Changes
On May 7, 2021 at 7:27:03 AM UTC, Lars Waser:
-
Updated description of Forest Type NFI from
This dataset presents an remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m x 3 m. The data available is a raster of 25 m x 25 m with the fraction of broadleaf trees. Two versions of the data are currently available: 2018 and 2016. The 2018 version presents a remote sensing-based approach for a countrywide mapping of the dominant leave type (DLT) with the two classes broadleaved and coniferous in Switzerland. The spatial resolution is 10 m with the fraction of the class broadleaf. The classification approach incorporates a random forest classifier, explanatory variables from multispectral Sentinel-2, multi-temporal Sentinel-1 data and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data. The models were calibrated using digitized training polygons and independently validated data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.97) and kappa (0.96) were achieved, the comparison of the tree type map with independent NFI data revealed deviations in mixed stands. In the 2016 version, the classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of 3.17%).
toTwo versions of the data are currently available: 2018 and 2016. The 2018 version presents a remote sensing-based approach for a countrywide mapping of the dominant leave type (DLT) with the two classes broadleaved and coniferous in Switzerland. The spatial resolution is 10 m with the fraction of the class broadleaf. The classification approach incorporates a random forest classifier, explanatory variables from multispectral Sentinel-2, multi-temporal Sentinel-1 data and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data. The models were calibrated using digitized training polygons and independently validated data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.97) and kappa (0.96) were achieved, the comparison of the tree type map with independent NFI data revealed deviations in mixed stands. In the 2016 version (3 m spatial resolution), the classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of 3.17%).
f | 1 | { | f | 1 | { |
2 | "author": "[{\"affiliation\": \"Swiss Federal Institute for Forest, | 2 | "author": "[{\"affiliation\": \"Swiss Federal Institute for Forest, | ||
3 | Snow and Landscape Research WSL \", \"affiliation_02\": \"\", | 3 | Snow and Landscape Research WSL \", \"affiliation_02\": \"\", | ||
4 | \"affiliation_03\": \"\", \"email\": \"lars.waser@wsl.ch\", | 4 | \"affiliation_03\": \"\", \"email\": \"lars.waser@wsl.ch\", | ||
5 | \"given_name\": \"Lars\", \"identifier\": \"D-5937-2011\", \"name\": | 5 | \"given_name\": \"Lars\", \"identifier\": \"D-5937-2011\", \"name\": | ||
6 | \"Waser\"}, {\"affiliation\": \"Swiss Federal Institute for Forest, | 6 | \"Waser\"}, {\"affiliation\": \"Swiss Federal Institute for Forest, | ||
7 | Snow and Landscape Research WSL \", \"affiliation_02\": \"\", | 7 | Snow and Landscape Research WSL \", \"affiliation_02\": \"\", | ||
8 | \"affiliation_03\": \"\", \"email\": \"christian.ginzler@wsl.ch\", | 8 | \"affiliation_03\": \"\", \"email\": \"christian.ginzler@wsl.ch\", | ||
9 | \"given_name\": \"Christian\", \"identifier\": \"E-9544-2012\", | 9 | \"given_name\": \"Christian\", \"identifier\": \"E-9544-2012\", | ||
10 | \"name\": \"Ginzler\"}]", | 10 | \"name\": \"Ginzler\"}]", | ||
11 | "author_email": null, | 11 | "author_email": null, | ||
12 | "creator_user_id": "6d44d5cd-9ac6-4100-bc2c-c02034a41b48", | 12 | "creator_user_id": "6d44d5cd-9ac6-4100-bc2c-c02034a41b48", | ||
13 | "date": "[{\"date\": \"2018-03-06\", \"date_type\": \"created\", | 13 | "date": "[{\"date\": \"2018-03-06\", \"date_type\": \"created\", | ||
14 | \"end_date\": \"\"}]", | 14 | \"end_date\": \"\"}]", | ||
15 | "doi": "10.16904/1000001.7", | 15 | "doi": "10.16904/1000001.7", | ||
16 | "funding": "[{\"grant_number\": \"\", \"institution\": \"Federal | 16 | "funding": "[{\"grant_number\": \"\", \"institution\": \"Federal | ||
17 | Office for the Environment (FOEN).\", \"institution_url\": \"\"}]", | 17 | Office for the Environment (FOEN).\", \"institution_url\": \"\"}]", | ||
18 | "groups": [], | 18 | "groups": [], | ||
19 | "id": "82d763fa-123a-4648-b827-0dec02a5efbc", | 19 | "id": "82d763fa-123a-4648-b827-0dec02a5efbc", | ||
20 | "isopen": false, | 20 | "isopen": false, | ||
21 | "language": "en", | 21 | "language": "en", | ||
22 | "license_id": "other-undefined", | 22 | "license_id": "other-undefined", | ||
23 | "license_title": "Other (Specified in the description)", | 23 | "license_title": "Other (Specified in the description)", | ||
24 | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | 24 | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | ||
25 | Forest, Snow and Landscape Research WSL \", \"email\": | 25 | Forest, Snow and Landscape Research WSL \", \"email\": | ||
26 | \"lars.waser@wsl.ch\", \"given_name\": \"Lars\", \"identifier\": | 26 | \"lars.waser@wsl.ch\", \"given_name\": \"Lars\", \"identifier\": | ||
27 | \"D-5937-2011\", \"name\": \"Waser\"}", | 27 | \"D-5937-2011\", \"name\": \"Waser\"}", | ||
28 | "maintainer_email": null, | 28 | "maintainer_email": null, | ||
29 | "metadata_created": "2018-04-03T10:38:30.545175", | 29 | "metadata_created": "2018-04-03T10:38:30.545175", | ||
n | 30 | "metadata_modified": "2021-05-07T07:13:35.659036", | n | 30 | "metadata_modified": "2021-05-07T07:27:03.921351", |
31 | "name": "forest-type-nfi", | 31 | "name": "forest-type-nfi", | ||
n | 32 | "notes": "This dataset presents an remote sensing based approach for | n | ||
33 | a countrywide mapping of broadleaved and coniferous trees in | ||||
34 | Switzerland with a spatial resolution of 3 m x 3 m. The data available | ||||
35 | is a raster of 25 m x 25 m with the fraction of broadleaf trees. | ||||
36 | \r\n\r\nTwo versions of the data are currently available: 2018 and | 32 | "notes": "Two versions of the data are currently available: 2018 and | ||
37 | 2016.\r\n\r\nThe 2018 version presents a remote sensing-based approach | 33 | 2016.\r\n\r\nThe 2018 version presents a remote sensing-based approach | ||
38 | for a countrywide mapping of the dominant leave type (DLT) with the | 34 | for a countrywide mapping of the dominant leave type (DLT) with the | ||
39 | two classes broadleaved and coniferous in Switzerland. The spatial | 35 | two classes broadleaved and coniferous in Switzerland. The spatial | ||
40 | resolution is 10 m with the fraction of the class broadleaf. The | 36 | resolution is 10 m with the fraction of the class broadleaf. The | ||
41 | classification approach incorporates a random forest classifier, | 37 | classification approach incorporates a random forest classifier, | ||
42 | explanatory variables from multispectral Sentinel-2, multi-temporal | 38 | explanatory variables from multispectral Sentinel-2, multi-temporal | ||
43 | Sentinel-1 data and a Digital Terrain Model (DTM) from Airborne Laser | 39 | Sentinel-1 data and a Digital Terrain Model (DTM) from Airborne Laser | ||
44 | Scanning (ALS) data. The models were calibrated using digitized | 40 | Scanning (ALS) data. The models were calibrated using digitized | ||
45 | training polygons and independently validated data from the National | 41 | training polygons and independently validated data from the National | ||
46 | Forest Inventory (NFI). Whereas high model overall accuracies (0.97) | 42 | Forest Inventory (NFI). Whereas high model overall accuracies (0.97) | ||
47 | and kappa (0.96) were achieved, the comparison of the tree type map | 43 | and kappa (0.96) were achieved, the comparison of the tree type map | ||
48 | with independent NFI data revealed deviations in mixed | 44 | with independent NFI data revealed deviations in mixed | ||
t | 49 | stands.\r\n\r\nIn the 2016 version, the classification approach | t | 45 | stands.\r\n\r\nIn the 2016 version (3 m spatial resolution), the |
50 | incorporates a random forest classifier, explanatory variables from | 46 | classification approach incorporates a random forest classifier, | ||
51 | multispectral aerial imagery and a Digital Terrain Model (DTM) from | 47 | explanatory variables from multispectral aerial imagery and a Digital | ||
52 | Airborne Laser Scanning (ALS) data, digitized training polygons and | 48 | Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized | ||
53 | independent validation data from the National Forest Inventory (NFI). | 49 | training polygons and independent validation data from the National | ||
54 | Whereas high model overall accuracies (0.99) and kappa (0.98) were | 50 | Forest Inventory (NFI). Whereas high model overall accuracies (0.99) | ||
55 | achieved, the comparison of the tree type map with independent NFI | 51 | and kappa (0.98) were achieved, the comparison of the tree type map | ||
56 | data revealed significant deviations that are related to | 52 | with independent NFI data revealed significant deviations that are | ||
57 | underestimations of broadleaved trees (median of 3.17%).", | 53 | related to underestimations of broadleaved trees (median of 3.17%).", | ||
58 | "num_resources": 4, | 54 | "num_resources": 4, | ||
59 | "num_tags": 5, | 55 | "num_tags": 5, | ||
60 | "organization": { | 56 | "organization": { | ||
61 | "approval_status": "approved", | 57 | "approval_status": "approved", | ||
62 | "created": "2017-04-20T16:51:21.920128", | 58 | "created": "2017-04-20T16:51:21.920128", | ||
63 | "description": "We develop and apply comprehensive and robust | 59 | "description": "We develop and apply comprehensive and robust | ||
64 | methods to extract and classify natural objects from continuous and | 60 | methods to extract and classify natural objects from continuous and | ||
65 | discrete raster datasets. Relevant features are acquired to describe | 61 | discrete raster datasets. Relevant features are acquired to describe | ||
66 | changes in landscape and land resources at different levels using | 62 | changes in landscape and land resources at different levels using | ||
67 | image data. Mathematical-statistical methods are adopted for automatic | 63 | image data. Mathematical-statistical methods are adopted for automatic | ||
68 | detection and description of image objects. Thus we contribute | 64 | detection and description of image objects. Thus we contribute | ||
69 | concepts, methods and data to describe/detect area wide changes and | 65 | concepts, methods and data to describe/detect area wide changes and | ||
70 | processes in the resources of landscape.\r\n\r\n### Tasks and main | 66 | processes in the resources of landscape.\r\n\r\n### Tasks and main | ||
71 | research\r\n\r\n* Development and application of methods to extract | 67 | research\r\n\r\n* Development and application of methods to extract | ||
72 | natural objects from continuous data.\r\n* Development of methods for | 68 | natural objects from continuous data.\r\n* Development of methods for | ||
73 | a comprehensive description of natural and anthropogenetic boundaries | 69 | a comprehensive description of natural and anthropogenetic boundaries | ||
74 | in continuous pattern (e.g. map signatures, vegetation transition, | 70 | in continuous pattern (e.g. map signatures, vegetation transition, | ||
75 | forest borders).\r\n* Development and application of methods to | 71 | forest borders).\r\n* Development and application of methods to | ||
76 | extract 3D-information from remotely sensed data for description of | 72 | extract 3D-information from remotely sensed data for description of | ||
77 | natural structures and changes. The main focus lies on wood and its | 73 | natural structures and changes. The main focus lies on wood and its | ||
78 | embedding/interaction within/with the landscape.\r\n* Conception and | 74 | embedding/interaction within/with the landscape.\r\n* Conception and | ||
79 | development of data acquisition based on high resolution remote | 75 | development of data acquisition based on high resolution remote | ||
80 | sensing data.\r\n* Conception, development and maintenance of the | 76 | sensing data.\r\n* Conception, development and maintenance of the | ||
81 | software interface in area wide data acquisition using airborne remote | 77 | software interface in area wide data acquisition using airborne remote | ||
82 | sensing data.\r\n* Scientific expert advice and support in the fields | 78 | sensing data.\r\n* Scientific expert advice and support in the fields | ||
83 | of photogrammetry and survey at WSL. Maintenance, enhancements and | 79 | of photogrammetry and survey at WSL. Maintenance, enhancements and | ||
84 | future development in these specific fields.\r\n* Adequate | 80 | future development in these specific fields.\r\n* Adequate | ||
85 | presentation of scientific results on national level and in noted | 81 | presentation of scientific results on national level and in noted | ||
86 | international journals and at international | 82 | international journals and at international | ||
87 | congresses/workshops/symposia.\r\n\r\n__Further information__: | 83 | congresses/workshops/symposia.\r\n\r\n__Further information__: | ||
88 | l/organization/research-units/landscape-dynamics/remote-sensing.html", | 84 | l/organization/research-units/landscape-dynamics/remote-sensing.html", | ||
89 | "id": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | 85 | "id": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | ||
90 | "image_url": "2018-07-10-102816.481589LogoWSL.svg", | 86 | "image_url": "2018-07-10-102816.481589LogoWSL.svg", | ||
91 | "is_organization": true, | 87 | "is_organization": true, | ||
92 | "name": "remote-sensing", | 88 | "name": "remote-sensing", | ||
93 | "state": "active", | 89 | "state": "active", | ||
94 | "title": "Remote Sensing", | 90 | "title": "Remote Sensing", | ||
95 | "type": "organization" | 91 | "type": "organization" | ||
96 | }, | 92 | }, | ||
97 | "owner_org": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | 93 | "owner_org": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | ||
98 | "private": false, | 94 | "private": false, | ||
99 | "publication": "{\"publication_year\": \"2021\", \"publisher\": | 95 | "publication": "{\"publication_year\": \"2021\", \"publisher\": | ||
100 | \"National Forest Inventory (NFI)\"}", | 96 | \"National Forest Inventory (NFI)\"}", | ||
101 | "publication_state": "published", | 97 | "publication_state": "published", | ||
102 | "related_datasets": "", | 98 | "related_datasets": "", | ||
103 | "related_publications": "", | 99 | "related_publications": "", | ||
104 | "relationships_as_object": [], | 100 | "relationships_as_object": [], | ||
105 | "relationships_as_subject": [], | 101 | "relationships_as_subject": [], | ||
106 | "resource_type": "dataset", | 102 | "resource_type": "dataset", | ||
107 | "resource_type_general": "dataset", | 103 | "resource_type_general": "dataset", | ||
108 | "resources": [ | 104 | "resources": [ | ||
109 | { | 105 | { | ||
110 | "cache_last_updated": null, | 106 | "cache_last_updated": null, | ||
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112 | "created": "2021-04-23T08:02:00.149511", | 108 | "created": "2021-04-23T08:02:00.149511", | ||
113 | "description": "This dataset presents a remote sensing-based | 109 | "description": "This dataset presents a remote sensing-based | ||
114 | approach for a countrywide mapping of the Dominant Leave Type (DLT) | 110 | approach for a countrywide mapping of the Dominant Leave Type (DLT) | ||
115 | with the two classes broadleaved and coniferous in Switzerland. The | 111 | with the two classes broadleaved and coniferous in Switzerland. The | ||
116 | spatial resolution is 10 m with the fraction of the class broadleaved. | 112 | spatial resolution is 10 m with the fraction of the class broadleaved. | ||
117 | The classification approach incorporates a random forest classifier, | 113 | The classification approach incorporates a random forest classifier, | ||
118 | explanatory variables from freely available Copernicus multispectral | 114 | explanatory variables from freely available Copernicus multispectral | ||
119 | Sentinel-2, multi-temporal Sentinel-1 data and a Digital Terrain Model | 115 | Sentinel-2, multi-temporal Sentinel-1 data and a Digital Terrain Model | ||
120 | (DTM) based on Airborne Laser Scanning (ALS) (SwissAlti3D) data. The | 116 | (DTM) based on Airborne Laser Scanning (ALS) (SwissAlti3D) data. The | ||
121 | models were calibrated using digitized training polygons and | 117 | models were calibrated using digitized training polygons and | ||
122 | independently validated data from the National Forest Inventory (NFI). | 118 | independently validated data from the National Forest Inventory (NFI). | ||
123 | Whereas high model overall accuracies (0.97) and kappa (0.96) were | 119 | Whereas high model overall accuracies (0.97) and kappa (0.96) were | ||
124 | achieved, the comparison of the tree type map with independent NFI | 120 | achieved, the comparison of the tree type map with independent NFI | ||
125 | data revealed deviations in mixed stands.\r\n\r\nData 'Forest Type NFI | 121 | data revealed deviations in mixed stands.\r\n\r\nData 'Forest Type NFI | ||
126 | 2018' available on request (lars.waser@wsl.ch).", | 122 | 2018' available on request (lars.waser@wsl.ch).", | ||
127 | "doi": "10.16904/1000001.6", | 123 | "doi": "10.16904/1000001.6", | ||
128 | "format": "GeoTIFF", | 124 | "format": "GeoTIFF", | ||
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149 | { | 145 | { | ||
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152 | "created": "2021-03-27T04:57:32.557727", | 148 | "created": "2021-03-27T04:57:32.557727", | ||
153 | "description": "Data 'Forest Type NFI 2016' available on request | 149 | "description": "Data 'Forest Type NFI 2016' available on request | ||
154 | (lars.waser@wsl.ch).", | 150 | (lars.waser@wsl.ch).", | ||
155 | "doi": "10.16904/1000001.3", | 151 | "doi": "10.16904/1000001.3", | ||
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189 | "name": "Wall-to-Wall Tree Type Mapping from Countrywide | 185 | "name": "Wall-to-Wall Tree Type Mapping from Countrywide | ||
190 | Airborne Remote Sensing Surveys", | 186 | Airborne Remote Sensing Surveys", | ||
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199 | "url": | 195 | "url": | ||
200 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14162", | 196 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14162", | ||
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219 | "resource_size": "", | 215 | "resource_size": "", | ||
220 | "resource_type": null, | 216 | "resource_type": null, | ||
221 | "restricted": "{\"allowed_users\": \"\", \"level\": | 217 | "restricted": "{\"allowed_users\": \"\", \"level\": | ||
222 | \"public\"}", | 218 | \"public\"}", | ||
223 | "size": 3072776, | 219 | "size": 3072776, | ||
224 | "state": "active", | 220 | "state": "active", | ||
225 | "url": | 221 | "url": | ||
226 | e4f37-c428-45dc-ba34-f40dd8368769/download/forest_type_thumbnail.jpg", | 222 | e4f37-c428-45dc-ba34-f40dd8368769/download/forest_type_thumbnail.jpg", | ||
227 | "url_type": "upload" | 223 | "url_type": "upload" | ||
228 | } | 224 | } | ||
229 | ], | 225 | ], | ||
230 | "spatial": | 226 | "spatial": | ||
231 | 80838],[10.49203,47.80838],[10.49203,45.81802],[5.95587,45.81802]]]}", | 227 | 80838],[10.49203,47.80838],[10.49203,45.81802],[5.95587,45.81802]]]}", | ||
232 | "spatial_info": "Switzerland", | 228 | "spatial_info": "Switzerland", | ||
233 | "state": "active", | 229 | "state": "active", | ||
234 | "subtitle": "", | 230 | "subtitle": "", | ||
235 | "tags": [ | 231 | "tags": [ | ||
236 | { | 232 | { | ||
237 | "display_name": "FOREST", | 233 | "display_name": "FOREST", | ||
238 | "id": "90cd0d8f-8df0-4b78-ac11-6e38c2a22106", | 234 | "id": "90cd0d8f-8df0-4b78-ac11-6e38c2a22106", | ||
239 | "name": "FOREST", | 235 | "name": "FOREST", | ||
240 | "state": "active", | 236 | "state": "active", | ||
241 | "vocabulary_id": null | 237 | "vocabulary_id": null | ||
242 | }, | 238 | }, | ||
243 | { | 239 | { | ||
244 | "display_name": "FOREST INVENTORY", | 240 | "display_name": "FOREST INVENTORY", | ||
245 | "id": "d1b2883f-be82-426b-8a5e-fd6d36bc2855", | 241 | "id": "d1b2883f-be82-426b-8a5e-fd6d36bc2855", | ||
246 | "name": "FOREST INVENTORY", | 242 | "name": "FOREST INVENTORY", | ||
247 | "state": "active", | 243 | "state": "active", | ||
248 | "vocabulary_id": null | 244 | "vocabulary_id": null | ||
249 | }, | 245 | }, | ||
250 | { | 246 | { | ||
251 | "display_name": "FOREST TYPE", | 247 | "display_name": "FOREST TYPE", | ||
252 | "id": "52ad8bb3-8ec8-4e9d-8798-858d7662c3e4", | 248 | "id": "52ad8bb3-8ec8-4e9d-8798-858d7662c3e4", | ||
253 | "name": "FOREST TYPE", | 249 | "name": "FOREST TYPE", | ||
254 | "state": "active", | 250 | "state": "active", | ||
255 | "vocabulary_id": null | 251 | "vocabulary_id": null | ||
256 | }, | 252 | }, | ||
257 | { | 253 | { | ||
258 | "display_name": "NFI", | 254 | "display_name": "NFI", | ||
259 | "id": "0a74eb64-cf7b-42df-b2d4-1a5ad8171458", | 255 | "id": "0a74eb64-cf7b-42df-b2d4-1a5ad8171458", | ||
260 | "name": "NFI", | 256 | "name": "NFI", | ||
261 | "state": "active", | 257 | "state": "active", | ||
262 | "vocabulary_id": null | 258 | "vocabulary_id": null | ||
263 | }, | 259 | }, | ||
264 | { | 260 | { | ||
265 | "display_name": "REMOTE SENSING", | 261 | "display_name": "REMOTE SENSING", | ||
266 | "id": "1bc9d51e-2500-44a7-857b-13710a59e4be", | 262 | "id": "1bc9d51e-2500-44a7-857b-13710a59e4be", | ||
267 | "name": "REMOTE SENSING", | 263 | "name": "REMOTE SENSING", | ||
268 | "state": "active", | 264 | "state": "active", | ||
269 | "vocabulary_id": null | 265 | "vocabulary_id": null | ||
270 | } | 266 | } | ||
271 | ], | 267 | ], | ||
272 | "title": "Forest Type NFI", | 268 | "title": "Forest Type NFI", | ||
273 | "type": "dataset", | 269 | "type": "dataset", | ||
274 | "url": null, | 270 | "url": null, | ||
275 | "version": "2018 (current)" | 271 | "version": "2018 (current)" | ||
276 | } | 272 | } |