Changes
On March 6, 2025 at 1:34:36 PM UTC,
-
Updated description of resource 2018 Forest Type NFI in Forest Type NFI from
**Important note:** None of the datasets is suitable for analysis at the individual tree crown level because pixel-level fractions (e.g., of the broadleaf class) are not allocated to individual trees. Analysis is recommended only for areas larger than 3x3 pixels, i.e. by calculating the mean values, except in rare cases of homogeneous forest stands (either broadleaf or coniferous class). This dataset uses a remote sensing-based approach for a countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying areas as either broadleaf or coniferous. These datasets have a spatial resolution of 10 m and provide the fractional representation (0-100%) of the class broadleaf at the pixel level (covering areas with vegetation height > 5m). The classification approach is based on a Random Forest (RF) classifier, that combines predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data with the SwissAlti3D terrain model. 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. The dataset 'Forest Type NFI 2018' is available on request (lars.waser@wsl.ch).
to**Important note: None of the datasets is suitable for analysis at the individual tree crown level because pixel-level fractions (e.g., of the broadleaf class) are not allocated to individual trees. Analysis is recommended only for areas larger than 3x3 pixels, i.e. by calculating the mean values, except in rare cases of homogeneous forest stands (either broadleaf or coniferous class). ** This dataset uses a remote sensing-based approach for a countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying areas as either broadleaf or coniferous. These datasets have a spatial resolution of 10 m and provide the fractional representation (0-100%) of the class broadleaf at the pixel level (covering areas with vegetation height > 5m). The classification approach is based on a Random Forest (RF) classifier, that combines predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data with the SwissAlti3D terrain model. 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. The dataset 'Forest Type NFI 2018' is available on request (lars.waser@wsl.ch).
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", | ||
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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", | ||
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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": "2025-03-06T13:33:40.403358", | n | 45 | "metadata_modified": "2025-03-06T13:34:35.963933", |
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", | ||
n | 166 | "description": "\n **Important note:** None of the datasets is | n | 166 | "description": "\n **Important note: None of the datasets is |
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 | ||
n | 172 | class).\n\nThis dataset uses a remote sensing-based approach for a | n | 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", | ||
t | 191 | "last_modified": "2025-03-06T14:33:39.750000", | t | 191 | "last_modified": "2025-03-06T14:34:35.321000", |
192 | "metadata_modified": "2025-03-06T13:33:40.409215", | 192 | "metadata_modified": "2025-03-06T13:34:35.968787", | ||
193 | "mimetype": null, | 193 | "mimetype": null, | ||
194 | "mimetype_inner": null, | 194 | "mimetype_inner": null, | ||
195 | "name": "2018 Forest Type NFI", | 195 | "name": "2018 Forest Type NFI", | ||
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\":\"\"}", | ||
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202 | "size": 191049626, | 202 | "size": 191049626, | ||
203 | "state": "active", | 203 | "state": "active", | ||
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206 | "url_type": "upload" | 206 | "url_type": "upload" | ||
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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", | ||
255 | "doi": "10.3390/rs9080766", | 255 | "doi": "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, | ||
279 | "cache_url": null, | 279 | "cache_url": null, | ||
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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298 | "size": 3072776, | 298 | "size": 3072776, | ||
299 | "state": "active", | 299 | "state": "active", | ||
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302 | "url_type": "upload" | 302 | "url_type": "upload" | ||
303 | }, | 303 | }, | ||
304 | { | 304 | { | ||
305 | "cache_last_updated": null, | 305 | "cache_last_updated": null, | ||
306 | "cache_url": null, | 306 | "cache_url": null, | ||
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": "\nImportant note: None of the datasets is | 339 | "description": "\nImportant note: None of the datasets is | ||
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": "2025-03-06T14:32:29.235000", | 364 | "last_modified": "2025-03-06T14:32:29.235000", | ||
365 | "metadata_modified": "2025-03-06T13:32:29.881018", | 365 | "metadata_modified": "2025-03-06T13:32:29.881018", | ||
366 | "mimetype": "image/tiff", | 366 | "mimetype": "image/tiff", | ||
367 | "mimetype_inner": null, | 367 | "mimetype_inner": null, | ||
368 | "name": "2023 Forest Type NFI", | 368 | "name": "2023 Forest Type NFI", | ||
369 | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | 369 | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | ||
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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": | ||
383 | 80838],[10.49203,47.80838],[10.49203,45.81802],[5.95587,45.81802]]]}", | 383 | 80838],[10.49203,47.80838],[10.49203,45.81802],[5.95587,45.81802]]]}", | ||
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 | } |