Changes
On March 9, 2025 at 9:37:56 PM UTC,
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Renamed resource ForestType 2023.JPG to Dataset preview in Forest Type NFI
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Updated description of resource Dataset preview in Forest Type NFI to
Preview of the dataset Forest Type NFI 2023.
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Changed value of field
resource_size
of resource Dataset preview to{"size_value":"","size_units":""}
(previously{"size_value":"","size_units":"kb"}
) 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": | 28 | "value": | ||
29 | ac-4bfd-936d-89c0a56eed36\",\"7b6e4f37-c428-45dc-ba34-f40dd8368769\"]" | 29 | ac-4bfd-936d-89c0a56eed36\",\"7b6e4f37-c428-45dc-ba34-f40dd8368769\"]" | ||
30 | } | 30 | } | ||
31 | ], | 31 | ], | ||
32 | "funding": "[{\"grant_number\":\"\",\"institution\":\"Federal Office | 32 | "funding": "[{\"grant_number\":\"\",\"institution\":\"Federal Office | ||
33 | for the Environment (FOEN).\",\"institution_url\":\"\"}]", | 33 | for the Environment (FOEN).\",\"institution_url\":\"\"}]", | ||
34 | "groups": [], | 34 | "groups": [], | ||
35 | "id": "82d763fa-123a-4648-b827-0dec02a5efbc", | 35 | "id": "82d763fa-123a-4648-b827-0dec02a5efbc", | ||
36 | "isopen": false, | 36 | "isopen": false, | ||
37 | "language": "en", | 37 | "language": "en", | ||
38 | "license_id": "other-undefined", | 38 | "license_id": "other-undefined", | ||
39 | "license_title": "Other (Specified in the description)", | 39 | "license_title": "Other (Specified in the description)", | ||
40 | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | 40 | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | ||
41 | Forest, Snow and Landscape Research WSL \", \"email\": | 41 | Forest, Snow and Landscape Research WSL \", \"email\": | ||
42 | \"lars.waser@wsl.ch\", \"given_name\": \"Lars\", \"identifier\": | 42 | \"lars.waser@wsl.ch\", \"given_name\": \"Lars\", \"identifier\": | ||
43 | \"D-5937-2011\", \"name\": \"Waser\"}", | 43 | \"D-5937-2011\", \"name\": \"Waser\"}", | ||
44 | "maintainer_email": null, | 44 | "maintainer_email": null, | ||
45 | "metadata_created": "2018-04-03T10:38:30.545175", | 45 | "metadata_created": "2018-04-03T10:38:30.545175", | ||
n | 46 | "metadata_modified": "2025-03-09T21:36:32.026583", | n | 46 | "metadata_modified": "2025-03-09T21:37:56.733697", |
47 | "name": "forest-type-nfi", | 47 | "name": "forest-type-nfi", | ||
48 | "notes": "A series of Forest Type NFI datasets covering Switzerland | 48 | "notes": "A series of Forest Type NFI datasets covering Switzerland | ||
49 | have been produced, and currently available for the years 2023, 2018, | 49 | have been produced, and currently available for the years 2023, 2018, | ||
50 | and 2016. These datasets provide the fractional representation | 50 | and 2016. These datasets provide the fractional representation | ||
51 | (0-100%) of the class broadleaf at the pixel level. The three datasets | 51 | (0-100%) of the class broadleaf at the pixel level. The three datasets | ||
52 | are based on different remote sensing data from different time spans | 52 | are based on different remote sensing data from different time spans | ||
53 | and with different spatial resolutions:\n\n- **Forest Type NFI 2023: | 53 | and with different spatial resolutions:\n\n- **Forest Type NFI 2023: | ||
54 | Sentinel-1/-2, 2021-2023, 10m (new dataset!)** \n- Forest Type NFI | 54 | Sentinel-1/-2, 2021-2023, 10m (new dataset!)** \n- Forest Type NFI | ||
55 | 2018: Sentinel-1/-2, 2016-2018, 10m\n- Forest Type NFI 2016: Aerial | 55 | 2018: Sentinel-1/-2, 2016-2018, 10m\n- Forest Type NFI 2016: Aerial | ||
56 | orthoimages, 2010-2015, 3m\n.\n---------------------\n.\n **Important | 56 | orthoimages, 2010-2015, 3m\n.\n---------------------\n.\n **Important | ||
57 | note:** None of the datasets is suitable for analysis at the | 57 | note:** None of the datasets is suitable for analysis at the | ||
58 | individual tree crown level because pixel-level fractions (e.g., of | 58 | individual tree crown level because pixel-level fractions (e.g., of | ||
59 | the broadleaf class) are not allocated to individual trees. Analysis | 59 | the broadleaf class) are not allocated to individual trees. Analysis | ||
60 | is recommended only for areas larger than 3x3 pixels, i.e. by | 60 | is recommended only for areas larger than 3x3 pixels, i.e. by | ||
61 | calculating the mean values, except in rare cases of homogeneous | 61 | calculating the mean values, except in rare cases of homogeneous | ||
62 | forest stands (either broadleaf or coniferous class).\n\n**User\u2019s | 62 | forest stands (either broadleaf or coniferous class).\n\n**User\u2019s | ||
63 | feedback:** \nWe are very motivated to improve the dataset further and | 63 | feedback:** \nWe are very motivated to improve the dataset further and | ||
64 | welcome any feedback or suggestions for corrections. Feel free to | 64 | welcome any feedback or suggestions for corrections. Feel free to | ||
65 | directly contact | 65 | directly contact | ||
66 | **lars.waser@wsl.ch**\n_________________\n.\n*Detailed | 66 | **lars.waser@wsl.ch**\n_________________\n.\n*Detailed | ||
67 | description:*\n\n**Forest Type NFI datasets, versions 2023 and 2018:** | 67 | description:*\n\n**Forest Type NFI datasets, versions 2023 and 2018:** | ||
68 | Both datasets use a remote sensing-based approach for a countrywide | 68 | Both datasets use a remote sensing-based approach for a countrywide | ||
69 | mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying | 69 | mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying | ||
70 | areas as either broadleaf or coniferous. These datasets have a spatial | 70 | areas as either broadleaf or coniferous. These datasets have a spatial | ||
71 | resolution of 10 m and provide the fractional representation (0-100%) | 71 | resolution of 10 m and provide the fractional representation (0-100%) | ||
72 | of the class broadleaf at the pixel level (covering all areas with | 72 | of the class broadleaf at the pixel level (covering all areas with | ||
73 | vegetation height > 5m version 2018, and >3 m version 2023). The | 73 | vegetation height > 5m version 2018, and >3 m version 2023). The | ||
74 | classification approach is based on a Random Forest (RF) classifier, | 74 | classification approach is based on a Random Forest (RF) classifier, | ||
75 | that combines predictors derived from multi-temporal Sentinel-1 and | 75 | that combines predictors derived from multi-temporal Sentinel-1 and | ||
76 | Sentinel-2 data with the SwissAlti3D terrain model. In addition to the | 76 | Sentinel-2 data with the SwissAlti3D terrain model. In addition to the | ||
77 | original Sentinel-2 spectral bands, vegetation indices such as GEMI, | 77 | original Sentinel-2 spectral bands, vegetation indices such as GEMI, | ||
78 | MSAVI2, NDVI, CLRE and CCCI were used in the final model.\nThe | 78 | MSAVI2, NDVI, CLRE and CCCI were used in the final model.\nThe | ||
79 | classification models were tested, trained and validated using up to | 79 | classification models were tested, trained and validated using up to | ||
80 | 400,000 labels representing the two classes broadleaf and coniferous, | 80 | 400,000 labels representing the two classes broadleaf and coniferous, | ||
81 | derived from aerial image delineations. For the 2023 data set, the | 81 | derived from aerial image delineations. For the 2023 data set, the | ||
82 | previously used labels were quality-checked for reliability and | 82 | previously used labels were quality-checked for reliability and | ||
83 | temporal consistency with the image data. Additionally, more labels | 83 | temporal consistency with the image data. Additionally, more labels | ||
84 | were collected in the regions where the previous version of the data | 84 | were collected in the regions where the previous version of the data | ||
85 | set performed less accurately \u2013 as reported by the | 85 | set performed less accurately \u2013 as reported by the | ||
86 | users.\nSimilar high model performances were achieved for both data | 86 | users.\nSimilar high model performances were achieved for both data | ||
87 | sets: overall accuracy = 0.96, kappa = 0.94, F1-score =0.96, precision | 87 | sets: overall accuracy = 0.96, kappa = 0.94, F1-score =0.96, precision | ||
88 | = 0.97, recall = 0.94 (2023), 0.95 (2018). Independent validation and | 88 | = 0.97, recall = 0.94 (2023), 0.95 (2018). Independent validation and | ||
89 | plausibility check included the comparison of the predicted results | 89 | plausibility check included the comparison of the predicted results | ||
90 | with aerial image interpretations of the National Forest Inventory | 90 | with aerial image interpretations of the National Forest Inventory | ||
91 | (NFI). For both data sets, deviations particularly occurred in mixed | 91 | (NFI). For both data sets, deviations particularly occurred in mixed | ||
92 | forest stands: over all accuracy = 0.81 (2018, 2023), kappa = 0.62 | 92 | forest stands: over all accuracy = 0.81 (2018, 2023), kappa = 0.62 | ||
93 | (2018), 0.59 (2023). For more details, see Waser et al. | 93 | (2018), 0.59 (2023). For more details, see Waser et al. | ||
94 | (2021).\n\n**The Forest Type NFI dataset, version 2016:** This dataset | 94 | (2021).\n\n**The Forest Type NFI dataset, version 2016:** This dataset | ||
95 | presents a countrywide map with the two classes broadleaf and | 95 | presents a countrywide map with the two classes broadleaf and | ||
96 | coniferous in Switzerland based on digital aerial imagery. The spatial | 96 | coniferous in Switzerland based on digital aerial imagery. The spatial | ||
97 | resolution of the data set is 3 m. The pixel values correspond to the | 97 | resolution of the data set is 3 m. The pixel values correspond to the | ||
98 | fraction (0-100 %) of the class broadleaf.\nThe classification | 98 | fraction (0-100 %) of the class broadleaf.\nThe classification | ||
99 | approach incorporates a RF classifier, predictors from multispectral | 99 | approach incorporates a RF classifier, predictors from multispectral | ||
100 | aerial imagery (ADS80) and the SwissAlti3D terrain model. The model | 100 | aerial imagery (ADS80) and the SwissAlti3D terrain model. The model | ||
101 | was tested, trained and validated using 90,000 digitized polygons and | 101 | was tested, trained and validated using 90,000 digitized polygons and | ||
102 | achieved an overall accuracy of 0.99 and a kappa of 0.98. Independent | 102 | achieved an overall accuracy of 0.99 and a kappa of 0.98. Independent | ||
103 | validation and plausibility check included the comparison of the | 103 | validation and plausibility check included the comparison of the | ||
104 | predicted results with aerial image interpretations of the NFI. | 104 | predicted results with aerial image interpretations of the NFI. | ||
105 | Significant deviations were observed, primarily due to an | 105 | Significant deviations were observed, primarily due to an | ||
106 | underestimation of broadleaved trees (median underestimation of | 106 | underestimation of broadleaved trees (median underestimation of | ||
107 | 3.17%), especially in mixed forest stands. For more details, see Waser | 107 | 3.17%), especially in mixed forest stands. For more details, see Waser | ||
108 | et al. (2017; https://doi.org/10.3390/rs9080766).\n\n", | 108 | et al. (2017; https://doi.org/10.3390/rs9080766).\n\n", | ||
109 | "num_resources": 7, | 109 | "num_resources": 7, | ||
110 | "num_tags": 6, | 110 | "num_tags": 6, | ||
111 | "organization": { | 111 | "organization": { | ||
112 | "approval_status": "approved", | 112 | "approval_status": "approved", | ||
113 | "created": "2017-04-20T16:51:21.920128", | 113 | "created": "2017-04-20T16:51:21.920128", | ||
114 | "description": "We develop and apply comprehensive and robust | 114 | "description": "We develop and apply comprehensive and robust | ||
115 | methods to extract and classify natural objects from continuous and | 115 | methods to extract and classify natural objects from continuous and | ||
116 | discrete raster datasets. Relevant features are acquired to describe | 116 | discrete raster datasets. Relevant features are acquired to describe | ||
117 | changes in landscape and land resources at different levels using | 117 | changes in landscape and land resources at different levels using | ||
118 | image data. Mathematical-statistical methods are adopted for automatic | 118 | image data. Mathematical-statistical methods are adopted for automatic | ||
119 | detection and description of image objects. Thus we contribute | 119 | detection and description of image objects. Thus we contribute | ||
120 | concepts, methods and data to describe/detect area wide changes and | 120 | concepts, methods and data to describe/detect area wide changes and | ||
121 | processes in the resources of landscape.\r\n\r\n### Tasks and main | 121 | processes in the resources of landscape.\r\n\r\n### Tasks and main | ||
122 | research\r\n\r\n* Development and application of methods to extract | 122 | research\r\n\r\n* Development and application of methods to extract | ||
123 | natural objects from continuous data.\r\n* Development of methods for | 123 | natural objects from continuous data.\r\n* Development of methods for | ||
124 | a comprehensive description of natural and anthropogenetic boundaries | 124 | a comprehensive description of natural and anthropogenetic boundaries | ||
125 | in continuous pattern (e.g. map signatures, vegetation transition, | 125 | in continuous pattern (e.g. map signatures, vegetation transition, | ||
126 | forest borders).\r\n* Development and application of methods to | 126 | forest borders).\r\n* Development and application of methods to | ||
127 | extract 3D-information from remotely sensed data for description of | 127 | extract 3D-information from remotely sensed data for description of | ||
128 | natural structures and changes. The main focus lies on wood and its | 128 | natural structures and changes. The main focus lies on wood and its | ||
129 | embedding/interaction within/with the landscape.\r\n* Conception and | 129 | embedding/interaction within/with the landscape.\r\n* Conception and | ||
130 | development of data acquisition based on high resolution remote | 130 | development of data acquisition based on high resolution remote | ||
131 | sensing data.\r\n* Conception, development and maintenance of the | 131 | sensing data.\r\n* Conception, development and maintenance of the | ||
132 | software interface in area wide data acquisition using airborne remote | 132 | software interface in area wide data acquisition using airborne remote | ||
133 | sensing data.\r\n* Scientific expert advice and support in the fields | 133 | sensing data.\r\n* Scientific expert advice and support in the fields | ||
134 | of photogrammetry and survey at WSL. Maintenance, enhancements and | 134 | of photogrammetry and survey at WSL. Maintenance, enhancements and | ||
135 | future development in these specific fields.\r\n* Adequate | 135 | future development in these specific fields.\r\n* Adequate | ||
136 | presentation of scientific results on national level and in noted | 136 | presentation of scientific results on national level and in noted | ||
137 | international journals and at international | 137 | international journals and at international | ||
138 | congresses/workshops/symposia.\r\n\r\n__Further information__: | 138 | congresses/workshops/symposia.\r\n\r\n__Further information__: | ||
139 | l/organization/research-units/landscape-dynamics/remote-sensing.html", | 139 | l/organization/research-units/landscape-dynamics/remote-sensing.html", | ||
140 | "id": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | 140 | "id": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | ||
141 | "image_url": "", | 141 | "image_url": "", | ||
142 | "is_organization": true, | 142 | "is_organization": true, | ||
143 | "name": "remote-sensing", | 143 | "name": "remote-sensing", | ||
144 | "state": "active", | 144 | "state": "active", | ||
145 | "title": "Remote Sensing", | 145 | "title": "Remote Sensing", | ||
146 | "type": "organization" | 146 | "type": "organization" | ||
147 | }, | 147 | }, | ||
148 | "owner_org": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | 148 | "owner_org": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | ||
149 | "private": false, | 149 | "private": false, | ||
150 | "publication": "{\"publication_year\": \"2025\", \"publisher\": | 150 | "publication": "{\"publication_year\": \"2025\", \"publisher\": | ||
151 | \"National Forest Inventory (NFI)\"}", | 151 | \"National Forest Inventory (NFI)\"}", | ||
152 | "publication_state": "published", | 152 | "publication_state": "published", | ||
153 | "related_datasets": "", | 153 | "related_datasets": "", | ||
154 | "related_publications": "wsl:28243\nWaser, L. T., Ginzler, C., & | 154 | "related_publications": "wsl:28243\nWaser, L. T., Ginzler, C., & | ||
155 | Rehush, N. (2017). Wall-to-wall tree type mapping from countrywide | 155 | Rehush, N. (2017). Wall-to-wall tree type mapping from countrywide | ||
156 | airborne remote sensing surveys. Remote Sensing, 9(8), 766 (24 pp.). | 156 | airborne remote sensing surveys. Remote Sensing, 9(8), 766 (24 pp.). | ||
157 | https://doi.org/10.3390/rs9080766", | 157 | https://doi.org/10.3390/rs9080766", | ||
158 | "relationships_as_object": [], | 158 | "relationships_as_object": [], | ||
159 | "relationships_as_subject": [], | 159 | "relationships_as_subject": [], | ||
160 | "resource_type": "dataset", | 160 | "resource_type": "dataset", | ||
161 | "resource_type_general": "dataset", | 161 | "resource_type_general": "dataset", | ||
162 | "resources": [ | 162 | "resources": [ | ||
163 | { | 163 | { | ||
164 | "cache_last_updated": null, | 164 | "cache_last_updated": null, | ||
165 | "cache_url": null, | 165 | "cache_url": null, | ||
166 | "created": "2021-04-23T08:02:00.149511", | 166 | "created": "2021-04-23T08:02:00.149511", | ||
167 | "description": "\n **Important note:** This dataset is NOT | 167 | "description": "\n **Important note:** This dataset is NOT | ||
168 | suitable for analysis at the individual tree crown level because | 168 | suitable for analysis at the individual tree crown level because | ||
169 | pixel-level fractions (e.g., of the broadleaf class) are not allocated | 169 | pixel-level fractions (e.g., of the broadleaf class) are not allocated | ||
170 | to individual trees. Analysis is recommended only for areas larger | 170 | to individual trees. Analysis is recommended only for areas larger | ||
171 | than 3x3 pixels, i.e. by calculating the mean values, except in rare | 171 | than 3x3 pixels, i.e. by calculating the mean values, except in rare | ||
172 | cases of homogeneous forest stands (either broadleaf or coniferous | 172 | cases of homogeneous forest stands (either broadleaf or coniferous | ||
173 | class). \n\nThis dataset uses a remote sensing-based approach for a | 173 | class). \n\nThis dataset uses a remote sensing-based approach for a | ||
174 | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | 174 | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | ||
175 | classifying areas as either broadleaf or coniferous. These datasets | 175 | classifying areas as either broadleaf or coniferous. These datasets | ||
176 | have a spatial resolution of 10 m and provide the fractional | 176 | have a spatial resolution of 10 m and provide the fractional | ||
177 | representation (0-100%) of the class broadleaf at the pixel level | 177 | representation (0-100%) of the class broadleaf at the pixel level | ||
178 | (covering areas with vegetation height > 5m).\nThe classification | 178 | (covering areas with vegetation height > 5m).\nThe classification | ||
179 | approach is based on a Random Forest (RF) classifier, that combines | 179 | approach is based on a Random Forest (RF) classifier, that combines | ||
180 | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | 180 | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | ||
181 | with the SwissAlti3D terrain model. The models were calibrated using | 181 | with the SwissAlti3D terrain model. The models were calibrated using | ||
182 | digitized training polygons and independently validated data from the | 182 | digitized training polygons and independently validated data from the | ||
183 | National Forest Inventory (NFI). Whereas high model overall accuracies | 183 | National Forest Inventory (NFI). Whereas high model overall accuracies | ||
184 | (0.97) and kappa (0.96) were achieved, the comparison of the tree type | 184 | (0.97) and kappa (0.96) were achieved, the comparison of the tree type | ||
185 | map with independent NFI data revealed deviations in mixed | 185 | map with independent NFI data revealed deviations in mixed | ||
186 | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | 186 | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | ||
187 | (lars.waser@wsl.ch).\n\n", | 187 | (lars.waser@wsl.ch).\n\n", | ||
188 | "doi": "10.16904/1000001.6", | 188 | "doi": "10.16904/1000001.6", | ||
189 | "format": "geotiff", | 189 | "format": "geotiff", | ||
190 | "hash": "", | 190 | "hash": "", | ||
191 | "id": "5a18924a-ef5a-4af2-a391-b0056a0874bb", | 191 | "id": "5a18924a-ef5a-4af2-a391-b0056a0874bb", | ||
192 | "last_modified": "2025-03-07T08:14:12.880000", | 192 | "last_modified": "2025-03-07T08:14:12.880000", | ||
193 | "metadata_modified": "2025-03-07T07:14:13.227379", | 193 | "metadata_modified": "2025-03-07T07:14:13.227379", | ||
194 | "mimetype": null, | 194 | "mimetype": null, | ||
195 | "mimetype_inner": null, | 195 | "mimetype_inner": null, | ||
196 | "name": "Forest Type NFI 2018", | 196 | "name": "Forest Type NFI 2018", | ||
197 | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | 197 | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | ||
198 | "position": 0, | 198 | "position": 0, | ||
199 | "resource_size": "{\"size_value\":\"\",\"size_units\":\"\"}", | 199 | "resource_size": "{\"size_value\":\"\",\"size_units\":\"\"}", | ||
200 | "resource_type": null, | 200 | "resource_type": null, | ||
201 | "restricted": | 201 | "restricted": | ||
202 | \"same_organization\",\"allowed_users\":\"\",\"shared_secret\":\"\"}", | 202 | \"same_organization\",\"allowed_users\":\"\",\"shared_secret\":\"\"}", | ||
203 | "size": 191049626, | 203 | "size": 191049626, | ||
204 | "state": "active", | 204 | "state": "active", | ||
205 | "url": | 205 | "url": | ||
206 | 18924a-ef5a-4af2-a391-b0056a0874bb/download/forest-type-nfi-2018.tif", | 206 | 18924a-ef5a-4af2-a391-b0056a0874bb/download/forest-type-nfi-2018.tif", | ||
207 | "url_type": "upload" | 207 | "url_type": "upload" | ||
208 | }, | 208 | }, | ||
209 | { | 209 | { | ||
210 | "cache_last_updated": null, | 210 | "cache_last_updated": null, | ||
211 | "cache_url": null, | 211 | "cache_url": null, | ||
212 | "created": "2021-03-27T04:57:32.557727", | 212 | "created": "2021-03-27T04:57:32.557727", | ||
213 | "description": "This dataset presents a countrywide map with the | 213 | "description": "This dataset presents a countrywide map with the | ||
214 | two classes broadleaf and coniferous in Switzerland based on digital | 214 | two classes broadleaf and coniferous in Switzerland based on digital | ||
215 | aerial imagery. The spatial resolution of the data set is 3 m. The | 215 | aerial imagery. The spatial resolution of the data set is 3 m. The | ||
216 | pixel values correspond to the fraction (0-100 %) of the class | 216 | pixel values correspond to the fraction (0-100 %) of the class | ||
217 | broadleaf.\nThe classification approach incorporates a RF classifier, | 217 | broadleaf.\nThe classification approach incorporates a RF classifier, | ||
218 | predictors from multispectral aerial imagery (ADS80) and the | 218 | predictors from multispectral aerial imagery (ADS80) and the | ||
219 | SwissAlti3D terrain model. The model was tested, trained and validated | 219 | SwissAlti3D terrain model. The model was tested, trained and validated | ||
220 | using 90,000 digitized polygons and achieved an overall accuracy of | 220 | using 90,000 digitized polygons and achieved an overall accuracy of | ||
221 | 0.99 and a kappa of 0.98. Independent validation and plausibility | 221 | 0.99 and a kappa of 0.98. Independent validation and plausibility | ||
222 | check included the comparison of the predicted results with aerial | 222 | check included the comparison of the predicted results with aerial | ||
223 | image interpretations of the NFI. Significant deviations were | 223 | image interpretations of the NFI. Significant deviations were | ||
224 | observed, primarily due to an underestimation of broadleaved trees | 224 | observed, primarily due to an underestimation of broadleaved trees | ||
225 | (median underestimation of 3.17%), especially in mixed forest stands. | 225 | (median underestimation of 3.17%), especially in mixed forest stands. | ||
226 | For more details, see Waser et al. (2017).\n\nData 'Forest Type NFI | 226 | For more details, see Waser et al. (2017).\n\nData 'Forest Type NFI | ||
227 | 2016' available on request (lars.waser@wsl.ch).", | 227 | 2016' available on request (lars.waser@wsl.ch).", | ||
228 | "doi": "10.16904/1000001.3", | 228 | "doi": "10.16904/1000001.3", | ||
229 | "format": "geotiff", | 229 | "format": "geotiff", | ||
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251 | "created": "2018-04-03T10:42:10.760550", | 251 | "created": "2018-04-03T10:42:10.760550", | ||
252 | "description": "Waser, L. T., Ginzler, C., & Rehush, N. (2017). | 252 | "description": "Waser, L. T., Ginzler, C., & Rehush, N. (2017). | ||
253 | Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote | 253 | Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote | ||
254 | Sensing Surveys. Remote Sensing, 9(8), 766. | 254 | Sensing Surveys. Remote Sensing, 9(8), 766. | ||
255 | https://doi.org/10.3390/rs9080766", | 255 | https://doi.org/10.3390/rs9080766", | ||
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260 | "last_modified": "2025-03-04T23:05:32.264000", | 260 | "last_modified": "2025-03-04T23:05:32.264000", | ||
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274 | "url": | 274 | "url": | ||
275 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14162", | 275 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:14162", | ||
276 | "url_type": null | 276 | "url_type": null | ||
277 | }, | 277 | }, | ||
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281 | "created": "2018-04-03T13:06:34.033220", | 281 | "created": "2018-04-03T13:06:34.033220", | ||
282 | "description": "Sample illustration of the Forest Type NFI | 282 | "description": "Sample illustration of the Forest Type NFI | ||
283 | dataset.", | 283 | dataset.", | ||
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308 | "created": "2025-03-04T22:11:00.261394", | 308 | "created": "2025-03-04T22:11:00.261394", | ||
309 | "description": "Waser, L. T., R\u00fcetschi, M., Psomas, A., | 309 | "description": "Waser, L. T., R\u00fcetschi, M., Psomas, A., | ||
310 | Small, D., & Rehush, N. (2021). Mapping dominant leaf type based on | 310 | Small, D., & Rehush, N. (2021). Mapping dominant leaf type based on | ||
311 | combined Sentinel-1/-2 data \u2013 Challenges for mountainous | 311 | combined Sentinel-1/-2 data \u2013 Challenges for mountainous | ||
312 | countries. ISPRS Journal of Photogrammetry and Remote Sensing, 180, | 312 | countries. ISPRS Journal of Photogrammetry and Remote Sensing, 180, | ||
313 | 209-226. https://doi.org/10.1016/j.isprsjprs.2021.08.017", | 313 | 209-226. https://doi.org/10.1016/j.isprsjprs.2021.08.017", | ||
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330 | "size": null, | 330 | "size": null, | ||
331 | "state": "active", | 331 | "state": "active", | ||
332 | "url": | 332 | "url": | ||
333 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:28243", | 333 | "https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:28243", | ||
334 | "url_type": null | 334 | "url_type": null | ||
335 | }, | 335 | }, | ||
336 | { | 336 | { | ||
337 | "cache_last_updated": null, | 337 | "cache_last_updated": null, | ||
338 | "cache_url": null, | 338 | "cache_url": null, | ||
339 | "created": "2025-03-06T13:23:45.237260", | 339 | "created": "2025-03-06T13:23:45.237260", | ||
340 | "description": "\n**Important note:** This dataset is NOT | 340 | "description": "\n**Important note:** This dataset is NOT | ||
341 | suitable for analysis at the individual tree crown level because | 341 | suitable for analysis at the individual tree crown level because | ||
342 | pixel-level fractions (e.g., of the broadleaf class) are not allocated | 342 | pixel-level fractions (e.g., of the broadleaf class) are not allocated | ||
343 | to individual trees. Analysis is recommended only for areas larger | 343 | to individual trees. Analysis is recommended only for areas larger | ||
344 | than 3x3 pixels, i.e. by calculating the mean values, except in rare | 344 | than 3x3 pixels, i.e. by calculating the mean values, except in rare | ||
345 | cases of homogeneous forest stands (either broadleaf or coniferous | 345 | cases of homogeneous forest stands (either broadleaf or coniferous | ||
346 | class).\n\nThis dataset uses a remote sensing-based approach for a | 346 | class).\n\nThis dataset uses a remote sensing-based approach for a | ||
347 | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | 347 | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | ||
348 | classifying areas as either broadleaf or coniferous. These datasets | 348 | classifying areas as either broadleaf or coniferous. These datasets | ||
349 | have a spatial resolution of 10 m and provide the fractional | 349 | have a spatial resolution of 10 m and provide the fractional | ||
350 | representation (0-100%) of the class broadleaf at the pixel level | 350 | representation (0-100%) of the class broadleaf at the pixel level | ||
351 | (covering areas with vegetation height > 3m).\nThe classification | 351 | (covering areas with vegetation height > 3m).\nThe classification | ||
352 | approach is based on a Random Forest (RF) classifier, that combines | 352 | approach is based on a Random Forest (RF) classifier, that combines | ||
353 | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | 353 | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | ||
354 | with the SwissAlti3D terrain model. The models were calibrated using | 354 | with the SwissAlti3D terrain model. The models were calibrated using | ||
355 | digitized training polygons and independently validated data from the | 355 | digitized training polygons and independently validated data from the | ||
356 | National Forest Inventory (NFI). Whereas high model overall accuracies | 356 | National Forest Inventory (NFI). Whereas high model overall accuracies | ||
357 | (0.97) and kappa (0.96) were achieved, the comparison of the tree type | 357 | (0.97) and kappa (0.96) were achieved, the comparison of the tree type | ||
358 | map with independent NFI data revealed deviations in mixed | 358 | map with independent NFI data revealed deviations in mixed | ||
359 | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | 359 | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | ||
360 | (lars.waser@wsl.ch).", | 360 | (lars.waser@wsl.ch).", | ||
361 | "doi": "", | 361 | "doi": "", | ||
362 | "format": "geotif", | 362 | "format": "geotif", | ||
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365 | "last_modified": "2025-03-07T08:15:14.960000", | 365 | "last_modified": "2025-03-07T08:15:14.960000", | ||
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380 | "url_type": "upload" | 380 | "url_type": "upload" | ||
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406 | "url_type": "upload" | 406 | "url_type": "upload" | ||
407 | } | 407 | } | ||
408 | ], | 408 | ], | ||
409 | "spatial": | 409 | "spatial": | ||
410 | inates\":[[[-175,-85],[-175,85],[175,85],[175,-85],[-175,-85]]]}]}]}", | 410 | inates\":[[[-175,-85],[-175,85],[175,85],[175,-85],[-175,-85]]]}]}]}", | ||
411 | "spatial_info": "Switzerland", | 411 | "spatial_info": "Switzerland", | ||
412 | "state": "active", | 412 | "state": "active", | ||
413 | "subtitle": "", | 413 | "subtitle": "", | ||
414 | "tags": [ | 414 | "tags": [ | ||
415 | { | 415 | { | ||
416 | "display_name": "DOMINANT LEAFTYPE", | 416 | "display_name": "DOMINANT LEAFTYPE", | ||
417 | "id": "1b334234-4bd0-44d4-bb78-039fe70bab90", | 417 | "id": "1b334234-4bd0-44d4-bb78-039fe70bab90", | ||
418 | "name": "DOMINANT LEAFTYPE", | 418 | "name": "DOMINANT LEAFTYPE", | ||
419 | "state": "active", | 419 | "state": "active", | ||
420 | "vocabulary_id": null | 420 | "vocabulary_id": null | ||
421 | }, | 421 | }, | ||
422 | { | 422 | { | ||
423 | "display_name": "FOREST", | 423 | "display_name": "FOREST", | ||
424 | "id": "90cd0d8f-8df0-4b78-ac11-6e38c2a22106", | 424 | "id": "90cd0d8f-8df0-4b78-ac11-6e38c2a22106", | ||
425 | "name": "FOREST", | 425 | "name": "FOREST", | ||
426 | "state": "active", | 426 | "state": "active", | ||
427 | "vocabulary_id": null | 427 | "vocabulary_id": null | ||
428 | }, | 428 | }, | ||
429 | { | 429 | { | ||
430 | "display_name": "FOREST INVENTORY", | 430 | "display_name": "FOREST INVENTORY", | ||
431 | "id": "d1b2883f-be82-426b-8a5e-fd6d36bc2855", | 431 | "id": "d1b2883f-be82-426b-8a5e-fd6d36bc2855", | ||
432 | "name": "FOREST INVENTORY", | 432 | "name": "FOREST INVENTORY", | ||
433 | "state": "active", | 433 | "state": "active", | ||
434 | "vocabulary_id": null | 434 | "vocabulary_id": null | ||
435 | }, | 435 | }, | ||
436 | { | 436 | { | ||
437 | "display_name": "FOREST TYPE", | 437 | "display_name": "FOREST TYPE", | ||
438 | "id": "52ad8bb3-8ec8-4e9d-8798-858d7662c3e4", | 438 | "id": "52ad8bb3-8ec8-4e9d-8798-858d7662c3e4", | ||
439 | "name": "FOREST TYPE", | 439 | "name": "FOREST TYPE", | ||
440 | "state": "active", | 440 | "state": "active", | ||
441 | "vocabulary_id": null | 441 | "vocabulary_id": null | ||
442 | }, | 442 | }, | ||
443 | { | 443 | { | ||
444 | "display_name": "NFI", | 444 | "display_name": "NFI", | ||
445 | "id": "0a74eb64-cf7b-42df-b2d4-1a5ad8171458", | 445 | "id": "0a74eb64-cf7b-42df-b2d4-1a5ad8171458", | ||
446 | "name": "NFI", | 446 | "name": "NFI", | ||
447 | "state": "active", | 447 | "state": "active", | ||
448 | "vocabulary_id": null | 448 | "vocabulary_id": null | ||
449 | }, | 449 | }, | ||
450 | { | 450 | { | ||
451 | "display_name": "REMOTE SENSING", | 451 | "display_name": "REMOTE SENSING", | ||
452 | "id": "1bc9d51e-2500-44a7-857b-13710a59e4be", | 452 | "id": "1bc9d51e-2500-44a7-857b-13710a59e4be", | ||
453 | "name": "REMOTE SENSING", | 453 | "name": "REMOTE SENSING", | ||
454 | "state": "active", | 454 | "state": "active", | ||
455 | "vocabulary_id": null | 455 | "vocabulary_id": null | ||
456 | } | 456 | } | ||
457 | ], | 457 | ], | ||
458 | "title": "Forest Type NFI", | 458 | "title": "Forest Type NFI", | ||
459 | "type": "dataset", | 459 | "type": "dataset", | ||
460 | "url": null, | 460 | "url": null, | ||
461 | "version": "2023" | 461 | "version": "2023" | ||
462 | } | 462 | } |