Item talk:Q266627
From geokb
{
"USGS Publications Warehouse": {
"@context": "https://schema.org",
"@type": "Article",
"additionalType": "Journal Article",
"name": "A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse IndexID",
"value": "70226855",
"url": "https://pubs.usgs.gov/publication/70226855"
},
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse Internal ID",
"value": 70226855
},
{
"@type": "PropertyValue",
"propertyID": "DOI",
"value": "10.1016/j.rse.2021.112670",
"url": "https://doi.org/10.1016/j.rse.2021.112670"
}
],
"journal": {
"@type": "Periodical",
"name": "Remote Sensing of Environment",
"volumeNumber": "266",
"issueNumber": null
},
"inLanguage": "en",
"isPartOf": [
{
"@type": "CreativeWorkSeries",
"name": "Remote Sensing of Environment"
}
],
"datePublished": "2021",
"dateModified": "2023-11-08",
"abstract": "The long record of\u00a0Landsat\u00a0imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic\u00a0phenology\u00a0learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same\u00a0geographical region\u00a0as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.",
"description": "112670, 19 p.",
"publisher": {
"@type": "Organization",
"name": "Elsevier"
},
"author": [
{
"@type": "Person",
"name": "Li, Congcong",
"givenName": "Congcong",
"familyName": "Li",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0002-4311-4169",
"url": "https://orcid.org/0000-0002-4311-4169"
},
"affiliation": [
{
"@type": "Organization",
"name": "ASRC Federal"
}
]
},
{
"@type": "Person",
"name": "Pengra, Bruce",
"givenName": "Bruce",
"familyName": "Pengra",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0003-2497-8284",
"url": "https://orcid.org/0000-0003-2497-8284"
},
"affiliation": [
{
"@type": "Organization",
"name": "KBR, Inc., under contract to USGS"
}
]
},
{
"@type": "Person",
"name": "Zhou, Qiang",
"givenName": "Qiang",
"familyName": "Zhou",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0002-1282-8177",
"url": "https://orcid.org/0000-0002-1282-8177"
},
"affiliation": [
{
"@type": "Organization",
"name": "AFDS, contractor to U.S. Geological Survey"
}
]
},
{
"@type": "Person",
"name": "Xian, George Z.",
"givenName": "George Z.",
"familyName": "Xian",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0001-5674-2204",
"url": "https://orcid.org/0000-0001-5674-2204"
},
"affiliation": [
{
"@type": "Organization",
"name": "Earth Resources Observation and Science (EROS) Center",
"url": "https://www.usgs.gov/centers/eros"
}
]
}
],
"funder": [
{
"@type": "Organization",
"name": "Earth Resources Observation and Science (EROS) Center",
"url": "https://www.usgs.gov/centers/eros"
},
{
"@type": "Organization",
"name": "Advanced Research Computing (ARC)",
"url": "https://www.usgs.gov/ecosystems/land-change-science-program"
}
]
},
"OpenAlex": {
"_id": "https://openalex.org/w3199869787",
"abstract_inverted_index": {
"The": [
0,
241,
276
],
"long": [
1
],
"record": [
2
],
"of": [
3,
10,
76,
112,
248,
317,
330,
360,
369
],
"Landsat": [
4,
57,
251
],
"imagery,": [
5
],
"which": [
6
],
"is": [
7
],
"the": [
8,
27,
30,
70,
95,
106,
109,
115,
121,
138,
188,
204,
214,
224,
230,
235,
246,
258,
284,
303,
311,
314,
318,
328,
343,
351,
367
],
"cornerstone": [
9
],
"Earth": [
11
],
"observation,": [
12
],
"provides": [
13,
354
],
"an": [
14,
99,
239,
355
],
"opportunity": [
15
],
"to": [
16,
53,
129,
140,
266,
274
],
"monitor": [
17
],
"land": [
18,
21,
86,
117,
142,
200,
216,
225,
269,
298,
307,
320,
362
],
"use": [
19,
359
],
"and": [
20,
25,
32,
47,
62,
73,
180,
199,
260,
279,
305,
335,
365
],
"cover": [
22,
87,
118,
143,
201,
217,
226,
270,
299,
308,
321,
363
],
"(LULC)": [
23
],
"change": [
24,
39,
60
],
"understand": [
26
],
"interactions": [
28
],
"between": [
29,
195
],
"climate": [
31
],
"earth": [
33
],
"system": [
34
],
"through": [
35
],
"time.": [
36
],
"A": [
37
],
"few": [
38
],
"detection": [
40,
61
],
"algorithms": [
41
],
"such": [
42
],
"as": [
43,
208,
238
],
"Continuous": [
44
],
"Change": [
45,
332
],
"Detection": [
46
],
"Classification": [
48
],
"(CCDC)": [
49
],
"have": [
50,
79
],
"been": [
51
],
"developed": [
52
],
"utilize": [
54
],
"all": [
55
],
"available": [
56
],
"images": [
58
],
"for": [
59,
83,
145,
219,
302,
357,
372
],
"characterization": [
63
],
"at": [
64,
89,
125
],
"local": [
65,
127
],
"or": [
66,
123
],
"global": [
67
],
"scales.": [
68
],
"However,": [
69
],
"reliable,": [
71
],
"rapid,": [
72
],
"reproducible": [
74
],
"collection": [
75
],
"training": [
77,
133,
290,
373
],
"samples": [
78,
113,
134,
151,
198,
291
],
"become": [
80
],
"a": [
81,
90,
126
],
"challenge": [
82
],
"time": [
84
],
"series": [
85
],
"classification": [
88
],
"large": [
91
],
"scale.": [
92
],
"To": [
93
],
"meet": [
94
],
"challenge,": [
96
],
"we": [
97,
186,
212
],
"proposed": [
98
],
"automatic": [
100
],
"phenology": [
101
],
"learning": [
102
],
"(APL)": [
103
],
"method": [
104,
139,
242,
286,
353
],
"with": [
105
],
"assumption": [
107
],
"that": [
108,
283,
350
],
"temporal": [
110
],
"profiles": [
111
],
"within": [
114
],
"same": [
116,
122,
205
],
"type": [
119
],
"are": [
120
],
"similar": [
124
],
"scale": [
128
],
"generate": [
130,
288
],
"evenly": [
131
],
"distributed": [
132
],
"automatically.": [
135
],
"We": [
136
],
"designed": [
137,
231
],
"build": [
141
],
"patterns": [
144,
202
],
"each": [
146,
220
],
"category": [
147,
218
],
"based": [
148,
228
],
"on": [
149,
229
],
"consensus": [
150
],
"derived": [
152
],
"from": [
153,
223,
272
],
"multiple": [
154
],
"existing": [
155
],
"scientific": [
156
],
"datasets": [
157
],
"including": [
158
],
"LANDFIRE's": [
159
],
"(LF)": [
160
],
"Existing": [
161
],
"Vegetation": [
162
],
"Type": [
163
],
"(EVT),": [
164
],
"USGS": [
165
],
"National": [
166,
171,
181
],
"Land": [
167,
331
],
"Cover": [
168
],
"Database": [
169
],
"(NLCD),": [
170
],
"Agricultural": [
172
],
"Statistics": [
173
],
"Service": [
174
],
"(NASS)": [
175
],
"Cropland": [
176
],
"Data": [
177,
254
],
"Layer": [
178
],
"(CDL),": [
179
],
"Wetlands": [
182
],
"Inventory": [
183
],
"(NWI).": [
184
],
"Then": [
185
],
"calculated": [
187
],
"Time-Weighted": [
189
],
"Dynamic": [
190
],
"Time": [
191
],
"Warping": [
192
],
"(twDTW)": [
193
],
"distance": [
194,
237
],
"any": [
196,
293
],
"undefined": [
197,
221
],
"in": [
203,
245,
257,
342
],
"geographical": [
206
],
"region": [
207
],
"prior": [
209
],
"knowledge.": [
210
],
"Finally,": [
211
],
"selected": [
213,
250
],
"optimal": [
215
],
"sample": [
222
],
"products": [
227,
364
],
"criteria": [
232
],
"iteratively": [
233
],
"using": [
234
],
"twDTW": [
236
],
"indicator.": [
240
],
"was": [
243,
323
],
"applied": [
244
],
"footprint": [
247
],
"10": [
249
],
"Analysis": [
252
],
"Ready": [
253
],
"(ARD)": [
255
],
"tiles": [
256
],
"eastern": [
259
],
"western": [
261
],
"conterminous": [
262
],
"United": [
263
],
"States": [
264
],
"(CONUS)": [
265
],
"produce": [
267
],
"annual": [
268,
319
],
"maps": [
271,
322
],
"1985": [
273
],
"2017.": [
275
],
"accuracy": [
277,
316,
329
],
"assessment": [
278
],
"visual": [
280
],
"comparison": [
281
],
"revealed": [
282
],
"APL": [
285,
312,
352
],
"can": [
287
],
"reliable": [
289
],
"without": [
292
],
"manual": [
294
],
"interpretation,": [
295
],
"producing": [
296
],
"better": [
297
],
"results": [
300,
347
],
"especially": [
301
],
"grass/shrub": [
304
],
"wetland": [
306
],
"classes.": [
309
],
"Applying": [
310
],
"method,": [
313
],
"overall": [
315
],
"improved": [
324
],
"by": [
325
],
"2%": [
326
],
"over": [
327
],
"Monitoring,": [
333
],
"Assessment,": [
334
],
"Projection": [
336
],
"(LCMAP)": [
337
],
"Collection": [
338
],
"1.0": [
339
],
"Science": [
340
],
"Products": [
341
],
"research": [
344
],
"regions.": [
345
],
"Our": [
346
],
"also": [
348
],
"indicate": [
349
],
"approach": [
356
],
"best": [
358
],
"different": [
361
],
"meets": [
366
],
"requirement": [
368
],
"intensive": [
370
],
"sampling": [
371
],
"data": [
374
],
"collection.": [
375
]
},
"apc_list": {
"value": 4070,
"currency": "USD",
"value_usd": 4070,
"provenance": "doaj"
},
"apc_paid": null,
"authorships": [
{
"author_position": "first",
"author": {
"id": "https://openalex.org/A5100332233",
"display_name": "Congcong Li",
"orcid": "https://orcid.org/0000-0001-7790-1112"
},
"institutions": [
{
"id": "https://openalex.org/I121847817",
"display_name": "The Graduate Center, CUNY",
"ror": "https://ror.org/00awd9g61",
"country_code": "US",
"type": "education",
"lineage": [
"https://openalex.org/I121847817"
]
},
{
"id": "https://openalex.org/I1286329397",
"display_name": "United States Geological Survey",
"ror": "https://ror.org/035a68863",
"country_code": "US",
"type": "government",
"lineage": [
"https://openalex.org/I1286329397",
"https://openalex.org/I1335927249"
]
}
],
"countries": [
"US"
],
"is_corresponding": true,
"raw_author_name": "Congcong Li",
"raw_affiliation_strings": [
"ASRC Federal Data Solutions, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Work Performed under USGS Contract 140G0119C0001, Sioux Falls, SD 57198, USA"
],
"affiliations": [
{
"raw_affiliation_string": "ASRC Federal Data Solutions, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Work Performed under USGS Contract 140G0119C0001, Sioux Falls, SD 57198, USA",
"institution_ids": [
"https://openalex.org/I121847817",
"https://openalex.org/I1286329397"
]
}
]
},
{
"author_position": "middle",
"author": {
"id": "https://openalex.org/A5012129280",
"display_name": "George Xian",
"orcid": "https://orcid.org/0000-0001-5674-2204"
},
"institutions": [
{
"id": "https://openalex.org/I1286329397",
"display_name": "United States Geological Survey",
"ror": "https://ror.org/035a68863",
"country_code": "US",
"type": "government",
"lineage": [
"https://openalex.org/I1286329397",
"https://openalex.org/I1335927249"
]
}
],
"countries": [
"US"
],
"is_corresponding": false,
"raw_author_name": "George Xian",
"raw_affiliation_strings": [
"U.S. Geological Survey, Earth Resources and Observation Science (EROS) Center, 47914 252nd St., Sioux Falls, SD 57198, USA"
],
"affiliations": [
{
"raw_affiliation_string": "U.S. Geological Survey, Earth Resources and Observation Science (EROS) Center, 47914 252nd St., Sioux Falls, SD 57198, USA",
"institution_ids": [
"https://openalex.org/I1286329397"
]
}
]
},
{
"author_position": "middle",
"author": {
"id": "https://openalex.org/A5020198396",
"display_name": "Qiang Zhou",
"orcid": "https://orcid.org/0000-0002-1282-8177"
},
"institutions": [
{
"id": "https://openalex.org/I121847817",
"display_name": "The Graduate Center, CUNY",
"ror": "https://ror.org/00awd9g61",
"country_code": "US",
"type": "education",
"lineage": [
"https://openalex.org/I121847817"
]
},
{
"id": "https://openalex.org/I1286329397",
"display_name": "United States Geological Survey",
"ror": "https://ror.org/035a68863",
"country_code": "US",
"type": "government",
"lineage": [
"https://openalex.org/I1286329397",
"https://openalex.org/I1335927249"
]
}
],
"countries": [
"US"
],
"is_corresponding": false,
"raw_author_name": "Qiang Zhou",
"raw_affiliation_strings": [
"ASRC Federal Data Solutions, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Work Performed under USGS Contract 140G0119C0001, Sioux Falls, SD 57198, USA"
],
"affiliations": [
{
"raw_affiliation_string": "ASRC Federal Data Solutions, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Work Performed under USGS Contract 140G0119C0001, Sioux Falls, SD 57198, USA",
"institution_ids": [
"https://openalex.org/I121847817",
"https://openalex.org/I1286329397"
]
}
]
},
{
"author_position": "last",
"author": {
"id": "https://openalex.org/A5029728349",
"display_name": "Bruce W. Pengra",
"orcid": "https://orcid.org/0000-0003-2497-8284"
},
"institutions": [
{
"id": "https://openalex.org/I1286329397",
"display_name": "United States Geological Survey",
"ror": "https://ror.org/035a68863",
"country_code": "US",
"type": "government",
"lineage": [
"https://openalex.org/I1286329397",
"https://openalex.org/I1335927249"
]
}
],
"countries": [
"US"
],
"is_corresponding": false,
"raw_author_name": "Bruce W. Pengra",
"raw_affiliation_strings": [
"KBRwyle, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA"
],
"affiliations": [
{
"raw_affiliation_string": "KBRwyle, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA",
"institution_ids": [
"https://openalex.org/I1286329397"
]
}
]
}
],
"best_oa_location": {
"is_oa": true,
"landing_page_url": "https://doi.org/10.1016/j.rse.2021.112670",
"pdf_url": "http://manuscript.elsevier.com/S0034425721003904/pdf/S0034425721003904.pdf",
"source": {
"id": "https://openalex.org/S141808269",
"display_name": "Remote Sensing of Environment",
"issn_l": "0034-4257",
"issn": [
"0034-4257",
"1879-0704"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": true,
"host_organization": "https://openalex.org/P4310320990",
"host_organization_name": "Elsevier BV",
"host_organization_lineage": [
"https://openalex.org/P4310320990"
],
"host_organization_lineage_names": [
"Elsevier BV"
],
"type": "journal"
},
"license": "publisher-specific-oa",
"license_id": "https://openalex.org/licenses/publisher-specific-oa",
"version": "acceptedVersion",
"is_accepted": true,
"is_published": false
},
"biblio": {
"volume": "266",
"issue": null,
"first_page": "112670",
"last_page": "112670"
},
"citation_normalized_percentile": {
"value": 0.751917,
"is_in_top_1_percent": false,
"is_in_top_10_percent": false
},
"cited_by_api_url": "https://api.openalex.org/works?filter=cites:W3199869787",
"cited_by_count": 27,
"cited_by_percentile_year": {
"min": 96,
"max": 97
},
"concepts": [
{
"id": "https://openalex.org/C2780648208",
"wikidata": "https://www.wikidata.org/wiki/Q3001793",
"display_name": "Land cover",
"level": 3,
"score": 0.8383758
},
{
"id": "https://openalex.org/C62649853",
"wikidata": "https://www.wikidata.org/wiki/Q199687",
"display_name": "Remote sensing",
"level": 1,
"score": 0.60915595
},
{
"id": "https://openalex.org/C2778755073",
"wikidata": "https://www.wikidata.org/wiki/Q10858537",
"display_name": "Scale (ratio)",
"level": 2,
"score": 0.6033404
},
{
"id": "https://openalex.org/C41008148",
"wikidata": "https://www.wikidata.org/wiki/Q21198",
"display_name": "Computer science",
"level": 0,
"score": 0.5497386
},
{
"id": "https://openalex.org/C203595873",
"wikidata": "https://www.wikidata.org/wiki/Q25389927",
"display_name": "Change detection",
"level": 2,
"score": 0.5279868
},
{
"id": "https://openalex.org/C88516994",
"wikidata": "https://www.wikidata.org/wiki/Q1268863",
"display_name": "Dynamic time warping",
"level": 2,
"score": 0.52182025
},
{
"id": "https://openalex.org/C198531522",
"wikidata": "https://www.wikidata.org/wiki/Q485146",
"display_name": "Sample (material)",
"level": 2,
"score": 0.5007179
},
{
"id": "https://openalex.org/C39399123",
"wikidata": "https://www.wikidata.org/wiki/Q1348989",
"display_name": "Earth observation",
"level": 3,
"score": 0.41900775
},
{
"id": "https://openalex.org/C39432304",
"wikidata": "https://www.wikidata.org/wiki/Q188847",
"display_name": "Environmental science",
"level": 0,
"score": 0.390546
},
{
"id": "https://openalex.org/C4792198",
"wikidata": "https://www.wikidata.org/wiki/Q1165944",
"display_name": "Land use",
"level": 2,
"score": 0.35486
},
{
"id": "https://openalex.org/C58640448",
"wikidata": "https://www.wikidata.org/wiki/Q42515",
"display_name": "Cartography",
"level": 1,
"score": 0.2506071
},
{
"id": "https://openalex.org/C154945302",
"wikidata": "https://www.wikidata.org/wiki/Q11660",
"display_name": "Artificial intelligence",
"level": 1,
"score": 0.21292034
},
{
"id": "https://openalex.org/C205649164",
"wikidata": "https://www.wikidata.org/wiki/Q1071",
"display_name": "Geography",
"level": 0,
"score": 0.19792578
},
{
"id": "https://openalex.org/C19269812",
"wikidata": "https://www.wikidata.org/wiki/Q26540",
"display_name": "Satellite",
"level": 2,
"score": 0.19104794
},
{
"id": "https://openalex.org/C185592680",
"wikidata": "https://www.wikidata.org/wiki/Q2329",
"display_name": "Chemistry",
"level": 0,
"score": 0.0
},
{
"id": "https://openalex.org/C147176958",
"wikidata": "https://www.wikidata.org/wiki/Q77590",
"display_name": "Civil engineering",
"level": 1,
"score": 0.0
},
{
"id": "https://openalex.org/C43617362",
"wikidata": "https://www.wikidata.org/wiki/Q170050",
"display_name": "Chromatography",
"level": 1,
"score": 0.0
},
{
"id": "https://openalex.org/C146978453",
"wikidata": "https://www.wikidata.org/wiki/Q3798668",
"display_name": "Aerospace engineering",
"level": 1,
"score": 0.0
},
{
"id": "https://openalex.org/C127413603",
"wikidata": "https://www.wikidata.org/wiki/Q11023",
"display_name": "Engineering",
"level": 0,
"score": 0.0
}
],
"corresponding_author_ids": [
"https://openalex.org/A5100332233"
],
"corresponding_institution_ids": [
"https://openalex.org/I121847817",
"https://openalex.org/I1286329397"
],
"countries_distinct_count": 1,
"counts_by_year": [
{
"year": 2024,
"cited_by_count": 7
},
{
"year": 2023,
"cited_by_count": 14
},
{
"year": 2022,
"cited_by_count": 6
}
],
"created_date": "2021-09-27",
"datasets": [],
"display_name": "A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping",
"doi": "https://doi.org/10.1016/j.rse.2021.112670",
"fwci": 3.726,
"grants": [],
"has_fulltext": false,
"id": "https://openalex.org/W3199869787",
"ids": {
"openalex": "https://openalex.org/W3199869787",
"doi": "https://doi.org/10.1016/j.rse.2021.112670",
"mag": "3199869787"
},
"indexed_in": [
"crossref"
],
"institutions_distinct_count": 2,
"is_paratext": false,
"is_retracted": false,
"keywords": [
{
"id": "https://openalex.org/keywords/land-cover",
"display_name": "Land cover",
"score": 0.8383758
},
{
"id": "https://openalex.org/keywords/dynamic-time-warping",
"display_name": "Dynamic time warping",
"score": 0.52182025
},
{
"id": "https://openalex.org/keywords/model-evaluation",
"display_name": "Model Evaluation",
"score": 0.506022
},
{
"id": "https://openalex.org/keywords/phenology",
"display_name": "Phenology",
"score": 0.505016
},
{
"id": "https://openalex.org/keywords/species-distribution-modeling",
"display_name": "Species Distribution Modeling",
"score": 0.502407
},
{
"id": "https://openalex.org/keywords/sample",
"display_name": "Sample (material)",
"score": 0.5007179
},
{
"id": "https://openalex.org/keywords/earth-observation",
"display_name": "Earth observation",
"score": 0.41900775
}
],
"language": "en",
"locations": [
{
"is_oa": true,
"landing_page_url": "https://doi.org/10.1016/j.rse.2021.112670",
"pdf_url": "http://manuscript.elsevier.com/S0034425721003904/pdf/S0034425721003904.pdf",
"source": {
"id": "https://openalex.org/S141808269",
"display_name": "Remote Sensing of Environment",
"issn_l": "0034-4257",
"issn": [
"0034-4257",
"1879-0704"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": true,
"host_organization": "https://openalex.org/P4310320990",
"host_organization_name": "Elsevier BV",
"host_organization_lineage": [
"https://openalex.org/P4310320990"
],
"host_organization_lineage_names": [
"Elsevier BV"
],
"type": "journal"
},
"license": "publisher-specific-oa",
"license_id": "https://openalex.org/licenses/publisher-specific-oa",
"version": "acceptedVersion",
"is_accepted": true,
"is_published": false
}
],
"locations_count": 1,
"mesh": [],
"ngrams_url": "https://api.openalex.org/works/W3199869787/ngrams",
"open_access": {
"is_oa": true,
"oa_status": "bronze",
"oa_url": "http://manuscript.elsevier.com/S0034425721003904/pdf/S0034425721003904.pdf",
"any_repository_has_fulltext": false
},
"primary_location": {
"is_oa": true,
"landing_page_url": "https://doi.org/10.1016/j.rse.2021.112670",
"pdf_url": "http://manuscript.elsevier.com/S0034425721003904/pdf/S0034425721003904.pdf",
"source": {
"id": "https://openalex.org/S141808269",
"display_name": "Remote Sensing of Environment",
"issn_l": "0034-4257",
"issn": [
"0034-4257",
"1879-0704"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": true,
"host_organization": "https://openalex.org/P4310320990",
"host_organization_name": "Elsevier BV",
"host_organization_lineage": [
"https://openalex.org/P4310320990"
],
"host_organization_lineage_names": [
"Elsevier BV"
],
"type": "journal"
},
"license": "publisher-specific-oa",
"license_id": "https://openalex.org/licenses/publisher-specific-oa",
"version": "acceptedVersion",
"is_accepted": true,
"is_published": false
},
"primary_topic": {
"id": "https://openalex.org/T10111",
"display_name": "Remote Sensing in Vegetation Monitoring and Phenology",
"score": 0.9997,
"subfield": {
"id": "https://openalex.org/subfields/2303",
"display_name": "Ecology"
},
"field": {
"id": "https://openalex.org/fields/23",
"display_name": "Environmental Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
"publication_date": "2021-12-01",
"publication_year": 2021,
"referenced_works": [
"https://openalex.org/W1213549907",
"https://openalex.org/W1781559127",
"https://openalex.org/W1976651503",
"https://openalex.org/W1979210946",
"https://openalex.org/W1979776441",
"https://openalex.org/W1981213426",
"https://openalex.org/W1982121855",
"https://openalex.org/W1988713966",
"https://openalex.org/W1996777760",
"https://openalex.org/W2001007226",
"https://openalex.org/W2001510610",
"https://openalex.org/W2006929658",
"https://openalex.org/W2030025097",
"https://openalex.org/W2055718260",
"https://openalex.org/W2063623478",
"https://openalex.org/W2064578974",
"https://openalex.org/W2078100709",
"https://openalex.org/W2092141993",
"https://openalex.org/W2117996123",
"https://openalex.org/W2119160928",
"https://openalex.org/W2126902408",
"https://openalex.org/W2129920251",
"https://openalex.org/W2140908571",
"https://openalex.org/W2150508029",
"https://openalex.org/W2155473890",
"https://openalex.org/W2161336494",
"https://openalex.org/W2170804038",
"https://openalex.org/W2283002322",
"https://openalex.org/W2344186514",
"https://openalex.org/W2418543604",
"https://openalex.org/W2463110810",
"https://openalex.org/W2540777836",
"https://openalex.org/W2553544826",
"https://openalex.org/W2560167313",
"https://openalex.org/W2584952387",
"https://openalex.org/W2596981200",
"https://openalex.org/W2619820913",
"https://openalex.org/W2625380067",
"https://openalex.org/W2700341059",
"https://openalex.org/W2734813859",
"https://openalex.org/W2735042947",
"https://openalex.org/W2763336038",
"https://openalex.org/W2793327769",
"https://openalex.org/W2794421859",
"https://openalex.org/W2914874661",
"https://openalex.org/W2922152173",
"https://openalex.org/W2936888209",
"https://openalex.org/W2950541263",
"https://openalex.org/W2967165937",
"https://openalex.org/W2990323597",
"https://openalex.org/W2990663070",
"https://openalex.org/W2991639467",
"https://openalex.org/W3003421670",
"https://openalex.org/W3005485289",
"https://openalex.org/W3008834779",
"https://openalex.org/W3009367059",
"https://openalex.org/W3017247426",
"https://openalex.org/W3044364573",
"https://openalex.org/W3090679658",
"https://openalex.org/W3102476541",
"https://openalex.org/W3122084549",
"https://openalex.org/W4247860859"
],
"referenced_works_count": 62,
"related_works": [
"https://openalex.org/W4389201442",
"https://openalex.org/W3192667092",
"https://openalex.org/W3133615129",
"https://openalex.org/W3011513067",
"https://openalex.org/W2386169820",
"https://openalex.org/W2373152553",
"https://openalex.org/W2188959887",
"https://openalex.org/W2153381734",
"https://openalex.org/W2087854757",
"https://openalex.org/W2050072374"
],
"sustainable_development_goals": [
{
"score": 0.52,
"display_name": "Life on land",
"id": "https://metadata.un.org/sdg/15"
}
],
"title": "A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping",
"topics": [
{
"id": "https://openalex.org/T10111",
"display_name": "Remote Sensing in Vegetation Monitoring and Phenology",
"score": 0.9997,
"subfield": {
"id": "https://openalex.org/subfields/2303",
"display_name": "Ecology"
},
"field": {
"id": "https://openalex.org/fields/23",
"display_name": "Environmental Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10895",
"display_name": "Species Distribution Modeling and Climate Change Impacts",
"score": 0.999,
"subfield": {
"id": "https://openalex.org/subfields/2302",
"display_name": "Ecological Modeling"
},
"field": {
"id": "https://openalex.org/fields/23",
"display_name": "Environmental Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T10226",
"display_name": "Global Analysis of Ecosystem Services and Land Use",
"score": 0.9881,
"subfield": {
"id": "https://openalex.org/subfields/2306",
"display_name": "Global and Planetary Change"
},
"field": {
"id": "https://openalex.org/fields/23",
"display_name": "Environmental Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
}
],
"type": "article",
"type_crossref": "journal-article",
"updated_date": "2024-08-11T14:46:19.084834",
"versions": []
}
}
