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\"email\": \"dominique.weber@wsl.ch\", \"given_name\": \"Dominique\", 
            
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\"christian.ginzler@wsl.ch\", \"given_name\": \"Christian\", 
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\"christian.ginzler@wsl.ch\", \"given_name\": \"Christian\", 
            
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\"identifier\": \"0000-0001-6365-2151\", \"name\": \"Ginzler\"}, 
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\"identifier\": \"0000-0001-6365-2151\", \"name\": \"Ginzler\"}, 
            
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\"bronwyn.price@wsl.ch\", \"given_name\": \"Bronwyn\", \"identifier\": 
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\"bronwyn.price@wsl.ch\", \"given_name\": \"Bronwyn\", \"identifier\": 
            
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  "date": "[{\"date\": \"2021-01-18\", \"date_type\": \"created\", 
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  "doi": "10.16904/envidat.205",
            
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  "funding": "[{\"grant_number\": \"\", \"institution\": \"WSL\", 
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  "language": "en",
            
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  "license_id": "odc-odbl",
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  "license_id": "odc-odbl",
            
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  "license_title": "ODbL with Database Contents License (DbCL)",
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  "license_title": "ODbL with Database Contents License (DbCL)",
            
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  "maintainer": "{\"affiliation\": \"Swiss Federal Research Institute 
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  "maintainer": "{\"affiliation\": \"Swiss Federal Research Institute 
            
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WSL\", \"email\": \"robert.pazur@wsl.ch\", \"given_name\": \"Robert\", 
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WSL\", \"email\": \"robert.pazur@wsl.ch\", \"given_name\": \"Robert\", 
            
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\"identifier\": \"0000-0002-9600-7420\", \"name\": \"Paz\u00far\"}",
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\"identifier\": \"0000-0002-9600-7420\", \"name\": \"Paz\u00far\"}",
            
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  "metadata_modified": "2021-03-10T12:26:36.618996",
            
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  "name": 
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  "name": 
            
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"cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data",
43
"cropland-and-grassland-map-of-switzerland-based-on-sentinel-2-data",
            
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  "notes": "We developed a map of cropland and grassland allocation 
44
  "notes": "We developed a map of cropland and grassland allocation 
            
45
for Switzerland based on several indices dominantly derived from 
45
for Switzerland based on several indices dominantly derived from 
            
46
Sentinel-2 satellite imagery captured over multiple growing seasons. 
46
Sentinel-2 satellite imagery captured over multiple growing seasons. 
            
47
The classification model was trained based on parcel-based data 
47
The classification model was trained based on parcel-based data 
            
48
derived from landholder reporting. The mapping was conducted on Google 
48
derived from landholder reporting. The mapping was conducted on Google 
            
49
Earth Engine platform using random forest classifier. Areas of high 
49
Earth Engine platform using random forest classifier. Areas of high 
            
50
vegetation, shrubland,  sealed surface and non-vegetated areas were 
50
vegetation, shrubland,  sealed surface and non-vegetated areas were 
            
51
masked out from the country-wide map. The resulting map has high 
51
masked out from the country-wide map. The resulting map has high 
            
52
accuracy in lowlands as well as mountainous areas. \r\n",
52
accuracy in lowlands as well as mountainous areas. \r\n",
            
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  "num_resources": 10,
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  "num_resources": 10,
            
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  "num_tags": 10,
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  "num_tags": 10,
            
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  "organization": {
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  "organization": {
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2017-04-20T16:51:21.920128",
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    "created": "2017-04-20T16:51:21.920128",
            
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    "description": "We develop and apply comprehensive and robust 
58
    "description": "We develop and apply comprehensive and robust 
            
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methods to extract and classify natural objects from continuous and 
59
methods to extract and classify natural objects from continuous and 
            
60
discrete raster datasets. Relevant features are acquired to describe 
60
discrete raster datasets. Relevant features are acquired to describe 
            
61
changes in landscape and land resources at different levels using 
61
changes in landscape and land resources at different levels using 
            
62
image data. Mathematical-statistical methods are adopted for automatic 
62
image data. Mathematical-statistical methods are adopted for automatic 
            
63
detection and description of image objects. Thus we contribute 
63
detection and description of image objects. Thus we contribute 
            
64
concepts, methods and data to describe/detect area wide changes and 
64
concepts, methods and data to describe/detect area wide changes and 
            
65
processes in the resources of landscape.\r\n\r\n### Tasks and main 
65
processes in the resources of landscape.\r\n\r\n### Tasks and main 
            
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research\r\n\r\n* Development and application of methods to extract 
66
research\r\n\r\n* Development and application of methods to extract 
            
67
natural objects from continuous data.\r\n* Development of methods for 
67
natural objects from continuous data.\r\n* Development of methods for 
            
68
a comprehensive description of natural and anthropogenetic boundaries 
68
a comprehensive description of natural and anthropogenetic boundaries 
            
69
in continuous pattern (e.g. map signatures, vegetation transition, 
69
in continuous pattern (e.g. map signatures, vegetation transition, 
            
70
forest borders).\r\n* Development and application of methods to 
70
forest borders).\r\n* Development and application of methods to 
            
71
extract 3D-information from remotely sensed data for description of 
71
extract 3D-information from remotely sensed data for description of 
            
72
natural structures and changes. The main focus lies on wood and its 
72
natural structures and changes. The main focus lies on wood and its 
            
73
embedding/interaction within/with the landscape.\r\n* Conception and 
73
embedding/interaction within/with the landscape.\r\n* Conception and 
            
74
development of data acquisition based on high resolution remote 
74
development of data acquisition based on high resolution remote 
            
75
sensing data.\r\n* Conception, development and maintenance of the 
75
sensing data.\r\n* Conception, development and maintenance of the 
            
76
software interface in area wide data acquisition using airborne remote 
76
software interface in area wide data acquisition using airborne remote 
            
77
sensing data.\r\n* Scientific expert advice and support in the fields 
77
sensing data.\r\n* Scientific expert advice and support in the fields 
            
78
of photogrammetry and survey at WSL. Maintenance, enhancements and 
78
of photogrammetry and survey at WSL. Maintenance, enhancements and 
            
79
future development in these specific fields.\r\n* Adequate 
79
future development in these specific fields.\r\n* Adequate 
            
80
presentation of scientific results on national level and in noted 
80
presentation of scientific results on national level and in noted 
            
81
international journals and at international 
81
international journals and at international 
            
82
congresses/workshops/symposia.\r\n\r\n__Further information__: 
82
congresses/workshops/symposia.\r\n\r\n__Further information__: 
            
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l/organization/research-units/landscape-dynamics/remote-sensing.html",
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l/organization/research-units/landscape-dynamics/remote-sensing.html",
            
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    "image_url": "2018-07-10-102816.481589LogoWSL.svg",
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    "is_organization": true,
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    "name": "remote-sensing",
            
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    "state": "active",
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    "state": "active",
            
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    "title": "Remote Sensing",
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    "title": "Remote Sensing",
            
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  "private": false,
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  "publication": "{\"publication_year\": \"2021\", \"publisher\": 
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  "publication": "{\"publication_year\": \"2021\", \"publisher\": 
            
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\"EnviDat\"}",
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  "publication_state": "published",
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      "created": "2021-01-25T11:06:04.548652",
            
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      "description": "The code used in Google Earth Engine to derive 
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      "description": "!!! Please check out the newer version in the 
            
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repository !!! The code used in Google Earth Engine to derive the 
            
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the cropland, grassland and shrubland map of Switzerland",
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cropland, grassland and shrubland map of Switzerland",
            
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      "doi": "",
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      "doi": "",
            
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      "name": "GEE (Google Earth Engine) script (JavaScript) to 
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derived the output map  (v1.0)",
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derived the output map  (v1.0)",
            
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      "url": 
            
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ownload/sentinel12_wholech_croplandgraslandshrubland_published_v1.js",
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ownload/sentinel12_wholech_croplandgraslandshrubland_published_v1.js",
            
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      "created": "2021-01-26T06:17:24.788237",
            
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      "description": "!!! Please check out the newer version in this 
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      "description": "!!! Please check out the newer version in this 
            
138
repository !!! (1) cropland; (2) grassland; other LC classes are 
139
repository !!! (1) cropland; (2) grassland; other LC classes are 
            
139
masked out from the map\r\n",
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masked out from the map\r\n",
            
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repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC 
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repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC 
            
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repository !!! Assuming that sealed surfaces have little or no 
198
repository !!! Assuming that sealed surfaces have little or no 
            
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phenological response we calculated a mask of unsealed surfaces based 
199
phenological response we calculated a mask of unsealed surfaces based 
            
199
on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold 
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on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold 
            
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      "description": "!!! Please check out the newer version in the 
            
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repository !!! \r\nMask of unsealed surfaces of Switzerland based on 3 
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repository !!! \r\nMask of unsealed surfaces of Switzerland based on 3 
            
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years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 
            
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phenological response we calculated a mask of unsealed surfaces based 
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phenological response we calculated a mask of unsealed surfaces based 
            
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on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold 
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      "description": "Assuming that sealed surfaces have little or no 
348
      "description": "Assuming that sealed surfaces have little or no 
            
348
phenological response we calculated a mask of unsealed surfaces based 
349
phenological response we calculated a mask of unsealed surfaces based 
            
349
on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold 
350
on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold 
            
350
value: 0.6.",
351
value: 0.6.",
            
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      "name": "Mask of unsealed surfaces of Switzerland based on 3 
360
      "name": "Mask of unsealed surfaces of Switzerland based on 3 
            
360
years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)",
361
years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)",
            
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      "size": 20648702,
            
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      "url": 
            
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      "created": "2021-03-08T08:24:54.688432",
            
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      "description": "The code used in Google Earth Engine to derive 
379
      "description": "The code used in Google Earth Engine to derive 
            
379
the cropland, grassland and shrubland map of Switzerland. Compare to 
380
the cropland, grassland and shrubland map of Switzerland. Compare to 
            
380
the previous version the newer version includes the shadow masking 
381
the previous version the newer version includes the shadow masking 
            
381
procedure which might be important in the mountainous areas.",
382
procedure which might be important in the mountainous areas.",
            
382
      "doi": "",
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      "doi": "",
            
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      "format": "JS",
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      "format": "JS",
            
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      "name": "GEE (Google Earth Engine) script (JavaScript) to 
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      "name": "GEE (Google Earth Engine) script (JavaScript) to 
            
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derived the output map  (v1.5)",
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derived the output map  (v1.5)",
            
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      "url": 
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      "url": 
            
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ownload/sentinel12_wholech_croplandgraslandshrubland_published_v2.js",
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ownload/sentinel12_wholech_croplandgraslandshrubland_published_v2.js",
            
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      "url_type": "upload"
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  "spatial_info": "Switzerland",
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  "subtitle": "",
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      "id": "97ac3564-bb88-46ad-9fe4-b7d606e0aa3a",
            
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      "name": "SWITZERLAND",
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      "name": "SWITZERLAND",
            
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      "state": "active",
            
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    },
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    },
            
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      "display_name": "VEGETATION INDICES",
            
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      "id": "9a6c8ef8-2980-4a11-a3c0-8e9f8a515c6c",
            
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    }
            
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  ],
            
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  "title": "Cropland and grassland map of Switzerland based on 
485
  "title": "Cropland and grassland map of Switzerland based on 
            
485
Sentinel-2 data",
486
Sentinel-2 data",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "url": null,
            
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  "version": "1.0"
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  "version": "1.0"
            
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}
490
}