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  "author": "[{\"name\": \"Waser\", \"affiliation\": \"Swiss Federal 
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  "author": "[{\"name\": \"Waser\", \"affiliation\": \"Swiss Federal 
            
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Institute for Forest, Snow and Landscape Research WSL \", 
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Institute for Forest, Snow and Landscape Research WSL \", 
            
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\"affiliation_03\": \"\", \"given_name\": \"Lars\", \"identifier\": 
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\"affiliation_03\": \"\", \"given_name\": \"Lars\", \"identifier\": 
            
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\"D-5937-2011\", \"email\": \"lars.waser@wsl.ch\", \"affiliation_02\": 
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\"D-5937-2011\", \"email\": \"lars.waser@wsl.ch\", \"affiliation_02\": 
            
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\"\"}, {\"name\": \"Ginzler\", \"affiliation\": \"Swiss Federal 
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\"\"}, {\"name\": \"Ginzler\", \"affiliation\": \"Swiss Federal 
            
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Institute for Forest, Snow and Landscape Research WSL \", 
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Institute for Forest, Snow and Landscape Research WSL \", 
            
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\"affiliation_03\": \"\", \"given_name\": \"Christian\", 
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\"affiliation_03\": \"\", \"given_name\": \"Christian\", 
            
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\"identifier\": \"E-9544-2012\", \"email\": 
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\"identifier\": \"E-9544-2012\", \"email\": 
            
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\"christian.ginzler@wsl.ch\", \"affiliation_02\": \"\"}]",
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\"christian.ginzler@wsl.ch\", \"affiliation_02\": \"\"}]",
            
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  "creator_user_id": "6d44d5cd-9ac6-4100-bc2c-c02034a41b48",
            
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  "date": "[{\"date\": \"2018-03-06\", \"date_type\": \"created\", 
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  "date": "[{\"date\": \"2018-03-06\", \"date_type\": \"created\", 
            
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\"end_date\": \"\"}]",
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\"end_date\": \"\"}]",
            
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  "doi": "10.16904/1000001.3",
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  "doi": "10.16904/1000001.3",
            
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  ],
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  ],
            
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  "funding": "[{\"grant_number\": \"\", \"institution\": \"Federal 
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  "funding": "[{\"grant_number\": \"\", \"institution\": \"Federal 
            
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Office for the Environment (FOEN).\", \"institution_url\": \"\"}]",
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Office for the Environment (FOEN).\", \"institution_url\": \"\"}]",
            
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  "groups": [],
            
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  "id": "82d763fa-123a-4648-b827-0dec02a5efbc",
            
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  "language": "en",
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  "language": "en",
            
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  "license_id": "other-undefined",
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  "license_id": "other-undefined",
            
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  "license_title": "Other (Specified in the description)",
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  "license_title": "Other (Specified in the description)",
            
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  "maintainer": "{\"affiliation\": \"Swiss Federal Institute for 
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  "maintainer": "{\"affiliation\": \"Swiss Federal Institute for 
            
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Forest, Snow and Landscape Research WSL \", \"identifier\": 
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Forest, Snow and Landscape Research WSL \", \"identifier\": 
            
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\"D-5937-2011\", \"given_name\": \"Lars\", \"email\": 
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\"D-5937-2011\", \"given_name\": \"Lars\", \"email\": 
            
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\"lars.waser@wsl.ch\", \"name\": \"Waser\"}",
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\"lars.waser@wsl.ch\", \"name\": \"Waser\"}",
            
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  "maintainer_email": null,
            
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  "metadata_created": "2018-04-03T10:38:30.545175",
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  "metadata_created": "2018-04-03T10:38:30.545175",
            
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  "metadata_modified": "2021-04-23T11:44:06.285477",
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  "metadata_modified": "2021-04-23T11:51:59.132760",
            
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  "name": "forest-type-nfi",
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  "name": "forest-type-nfi",
            
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  "notes": "This dataset presents an remote sensing based approach for 
38
  "notes": "This dataset presents an remote sensing based approach for 
            
39
a countrywide mapping of broadleaved and coniferous trees in 
39
a countrywide mapping of broadleaved and coniferous trees in 
            
40
Switzerland with a spatial resolution of 3 m x 3 m. The data available 
40
Switzerland with a spatial resolution of 3 m x 3 m. The data available 
            
41
is a raster of 25 m x 25 m with the fraction of broadleaf trees. The 
41
is a raster of 25 m x 25 m with the fraction of broadleaf trees. The 
            
42
classification approach incorporates a random forest classifier, 
42
classification approach incorporates a random forest classifier, 
            
43
explanatory variables from multispectral aerial imagery and a Digital 
43
explanatory variables from multispectral aerial imagery and a Digital 
            
44
Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized 
44
Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized 
            
45
training polygons and independent validation data from the National 
45
training polygons and independent validation data from the National 
            
46
Forest Inventory (NFI). Whereas high model overall accuracies 
46
Forest Inventory (NFI). Whereas high model overall accuracies 
            
47
(0.99)\r\nand kappa (0.98) were achieved, the comparison of the tree 
47
(0.99)\r\nand kappa (0.98) were achieved, the comparison of the tree 
            
48
type map with independent NFI data revealed significant deviations 
48
type map with independent NFI data revealed significant deviations 
            
49
that are related to underestimations of broadleaved trees (median of 
49
that are related to underestimations of broadleaved trees (median of 
            
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3.17%).",
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3.17%).",
            
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  "num_resources": 4,
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  "num_resources": 4,
            
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  "num_tags": 5,
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  "num_tags": 5,
            
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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 
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    "description": "We develop and apply comprehensive and robust 
            
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methods to extract and classify natural objects from continuous and 
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methods to extract and classify natural objects from continuous and 
            
58
discrete raster datasets. Relevant features are acquired to describe 
58
discrete raster datasets. Relevant features are acquired to describe 
            
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changes in landscape and land resources at different levels using 
59
changes in landscape and land resources at different levels using 
            
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image data. Mathematical-statistical methods are adopted for automatic 
60
image data. Mathematical-statistical methods are adopted for automatic 
            
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detection and description of image objects. Thus we contribute 
61
detection and description of image objects. Thus we contribute 
            
62
concepts, methods and data to describe/detect area wide changes and 
62
concepts, methods and data to describe/detect area wide changes and 
            
63
processes in the resources of landscape.\r\n\r\n### Tasks and main 
63
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 
64
research\r\n\r\n* Development and application of methods to extract 
            
65
natural objects from continuous data.\r\n* Development of methods for 
65
natural objects from continuous data.\r\n* Development of methods for 
            
66
a comprehensive description of natural and anthropogenetic boundaries 
66
a comprehensive description of natural and anthropogenetic boundaries 
            
67
in continuous pattern (e.g. map signatures, vegetation transition, 
67
in continuous pattern (e.g. map signatures, vegetation transition, 
            
68
forest borders).\r\n* Development and application of methods to 
68
forest borders).\r\n* Development and application of methods to 
            
69
extract 3D-information from remotely sensed data for description of 
69
extract 3D-information from remotely sensed data for description of 
            
70
natural structures and changes. The main focus lies on wood and its 
70
natural structures and changes. The main focus lies on wood and its 
            
71
embedding/interaction within/with the landscape.\r\n* Conception and 
71
embedding/interaction within/with the landscape.\r\n* Conception and 
            
72
development of data acquisition based on high resolution remote 
72
development of data acquisition based on high resolution remote 
            
73
sensing data.\r\n* Conception, development and maintenance of the 
73
sensing data.\r\n* Conception, development and maintenance of the 
            
74
software interface in area wide data acquisition using airborne remote 
74
software interface in area wide data acquisition using airborne remote 
            
75
sensing data.\r\n* Scientific expert advice and support in the fields 
75
sensing data.\r\n* Scientific expert advice and support in the fields 
            
76
of photogrammetry and survey at WSL. Maintenance, enhancements and 
76
of photogrammetry and survey at WSL. Maintenance, enhancements and 
            
77
future development in these specific fields.\r\n* Adequate 
77
future development in these specific fields.\r\n* Adequate 
            
78
presentation of scientific results on national level and in noted 
78
presentation of scientific results on national level and in noted 
            
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international journals and at international 
79
international journals and at international 
            
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congresses/workshops/symposia.\r\n\r\n__Further information__: 
80
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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  "publication": "{\"publisher\": \"National Forest Inventory (NFI)\", 
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\"publication_year\": \"2018\"}",
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      "description": "Data 'Vegetation Height Model NFI 2016' 
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      "description": "Data 'Forest Type NFI 2016' available on request 
            
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available on request (lars.waser@wsl.ch).",
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(lars.waser@wsl.ch).",
            
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      "doi": "10.16904/1000001.3",
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      "doi": "10.16904/1000001.3",
            
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      "name": "Wall-to-Wall Tree Type Mapping from Countrywide 
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      "name": "Wall-to-Wall Tree Type Mapping from Countrywide 
            
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Airborne Remote Sensing Surveys",
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Airborne Remote Sensing Surveys",
            
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