f | { | f | { |
| "author": "[{\"affiliation\": \"Swiss Federal Institute for Forest, | | "author": "[{\"affiliation\": \"Swiss Federal Institute for Forest, |
| Snow and Landscape Research WSL \", \"affiliation_02\": \"\", | | Snow and Landscape Research WSL \", \"affiliation_02\": \"\", |
| \"affiliation_03\": \"\", \"email\": \"lars.waser@wsl.ch\", | | \"affiliation_03\": \"\", \"email\": \"lars.waser@wsl.ch\", |
| \"given_name\": \"Lars\", \"identifier\": \"D-5937-2011\", \"name\": | | \"given_name\": \"Lars\", \"identifier\": \"D-5937-2011\", \"name\": |
| \"Waser\"}, {\"affiliation\": \"Swiss Federal Institute for Forest, | | \"Waser\"}, {\"affiliation\": \"Swiss Federal Institute for Forest, |
| Snow and Landscape Research WSL \", \"affiliation_02\": \"\", | | Snow and Landscape Research WSL \", \"affiliation_02\": \"\", |
| \"affiliation_03\": \"\", \"email\": \"christian.ginzler@wsl.ch\", | | \"affiliation_03\": \"\", \"email\": \"christian.ginzler@wsl.ch\", |
| \"given_name\": \"Christian\", \"identifier\": \"E-9544-2012\", | | \"given_name\": \"Christian\", \"identifier\": \"E-9544-2012\", |
| \"name\": \"Ginzler\"}, {\"affiliation\": \"WSL\", \"affiliation_02\": | | \"name\": \"Ginzler\"}, {\"affiliation\": \"WSL\", \"affiliation_02\": |
| \"\", \"affiliation_03\": \"\", \"email\": | | \"\", \"affiliation_03\": \"\", \"email\": |
| \"achilleas.psomas@wsl.ch\", \"given_name\": \"Achilleas\", | | \"achilleas.psomas@wsl.ch\", \"given_name\": \"Achilleas\", |
| \"identifier\": \"\", \"name\": \"Psomas\"}, {\"affiliation\": | | \"identifier\": \"\", \"name\": \"Psomas\"}, {\"affiliation\": |
| \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | | \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", |
| \"email\": \"marius.rueetschi@wsl.ch\", \"given_name\": \"Marius\", | | \"email\": \"marius.rueetschi@wsl.ch\", \"given_name\": \"Marius\", |
| \"identifier\": \"\", \"name\": \"R\\u00fcetschi\"}, {\"affiliation\": | | \"identifier\": \"\", \"name\": \"R\\u00fcetschi\"}, {\"affiliation\": |
| \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", | | \"WSL\", \"affiliation_02\": \"\", \"affiliation_03\": \"\", |
| \"email\": \"nataliia.rehush@wsl.ch\", \"given_name\": \"Nataliia\", | | \"email\": \"nataliia.rehush@wsl.ch\", \"given_name\": \"Nataliia\", |
| \"identifier\": \"\", \"name\": \"Rehush\"}]", | | \"identifier\": \"\", \"name\": \"Rehush\"}]", |
| "author_email": null, | | "author_email": null, |
| "creator_user_id": "6d44d5cd-9ac6-4100-bc2c-c02034a41b48", | | "creator_user_id": "6d44d5cd-9ac6-4100-bc2c-c02034a41b48", |
| "date": "[{\"date\": \"2025-01-05\", \"date_type\": \"created\", | | "date": "[{\"date\": \"2025-01-05\", \"date_type\": \"created\", |
| \"end_date\": \"\"}]", | | \"end_date\": \"\"}]", |
| "doi": "10.16904/1000001.7", | | "doi": "10.16904/1000001.7", |
| "extras": [ | | "extras": [ |
| { | | { |
| "key": "deprecatedResources", | | "key": "deprecatedResources", |
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| } | | } |
| ], | | ], |
| "funding": "[{\"grant_number\":\"\",\"institution\":\"Federal Office | | "funding": "[{\"grant_number\":\"\",\"institution\":\"Federal Office |
| for the Environment (FOEN).\",\"institution_url\":\"\"}]", | | for the Environment (FOEN).\",\"institution_url\":\"\"}]", |
| "groups": [], | | "groups": [], |
| "id": "82d763fa-123a-4648-b827-0dec02a5efbc", | | "id": "82d763fa-123a-4648-b827-0dec02a5efbc", |
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| "language": "en", | | "language": "en", |
| "license_id": "other-undefined", | | "license_id": "other-undefined", |
| "license_title": "Other (Specified in the description)", | | "license_title": "Other (Specified in the description)", |
| "maintainer": "{\"affiliation\": \"Swiss Federal Institute for | | "maintainer": "{\"affiliation\": \"Swiss Federal Institute for |
| Forest, Snow and Landscape Research WSL \", \"email\": | | Forest, Snow and Landscape Research WSL \", \"email\": |
| \"lars.waser@wsl.ch\", \"given_name\": \"Lars\", \"identifier\": | | \"lars.waser@wsl.ch\", \"given_name\": \"Lars\", \"identifier\": |
| \"D-5937-2011\", \"name\": \"Waser\"}", | | \"D-5937-2011\", \"name\": \"Waser\"}", |
| "maintainer_email": null, | | "maintainer_email": null, |
| "metadata_created": "2018-04-03T10:38:30.545175", | | "metadata_created": "2018-04-03T10:38:30.545175", |
n | "metadata_modified": "2025-03-09T21:37:56.733697", | n | "metadata_modified": "2025-03-10T19:40:26.291700", |
| "name": "forest-type-nfi", | | "name": "forest-type-nfi", |
| "notes": "A series of Forest Type NFI datasets covering Switzerland | | "notes": "A series of Forest Type NFI datasets covering Switzerland |
| have been produced, and currently available for the years 2023, 2018, | | have been produced, and currently available for the years 2023, 2018, |
n | and 2016. These datasets provide the fractional representation | n | and 2016. These datasets provide the probability (0-100%) of the class |
| (0-100%) of the class broadleaf at the pixel level. The three datasets | | broadleaf at the pixel level. While 0% probability corresponds to the |
| | | pure class coniferous, 100% correspond to the class pure broadleaf. |
| | | Values around 50% probability weren't assigned to one of the two |
| | | classes and might belong to mixed stands. The three datasets are based |
| are based on different remote sensing data from different time spans | | on different remote sensing data from different time spans and with |
| and with different spatial resolutions:\n\n- **Forest Type NFI 2023: | | different spatial resolutions:\n\n- **Forest Type NFI 2023: |
| Sentinel-1/-2, 2021-2023, 10m (new dataset!)** \n- Forest Type NFI | | Sentinel-1/-2, 2021-2023, 10m (new dataset!)** \n- Forest Type NFI |
| 2018: Sentinel-1/-2, 2016-2018, 10m\n- Forest Type NFI 2016: Aerial | | 2018: Sentinel-1/-2, 2016-2018, 10m\n- Forest Type NFI 2016: Aerial |
| orthoimages, 2010-2015, 3m\n.\n---------------------\n.\n **Important | | orthoimages, 2010-2015, 3m\n.\n---------------------\n.\n **Important |
| note:** None of the datasets is suitable for analysis at the | | note:** None of the datasets is suitable for analysis at the |
n | individual tree crown level because pixel-level fractions (e.g., of | n | individual tree crown level because pixel-level probabilities are not |
| the broadleaf class) are not allocated to individual trees. Analysis | | allocated to individual trees. Analysis is recommended only for areas |
| is recommended only for areas larger than 3x3 pixels, i.e. by | | larger than 3x3 pixels, i.e. by calculating the mean values, except in |
| calculating the mean values, except in rare cases of homogeneous | | rare cases of homogeneous forest stands (either broadleaf or |
| forest stands (either broadleaf or coniferous class).\n\n**User\u2019s | | coniferous class).\n\n**User\u2019s feedback:** \nWe are very |
| feedback:** \nWe are very motivated to improve the dataset further and | | motivated to improve the dataset further and welcome any feedback or |
| welcome any feedback or suggestions for corrections. Feel free to | | suggestions for corrections. Feel free to directly contact |
| directly contact | | |
| **lars.waser@wsl.ch**\n_________________\n.\n*Detailed | | **lars.waser@wsl.ch**\n_________________\n.\n*Detailed |
| description:*\n\n**Forest Type NFI datasets, versions 2023 and 2018:** | | description:*\n\n**Forest Type NFI datasets, versions 2023 and 2018:** |
| Both datasets use a remote sensing-based approach for a countrywide | | Both datasets use a remote sensing-based approach for a countrywide |
| mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying | | mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying |
| areas as either broadleaf or coniferous. These datasets have a spatial | | areas as either broadleaf or coniferous. These datasets have a spatial |
n | resolution of 10 m and provide the fractional representation (0-100%) | n | resolution of 10 m and provide the probability (0-100%) of the class |
| of the class broadleaf at the pixel level (covering all areas with | | broadleaf at the pixel level (covering all areas with vegetation |
| vegetation height > 5m version 2018, and >3 m version 2023). The | | height > 5m version 2018, and >3 m version 2023). The classification |
| classification approach is based on a Random Forest (RF) classifier, | | approach is based on a Random Forest (RF) classifier, that combines |
| that combines predictors derived from multi-temporal Sentinel-1 and | | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data |
| Sentinel-2 data with the SwissAlti3D terrain model. In addition to the | | with the SwissAlti3D terrain model. In addition to the original |
| original Sentinel-2 spectral bands, vegetation indices such as GEMI, | | Sentinel-2 spectral bands, vegetation indices such as GEMI, MSAVI2, |
| MSAVI2, NDVI, CLRE and CCCI were used in the final model.\nThe | | NDVI, CLRE and CCCI were used in the final model.\nThe classification |
| classification models were tested, trained and validated using up to | | models were tested, trained and validated using up to 400,000 labels |
| 400,000 labels representing the two classes broadleaf and coniferous, | | representing the two classes broadleaf and coniferous, derived from |
| derived from aerial image delineations. For the 2023 data set, the | | aerial image delineations. For the 2023 data set, the previously used |
| previously used labels were quality-checked for reliability and | | labels were quality-checked for reliability and temporal consistency |
| temporal consistency with the image data. Additionally, more labels | | with the image data. Additionally, more labels were collected in the |
| were collected in the regions where the previous version of the data | | regions where the previous version of the data set performed less |
| set performed less accurately \u2013 as reported by the | | accurately \u2013 as reported by the users.\nSimilar high model |
| users.\nSimilar high model performances were achieved for both data | | performances were achieved for both data sets: overall accuracy = |
| sets: overall accuracy = 0.96, kappa = 0.94, F1-score =0.96, precision | | 0.96, kappa = 0.94, F1-score =0.96, precision = 0.97, recall = 0.94 |
| = 0.97, recall = 0.94 (2023), 0.95 (2018). Independent validation and | | (2023), 0.95 (2018). Independent validation and plausibility check |
| | | included the comparison of the predicted results with aerial image |
| | | interpretations of the National Forest Inventory (NFI). For both data |
| | | sets, deviations particularly occurred in mixed forest stands: over |
| | | all accuracy = 0.81 (2018, 2023), kappa = 0.62 (2018), 0.59 (2023). |
| | | For more details, see Waser et al. (2021).\n\n**The Forest Type NFI |
| | | dataset, version 2016:** This dataset presents a countrywide map with |
| | | the two classes broadleaf and coniferous in Switzerland based on |
| | | digital aerial imagery. The spatial resolution of the data set is 3 m. |
| | | The pixel values correspond to the probabilities (0-100 %) of the |
| | | class broadleaf.\nThe classification approach incorporates a RF |
| | | classifier, predictors from multispectral aerial imagery (ADS80) and |
| | | the SwissAlti3D terrain model. The model was tested, trained and |
| | | validated using 90,000 digitized polygons and achieved an overall |
| | | accuracy of 0.99 and a kappa of 0.98. Independent validation and |
| plausibility check included the comparison of the predicted results | | plausibility check included the comparison of the predicted results |
t | with aerial image interpretations of the National Forest Inventory | t | with aerial image interpretations of the NFI. Significant deviations |
| (NFI). For both data sets, deviations particularly occurred in mixed | | were observed, primarily due to an underestimation of broadleaved |
| forest stands: over all accuracy = 0.81 (2018, 2023), kappa = 0.62 | | trees (median underestimation of 3.17%), especially in mixed forest |
| (2018), 0.59 (2023). For more details, see Waser et al. | | stands. For more details, see Waser et al. (2017; |
| (2021).\n\n**The Forest Type NFI dataset, version 2016:** This dataset | | |
| presents a countrywide map with the two classes broadleaf and | | |
| coniferous in Switzerland based on digital aerial imagery. The spatial | | |
| resolution of the data set is 3 m. The pixel values correspond to the | | |
| fraction (0-100 %) of the class broadleaf.\nThe classification | | |
| approach incorporates a RF classifier, predictors from multispectral | | |
| aerial imagery (ADS80) and the SwissAlti3D terrain model. The model | | |
| was tested, trained and validated using 90,000 digitized polygons and | | |
| achieved an overall accuracy of 0.99 and a kappa of 0.98. Independent | | |
| validation and plausibility check included the comparison of the | | |
| predicted results with aerial image interpretations of the NFI. | | |
| Significant deviations were observed, primarily due to an | | |
| underestimation of broadleaved trees (median underestimation of | | |
| 3.17%), especially in mixed forest stands. For more details, see Waser | | |
| et al. (2017; https://doi.org/10.3390/rs9080766).\n\n", | | https://doi.org/10.3390/rs9080766).\n\n", |
| "num_resources": 7, | | "num_resources": 7, |
| "num_tags": 6, | | "num_tags": 6, |
| "organization": { | | "organization": { |
| "approval_status": "approved", | | "approval_status": "approved", |
| "created": "2017-04-20T16:51:21.920128", | | "created": "2017-04-20T16:51:21.920128", |
| "description": "We develop and apply comprehensive and robust | | "description": "We develop and apply comprehensive and robust |
| methods to extract and classify natural objects from continuous and | | methods to extract and classify natural objects from continuous and |
| discrete raster datasets. Relevant features are acquired to describe | | discrete raster datasets. Relevant features are acquired to describe |
| changes in landscape and land resources at different levels using | | changes in landscape and land resources at different levels using |
| image data. Mathematical-statistical methods are adopted for automatic | | image data. Mathematical-statistical methods are adopted for automatic |
| detection and description of image objects. Thus we contribute | | detection and description of image objects. Thus we contribute |
| concepts, methods and data to describe/detect area wide changes and | | concepts, methods and data to describe/detect area wide changes and |
| processes in the resources of landscape.\r\n\r\n### Tasks and main | | processes in the resources of landscape.\r\n\r\n### Tasks and main |
| research\r\n\r\n* Development and application of methods to extract | | research\r\n\r\n* Development and application of methods to extract |
| natural objects from continuous data.\r\n* Development of methods for | | natural objects from continuous data.\r\n* Development of methods for |
| a comprehensive description of natural and anthropogenetic boundaries | | a comprehensive description of natural and anthropogenetic boundaries |
| in continuous pattern (e.g. map signatures, vegetation transition, | | in continuous pattern (e.g. map signatures, vegetation transition, |
| forest borders).\r\n* Development and application of methods to | | forest borders).\r\n* Development and application of methods to |
| extract 3D-information from remotely sensed data for description of | | extract 3D-information from remotely sensed data for description of |
| natural structures and changes. The main focus lies on wood and its | | natural structures and changes. The main focus lies on wood and its |
| embedding/interaction within/with the landscape.\r\n* Conception and | | embedding/interaction within/with the landscape.\r\n* Conception and |
| development of data acquisition based on high resolution remote | | development of data acquisition based on high resolution remote |
| sensing data.\r\n* Conception, development and maintenance of the | | sensing data.\r\n* Conception, development and maintenance of the |
| software interface in area wide data acquisition using airborne remote | | software interface in area wide data acquisition using airborne remote |
| sensing data.\r\n* Scientific expert advice and support in the fields | | sensing data.\r\n* Scientific expert advice and support in the fields |
| of photogrammetry and survey at WSL. Maintenance, enhancements and | | of photogrammetry and survey at WSL. Maintenance, enhancements and |
| future development in these specific fields.\r\n* Adequate | | future development in these specific fields.\r\n* Adequate |
| presentation of scientific results on national level and in noted | | presentation of scientific results on national level and in noted |
| international journals and at international | | international journals and at international |
| congresses/workshops/symposia.\r\n\r\n__Further information__: | | congresses/workshops/symposia.\r\n\r\n__Further information__: |
| l/organization/research-units/landscape-dynamics/remote-sensing.html", | | l/organization/research-units/landscape-dynamics/remote-sensing.html", |
| "id": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | | "id": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", |
| "image_url": "", | | "image_url": "", |
| "is_organization": true, | | "is_organization": true, |
| "name": "remote-sensing", | | "name": "remote-sensing", |
| "state": "active", | | "state": "active", |
| "title": "Remote Sensing", | | "title": "Remote Sensing", |
| "type": "organization" | | "type": "organization" |
| }, | | }, |
| "owner_org": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", | | "owner_org": "5243fbb4-e4e6-4779-9672-32a7ef33d5f9", |
| "private": false, | | "private": false, |
| "publication": "{\"publication_year\": \"2025\", \"publisher\": | | "publication": "{\"publication_year\": \"2025\", \"publisher\": |
| \"National Forest Inventory (NFI)\"}", | | \"National Forest Inventory (NFI)\"}", |
| "publication_state": "published", | | "publication_state": "published", |
| "related_datasets": "", | | "related_datasets": "", |
| "related_publications": "wsl:28243\nWaser, L. T., Ginzler, C., & | | "related_publications": "wsl:28243\nWaser, L. T., Ginzler, C., & |
| Rehush, N. (2017). Wall-to-wall tree type mapping from countrywide | | Rehush, N. (2017). Wall-to-wall tree type mapping from countrywide |
| airborne remote sensing surveys. Remote Sensing, 9(8), 766 (24 pp.). | | airborne remote sensing surveys. Remote Sensing, 9(8), 766 (24 pp.). |
| https://doi.org/10.3390/rs9080766", | | https://doi.org/10.3390/rs9080766", |
| "relationships_as_object": [], | | "relationships_as_object": [], |
| "relationships_as_subject": [], | | "relationships_as_subject": [], |
| "resource_type": "dataset", | | "resource_type": "dataset", |
| "resource_type_general": "dataset", | | "resource_type_general": "dataset", |
| "resources": [ | | "resources": [ |
| { | | { |
| "cache_last_updated": null, | | "cache_last_updated": null, |
| "cache_url": null, | | "cache_url": null, |
| "created": "2021-04-23T08:02:00.149511", | | "created": "2021-04-23T08:02:00.149511", |
| "description": "\n **Important note:** This dataset is NOT | | "description": "\n **Important note:** This dataset is NOT |
| suitable for analysis at the individual tree crown level because | | suitable for analysis at the individual tree crown level because |
| pixel-level fractions (e.g., of the broadleaf class) are not allocated | | pixel-level fractions (e.g., of the broadleaf class) are not allocated |
| to individual trees. Analysis is recommended only for areas larger | | to individual trees. Analysis is recommended only for areas larger |
| than 3x3 pixels, i.e. by calculating the mean values, except in rare | | than 3x3 pixels, i.e. by calculating the mean values, except in rare |
| cases of homogeneous forest stands (either broadleaf or coniferous | | cases of homogeneous forest stands (either broadleaf or coniferous |
| class). \n\nThis dataset uses a remote sensing-based approach for a | | class). \n\nThis dataset uses a remote sensing-based approach for a |
| countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, |
| classifying areas as either broadleaf or coniferous. These datasets | | classifying areas as either broadleaf or coniferous. These datasets |
| have a spatial resolution of 10 m and provide the fractional | | have a spatial resolution of 10 m and provide the fractional |
| representation (0-100%) of the class broadleaf at the pixel level | | representation (0-100%) of the class broadleaf at the pixel level |
| (covering areas with vegetation height > 5m).\nThe classification | | (covering areas with vegetation height > 5m).\nThe classification |
| approach is based on a Random Forest (RF) classifier, that combines | | approach is based on a Random Forest (RF) classifier, that combines |
| predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data |
| with the SwissAlti3D terrain model. The models were calibrated using | | with the SwissAlti3D terrain model. The models were calibrated using |
| digitized training polygons and independently validated data from the | | digitized training polygons and independently validated data from the |
| National Forest Inventory (NFI). Whereas high model overall accuracies | | National Forest Inventory (NFI). Whereas high model overall accuracies |
| (0.97) and kappa (0.96) were achieved, the comparison of the tree type | | (0.97) and kappa (0.96) were achieved, the comparison of the tree type |
| map with independent NFI data revealed deviations in mixed | | map with independent NFI data revealed deviations in mixed |
| stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request |
| (lars.waser@wsl.ch).\n\n", | | (lars.waser@wsl.ch).\n\n", |
| "doi": "10.16904/1000001.6", | | "doi": "10.16904/1000001.6", |
| "format": "geotiff", | | "format": "geotiff", |
| "hash": "", | | "hash": "", |
| "id": "5a18924a-ef5a-4af2-a391-b0056a0874bb", | | "id": "5a18924a-ef5a-4af2-a391-b0056a0874bb", |
| "last_modified": "2025-03-07T08:14:12.880000", | | "last_modified": "2025-03-07T08:14:12.880000", |
| "metadata_modified": "2025-03-07T07:14:13.227379", | | "metadata_modified": "2025-03-07T07:14:13.227379", |
| "mimetype": null, | | "mimetype": null, |
| "mimetype_inner": null, | | "mimetype_inner": null, |
| "name": "Forest Type NFI 2018", | | "name": "Forest Type NFI 2018", |
| "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", | | "package_id": "82d763fa-123a-4648-b827-0dec02a5efbc", |
| "position": 0, | | "position": 0, |
| "resource_size": "{\"size_value\":\"\",\"size_units\":\"\"}", | | "resource_size": "{\"size_value\":\"\",\"size_units\":\"\"}", |
| "resource_type": null, | | "resource_type": null, |
| "restricted": | | "restricted": |
| \"same_organization\",\"allowed_users\":\"\",\"shared_secret\":\"\"}", | | \"same_organization\",\"allowed_users\":\"\",\"shared_secret\":\"\"}", |
| "size": 191049626, | | "size": 191049626, |
| "state": "active", | | "state": "active", |
| "url": | | "url": |
| 18924a-ef5a-4af2-a391-b0056a0874bb/download/forest-type-nfi-2018.tif", | | 18924a-ef5a-4af2-a391-b0056a0874bb/download/forest-type-nfi-2018.tif", |
| "url_type": "upload" | | "url_type": "upload" |
| }, | | }, |
| { | | { |
| "cache_last_updated": null, | | "cache_last_updated": null, |
| "cache_url": null, | | "cache_url": null, |
| "created": "2021-03-27T04:57:32.557727", | | "created": "2021-03-27T04:57:32.557727", |
| "description": "This dataset presents a countrywide map with the | | "description": "This dataset presents a countrywide map with the |
| two classes broadleaf and coniferous in Switzerland based on digital | | two classes broadleaf and coniferous in Switzerland based on digital |
| aerial imagery. The spatial resolution of the data set is 3 m. The | | aerial imagery. The spatial resolution of the data set is 3 m. The |
| pixel values correspond to the fraction (0-100 %) of the class | | pixel values correspond to the fraction (0-100 %) of the class |
| broadleaf.\nThe classification approach incorporates a RF classifier, | | broadleaf.\nThe classification approach incorporates a RF classifier, |
| predictors from multispectral aerial imagery (ADS80) and the | | predictors from multispectral aerial imagery (ADS80) and the |
| SwissAlti3D terrain model. The model was tested, trained and validated | | SwissAlti3D terrain model. The model was tested, trained and validated |
| using 90,000 digitized polygons and achieved an overall accuracy of | | using 90,000 digitized polygons and achieved an overall accuracy of |
| 0.99 and a kappa of 0.98. Independent validation and plausibility | | 0.99 and a kappa of 0.98. Independent validation and plausibility |
| check included the comparison of the predicted results with aerial | | check included the comparison of the predicted results with aerial |
| image interpretations of the NFI. Significant deviations were | | image interpretations of the NFI. Significant deviations were |
| observed, primarily due to an underestimation of broadleaved trees | | observed, primarily due to an underestimation of broadleaved trees |
| (median underestimation of 3.17%), especially in mixed forest stands. | | (median underestimation of 3.17%), especially in mixed forest stands. |
| For more details, see Waser et al. (2017).\n\nData 'Forest Type NFI | | For more details, see Waser et al. (2017).\n\nData 'Forest Type NFI |
| 2016' available on request (lars.waser@wsl.ch).", | | 2016' available on request (lars.waser@wsl.ch).", |
| "doi": "10.16904/1000001.3", | | "doi": "10.16904/1000001.3", |
| "format": "geotiff", | | "format": "geotiff", |
| "hash": "", | | "hash": "", |
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| Small, D., & Rehush, N. (2021). Mapping dominant leaf type based on | | Small, D., & Rehush, N. (2021). Mapping dominant leaf type based on |
| combined Sentinel-1/-2 data \u2013 Challenges for mountainous | | combined Sentinel-1/-2 data \u2013 Challenges for mountainous |
| countries. ISPRS Journal of Photogrammetry and Remote Sensing, 180, | | countries. ISPRS Journal of Photogrammetry and Remote Sensing, 180, |
| 209-226. https://doi.org/10.1016/j.isprsjprs.2021.08.017", | | 209-226. https://doi.org/10.1016/j.isprsjprs.2021.08.017", |
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| "description": "\n**Important note:** This dataset is NOT | | "description": "\n**Important note:** This dataset is NOT |
| suitable for analysis at the individual tree crown level because | | suitable for analysis at the individual tree crown level because |
| pixel-level fractions (e.g., of the broadleaf class) are not allocated | | pixel-level fractions (e.g., of the broadleaf class) are not allocated |
| to individual trees. Analysis is recommended only for areas larger | | to individual trees. Analysis is recommended only for areas larger |
| than 3x3 pixels, i.e. by calculating the mean values, except in rare | | than 3x3 pixels, i.e. by calculating the mean values, except in rare |
| cases of homogeneous forest stands (either broadleaf or coniferous | | cases of homogeneous forest stands (either broadleaf or coniferous |
| class).\n\nThis dataset uses a remote sensing-based approach for a | | class).\n\nThis dataset uses a remote sensing-based approach for a |
| countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, | | countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, |
| classifying areas as either broadleaf or coniferous. These datasets | | classifying areas as either broadleaf or coniferous. These datasets |
| have a spatial resolution of 10 m and provide the fractional | | have a spatial resolution of 10 m and provide the fractional |
| representation (0-100%) of the class broadleaf at the pixel level | | representation (0-100%) of the class broadleaf at the pixel level |
| (covering areas with vegetation height > 3m).\nThe classification | | (covering areas with vegetation height > 3m).\nThe classification |
| approach is based on a Random Forest (RF) classifier, that combines | | approach is based on a Random Forest (RF) classifier, that combines |
| predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data | | predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data |
| with the SwissAlti3D terrain model. The models were calibrated using | | with the SwissAlti3D terrain model. The models were calibrated using |
| digitized training polygons and independently validated data from the | | digitized training polygons and independently validated data from the |
| National Forest Inventory (NFI). Whereas high model overall accuracies | | National Forest Inventory (NFI). Whereas high model overall accuracies |
| (0.97) and kappa (0.96) were achieved, the comparison of the tree type | | (0.97) and kappa (0.96) were achieved, the comparison of the tree type |
| map with independent NFI data revealed deviations in mixed | | map with independent NFI data revealed deviations in mixed |
| stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request | | stands.\n\nThe dataset 'Forest Type NFI 2018' is available on request |
| (lars.waser@wsl.ch).", | | (lars.waser@wsl.ch).", |
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| "title": "Forest Type NFI", | | "title": "Forest Type NFI", |
| "type": "dataset", | | "type": "dataset", |
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| "version": "2023" | | "version": "2023" |
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