forked from gee-community/msslib
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmsslib.js
1110 lines (1029 loc) · 38.6 KB
/
msslib.js
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
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/**
* @license
* Copyright 2020 Justin Braaten
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// #############################################################################
// ### VERSION ###
// #############################################################################
exports.version = '0.1.2';
// #############################################################################
// ### CONSTANTS ###
// #############################################################################
/**
* A dictionary of false color visualization parameters for MSS DN images.
*
* @constant {Object}
* @example
* // Get an MSS image.
* var mssDnImg = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* yearRange: [1987, 1987],
* doyRange: [170, 240],
* wrs: '2'
* }).first();
*
* // Use with Map.addLayer().
* Map.centerObject(mssDnImg, 8);
* Map.addLayer(mssDnImg, msslib.visDn, 'From Map.addLayer()');
*
* // Use with ee.Image.visualize().
* var visImg = mssDnImg.visualize(msslib.visDn);
* Map.addLayer(visImg, null, 'From ee.Image.visualize()');
*/
var visDn = {
bands: ['nir', 'red', 'green'],
min: [47, 20, 27],
max: [142, 92, 71],
gamma: [1.2, 1.2, 1.2]
};
exports.visDn = visDn;
/**
* A dictionary of false color visualization parameters for MSS radiance images.
*
* @constant {Object}
* @example
* // Get an MSS image.
* var mssDnImg = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* yearRange: [1987, 1987],
* doyRange: [170, 240],
* wrs: '2'
* }).first();
*
* // Convert DN to radiance.
* var mssRadImg = msslib.calcRad(mssDnImg);
*
* // Use with Map.addLayer().
* Map.centerObject(mssRadImg, 8);
* Map.addLayer(mssRadImg, msslib.visRad, 'From Map.addLayer()');
*
* // Use with ee.Image.visualize().
* var visImg = mssRadImg.visualize(msslib.visRad);
* Map.addLayer(visImg, null, 'From ee.Image.visualize()');
*/
var visRad = {
bands: ['nir', 'red', 'green'],
min: [23, 15, 25],
max: [67, 62, 64],
gamma: [1.2, 1.2, 1.2]
};
exports.visRad = visRad;
/**
* A dictionary of false color visualization parameters for MSS TOA reflectance
* images.
*
* @constant {Object}
* @example
* // Get an MSS image.
* var mssDnImg = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* yearRange: [1987, 1987],
* doyRange: [170, 240],
* wrs: '2'
* }).first();
*
* // Convert DN to TOA.
* var mssToaImg = msslib.calcToa(mssDnImg);
*
* // Use with Map.addLayer().
* Map.centerObject(mssToaImg, 8);
* Map.addLayer(mssToaImg, msslib.visToa, 'From Map.addLayer()');
*
* // Use with ee.Image.visualize().
* var visImg = mssToaImg.visualize(msslib.visToa);
* Map.addLayer(visImg, null, 'From ee.Image.visualize()');
*/
var visToa = {
bands: ['nir', 'red', 'green'],
min: [0.0896, 0.0322, 0.0464],
max: [0.2627, 0.1335, 0.1177],
gamma: [1.2, 1.2, 1.2]
};
exports.visToa = visToa;
/**
* A dictionary of visualization parameters for MSS NDVI images.
*
* @constant {Object}
* @example
* // Get an MSS image.
* var mssDnImg = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* yearRange: [1987, 1987],
* doyRange: [170, 240],
* wrs: '2'
* }).first();
*
* // Convert DN to TOA and add NDVI band.
* var mssNdviImg = msslib.addNdvi(msslib.calcToa(mssDnImg));
*
* // Use with Map.addLayer().
* Map.centerObject(mssNdviImg, 8);
* Map.addLayer(mssNdviImg, msslib.visNdvi, 'From Map.addLayer()');
*
* // Use with ee.Image.visualize().
* var visImg = mssNdviImg.visualize(msslib.visNdvi);
* Map.addLayer(visImg, null, 'From ee.Image.visualize()');
*/
var visNdvi = {
bands: ['ndvi'], min: 0.1, max: 0.8
};
exports.visNdvi = visNdvi;
/**
* An example MSS 5 image.
*
* @constant {ee.Image}
* @ignore
*/
var exMss5 = ee.Image('LANDSAT/LM05/C01/T2/LM05_045029_19840728');
exports.exMss5 = exMss5;
// #############################################################################
// ### FETCH COLLECTIONS ###
// #############################################################################
/**
* Generates the PPPRRR path/row granuale ID.
*
* @param {ee.Image} img A Landsat MSS image.
* @returns {ee.String} A Landsat PPPRRR path/row granuale ID.
* @ignore
*/
function getPr(img) {
var path = ee.String('000').cat(
ee.String(ee.Number(img.get('WRS_PATH')).toShort())).slice(-3);
var row = ee.String('000').cat(
ee.String(ee.Number(img.get('WRS_ROW')).toShort())).slice(-3);
return ee.String(path.cat(row));
}
// TODO: describe the returned dictionary better, it may not be clear what the
// keys and values are. Also, why not add the 40 km buffer as needed later,
// seems strange to include it here.
/**
* Get the geometry for a given WRS-1 granule. Returns a dictionary with three
* elements: 'granule' a `ee.Feature`, granule 'centroid' a `ee.Geometry`, and
* granule 'bounds' `ee.Geometry` with a 40 km buffer. Note that it will only
* return results for granules that intersect land on the descending path.
*
* @param {string} granuleId The PPPRRR granule ID.
* @returns {ee.Dictionary}
* @example
* // Get granule geometry for WRS-1 path/row granule 049030.
* var granuleGeom = msslib.getWrs1GranuleGeom('049030');
*
* // Print the results.
* print(granuleGeom);
*
* // Display the results.
* var granule = ee.Feature(granuleGeom.get('granule'));
* var centroid = ee.Geometry(granuleGeom.get('centroid'));
* var bounds = ee.Geometry(granuleGeom.get('bounds'));
* Map.centerObject(centroid, 8);
* Map.addLayer(bounds, {color: 'blue'}, 'Bounds');
* Map.addLayer(granule, {color: 'black'}, 'Granule');
* Map.addLayer(centroid, {color: 'red'}, 'Centroid');
*/
function getWrs1GranuleGeom(granuleId) {
var granule = ee.Feature(
ee.FeatureCollection('users/jstnbraaten/wrs/wrs1_descending_land')
.filter(ee.Filter.eq('PR', granuleId)).first());
var centroid = granule.centroid(300).geometry(300);
var bounds = granule.geometry(300).buffer(40000);
return ee.Dictionary({
granule: granule,
centroid: centroid,
bounds: bounds
});
}
exports.getWrs1GranuleGeom = getWrs1GranuleGeom;
/**
* Excludes an image from a collection by image ID. Used as the `algorithm`
* input to the `ee.List.iterate()` function in the `msslib.filterById()`
* function.
*
* @param {string} id The image ID to filter out of the image collection, given
* as the value of the image's 'LANDSAT_SCENE_ID' property.
* @param {ee.ImageCollection} col The image collection to filter.
* @returns {ee.ImageCollection} The filtered image collection.
* @ignore
*/
function _filterById(id, col) {
return ee.ImageCollection(col).filter(
ee.Filter.neq('LANDSAT_SCENE_ID', ee.String(id)));
}
/**
* Excludes a list of images from a collection by image ID. It is used in the
* `msslib.filterCol()` function.
*
* @param {ee.ImageCollection} col The image collection to filter.
* @param {Array} imgList A list of image IDs to filter out of the image
* collection, given as the value of the image's 'system:index' property.
* @returns {ee.ImageCollection} The filtered image collection.
* @ignore
*/
function filterById(col, imgList) {
return ee.ImageCollection(ee.List(imgList).iterate(_filterById, col));
}
/**
* Filters an MSS image collection by bounds, date, and quality properties.
* By default, it excludes images that do not have all four reflectance bands
* present and/or are only processed to level L1G. It is intended to handle
* only one MSS collection at a time i.e. no merged collections. Used by the
* `msslib.getCol()` function.
*
* @param {ee.ImageCollection} col The image collection to filter.
* @param {Object} params See `getCol`.
* @param {string} wrs An indicator for whether the image collection contains
* WRS-1 ('wrs1') or WRS-2 ('wrs2') images.
* @returns {ee.ImageCollection} The filtered image collection.
* @ignore
*/
function filterCol(col, params, wrs) {
// Adjust band present property names depending on WRS (1 or 2).
var bandsPresent = {
wrs1: [
'PRESENT_BAND_4', 'PRESENT_BAND_5', 'PRESENT_BAND_6', 'PRESENT_BAND_7'
],
wrs2: [
'PRESENT_BAND_1', 'PRESENT_BAND_2', 'PRESENT_BAND_3', 'PRESENT_BAND_4'
],
};
if (params.aoi) {
col = col.filterBounds(params.aoi);
}
col = col.filter(ee.Filter.neq('DATA_TYPE', 'L1G'))
.filter(ee.Filter.eq(bandsPresent[wrs][0], 'Y'))
.filter(ee.Filter.eq(bandsPresent[wrs][1], 'Y'))
.filter(ee.Filter.eq(bandsPresent[wrs][2], 'Y'))
.filter(ee.Filter.eq(bandsPresent[wrs][3], 'Y'))
.filter(ee.Filter.lte('GEOMETRIC_RMSE_VERIFY', params.maxRmseVerify))
.filter(ee.Filter.lte('CLOUD_COVER', params.maxCloudCover));
if (params.yearRange) {
col = col.filter(ee.Filter.calendarRange(
params.yearRange[0], params.yearRange[1], 'year'));
}
if (params.doyRange) {
col = col.filter(ee.Filter.calendarRange(
params.doyRange[0], params.doyRange[1], 'day_of_year'));
}
if (params.excludeIds) {
col = filterById(col, params.excludeIds);
}
return col;
}
/**
* Assembles a Landsat MSS image collection from USGS Collection 1 T1 and T2
* images acquired by satellites 1-5. Removes L1G images and images without a
* complete set of reflectance bands. Additional default and optional filtering
* criteria are applied, including by bounds, geometric error, cloud cover,
* year, and day of year. All image bands are named consistently:
* ['green', 'red', 'red_edge', 'nir', 'BQA']. Adds 'wrs' property to all images
* designating them as 'WRS-1' or 'WRS-2'.
*
* @param {Object} params An object that provides filtering parameters.
* @param {ee.Geometry} [params.aoi=null] The geometry to filter images by
* intersection; those intersecting the geometry are included in the
* collection.
* @param {number} [params.maxRmseVerify=0.5] The maximum geometric RMSE of a
* given image allowed in the collection, provided in units of pixels
* (60 m), conditioned on the 'GEOMETRIC_RMSE_VERIFY' image property.
* @param {number} [params.maxCloudCover=50] The maximum cloud cover of a given
* image allowed in the collection, provided as a percent, conditioned on
* the 'CLOUD_COVER' image property.
* @param {string} [params.wrs=1&2] An indicator for what World Reference
* System types to allow in the collection. MSS images from Landsat
* satellites 1-3 use WRS-1, while 4-5 use WRS-2. Options include: '1'
* (WRS-1 only), '2' (WRS-2 only), and '1&2' (both WRS-1 and WRS-2).
* @param {Array} [params.yearRange=[1972, 2000]] An array with two integers that define
* the range of years to include in the collection. The first defines the
* start year (inclusive) and the second defines the end year (inclusive).
* Ex: [1972, 1990].
* @param {Array} [params.doyRange=[1, 365]] An array with two integers that define
* the range of days to include in the collection. The first defines the
* start day of year (inclusive) and the second defines the end day of year
* (inclusive). Note that the start day can be less than the end day, which
* indicates that the day range crosses the new year. Ex: [180, 240]
* (dates for northern hemisphere summer images), [330, 90] (dates for
* southern hemisphere summer images).
* @param {Array} [params.excludeIds=null] A list of image IDs to filter out of
* the image collection, given as the value of the image's
* 'LANDSAT_SCENE_ID' property.
* @returns {ee.ImageCollection} An MSS image collection.
* @example
* // Filter by geometry intersection, cloud cover, and geometric RMSE.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* maxCloudCover: 25,
* maxRmseVerify: 0.25
* });
*
* // Filter by geometry intersection, year range, and day of year.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* yearRange: [1975, 1980],
* doyRange: [170, 240]
* });
*
* // Filter by geometry intersection and exclude two images by ID.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* excludeIds: ['LM10490291972246AAA04', 'LM10480291973113AAA02']
* });
*/
function getCol(params) {
// Define default filter parameters.
var _params = {
'aoi': null,
'maxRmseVerify': 0.5,
'maxCloudCover': 50,
'wrs': '1&2',
'yearRange': [1972, 2000],
'doyRange': [1, 365],
'excludeIds': null
};
// Replace default params with provided params.
if (params) {
for (var param in params) {
if (params[param] != null) {
_params[param] = params[param];
}
}
}
// Initialize WRS-1 and WRS-2 collections.
var wrs1Col = ee.ImageCollection([]);
var wrs2Col = ee.ImageCollection([]);
// Gather MSS WRS-1 images, filter as requested, designate as 'WRS-1'.
if (_params.wrs.indexOf('1') !== -1) {
var mss1T1 = filterCol(
ee.ImageCollection('LANDSAT/LM01/C01/T1'), _params, 'wrs1');
var mss1T2 = filterCol(
ee.ImageCollection('LANDSAT/LM01/C01/T2'), _params, 'wrs1');
var mss2T1 = filterCol(
ee.ImageCollection('LANDSAT/LM02/C01/T1'), _params, 'wrs1');
var mss2T2 = filterCol(
ee.ImageCollection('LANDSAT/LM02/C01/T2'), _params, 'wrs1');
var mss3T1 = filterCol(
ee.ImageCollection('LANDSAT/LM03/C01/T1'), _params, 'wrs1');
var mss3T2 = filterCol(
ee.ImageCollection('LANDSAT/LM03/C01/T2'), _params, 'wrs1');
wrs1Col =
mss1T1.merge(mss1T2)
.merge(mss2T1)
.merge(mss2T2)
.merge(mss3T1)
.merge(mss3T2)
.map(function(img) {
return img.rename(['green', 'red', 'red_edge', 'nir', 'BQA'])
.set('wrs', 'WRS-1');
});
}
// Gather MSS WRS-2 images, filter as requested, designate as 'WRS-2'.
if (_params.wrs.indexOf('2') !== -1) {
var mss4T1 = filterCol(
ee.ImageCollection('LANDSAT/LM04/C01/T1'), _params, 'wrs2');
var mss4T2 = filterCol(
ee.ImageCollection('LANDSAT/LM04/C01/T2'), _params, 'wrs2');
var mss5T1 = filterCol(
ee.ImageCollection('LANDSAT/LM05/C01/T1'), _params, 'wrs2');
var mss5T2 = filterCol(
ee.ImageCollection('LANDSAT/LM05/C01/T2'), _params, 'wrs2');
wrs2Col =
mss4T1.merge(mss4T2).merge(mss5T1).merge(mss5T2).map(function(img) {
return img.rename(['green', 'red', 'red_edge', 'nir', 'BQA'])
.set('wrs', 'WRS-2');
});
}
// Return time-sorted, merged, WRS-1 and WRS-2 collection with filter params
// attached.
return wrs1Col
.merge(wrs2Col)
.map(function(img) {
var date = img.date();
return img.set({
start_doy: _params.doyRange[0],
end_doy: _params.doyRange[1],
year: date.get('year'),
doy: date.getRelative('day', 'year'),
pr: getPr(img)
// composite_year: // TODO
});
})
.sort('system:time_start');
}
exports.getCol = getCol;
// #############################################################################
// ### IMAGE ASSESSMENT ###
// #############################################################################
// TODO: add example(s) that shows how to use `display` and `visParams`.
/**
* Prints image collection thumbnails to the console with accompanying image
* IDs for use in quickly evaluating a collection. The image IDs can be recorded
* and used as entries in the `params.excludeIds` list of the `msslib.getCol()`
* function to exclude the given image(s).
*
* @param {ee.ImageCollection} col MSS DN image collection originating from the
* `msslib.getCol()` function.
* @param {Object} params An object that provides visualization parameters.
* @param {string} [params.unit=toa] An indicator for what units to use in the
* display image. Use: 'dn' (raw digital number), 'rad' (radiance), or
* 'toa' (TOA reflectance). The selected unit will be calculated on-the-fly.
* @param {string} [params.display=nir\|red\|green] An indicator for how to
* display the image thumbnail. Use 'nir\|red\|green' (RGB) or 'ndvi'
* (grayscale). Default visualization parameters for color stretch are
* applied.
* @param {Object} [params.visParams=null] A custom visualization parameter
* dictionary as described [here](https://developers.google.com/earth-engine/image_visualization#mapVisParamTable).
* If set, overrides the `params.display` option and default.
* @example
* // Get an MSS image collection.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* doyRange: [170, 240]
* });
*
* // View DN image thumbnails in the console.
* viewThumbnails(mssDnCol, {unit: 'dn'});
*/
function viewThumbnails(col, params) {
print('Please wait patiently, images may not load immediately');
var _params = {
unit: 'toa',
display: 'nir|red|green',
visParams: null
};
if (params) {
for (var param in params) {
_params[param] = params[param] || _params[param];
}
}
var settings = {
unit: {
dn: function(img) {return img},
rad: calcRad,
toa: calcToa
},
display: {
'nir|red|green': {
dn: visDn,
rad: visRad,
toa: visToa
},
'ndvi': {
dn: visNdvi,
rad: visNdvi,
toa: visNdvi
}
}
};
var imgList = col.sort('system:time_start').toList(col.size());
imgList.evaluate(function(imgList) {
for (var i = 0; i < imgList.length; i++) {
var id = imgList[i].id;
var img = ee.Image(id).rename(['green', 'red', 'red_edge', 'nir', 'BQA']);
img = settings.unit[_params.unit](img);
if(_params.display == 'ndvi') {
img = addNdvi(img);
}
var visParams = settings.display[_params.display][_params.unit];
if(_params.visParams) {
visParams = _params.visParams;
}
var imgVis = img.visualize(visParams);
print(img.get('LANDSAT_SCENE_ID'));
print(ui.Thumbnail(imgVis, {
dimensions: 512,
crs: 'EPSG:3857',
}));
}
});
}
exports.viewThumbnails = viewThumbnails;
// #############################################################################
// ### IMAGE MANIPULATION ###
// #############################################################################
/**
* Converts DN values to either radiance or TOA reflectance.
*
* @param {ee.Image} img MSS DN image originating from the `msslib.getCol()`
* function.
* @param {string} unit Indicator for whether to convert DN to units of radiance
* ('radiance') or TOA reflectance ('reflectance').
* @return {ee.Image}
* @ignore
*/
function scaleDn(img, unit) {
var mult = 'REFLECTANCE_MULT_BAND', add = 'REFLECTANCE_ADD_BAND';
if (unit == 'radiance') {
mult = 'RADIANCE_MULT_BAND';
add = 'RADIANCE_ADD_BAND';
}
var gainBands = ee.List(img.propertyNames())
.filter(ee.Filter.stringContains('item', mult))
.sort();
var biasBands = ee.List(img.propertyNames())
.filter(ee.Filter.stringContains('item', add))
.sort();
var gainImg = ee.Image.cat(
ee.Image.constant(img.get(gainBands.getString(0))),
ee.Image.constant(img.get(gainBands.getString(1))),
ee.Image.constant(img.get(gainBands.getString(2))),
ee.Image.constant(img.get(gainBands.getString(3)))).toFloat();
var biasImg = ee.Image.cat(
ee.Image.constant(img.get(biasBands.getString(0))),
ee.Image.constant(img.get(biasBands.getString(1))),
ee.Image.constant(img.get(biasBands.getString(2))),
ee.Image.constant(img.get(biasBands.getString(3)))).toFloat();
var dnImg = img.select([0, 1, 2, 3]);
return ee.Image(
dnImg.multiply(gainImg)
.add(biasImg)
.toFloat()
.addBands(img.select('BQA'))
.copyProperties(img, img.propertyNames()));
}
/**
* Converts DN values to radiance.
*
* @param {ee.Image} img MSS DN image originating from the `msslib.getCol()`
* function.
* @return {ee.Image}
* @example
* // Get an MSS image collection.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* doyRange: [170, 240]
* });
*
* // Convert DN to radiance for a single image.
* var mssRadImg = msslib.calcRad(mssDnCol.first());
*
* // Convert DN to radiance for all images in a collection.
* var mssRadCol = mssDnCol.map(msslib.calcRad);
*/
function calcRad(img) {
return scaleDn(img, 'radiance');
}
exports.calcRad = calcRad;
/**
* Converts DN values to TOA reflectance.
*
* @param {ee.Image} img MSS DN image originating from the `msslib.getCol()`
* function.
* @return {ee.Image}
* @example
* // Get an MSS image collection.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* doyRange: [170, 240]
* });
*
* // Convert DN to TOA for a single image.
* var mssToaImg = msslib.calcToa(mssDnCol.first());
*
* // Convert DN to TOA for all images in a collection.
* var mssToaCol = mssDnCol.map(msslib.calcToa);
*/
function calcToa(img) {
return scaleDn(img, 'reflectance');
}
exports.calcToa = calcToa;
// TODO: add example of applying to a single image.
/**
* Adds NDVI transformation as a band ('ndvi') to the input image.
*
* @param {ee.Image} img MSS image originating from the `msslib.getCol()`
* function. It is recommended that the image be in units of radiance or
* TOA reflectance (see `msslib.calcRad()` and `msslib.calcToa()`).
* @return {ee.Image}
* @example
* // Get an MSS image collection.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* doyRange: [170, 240]
* });
*
* // Convert DN to TOA for all images in a collection.
* var mssToaCol = mssDnCol.map(msslib.calcToa);
*
* // Add NDVI band to each image in a collection.
* var mssToaColNdvi = mssToaCol.map(msslib.addNdvi);
*/
function addNdvi(img) {
var ndvi = img.normalizedDifference(['nir', 'red']).rename('ndvi');
return ee.Image(img.addBands(ndvi).copyProperties(img, img.propertyNames()));
}
exports.addNdvi = addNdvi;
// TODO: Need to ensure use of the proper units - paper seems to suggest DN
// and also the use of an offset - see section IV, eq 1. Should it be
// capitalized?
/**
* Adds Tasseled Cap indices brightness ('tcb'), greenness ('tcg'), yellowness
* ('tcy'), and angle ('tca') to the input image. See [Kauth and Thomas, 1976](https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1160&context=lars_symp)
*
* @param {ee.Image} img MSS image originating from the `msslib.getCol()`
* function. It is recommended that the image be in units of radiance or
* TOA reflectance (see `msslib.calcRad()` and `msslib.calcToa()`).
* @return {ee.Image}
* @example
* // Get an MSS image collection.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* doyRange: [170, 240]
* });
*
* // Convert DN to TOA for all images in a collection.
* var mssToaCol = mssDnCol.map(msslib.calcToa);
*
* // Add Tasseled Cap band to each image in a collection.
* var mssToaColTc = mssToaCol.map(msslib.addTc);
* @ignore
*/
function addTc(img) {
var bands = img.select([0, 1, 2, 3]);
var tcbCoeffs = ee.Image.constant([0.433, 0.632, 0.586, 0.264]);
var tcgCoeffs = ee.Image.constant([-0.290, -0.562, 0.600, 0.491]);
var tcyCoeffs = ee.Image.constant([-0.829, 0.522, -0.039, 0.194]);
var tcb = bands.multiply(tcbCoeffs).reduce(ee.Reducer.sum()).toFloat();
var tcg = bands.multiply(tcgCoeffs).reduce(ee.Reducer.sum()).toFloat();
var tcy = bands.multiply(tcyCoeffs).reduce(ee.Reducer.sum()).toFloat();
var tca = (tcg.divide(tcb)).atan().multiply(180 / Math.PI).toFloat();
var tc = ee.Image.cat(tcb, tcg, tcy, tca).rename('tcb', 'tcg', 'tcy', 'tca');
return ee.Image(img.addBands(tc).copyProperties(img, img.propertyNames()));
}
exports.addTc = addTc;
// #############################################################################
// ### BQA MASK ###
// #############################################################################
/**
* Get the 'BQA' quality band as a Boolean layer indicating good (1) and bad (0)
* pixels. [Learn more about the 'BQA' band](https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band).
*
* @param {ee.Image} img MSS image originating from the `msslib.getCol()`
* function.
* @return {ee.Image}
* @ignore
*/
function getQaMask(img) {
return img.select('BQA').eq(32).rename('BQA_mask');
}
/**
* Adds the 'BQA' quality band as mask band ('BQA_mask') indicating good (1) and
* bad (0) pixels. [Learn more about the 'BQA' band](https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band).
*
* @param {ee.Image} img MSS image originating from the `msslib.getCol()`
* function.
* @return {ee.Image}
* @example
* // Get an MSS image collection.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* doyRange: [170, 240]
* });
*
* // Select a single image.
* var mssDnImg = mssDnCol.filter(
* ee.Filter.eq('LANDSAT_SCENE_ID', 'LM30490291982193AAA03')).first();
*
* // Add BQA mask band to the single image.
* var mssDnImgQaMask = msslib.addQaMask(mssDnImg);
*
* // Display the results.
* Map.centerObject(mssDnImgQaMask, 9);
* Map.addLayer(mssDnImgQaMask, msslib.visDn, 'DN image');
* Map.addLayer(mssDnImgQaMask, {
* bands: ['BQA_mask'],
* min: 0,
* max: 1,
* palette: ['grey', 'green']
* }, 'BQA mask');
*
* // Add BQA mask band to all images in collection.
* var mssDnColQaMask = mssDnCol.map(msslib.addQaMask);
* print(mssDnColQaMask.limit(5));
*/
function addQaMask(img) {
return img.addBands(getQaMask(img));
}
exports.addQaMask = addQaMask;
/**
* Applies the 'BQA' quality band to an image as a mask. It masks out cloud
* pixels and those exhibiting radiometric saturation, as well pixels associated
* with missing data. Cloud identification is limited to mostly thick cumulus
* clouds; note that snow and very bright surface features are often mislabeled
* as cloud. Radiometric saturation in MSS images usually manifests as entire
* or partial image pixel rows being highly biased toward high values in a
* single band, which when visualized, can appear as tinted red, green, or
* blue. [Learn more about the 'BQA' band](https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band).
*
* @param {ee.Image} img MSS image originating from the `msslib.getCol()`
* function.
* @return {ee.Image}
* @example
* // Get an MSS image collection.
* var mssDnCol = msslib.getCol({
* aoi: ee.Geometry.Point([-122.239, 44.018]),
* doyRange: [170, 240]
* });
*
* // Select a single image.
* var mssDnImg = mssDnCol.filter(
* ee.Filter.eq('LANDSAT_SCENE_ID', 'LM30490291982193AAA03')).first();
*
* // Apply BQA mask to the single image.
* var mssDnImgQaMask = msslib.applyQaMask(mssDnImg);
*
* // Display the results.
* Map.centerObject(mssDnImgQaMask, 9);
* Map.setOptions('SATELLITE');
* Map.addLayer(mssDnImg, msslib.visDn, 'DN image');
* Map.addLayer(mssDnImgQaMask, msslib.visDn, 'DN image masked');
*
* // Apply BQA mask to all images in collection.
* var mssDnColQaMask = mssDnCol.map(msslib.applyQaMask);
* print(mssDnColQaMask.limit(5));
*/
function applyQaMask(img) {
return img.updateMask(getQaMask(img));
}
exports.applyQaMask = applyQaMask;
// #############################################################################
// ### MSSCVM ###
// #############################################################################
/**
* Returns MSScvm cloud layer.
*
* @param {ee.Image} img MSS TOA image originating from `msslib.getCol()`
* and `msslib.calcToa()`.
* @return {ee.Image}
* @ignore
*/
function cloudLayer(img) {
// Identify cloud pixels.
var cloudPixels = img.normalizedDifference(['green', 'red'])
.gt(0)
.multiply(img.select('green').gt(0.175)) // 1750
.add(img.select('green').gt(0.39)) // 3900
.gt(0);
// Nine-pixel minimum connected component sieve.
cloudPixels = cloudPixels.selfMask()
.connectedPixelCount(10, true)
.reproject(img.projection())
.gte(0)
.unmask(0)
.rename('cloudtest');
// Define kernel for buffer.
var kernel = ee.Kernel.circle({radius: 2, units: 'pixels', normalize: true});
// Two pixel buffer, eight neighbor rule.
return cloudPixels.focal_max({radius: 2, kernel: kernel})
.reproject(img.projection())
.rename('clouds');
}
/**
* Returns MSScvm water layer.
*
* @param {ee.Image} img MSS TOA image originating from `msslib.getCol()`
* and `msslib.calcToa()`.
* @return {ee.Image}
* @ignore
*/
function waterLayer(img) {
// Threshold on NDVI.
var mssWater = img.normalizedDifference(['nir', 'red']).lt(-0.085);
// Get max extent of water 1985-2018.
var waterExtent =
ee.Image('JRC/GSW1_1/GlobalSurfaceWater').select('max_extent');
// Get intersection of MSS water and max extent.
return mssWater.multiply(waterExtent)
.reproject(img.projection())
.rename('water');
}
/**
* Assembles a global DEM from several sources, returned in the projection of
* the input image.
*
* @param {ee.Image} img MSS TOA image originating from `msslib.getCol()`
* and `msslib.calcToa()`.
* @return {ee.Image}
* @ignore
*/
function getDem(img) {
var aw3d30 =
ee.Image('JAXA/ALOS/AW3D30/V2_2').select('AVE_DSM').rename('elev');
var GMTED2010 = ee.Image('USGS/GMTED2010').rename('elev');
return ee.ImageCollection([GMTED2010, aw3d30])
.mosaic()
.reproject(img.projection());
}
exports.getDem = getDem;
/**
* Converts degrees to radians.
*
* @param {ee.Image} img An image with pixel values in units of degrees.
* @return {ee.Image}
* @ignore
*/
function radians(img) {
return img.toFloat().multiply(Math.PI).divide(180);
}
/**
* Returns terrain illumination image.
*
* @param {ee.Image} img MSS TOA image originating from `msslib.getCol()`
* and `msslib.calcToa()`.
* @param {ee.Image} slope A terrain slope image in units of degrees.
* @param {ee.Image} aspect A terrain aspect image in units of degrees.
* @return {ee.Image}
* @ignore
*/
function getIll(img, slope, aspect) {
// Get sun info.
var azimuth = img.get('SUN_AZIMUTH');
var zenith = ee.Number(90).subtract(img.getNumber('SUN_ELEVATION'));
// Convert slope and aspect degrees to radians.
var slopeRad = radians(slope);
var aspectRad = radians(aspect);
// Calculate illumination.
var azimuthImg = radians(ee.Image.constant(azimuth));
var zenithImg = radians(ee.Image.constant(zenith));
var left = zenithImg.cos().multiply(slopeRad.cos());
var right = zenithImg.sin()
.multiply(slopeRad.sin())
.multiply(azimuthImg.subtract(aspectRad).cos());
return left.add(right);
}
/**
* Returns MSS NIR TOA reflectance band corrected for topography via
* Minnaert correction.
*
* @param {ee.Image} img MSS TOA image originating from `msslib.getCol()`
* and `msslib.calcToa()`.
* @param {ee.Image} dem A digital elevation model.
* @return {ee.Image}
* @ignore
*/
function topoCorrB4(img, dem) {
// Get terrain layers.
var terrain = ee.Algorithms.Terrain(dem);
var slope = terrain.select(['slope']);
var aspect = terrain.select(['aspect']);
// Get k image.
// define polynomial coefficients to calc Minnaert value as function of slope
// Ge, H., Lu, D., He, S., Xu, A., Zhou, G., & Du, H. (2008). Pixel-based
// Minnaert correction method for reducing topographic effects on a Landsat 7
// ETM+ image. Photogrammetric Engineering & Remote Sensing, 74(11),
// 1343-1350. |
// https://orst.library.ingentaconnect.com/content/asprs/pers/2008/00000074/00000011/art00003?crawler=true&mimetype=application/pdf
var kImg = slope.resample('bilinear')
.where(
slope.gt(50),
50) // Set max slope at 50 degrees - paper does not sample
// past - authors recommend no extrapolation.
.polynomial([
1.0021313684, -0.1308793751, 0.0106861276, -0.0004051135,
0.0000071825, -4.88e-8
]);
// Get illumination.
var ill = getIll(img, slope, aspect);
// Correct NIR reflectance for topography.
var cosTheta = radians(ee.Image.constant(ee.Number(90).subtract(
ee.Number(img.get('SUN_ELEVATION')))))
.cos();
var correction = (cosTheta.divide(ill)).pow(kImg);
return img.select('nir').multiply(correction);
}
exports.topoCorrB4 = topoCorrB4;
/**
* Returns MSScvm shadow layer.
*
* @param {ee.Image} img MSS TOA image originating from `msslib.getCol()`
* and `msslib.calcToa()`.
* @param {ee.Image} dem A digital elevation model.
* @param {ee.Image} clouds The result of `msslib.cloudLayer()`.
* @return {ee.Image}
* @ignore
*/
function shadowLayer(img, dem, clouds) {
// Correct B4 reflectance for topography.
var b4c = topoCorrB4(img, dem);
// Threshold B4 - target dark pixels.
var shadows = b4c.lt(0.11); // Make this true for all pixels to use full cloud projection.
// Project clouds as potential shadow.
var shadow_azimuth =
ee.Number(90).subtract(ee.Number(img.get('SUN_AZIMUTH')));
var cloudProj = clouds.directionalDistanceTransform(shadow_azimuth, 50)
.reproject({crs: img.projection(), scale: 60})