541	34	11.1064
731	0.2090	52	0.0713	0	2.9304	0.1992	40	0.0735	12	2.7110	...	0.2529	52	0.1161	0	2.1778	0.2505	52	0.0940	0	2.6662
732	0.1907	75	0.0376	1	5.0693	0.2057	71	0.0631	5	3.2603	...	0.2429	76	0.0557	0	4.3587	0.2266	73	0.0577	3	3.9278

	mean__16012020	mean__12022020	mean__16032020	mean__17042020	mean__20052020	mean__21062020	mean__19072020	mean__13082020	mean__14092020	mean__19102020	mean__18112020	mean__18122020
0	0.7395	0.1272	0.8413	0.8236	0.6891	0.7709	0.7815	0.4859	0.5481	0.6841	0.7976	0.7610
1	0.7475	NaN	0.7638	0.8128	0.7638	0.5150	0.7029	0.3949	0.6004	0.6972	0.7914	0.7185
2	0.7392	NaN	0.7566	0.5829	0.5943	0.7457	0.7798	0.5395	0.4648	0.5568	0.6331	0.6759
3	0.6598	0.6801	0.6979	0.8038	0.8918	0.7204	0.6554	0.6826	0.7663	0.8264	0.6573	0.6309
4	0.7268	NaN	0.7962	0.8004	0.5363	0.7735	0.8932	0.6061	0.5772	0.5625	0.8323	0.8127
...	...	...	...	...	...	...	...	...	...	...	...	...
728	NaN	0.3371	0.5573	0.6364	0.9057	0.6302	0.9453	0.8781	0.8661	0.8224	0.6595	0.5909
729	0.5909	0.1317	0.4802	0.6402	0.7913	0.5485	0.8005	0.7715	0.8120	0.7472	0.6570	0.5920
730	0.6248	0.2534	0.5915	NaN	0.8734	0.9049	0.9240	0.8966	0.8531	0.8123	0.6661	0.6007
731	0.2090	0.1992	0.1808	0.1673	0.1392	0.1767	0.1832	0.2076	0.1431	0.2825	0.2529	0.2505
732	0.1907	0.2057	0.1596	NaN	0.1336	0.1465	0.1570	0.1960	0.1671	0.2447	0.2429	0.2266

	date	parcel_id	value	sub_nb	sub	class_nb	class
0	2020-01-16	0	0.7395	3199	Grassland and meadows	319	Grassland and meadows
1	2020-02-12	0	0.1272	3199	Grassland and meadows	319	Grassland and meadows
2	2020-03-16	0	0.8413	3199	Grassland and meadows	319	Grassland and meadows
3	2020-04-17	0	0.8236	3199	Grassland and meadows	319	Grassland and meadows
4	2020-05-20	0	0.6891	3199	Grassland and meadows	319	Grassland and meadows
...	...	...	...	...	...	...	...
8791	2020-08-13	732	0.1960	7211	Bare soils	721	Bare soils
8792	2020-09-14	732	0.1671	7211	Bare soils	721	Bare soils
8793	2020-10-19	732	0.2447	7211	Bare soils	721	Bare soils
8794	2020-11-18	732	0.2429	7211	Bare soils	721	Bare soils
8795	2020-12-18	732	0.2266	7211	Bare soils	721	Bare soils

	id	lc_nb	lc	grp_nb	grp	class_nb	class	sub_nb	sub	grp_1_nb	...	mean__18112020	valid_18112020	std___18112020	nan___18112020	mDIVs_18112020	mean__18122020	valid_18122020	std___18122020	nan___18122020	mDIVs_18122020
0	0	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.7976	25	0.0516	0	15.4615	0.7610	25	0.0409	0	18.6177
1	1	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.7914	26	0.0376	0	21.0200	0.7185	26	0.0311	0	23.0900
2	2	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.6331	27	0.1587	0	3.9892	0.6759	26	0.1439	1	4.6973
3	3	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.6573	88	0.0958	0	6.8626	0.6309	62	0.0733	26	8.6033
4	4	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.8323	206	0.0724	2	11.5005	0.8127	204	0.0597	4	13.6152
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
728	727	6	Forest	69	Forest	699	Forest	6999	Forest	69	...	0.6595	15	0.0514	41	12.8283	0.5909	5	0.0741	51	7.9720
729	730	6	Forest	69	Forest	699	Forest	6999	Forest	69	...	0.6570	118	0.1176	18	5.5887	0.5920	68	0.0652	68	9.0778
730	731	6	Forest	69	Forest	699	Forest	6999	Forest	69	...	0.6661	96	0.0597	8	11.1671	0.6007	70	0.0541	34	11.1064
731	711	7	Bare soil	72	Bare soils	721	Bare soils	7211	Bare soils	72	...	0.2529	52	0.1161	0	2.1778	0.2505	52	0.0940	0	2.6662
732	732	7	Bare soil	72	Bare soils	721	Bare soils	7211	Bare soils	72	...	0.2429	76	0.0557	0	4.3587	0.2266	73	0.0577	3	3.9278

	id	lc_nb	lc	grp_nb	grp	class_nb	class	sub_nb	sub	grp_1_nb	...	mean__18112020	valid_18112020	std___18112020	nan___18112020	mDIVs_18112020	mean__18122020	valid_18122020	std___18122020	nan___18122020	mDIVs_18122020
0	0	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.7976	25	0.0516	0	15.4615	0.7610	25	0.0409	0	18.6177
1	1	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.7914	26	0.0376	0	21.0200	0.7185	26	0.0311	0	23.0900
2	2	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.6331	27	0.1587	0	3.9892	0.6759	26	0.1439	1	4.6973
3	3	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.6573	88	0.0958	0	6.8626	0.6309	62	0.0733	26	8.6033
4	4	3	Grassland and meadows	31	Grassland and meadows	319	Grassland and meadows	3199	Grassland and meadows	3	...	0.8323	206	0.0724	2	11.5005	0.8127	204	0.0597	4	13.6152
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
728	727	6	Forest	69	Forest	699	Forest	6999	Forest	69	...	0.6595	15	0.0514	41	12.8283	0.5909	5	0.0741	51	7.9720
729	730	6	Forest	69	Forest	699	Forest	6999	Forest	69	...	0.6570	118	0.1176	18	5.5887	0.5920	68	0.0652	68	9.0778
730	731	6	Forest	69	Forest	699	Forest	6999	Forest	69	...	0.6661	96	0.0597	8	11.1671	0.6007	70	0.0541	34	11.1064
731	711	7	Bare soil	72	Bare soils	721	Bare soils	7211	Bare soils	72	...	0.2529	52	0.1161	0	2.1778	0.2505	52	0.0940	0	2.6662
732	732	7	Bare soil	72	Bare soils	721	Bare soils	7211	Bare soils	72	...	0.2429	76	0.0557	0	4.3587	0.2266	73	0.0577	3	3.9278

Zonal Statistics - continuous values¶

Set parameters¶

Date¶

Set directory¶

Check if raster and vector are projected to the same CRS¶

Get date of each image¶

Select images inside a period¶

Compute zonal statistics¶

Plot timeseries¶

Write output to shapefile¶

Write output to CSV¶

	date	im_path
0	2020-01-16	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
1	2020-02-12	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
2	2020-03-16	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
3	2020-04-17	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
4	2020-05-20	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
5	2020-06-21	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
6	2020-07-19	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
7	2020-08-13	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
8	2020-09-14	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
9	2020-10-19	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
10	2020-11-18	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...
11	2020-12-18	/export/miro/ndeffense/LBRAT2104/GROUP_X/WORK/...