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SUM_124063.py
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import datetime
import math
from datetime import datetime, date
import numpy as np
import glob
import os
from osgeo import gdal
from scipy.stats import linregress
import pandas as pd
# from dict import dict
import numexpr
#Load Metadata
path_f = 'D:/FORESTS2020/DATA/LANDSAT/PATH ROW/124063/*.txt'#open file for reading
glob_f= glob.glob(path_f)
f=open(glob_f[1])
def build_data(f):
output = {}
for line in f.readlines():
if "=" in line:
l = line.split("=")
output[l[0].strip()] = l[1].strip()
return output
data = build_data(f)
#Load data raster
print "Loading Data Raster..."
#Load data raster
path='D:/FORESTS2020/DATA/LANDSAT/PATH ROW/124063/'
raster_list=glob.glob(path+ '*.TIF')
read=[]
for i in raster_list:
band=gdal.Open(i)
read.append(band.GetRasterBand(1).ReadAsArray().astype(float))
# read_f=[]
# for i in read:
# landsat= np.array(i)
# landsat[landsat==0]= np.nan
# read_f.append(landsat)
filename=[]
for a in [os.path.basename(x) for x in glob.glob(path + '*.TIF')]:
p=os.path.splitext(a)[0]
filename.append(p)
my_dict= dict(zip(filename, read))
#Load data raster aspect & slope
pathname='D:/FORESTS2020/DATA/LANDSAT/DEM/'
raster_list_dem=glob.glob(pathname+filename[0][10:16]+'/*.TIF')
filename_dem=[]
for b in [os.path.basename(z) for z in glob.glob(pathname+filename[0][10:16]+'/*.TIF')]:
c=os.path.splitext(b)[0]
filename_dem.append(c)
read2=[]
for d in raster_list_dem:
band2=gdal.Open(d)
read2.append(band2.GetRasterBand(1).ReadAsArray())
dem_dict= dict(zip(filename_dem, read2))
#Load data raster sample area based on Baplan
pathsample='D:/FORESTS2020/DATA/LANDSAT/SAMPLE/'
sampledata=gdal.Open(pathsample +filename[0][10:16]+'/baplan.TIF')
sample=np.array(sampledata.GetRasterBand(1).ReadAsArray())
def year_date():
year_file=data['DATE_ACQUIRED']
date_file=data['SCENE_CENTER_TIME']
date_file2= date_file [1:16]
all= year_file+" "+date_file2
parsing = datetime.strptime(all, '%Y-%m-%d %H:%M:%S.%f')
return parsing
dt=year_date()
def hour():
h=dt.hour+7
return h
def second():
s= float(dt.microsecond)/1000000+dt.second
return s
def leap():
if (dt.year % 4) == 0:
if (dt.year % 100) == 0:
if (dt.year % 400) == 0:
a = int(366)
else:
a = int(365)
else:
a= int(366)
else:
a= int(365)
return a
def cos(x):
cos= np.cos(np.deg2rad(x))
return cos
def sin(x):
sin=np.sin(np.deg2rad(x))
return sin
def day():
day_date= date(dt.year, dt.month, dt.day)
sum_of_day=int(day_date.strftime('%j'))
return sum_of_day
print "Calculating Solar Position..."
# Solar Position Calculation
gamma=((2 * math.pi) / leap()) * ((day() - 1) + (((hour()+dt.minute/60+second()/3600) - 12) / 24) )# degree
#sun declination angle
decl=0.006918 - 0.399912 * cos(gamma) + 0.070257 * sin(gamma) - 0.006758 * cos (2 * gamma)\
+ 0.000907 * sin (2 * gamma) - 0.002697 * cos (3 * gamma) + 0.00148 * sin (3 * gamma) #radians
decl_deg= (360 / (2 * math.pi)) * decl
#lat long value
# get columns and rows of your image from gdalinfo
xoff, a, b, yoff, d, e = band.GetGeoTransform()
def pixel2coord(x, y):
xp = a * x + b * y + xoff
yp = d * x + e * y + yoff
return(xp, yp)
rows=read[0].shape[0]
colms=read[0].shape[1]
coordinate=[]
for row in range(0,rows):
for col in range(0,colms):
coordinate.append(pixel2coord(col,row))
coor_2=np.array(coordinate, dtype=float)
long=coor_2[:,0]
lat=coor_2[:,1]
long_n=long.reshape(rows,colms)
lat_n=lat.reshape(rows,colms)
#eqtime
eqtime = 229.18 * (0.000075 + 0.001868 * cos(gamma) - 0.032077 * sin(gamma) - 0.014615 * cos(2 * gamma) - 0.040849 * sin(2 * gamma)) # minutes
timeoff= eqtime - 4 * long_n + 60 * 7 #minutes
tst=hour() * 60 + dt.minute + second() / 60 + timeoff #minutes
ha=(tst /4)-180 #degree
#sun zenith angle
zenit1 =sin(lat_n)* sin(decl_deg) + cos (lat_n)* cos(decl_deg) * cos(ha)
zenit2=np.arccos(zenit1) #radians
zenit_angle= np.rad2deg(zenit2)
#sun azimuth angle
theta1= -1 * ((sin(lat_n)) * cos(zenit_angle)- sin(decl_deg)/(cos (lat_n) * sin (zenit_angle)))
theta2=np.arccos(theta1) #radians
theta3=np.rad2deg(theta2)#degree
azimuth_angle=180 - theta3 #degrees
# IC calculation
delta=azimuth_angle - dem_dict['aspect']
IC=(cos(zenit_angle)* cos (dem_dict['slope'])) + (sin(zenit_angle) * sin (dem_dict['slope']) * cos(delta))#radians
#Reflectance
reflectance_band1=(float(data['REFLECTANCE_MULT_BAND_1'])*my_dict[filename[0][:-2]+'B1']+float(data['REFLECTANCE_ADD_BAND_1']))/cos(zenit_angle)
reflectance_band2=(float(data['REFLECTANCE_MULT_BAND_2'])*my_dict[filename[0][:-2]+'B2']+float(data['REFLECTANCE_ADD_BAND_2']))/cos(zenit_angle)
reflectance_band3=(float(data['REFLECTANCE_MULT_BAND_3'])*my_dict[filename[0][:-2]+'B3']+float(data['REFLECTANCE_ADD_BAND_3']))/cos(zenit_angle)
reflectance_band4=(float(data['REFLECTANCE_MULT_BAND_4'])*my_dict[filename[0][:-2]+'B4']+float(data['REFLECTANCE_ADD_BAND_4']))/cos(zenit_angle)
reflectance_band5=(float(data['REFLECTANCE_MULT_BAND_5'])*my_dict[filename[0][:-2]+'B5']+float(data['REFLECTANCE_ADD_BAND_5']))/cos(zenit_angle)
reflectance_band6=(float(data['REFLECTANCE_MULT_BAND_6'])*my_dict[filename[0][:-2]+'B6']+float(data['REFLECTANCE_ADD_BAND_6']))/cos(zenit_angle)
reflectance_band7=(float(data['REFLECTANCE_MULT_BAND_7'])*my_dict[filename[0][:-2]+'B7']+float(data['REFLECTANCE_ADD_BAND_7']))/cos(zenit_angle)
reflectance_band9=(float(data['REFLECTANCE_MULT_BAND_9'])*my_dict[filename[0][:-2]+'B9']+float(data['REFLECTANCE_ADD_BAND_9']))/cos(zenit_angle)
reflectance_f= {filename[0][:-2]+'B1':reflectance_band1, filename[0][:-2]+'B2':reflectance_band2,filename[0][:-2]+'B3':reflectance_band3, filename[0][:-2]+'B4':reflectance_band4, filename[0][:-2]+'B5':reflectance_band5, filename[0][:-2]+'B6':reflectance_band6, filename[0][:-2]+'B7':reflectance_band7, filename[0][:-2]+'B9':reflectance_band9}
# sample
NDVI=numexpr.evaluate("(reflectance_band5 - reflectance_band4) / (reflectance_band5 + reflectance_band4)")
hutan= sample == 1
sample_ndvi= numexpr.evaluate("(NDVI >0.5) & (hutan==True)")
area_true= sample_ndvi.nonzero() #outputnya index row n col
a_true=area_true[0]
b_true=area_true[1]
#correction or Rotation Model
cos_zenith= cos(zenit_angle)
temp={}
IC_final={}
for y in reflectance_f:
val2=reflectance_f[y]
temp[y]=val2[a_true,b_true].ravel()
IC_true=IC[a_true,b_true].ravel()
slope=linregress(IC_true, temp[y])
IC_final[y]=reflectance_f[y]-(slope[0]*(IC-cos_zenith))
#export auto
for item in IC_final:
geo = band.GetGeoTransform()
proj = band.GetProjection()
shape = my_dict[filename[0][:-2]+'B1'].shape
driver = gdal.GetDriverByName("GTiff")
dst_ds = driver.Create("D:/FORESTS2020/TRAINING/Python/RESULT/TOPO/TP230118/124063/" +item + "topo.TIF", shape[1], shape[0], 1, gdal.GDT_Float64)
dst_ds.SetGeoTransform(geo)
dst_ds.SetProjection(proj)
ds = dst_ds.GetRasterBand(1)
ds.SetNoDataValue(-9999)
ds.WriteArray(IC_final[item])
dst_ds.FlushCache()
dst_ds = None # save, close""
def export_array(in_array, output_path):
"""This function is used to produce output of array as a map."""
global proj, geotrans, row, col
proj = band.GetProjection()
geotrans = band.GetGeoTransform()
row = band.RasterYSize
col = band.RasterXSize
driver = gdal.GetDriverByName("GTiff")
outdata = driver.Create(output_path, col, row, 1, gdal.GDT_Float32)
outband = outdata.GetRasterBand(1)
outband.SetNoDataValue(-9999)
outband.WriteArray(in_array)
# Georeference the image
outdata.SetGeoTransform(geotrans)
# Write projection information
outdata.SetProjection(proj)
outdata.FlushCache()
outdata = None
export_array(IC, "D:/FORESTS2020/TRAINING/Python/RESULT/TOPO/TP230118/124063/IC.TIF")
csv2 = {}
for c in IC_final:
val4=IC_final[c]
csv2[c]= val4[a_true,b_true].ravel()
#print "hasil", csv2
df=pd.DataFrame(csv2)
df2=pd.DataFrame(temp)
df3=pd.DataFrame({'IC':IC_true})
dfn=pd.concat([df3, df, df2], axis=1)
dfn.to_csv("D:/FORESTS2020/TRAINING/PyQgis/RESULT/Landsat8/TOPO/TP040118/sample12562.csv", index= False)