-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathSUM_127058.py
229 lines (207 loc) · 8.15 KB
/
SUM_127058.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
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/127058/*.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/127058/'
raster_list=glob.glob(path+ '*.TIF')
read=[]
for i in raster_list:
band=gdal.Open(i)
read.append(band.GetRasterBand(1).ReadAsArray())
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..."
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
print "Calculating Reflectances..."
#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}
print "Sampling..."
# sample
NDVI=numexpr.evaluate("(reflectance_band5 - reflectance_band4) / (reflectance_band5 + reflectance_band4)")
hutan= sample == 1
sample_ndvi= numexpr.evaluate("(NDVI >0.5) & (hutan==True)")
# sample_ndvi= NDVI > 0.5
area_true= sample_ndvi.nonzero() #outputnya index row n col
a_true=area_true[0]
b_true=area_true[1]
#correction
cos_zenith= cos(zenit_angle)
#auto
#def IC_all(my_dict):
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))
print "Exporting to GeoTIFF..."
#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/TP220118/127058/" +item + ".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])
# outband.SetNoDataValue(-9999)
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(sample_ndvi, "D:/FORESTS2020/TRAINING/Python/RESULT/TOPO/TP220118/127059/sample.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/TP020118/sample12363.csv", index= False)