Changes
On March 8, 2025 at 11:41:04 AM UTC,
-
Changed value of field
spatial
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in Forest Type NFI
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\"}, {\"affiliation\": \"WSL\", \"affiliation_02\": | 10 | \"name\": \"Ginzler\"}, {\"affiliation\": \"WSL\", \"affiliation_02\": | ||
11 | \"\", \"affiliation_03\": \"\", \"email\": | 11 | \"\", \"affiliation_03\": \"\", \"email\": | ||
12 | \"achilleas.psomas@wsl.ch\", \"given_name\": \"Achilleas\", | 12 | \"achilleas.psomas@wsl.ch\", \"given_name\": \"Achilleas\", | ||
13 | \"identifier\": \"\", \"name\": \"Psomas\"}, {\"affiliation\": | 13 | \"identifier\": \"\", \"name\": \"Psomas\"}, {\"affiliation\": | ||
14 | \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | 14 | \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | ||
15 | \"email\": \"marius.rueetschi@wsl.ch\", \"given_name\": \"Marius\", | 15 | \"email\": \"marius.rueetschi@wsl.ch\", \"given_name\": \"Marius\", | ||
16 | \"identifier\": \"\", \"name\": \"R\\u00fcetschi\"}, {\"affiliation\": | 16 | \"identifier\": \"\", \"name\": \"R\\u00fcetschi\"}, {\"affiliation\": | ||
17 | \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | 17 | \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | ||
18 | \"email\": \"nataliia.rehush@wsl.ch\", \"given_name\": \"Nataliia\", | 18 | \"email\": \"nataliia.rehush@wsl.ch\", \"given_name\": \"Nataliia\", | ||
19 | \"identifier\": \"\", \"name\": \"Rehush\"}]", | 19 | \"identifier\": \"\", \"name\": \"Rehush\"}]", | ||
20 | "author_email": null, | 20 | "author_email": null, | ||
21 | "creator_user_id": "6d44d5cd-9ac6-4100-bc2c-c02034a41b48", | 21 | "creator_user_id": "6d44d5cd-9ac6-4100-bc2c-c02034a41b48", | ||
22 | "date": "[{\"date\": \"2025-01-05\", \"date_type\": \"created\", | 22 | "date": "[{\"date\": \"2025-01-05\", \"date_type\": \"created\", | ||
23 | \"end_date\": \"\"}]", | 23 | \"end_date\": \"\"}]", | ||
24 | "doi": "10.16904/1000001.7", | 24 | "doi": "10.16904/1000001.7", | ||
25 | "extras": [ | 25 | "extras": [ | ||
26 | { | 26 | { | ||
27 | "key": "deprecatedResources", | 27 | "key": "deprecatedResources", | ||
28 | "value": "[\"0f22d071-5aac-4bfd-936d-89c0a56eed36\"]" | 28 | "value": "[\"0f22d071-5aac-4bfd-936d-89c0a56eed36\"]" | ||
29 | } | 29 | } | ||
30 | ], | 30 | ], | ||
31 | "funding": "[{\"grant_number\":\"\",\"institution\":\"Federal Office | 31 | "funding": "[{\"grant_number\":\"\",\"institution\":\"Federal Office | ||
32 | for the Environment (FOEN).\",\"institution_url\":\"\"}]", | 32 | for the Environment (FOEN).\",\"institution_url\":\"\"}]", | ||
33 | "groups": [], | 33 | "groups": [], | ||
34 | "id": "82d763fa-123a-4648-b827-0dec02a5efbc", | 34 | "id": "82d763fa-123a-4648-b827-0dec02a5efbc", | ||
35 | "isopen": false, | 35 | "isopen": false, | ||
36 | "language": "en", | 36 | "language": "en", | ||
37 | "license_id": "other-undefined", | 37 | "license_id": "other-undefined", | ||
38 | "license_title": "Other (Specified in the description)", | 38 | "license_title": "Other (Specified in the description)", | ||
39 | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | 39 | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | ||
40 | Forest, Snow and Landscape Research WSL \", \"email\": | 40 | Forest, Snow and Landscape Research WSL \", \"email\": | ||
41 | \"lars.waser@wsl.ch\", \"given_name\": \"Lars\", \"identifier\": | 41 | \"lars.waser@wsl.ch\", \"given_name\": \"Lars\", \"identifier\": | ||
42 | \"D-5937-2011\", \"name\": \"Waser\"}", | 42 | \"D-5937-2011\", \"name\": \"Waser\"}", | ||
43 | "maintainer_email": null, | 43 | "maintainer_email": null, | ||
44 | "metadata_created": "2018-04-03T10:38:30.545175", | 44 | "metadata_created": "2018-04-03T10:38:30.545175", | ||
n | 45 | "metadata_modified": "2025-03-07T07:15:15.288389", | n | 45 | "metadata_modified": "2025-03-08T11:41:04.585469", |
46 | "name": "forest-type-nfi", | 46 | "name": "forest-type-nfi", | ||
47 | "notes": "A series of Forest Type NFI datasets covering Switzerland | 47 | "notes": "A series of Forest Type NFI datasets covering Switzerland | ||
48 | have been produced, and currently available for the years 2023, 2018, | 48 | have been produced, and currently available for the years 2023, 2018, | ||
49 | and 2016. These datasets provide the fractional representation | 49 | and 2016. These datasets provide the fractional representation | ||
50 | (0-100%) of the class broadleaf at the pixel level. The three datasets | 50 | (0-100%) of the class broadleaf at the pixel level. The three datasets | ||
51 | are based on different remote sensing data from different time spans | 51 | are based on different remote sensing data from different time spans | ||
52 | and with different spatial resolutions:\n\n- **Forest Type NFI 2023: | 52 | and with different spatial resolutions:\n\n- **Forest Type NFI 2023: | ||
53 | Sentinel-1/-2, 2021-2023, 10m (new dataset!)** \n- Forest Type NFI | 53 | Sentinel-1/-2, 2021-2023, 10m (new dataset!)** \n- Forest Type NFI | ||
54 | 2018: Sentinel-1/-2, 2016-2018, 10m\n- Forest Type NFI 2016: Aerial | 54 | 2018: Sentinel-1/-2, 2016-2018, 10m\n- Forest Type NFI 2016: Aerial | ||
55 | orthoimages, 2010-2015, 3m\n.\n---------------------\n.\n **Important | 55 | orthoimages, 2010-2015, 3m\n.\n---------------------\n.\n **Important | ||
56 | note:** None of the datasets is suitable for analysis at the | 56 | note:** None of the datasets is suitable for analysis at the | ||
57 | individual tree crown level because pixel-level fractions (e.g., of | 57 | individual tree crown level because pixel-level fractions (e.g., of | ||
58 | the broadleaf class) are not allocated to individual trees. Analysis | 58 | the broadleaf class) are not allocated to individual trees. Analysis | ||
59 | is recommended only for areas larger than 3x3 pixels, i.e. by | 59 | is recommended only for areas larger than 3x3 pixels, i.e. by | ||
60 | calculating the mean values, except in rare cases of homogeneous | 60 | calculating the mean values, except in rare cases of homogeneous | ||
61 | forest stands (either broadleaf or coniferous class).\n\n**User\u2019s | 61 | forest stands (either broadleaf or coniferous class).\n\n**User\u2019s | ||
62 | feedback:** \nWe are very motivated to improve the dataset further and | 62 | feedback:** \nWe are very motivated to improve the dataset further and | ||
63 | welcome any feedback or suggestions for corrections. Feel free to | 63 | welcome any feedback or suggestions for corrections. Feel free to | ||
64 | directly contact | 64 | directly contact | ||
65 | **lars.waser@wsl.ch**\n_________________\n.\n*Detailed | 65 | **lars.waser@wsl.ch**\n_________________\n.\n*Detailed | ||
66 | description:*\n\n**Forest Type NFI datasets, versions 2023 and 2018:** | 66 | description:*\n\n**Forest Type NFI datasets, versions 2023 and 2018:** | ||
67 | Both datasets use a remote sensing-based approach for a countrywide | 67 | Both datasets use a remote sensing-based approach for a countrywide | ||
68 | mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying | 68 | mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying | ||
69 | areas as either broadleaf or coniferous. These datasets have a spatial | 69 | areas as either broadleaf or coniferous. These datasets have a spatial | ||
70 | resolution of 10 m and provide the fractional representation (0-100%) | 70 | resolution of 10 m and provide the fractional representation (0-100%) | ||
71 | of the class broadleaf at the pixel level (covering all areas with | 71 | of the class broadleaf at the pixel level (covering all areas with | ||
72 | vegetation height > 5m version 2018, and >3 m version 2023). The | 72 | vegetation height > 5m version 2018, and >3 m version 2023). The | ||
73 | classification approach is based on a Random Forest (RF) classifier, | 73 | classification approach is based on a Random Forest (RF) classifier, | ||
74 | that combines predictors derived from multi-temporal Sentinel-1 and | 74 | that combines predictors derived from multi-temporal Sentinel-1 and | ||
75 | Sentinel-2 data with the SwissAlti3D terrain model. In addition to the | 75 | Sentinel-2 data with the SwissAlti3D terrain model. In addition to the | ||
76 | original Sentinel-2 spectral bands, vegetation indices such as GEMI, | 76 | original Sentinel-2 spectral bands, vegetation indices such as GEMI, | ||
77 | MSAVI2, NDVI, CLRE and CCCI were used in the final model.\nThe | 77 | MSAVI2, NDVI, CLRE and CCCI were used in the final model.\nThe | ||
78 | classification models were tested, trained and validated using up to | 78 | classification models were tested, trained and validated using up to | ||
79 | 400,000 labels representing the two classes broadleaf and coniferous, | 79 | 400,000 labels representing the two classes broadleaf and coniferous, | ||
80 | derived from aerial image delineations. For the 2023 data set, the | 80 | derived from aerial image delineations. For the 2023 data set, the | ||
81 | previously used labels were quality-checked for reliability and | 81 | previously used labels were quality-checked for reliability and | ||
82 | temporal consistency with the image data. Additionally, more labels | 82 | temporal consistency with the image data. Additionally, more labels | ||
83 | were collected in the regions where the previous version of the data | 83 | were collected in the regions where the previous version of the data | ||
84 | set performed less accurately \u2013 as reported by the | 84 | set performed less accurately \u2013 as reported by the | ||
85 | users.\nSimilar high model performances were achieved for both data | 85 | users.\nSimilar high model performances were achieved for both data | ||
86 | sets: overall accuracy = 0.96, kappa = 0.94, F1-score =0.96, precision | 86 | sets: overall accuracy = 0.96, kappa = 0.94, F1-score =0.96, precision | ||
87 | = 0.97, recall = 0.94 (2023), 0.95 (2018). Independent validation and | 87 | = 0.97, recall = 0.94 (2023), 0.95 (2018). Independent validation and | ||
88 | plausibility check included the comparison of the predicted results | 88 | plausibility check included the comparison of the predicted results | ||
89 | with aerial image interpretations of the National Forest Inventory | 89 | with aerial image interpretations of the National Forest Inventory | ||
90 | (NFI). For both data sets, deviations particularly occurred in mixed | 90 | (NFI). For both data sets, deviations particularly occurred in mixed | ||
91 | forest stands: over all accuracy = 0.81 (2018, 2023), kappa = 0.62 | 91 | forest stands: over all accuracy = 0.81 (2018, 2023), kappa = 0.62 | ||
92 | (2018), 0.59 (2023). For more details, see Waser et al. | 92 | (2018), 0.59 (2023). For more details, see Waser et al. | ||
93 | (2021).\n\n**The Forest Type NFI dataset, version 2016:** This dataset | 93 | (2021).\n\n**The Forest Type NFI dataset, version 2016:** This dataset | ||
94 | presents a countrywide map with the two classes broadleaf and | 94 | presents a countrywide map with the two classes broadleaf and | ||
95 | coniferous in Switzerland based on digital aerial imagery. The spatial | 95 | coniferous in Switzerland based on digital aerial imagery. The spatial | ||
96 | resolution of the data set is 3 m. The pixel values correspond to the | 96 | resolution of the data set is 3 m. The pixel values correspond to the | ||
97 | fraction (0-100 %) of the class broadleaf.\nThe classification | 97 | fraction (0-100 %) of the class broadleaf.\nThe classification | ||
98 | approach incorporates a RF classifier, predictors from multispectral | 98 | approach incorporates a RF classifier, predictors from multispectral | ||
99 | aerial imagery (ADS80) and the SwissAlti3D terrain model. The model | 99 | aerial imagery (ADS80) and the SwissAlti3D terrain model. The model | ||
100 | was tested, trained and validated using 90,000 digitized polygons and | 100 | was tested, trained and validated using 90,000 digitized polygons and | ||
101 | achieved an overall accuracy of 0.99 and a kappa of 0.98. Independent | 101 | achieved an overall accuracy of 0.99 and a kappa of 0.98. Independent | ||
102 | validation and plausibility check included the comparison of the | 102 | validation and plausibility check included the comparison of the | ||
103 | predicted results with aerial image interpretations of the NFI. | 103 | predicted results with aerial image interpretations of the NFI. | ||
104 | Significant deviations were observed, primarily due to an | 104 | Significant deviations were observed, primarily due to an | ||
105 | underestimation of broadleaved trees (median underestimation of | 105 | underestimation of broadleaved trees (median underestimation of | ||
106 | 3.17%), especially in mixed forest stands. For more details, see Waser | 106 | 3.17%), especially in mixed forest stands. For more details, see Waser | ||
107 | et al. (2017; https://doi.org/10.3390/rs9080766).\n\n", | 107 | et al. (2017; https://doi.org/10.3390/rs9080766).\n\n", | ||
108 | "num_resources": 6, | 108 | "num_resources": 6, | ||
109 | "num_tags": 6, | 109 | "num_tags": 6, | ||
110 | "organization": { | 110 | "organization": { | ||
111 | "approval_status": "approved", | 111 | "approval_status": "approved", | ||
112 | "created": "2017-04-20T16:51:21.920128", | 112 | "created": "2017-04-20T16:51:21.920128", | ||
113 | "description": "We develop and apply comprehensive and robust | 113 | "description": "We develop and apply comprehensive and robust | ||
114 | methods to extract and classify natural objects from continuous and | 114 | methods to extract and classify natural objects from continuous and | ||
115 | discrete raster datasets. Relevant features are acquired to describe | 115 | discrete raster datasets. Relevant features are acquired to describe | ||
116 | changes in landscape and land resources at different levels using | 116 | changes in landscape and land resources at different levels using | ||
117 | image data. Mathematical-statistical methods are adopted for automatic | 117 | image data. Mathematical-statistical methods are adopted for automatic | ||
118 | detection and description of image objects. Thus we contribute | 118 | detection and description of image objects. Thus we contribute | ||
119 | concepts, methods and data to describe/detect area wide changes and | 119 | concepts, methods and data to describe/detect area wide changes and | ||
120 | processes in the resources of landscape.\r\n\r\n### Tasks and main | 120 | processes in the resources of landscape.\r\n\r\n### Tasks and main | ||
121 | research\r\n\r\n* Development and application of methods to extract | 121 | research\r\n\r\n* Development and application of methods to extract | ||
122 | natural objects from continuous data.\r\n* Development of methods for | 122 | natural objects from continuous data.\r\n* Development of methods for | ||
123 | a comprehensive description of natural and anthropogenetic boundaries | 123 | a comprehensive description of natural and anthropogenetic boundaries | ||
124 | in continuous pattern (e.g. map signatures, vegetation transition, | 124 | in continuous pattern (e.g. map signatures, vegetation transition, | ||
125 | forest borders).\r\n* Development and application of methods to | 125 | forest borders).\r\n* Development and application of methods to | ||
126 | extract 3D-information from remotely sensed data for description of | 126 | extract 3D-information from remotely sensed data for description of | ||
127 | natural structures and changes. The main focus lies on wood and its | 127 | natural structures and changes. The main focus lies on wood and its | ||
128 | embedding/interaction within/with the landscape.\r\n* Conception and | 128 | embedding/interaction within/with the landscape.\r\n* Conception and | ||
129 | development of data acquisition based on high resolution remote | 129 | development of data acquisition based on high resolution remote | ||
130 | sensing data.\r\n* Conception, development and maintenance of the | 130 | sensing data.\r\n* Conception, development and maintenance of the | ||
131 | software interface in area wide data acquisition using airborne remote | 131 | software interface in area wide data acquisition using airborne remote | ||
132 | sensing data.\r\n* Scientific expert advice and support in the fields | 132 | sensing data.\r\n* Scientific expert advice and support in the fields | ||
133 | of photogrammetry and survey at WSL. Maintenance, enhancements and | 133 | of photogrammetry and survey at WSL. Maintenance, enhancements and | ||
134 | future development in these specific fields.\r\n* Adequate | 134 | future development in these specific fields.\r\n* Adequate | ||
135 | presentation of scientific results on national level and in noted | 135 | presentation of scientific results on national level and in noted | ||
136 | international journals and at international | 136 | international journals and at international | ||
137 | congresses/workshops/symposia.\r\n\r\n__Further information__: | 137 | congresses/workshops/symposia.\r\n\r\n__Further information__: | ||
138 | l/organization/research-units/landscape-dynamics/remote-sensing.html", | 138 | l/organization/research-units/landscape-dynamics/remote-sensing.html", | ||
139 | "id": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | 139 | "id": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | ||
140 | "image_url": "", | 140 | "image_url": "", | ||
141 | "is_organization": true, | 141 | "is_organization": true, | ||
142 | "name": "remote-sensing", | 142 | "name": "remote-sensing", | ||
143 | "state": "active", | 143 | "state": "active", | ||
144 | "title": "Remote Sensing", | 144 | "title": "Remote Sensing", | ||
145 | "type": "organization" | 145 | "type": "organization" | ||
146 | }, | 146 | }, | ||
147 | "owner_org": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | 147 | "owner_org": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | ||
148 | "private": false, | 148 | "private": false, | ||
149 | "publication": "{\"publication_year\": \"2025\", \"publisher\": | 149 | "publication": "{\"publication_year\": \"2025\", \"publisher\": | ||
150 | \"National Forest Inventory (NFI)\"}", | 150 | \"National Forest Inventory (NFI)\"}", | ||
151 | "publication_state": "published", | 151 | "publication_state": "published", | ||
152 | "related_datasets": "", | 152 | "related_datasets": "", | ||
153 | "related_publications": "wsl:28243\nWaser, L. T., Ginzler, C., & | 153 | "related_publications": "wsl:28243\nWaser, L. T., Ginzler, C., & | ||
154 | Rehush, N. (2017). Wall-to-wall tree type mapping from countrywide | 154 | Rehush, N. (2017). Wall-to-wall tree type mapping from countrywide | ||
155 | airborne remote sensing surveys. Remote Sensing, 9(8), 766 (24 pp.). | 155 | airborne remote sensing surveys. Remote Sensing, 9(8), 766 (24 pp.). | ||
156 | https://doi.org/10.3390/rs9080766", | 156 | https://doi.org/10.3390/rs9080766", | ||
157 | "relationships_as_object": [], | 157 | "relationships_as_object": [], | ||
158 | "relationships_as_subject": [], | 158 | "relationships_as_subject": [], | ||
159 | "resource_type": "dataset", | 159 | "resource_type": "dataset", | ||
160 | "resource_type_general": "dataset", | 160 | "resource_type_general": "dataset", | ||
161 | "resources": [ | 161 | "resources": [ | ||
162 | { | 162 | { | ||
163 | "cache_last_updated": null, | 163 | "cache_last_updated": null, | ||
164 | "cache_url": null, | 164 | "cache_url": null, | ||
165 | "created": "2021-04-23T08:02:00.149511", | 165 | "created": "2021-04-23T08:02:00.149511", | ||
166 | "description": "\n **Important note:** This dataset is NOT | 166 | "description": "\n **Important note:** This dataset is NOT | ||
167 | suitable for analysis at the individual tree crown level because | 167 | suitable for analysis at the individual tree crown level because | ||
168 | pixel-level fractions (e.g., of the broadleaf class) are not allocated | 168 | pixel-level fractions (e.g., of the broadleaf class) are not allocated | ||
169 | to individual trees. Analysis is recommended only for areas larger | 169 | to individual trees. Analysis is recommended only for areas larger | ||
170 | than 3x3 pixels, i.e. by calculating the mean values, except in rare | 170 | than 3x3 pixels, i.e. by calculating the mean values, except in rare | ||
171 | cases of homogeneous forest stands (either broadleaf or coniferous | 171 | cases of homogeneous forest stands (either broadleaf or coniferous | ||
172 | class). \n\nThis dataset uses a remote sensing-based approach for a | 172 | class). \n\nThis dataset uses a remote sensing-based approach for a | ||
173 | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | 173 | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | ||
174 | classifying areas as either broadleaf or coniferous. These datasets | 174 | classifying areas as either broadleaf or coniferous. These datasets | ||
175 | have a spatial resolution of 10 m and provide the fractional | 175 | have a spatial resolution of 10 m and provide the fractional | ||
176 | representation (0-100%) of the class broadleaf at the pixel level | 176 | representation (0-100%) of the class broadleaf at the pixel level | ||
177 | (covering areas with vegetation height > 5m).\nThe classification | 177 | (covering areas with vegetation height > 5m).\nThe classification | ||
178 | approach is based on a Random Forest (RF) classifier, that combines | 178 | approach is based on a Random Forest (RF) classifier, that combines | ||
179 | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | 179 | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | ||
180 | with the SwissAlti3D terrain model. The models were calibrated using | 180 | with the SwissAlti3D terrain model. The models were calibrated using | ||
181 | digitized training polygons and independently validated data from the | 181 | digitized training polygons and independently validated data from the | ||
182 | National Forest Inventory (NFI). Whereas high model overall accuracies | 182 | National Forest Inventory (NFI). Whereas high model overall accuracies | ||
183 | (0.97) and kappa (0.96) were achieved, the comparison of the tree type | 183 | (0.97) and kappa (0.96) were achieved, the comparison of the tree type | ||
184 | map with independent NFI data revealed deviations in mixed | 184 | map with independent NFI data revealed deviations in mixed | ||
185 | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | 185 | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | ||
186 | (lars.waser@wsl.ch).\n\n", | 186 | (lars.waser@wsl.ch).\n\n", | ||
187 | "doi": "10.16904/1000001.6", | 187 | "doi": "10.16904/1000001.6", | ||
188 | "format": "geotiff", | 188 | "format": "geotiff", | ||
189 | "hash": "", | 189 | "hash": "", | ||
190 | "id": "5a18924a-ef5a-4af2-a391-b0056a0874bb", | 190 | "id": "5a18924a-ef5a-4af2-a391-b0056a0874bb", | ||
191 | "last_modified": "2025-03-07T08:14:12.880000", | 191 | "last_modified": "2025-03-07T08:14:12.880000", | ||
192 | "metadata_modified": "2025-03-07T07:14:13.227379", | 192 | "metadata_modified": "2025-03-07T07:14:13.227379", | ||
193 | "mimetype": null, | 193 | "mimetype": null, | ||
194 | "mimetype_inner": null, | 194 | "mimetype_inner": null, | ||
195 | "name": "Forest Type NFI 2018", | 195 | "name": "Forest Type NFI 2018", | ||
196 | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | 196 | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | ||
197 | "position": 0, | 197 | "position": 0, | ||
198 | "resource_size": "{\"size_value\":\"\",\"size_units\":\"\"}", | 198 | "resource_size": "{\"size_value\":\"\",\"size_units\":\"\"}", | ||
199 | "resource_type": null, | 199 | "resource_type": null, | ||
200 | "restricted": | 200 | "restricted": | ||
201 | \"same_organization\",\"allowed_users\":\"\",\"shared_secret\":\"\"}", | 201 | \"same_organization\",\"allowed_users\":\"\",\"shared_secret\":\"\"}", | ||
202 | "size": 191049626, | 202 | "size": 191049626, | ||
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205 | 18924a-ef5a-4af2-a391-b0056a0874bb/download/forest-type-nfi-2018.tif", | 205 | 18924a-ef5a-4af2-a391-b0056a0874bb/download/forest-type-nfi-2018.tif", | ||
206 | "url_type": "upload" | 206 | "url_type": "upload" | ||
207 | }, | 207 | }, | ||
208 | { | 208 | { | ||
209 | "cache_last_updated": null, | 209 | "cache_last_updated": null, | ||
210 | "cache_url": null, | 210 | "cache_url": null, | ||
211 | "created": "2021-03-27T04:57:32.557727", | 211 | "created": "2021-03-27T04:57:32.557727", | ||
212 | "description": "This dataset presents a countrywide map with the | 212 | "description": "This dataset presents a countrywide map with the | ||
213 | two classes broadleaf and coniferous in Switzerland based on digital | 213 | two classes broadleaf and coniferous in Switzerland based on digital | ||
214 | aerial imagery. The spatial resolution of the data set is 3 m. The | 214 | aerial imagery. The spatial resolution of the data set is 3 m. The | ||
215 | pixel values correspond to the fraction (0-100 %) of the class | 215 | pixel values correspond to the fraction (0-100 %) of the class | ||
216 | broadleaf.\nThe classification approach incorporates a RF classifier, | 216 | broadleaf.\nThe classification approach incorporates a RF classifier, | ||
217 | predictors from multispectral aerial imagery (ADS80) and the | 217 | predictors from multispectral aerial imagery (ADS80) and the | ||
218 | SwissAlti3D terrain model. The model was tested, trained and validated | 218 | SwissAlti3D terrain model. The model was tested, trained and validated | ||
219 | using 90,000 digitized polygons and achieved an overall accuracy of | 219 | using 90,000 digitized polygons and achieved an overall accuracy of | ||
220 | 0.99 and a kappa of 0.98. Independent validation and plausibility | 220 | 0.99 and a kappa of 0.98. Independent validation and plausibility | ||
221 | check included the comparison of the predicted results with aerial | 221 | check included the comparison of the predicted results with aerial | ||
222 | image interpretations of the NFI. Significant deviations were | 222 | image interpretations of the NFI. Significant deviations were | ||
223 | observed, primarily due to an underestimation of broadleaved trees | 223 | observed, primarily due to an underestimation of broadleaved trees | ||
224 | (median underestimation of 3.17%), especially in mixed forest stands. | 224 | (median underestimation of 3.17%), especially in mixed forest stands. | ||
225 | For more details, see Waser et al. (2017).\n\nData 'Forest Type NFI | 225 | For more details, see Waser et al. (2017).\n\nData 'Forest Type NFI | ||
226 | 2016' available on request (lars.waser@wsl.ch).", | 226 | 2016' available on request (lars.waser@wsl.ch).", | ||
227 | "doi": "10.16904/1000001.3", | 227 | "doi": "10.16904/1000001.3", | ||
228 | "format": "geotiff", | 228 | "format": "geotiff", | ||
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250 | "created": "2018-04-03T10:42:10.760550", | 250 | "created": "2018-04-03T10:42:10.760550", | ||
251 | "description": "Waser, L. T., Ginzler, C., & Rehush, N. (2017). | 251 | "description": "Waser, L. T., Ginzler, C., & Rehush, N. (2017). | ||
252 | Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote | 252 | Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote | ||
253 | Sensing Surveys. Remote Sensing, 9(8), 766. | 253 | Sensing Surveys. Remote Sensing, 9(8), 766. | ||
254 | https://doi.org/10.3390/rs9080766", | 254 | https://doi.org/10.3390/rs9080766", | ||
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259 | "last_modified": "2025-03-04T23:05:32.264000", | 259 | "last_modified": "2025-03-04T23:05:32.264000", | ||
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266 | "resource_size": | 266 | "resource_size": | ||
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271 | "size": null, | 271 | "size": null, | ||
272 | "state": "active", | 272 | "state": "active", | ||
273 | "url": | 273 | "url": | ||
274 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14162", | 274 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14162", | ||
275 | "url_type": null | 275 | "url_type": null | ||
276 | }, | 276 | }, | ||
277 | { | 277 | { | ||
278 | "cache_last_updated": null, | 278 | "cache_last_updated": null, | ||
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280 | "created": "2018-04-03T13:06:34.033220", | 280 | "created": "2018-04-03T13:06:34.033220", | ||
281 | "description": "Sample illustration of the Forest Type NFI | 281 | "description": "Sample illustration of the Forest Type NFI | ||
282 | dataset.", | 282 | dataset.", | ||
283 | "doi": "", | 283 | "doi": "", | ||
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299 | "state": "active", | 299 | "state": "active", | ||
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302 | "url_type": "upload" | 302 | "url_type": "upload" | ||
303 | }, | 303 | }, | ||
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305 | "cache_last_updated": null, | 305 | "cache_last_updated": null, | ||
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307 | "created": "2025-03-04T22:11:00.261394", | 307 | "created": "2025-03-04T22:11:00.261394", | ||
308 | "description": "Waser, L. T., R\u00fcetschi, M., Psomas, A., | 308 | "description": "Waser, L. T., R\u00fcetschi, M., Psomas, A., | ||
309 | Small, D., & Rehush, N. (2021). Mapping dominant leaf type based on | 309 | Small, D., & Rehush, N. (2021). Mapping dominant leaf type based on | ||
310 | combined Sentinel-1/-2 data \u2013 Challenges for mountainous | 310 | combined Sentinel-1/-2 data \u2013 Challenges for mountainous | ||
311 | countries. ISPRS Journal of Photogrammetry and Remote Sensing, 180, | 311 | countries. ISPRS Journal of Photogrammetry and Remote Sensing, 180, | ||
312 | 209-226. https://doi.org/10.1016/j.isprsjprs.2021.08.017", | 312 | 209-226. https://doi.org/10.1016/j.isprsjprs.2021.08.017", | ||
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317 | "last_modified": "2025-03-04T23:13:18.542000", | 317 | "last_modified": "2025-03-04T23:13:18.542000", | ||
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329 | "size": null, | 329 | "size": null, | ||
330 | "state": "active", | 330 | "state": "active", | ||
331 | "url": | 331 | "url": | ||
332 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:28243", | 332 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:28243", | ||
333 | "url_type": null | 333 | "url_type": null | ||
334 | }, | 334 | }, | ||
335 | { | 335 | { | ||
336 | "cache_last_updated": null, | 336 | "cache_last_updated": null, | ||
337 | "cache_url": null, | 337 | "cache_url": null, | ||
338 | "created": "2025-03-06T13:23:45.237260", | 338 | "created": "2025-03-06T13:23:45.237260", | ||
339 | "description": "\n**Important note:** This dataset is NOT | 339 | "description": "\n**Important note:** This dataset is NOT | ||
340 | suitable for analysis at the individual tree crown level because | 340 | suitable for analysis at the individual tree crown level because | ||
341 | pixel-level fractions (e.g., of the broadleaf class) are not allocated | 341 | pixel-level fractions (e.g., of the broadleaf class) are not allocated | ||
342 | to individual trees. Analysis is recommended only for areas larger | 342 | to individual trees. Analysis is recommended only for areas larger | ||
343 | than 3x3 pixels, i.e. by calculating the mean values, except in rare | 343 | than 3x3 pixels, i.e. by calculating the mean values, except in rare | ||
344 | cases of homogeneous forest stands (either broadleaf or coniferous | 344 | cases of homogeneous forest stands (either broadleaf or coniferous | ||
345 | class).\n\nThis dataset uses a remote sensing-based approach for a | 345 | class).\n\nThis dataset uses a remote sensing-based approach for a | ||
346 | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | 346 | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | ||
347 | classifying areas as either broadleaf or coniferous. These datasets | 347 | classifying areas as either broadleaf or coniferous. These datasets | ||
348 | have a spatial resolution of 10 m and provide the fractional | 348 | have a spatial resolution of 10 m and provide the fractional | ||
349 | representation (0-100%) of the class broadleaf at the pixel level | 349 | representation (0-100%) of the class broadleaf at the pixel level | ||
350 | (covering areas with vegetation height > 3m).\nThe classification | 350 | (covering areas with vegetation height > 3m).\nThe classification | ||
351 | approach is based on a Random Forest (RF) classifier, that combines | 351 | approach is based on a Random Forest (RF) classifier, that combines | ||
352 | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | 352 | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | ||
353 | with the SwissAlti3D terrain model. The models were calibrated using | 353 | with the SwissAlti3D terrain model. The models were calibrated using | ||
354 | digitized training polygons and independently validated data from the | 354 | digitized training polygons and independently validated data from the | ||
355 | National Forest Inventory (NFI). Whereas high model overall accuracies | 355 | National Forest Inventory (NFI). Whereas high model overall accuracies | ||
356 | (0.97) and kappa (0.96) were achieved, the comparison of the tree type | 356 | (0.97) and kappa (0.96) were achieved, the comparison of the tree type | ||
357 | map with independent NFI data revealed deviations in mixed | 357 | map with independent NFI data revealed deviations in mixed | ||
358 | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | 358 | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | ||
359 | (lars.waser@wsl.ch).", | 359 | (lars.waser@wsl.ch).", | ||
360 | "doi": "", | 360 | "doi": "", | ||
361 | "format": "geotif", | 361 | "format": "geotif", | ||
362 | "hash": "", | 362 | "hash": "", | ||
363 | "id": "884141d9-164a-4088-bc3a-5b4d4bb53d8f", | 363 | "id": "884141d9-164a-4088-bc3a-5b4d4bb53d8f", | ||
364 | "last_modified": "2025-03-07T08:15:14.960000", | 364 | "last_modified": "2025-03-07T08:15:14.960000", | ||
365 | "metadata_modified": "2025-03-07T07:15:15.294902", | 365 | "metadata_modified": "2025-03-07T07:15:15.294902", | ||
366 | "mimetype": "image/tiff", | 366 | "mimetype": "image/tiff", | ||
367 | "mimetype_inner": null, | 367 | "mimetype_inner": null, | ||
368 | "name": "Forest Type NFI 2023", | 368 | "name": "Forest Type NFI 2023", | ||
369 | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | 369 | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | ||
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374 | \"same_organization\",\"allowed_users\":\"\",\"shared_secret\":\"\"}", | 374 | \"same_organization\",\"allowed_users\":\"\",\"shared_secret\":\"\"}", | ||
375 | "size": 3329049504, | 375 | "size": 3329049504, | ||
376 | "state": "active", | 376 | "state": "active", | ||
377 | "url": | 377 | "url": | ||
378 | -4088-bc3a-5b4d4bb53d8f/download/waldmischungsgrad_2023_10m_2056.tif", | 378 | -4088-bc3a-5b4d4bb53d8f/download/waldmischungsgrad_2023_10m_2056.tif", | ||
379 | "url_type": "upload" | 379 | "url_type": "upload" | ||
380 | } | 380 | } | ||
381 | ], | 381 | ], | ||
382 | "spatial": | 382 | "spatial": | ||
t | 383 | 80838],[10.49203,47.80838],[10.49203,45.81802],[5.95587,45.81802]]]}", | t | 383 | inates\":[[[-175,-85],[-175,85],[175,85],[175,-85],[-175,-85]]]}]}]}", |
384 | "spatial_info": "Switzerland", | 384 | "spatial_info": "Switzerland", | ||
385 | "state": "active", | 385 | "state": "active", | ||
386 | "subtitle": "", | 386 | "subtitle": "", | ||
387 | "tags": [ | 387 | "tags": [ | ||
388 | { | 388 | { | ||
389 | "display_name": "DOMINANT LEAFTYPE", | 389 | "display_name": "DOMINANT LEAFTYPE", | ||
390 | "id": "1b334234-4bd0-44d4-bb78-039fe70bab90", | 390 | "id": "1b334234-4bd0-44d4-bb78-039fe70bab90", | ||
391 | "name": "DOMINANT LEAFTYPE", | 391 | "name": "DOMINANT LEAFTYPE", | ||
392 | "state": "active", | 392 | "state": "active", | ||
393 | "vocabulary_id": null | 393 | "vocabulary_id": null | ||
394 | }, | 394 | }, | ||
395 | { | 395 | { | ||
396 | "display_name": "FOREST", | 396 | "display_name": "FOREST", | ||
397 | "id": "90cd0d8f-8df0-4b78-ac11-6e38c2a22106", | 397 | "id": "90cd0d8f-8df0-4b78-ac11-6e38c2a22106", | ||
398 | "name": "FOREST", | 398 | "name": "FOREST", | ||
399 | "state": "active", | 399 | "state": "active", | ||
400 | "vocabulary_id": null | 400 | "vocabulary_id": null | ||
401 | }, | 401 | }, | ||
402 | { | 402 | { | ||
403 | "display_name": "FOREST INVENTORY", | 403 | "display_name": "FOREST INVENTORY", | ||
404 | "id": "d1b2883f-be82-426b-8a5e-fd6d36bc2855", | 404 | "id": "d1b2883f-be82-426b-8a5e-fd6d36bc2855", | ||
405 | "name": "FOREST INVENTORY", | 405 | "name": "FOREST INVENTORY", | ||
406 | "state": "active", | 406 | "state": "active", | ||
407 | "vocabulary_id": null | 407 | "vocabulary_id": null | ||
408 | }, | 408 | }, | ||
409 | { | 409 | { | ||
410 | "display_name": "FOREST TYPE", | 410 | "display_name": "FOREST TYPE", | ||
411 | "id": "52ad8bb3-8ec8-4e9d-8798-858d7662c3e4", | 411 | "id": "52ad8bb3-8ec8-4e9d-8798-858d7662c3e4", | ||
412 | "name": "FOREST TYPE", | 412 | "name": "FOREST TYPE", | ||
413 | "state": "active", | 413 | "state": "active", | ||
414 | "vocabulary_id": null | 414 | "vocabulary_id": null | ||
415 | }, | 415 | }, | ||
416 | { | 416 | { | ||
417 | "display_name": "NFI", | 417 | "display_name": "NFI", | ||
418 | "id": "0a74eb64-cf7b-42df-b2d4-1a5ad8171458", | 418 | "id": "0a74eb64-cf7b-42df-b2d4-1a5ad8171458", | ||
419 | "name": "NFI", | 419 | "name": "NFI", | ||
420 | "state": "active", | 420 | "state": "active", | ||
421 | "vocabulary_id": null | 421 | "vocabulary_id": null | ||
422 | }, | 422 | }, | ||
423 | { | 423 | { | ||
424 | "display_name": "REMOTE SENSING", | 424 | "display_name": "REMOTE SENSING", | ||
425 | "id": "1bc9d51e-2500-44a7-857b-13710a59e4be", | 425 | "id": "1bc9d51e-2500-44a7-857b-13710a59e4be", | ||
426 | "name": "REMOTE SENSING", | 426 | "name": "REMOTE SENSING", | ||
427 | "state": "active", | 427 | "state": "active", | ||
428 | "vocabulary_id": null | 428 | "vocabulary_id": null | ||
429 | } | 429 | } | ||
430 | ], | 430 | ], | ||
431 | "title": "Forest Type NFI", | 431 | "title": "Forest Type NFI", | ||
432 | "type": "dataset", | 432 | "type": "dataset", | ||
433 | "url": null, | 433 | "url": null, | ||
434 | "version": "2023" | 434 | "version": "2023" | ||
435 | } | 435 | } |