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SUM_131056.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/OLAH/PATH ROW/131056/*.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/OLAH/PATH ROW/131056/'
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/OLAH/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/OLAH/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/PyQgis/RESULT/Landsat8/TOPO/TP160118/" +item + "_I.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/PyQgis/RESULT/Landsat8/TOPO/TP160118/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)