Census of individual trees of Tucunare Vichada (2023)
This dataset contains information of estimated individual trees in Tucunaré, Vichada-Colombia extracted from remote sensing imagery processed using AI models and the TreeEyed QGIS Plugin. The dataset includes a CSV file with attributes related to tree dimensions and geolocation and a shapefile with...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Conjunto de datos |
| Language: | Inglés |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/163198 |
| _version_ | 1855540617932701696 |
|---|---|
| author | Ruiz-Hurtado, Andres Felipe Pérez Bolaños, Juliana Arrechea-Castillo, Darwin Alexis Matiz Rubio, Natalia Costa Junior, Ciniro Arango Mejia, Jacobo Cardoso Arango, Juan Andres |
| author_browse | Arango Mejia, Jacobo Arrechea-Castillo, Darwin Alexis Cardoso Arango, Juan Andres Costa Junior, Ciniro Matiz Rubio, Natalia Pérez Bolaños, Juliana Ruiz-Hurtado, Andres Felipe |
| author_facet | Ruiz-Hurtado, Andres Felipe Pérez Bolaños, Juliana Arrechea-Castillo, Darwin Alexis Matiz Rubio, Natalia Costa Junior, Ciniro Arango Mejia, Jacobo Cardoso Arango, Juan Andres |
| author_sort | Ruiz-Hurtado, Andres Felipe |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This dataset contains information of estimated individual trees in Tucunaré, Vichada-Colombia extracted from remote sensing imagery processed using AI models and the TreeEyed QGIS Plugin. The dataset includes a CSV file with attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree.
Metodology: The tree information for this dataset was obtained using a custom QGIS plugin (TreeEyed) and python scripting, designed to process high-resolution RGB remote sensing imagery to derive tree data. Leveraging AI pretrained models, in this case Meta's HighResCanopyHeight model was employed. The methodology involved processing RGB imagery with a resolution of 0.5 meters and dimensions of 20,406x19,871 pixels for the region of interest, a subdivision in 401 tiles of 1024x1024 pixels to process and obtain canopy tree height rasters, merging, vectorizing tree instances, and filtering to isolate individual trees. Zonal statistics were then applied to assign estimated tree height values to each instance by extracting the maximum pixel value within the tree crown polygon boundary. This approach allowed for flexible and localized analysis, enabling the derivation of detailed tree metrics such as height, crown area and perimeter, and equivalent diameter from high-resolution RGB imagery. |
| format | Conjunto de datos |
| id | CGSpace163198 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | CGSpace1631982025-06-03T13:36:44Z Census of individual trees of Tucunare Vichada (2023) Ruiz-Hurtado, Andres Felipe Pérez Bolaños, Juliana Arrechea-Castillo, Darwin Alexis Matiz Rubio, Natalia Costa Junior, Ciniro Arango Mejia, Jacobo Cardoso Arango, Juan Andres remote sensing artificial intelligence This dataset contains information of estimated individual trees in Tucunaré, Vichada-Colombia extracted from remote sensing imagery processed using AI models and the TreeEyed QGIS Plugin. The dataset includes a CSV file with attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree. Metodology: The tree information for this dataset was obtained using a custom QGIS plugin (TreeEyed) and python scripting, designed to process high-resolution RGB remote sensing imagery to derive tree data. Leveraging AI pretrained models, in this case Meta's HighResCanopyHeight model was employed. The methodology involved processing RGB imagery with a resolution of 0.5 meters and dimensions of 20,406x19,871 pixels for the region of interest, a subdivision in 401 tiles of 1024x1024 pixels to process and obtain canopy tree height rasters, merging, vectorizing tree instances, and filtering to isolate individual trees. Zonal statistics were then applied to assign estimated tree height values to each instance by extracting the maximum pixel value within the tree crown polygon boundary. This approach allowed for flexible and localized analysis, enabling the derivation of detailed tree metrics such as height, crown area and perimeter, and equivalent diameter from high-resolution RGB imagery. 2024 2024-12-09T07:55:46Z 2024-12-09T07:55:46Z Dataset https://hdl.handle.net/10568/163198 en Open Access Ruiz Hurtado, A.F.; Perez Bolanos, J.; Arrechea Castillo, D.A.; Matiz Rubio, N.; Costa Junior, C.; Arango Mejia, J.; Cardoso Arango, J.A. (2024) Census of individual trees of Tucunare Vichada (2023). https://doi.org/10.7910/DVN/JRLV83 |
| spellingShingle | remote sensing artificial intelligence Ruiz-Hurtado, Andres Felipe Pérez Bolaños, Juliana Arrechea-Castillo, Darwin Alexis Matiz Rubio, Natalia Costa Junior, Ciniro Arango Mejia, Jacobo Cardoso Arango, Juan Andres Census of individual trees of Tucunare Vichada (2023) |
| title | Census of individual trees of Tucunare Vichada (2023) |
| title_full | Census of individual trees of Tucunare Vichada (2023) |
| title_fullStr | Census of individual trees of Tucunare Vichada (2023) |
| title_full_unstemmed | Census of individual trees of Tucunare Vichada (2023) |
| title_short | Census of individual trees of Tucunare Vichada (2023) |
| title_sort | census of individual trees of tucunare vichada 2023 |
| topic | remote sensing artificial intelligence |
| url | https://hdl.handle.net/10568/163198 |
| work_keys_str_mv | AT ruizhurtadoandresfelipe censusofindividualtreesoftucunarevichada2023 AT perezbolanosjuliana censusofindividualtreesoftucunarevichada2023 AT arrecheacastillodarwinalexis censusofindividualtreesoftucunarevichada2023 AT matizrubionatalia censusofindividualtreesoftucunarevichada2023 AT costajuniorciniro censusofindividualtreesoftucunarevichada2023 AT arangomejiajacobo censusofindividualtreesoftucunarevichada2023 AT cardosoarangojuanandres censusofindividualtreesoftucunarevichada2023 |