Persistent Identifier
|
doi:10.7910/DVN/5TEA8E |
Publication Date
|
2024-11-22 |
Title
| Dataset: Census of individual trees of Hacienda San Jose Vichada (2020) |
Author
| Ruiz Hurtado, Andres FelipeInternational Center for Tropical Agriculture - CIATORCIDhttps://orcid.org/0000-0003-1293-8736
Perez Bolanos, JulianaInternational Center for Tropical Agriculture - CIATORCIDhttps://orcid.org/0009-0008-8076-724X
Arrechea Castillo, Darwin AlexisInternational Center for Tropical Agriculture - CIATORCIDhttps://orcid.org/0000-0002-2395-2181
Matiz Rubio, NataliaInternational Center for Tropical Agriculture - CIAT
Costa Junior, CiniroInternational Center for Tropical Agriculture - CIATORCIDhttps://orcid.org/0000-0003-1854-2927
Arango Mejia, JacoboInternational Center for Tropical Agriculture - CIATORCIDhttps://orcid.org/0000-0002-4828-9398
Cardoso Arango, Juan AndrésInternational Center for Tropical Agriculture - CIATORCIDhttps://orcid.org/0000-0002-0252-4655 |
Point of Contact
|
Use email button above to contact.
Alliance Data Management (Bioversity International and the International Center for Tropical Agriculture) |
Description
| This dataset contains information of individual trees in Hacienda San Jose (HSJ), Vichada-Colombia extracted from remote sensing imagery processed using AI models. Information from the High Resolution 1-meter Global Canopy Heights map from Meta-AI. The dataset includes a CSV file containing attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree.
Methodology:Tree information was obtained by leveraging the High-Resolution 1 m Global Canopy Height map generated by Meta's AI model, trained on a large dataset of satellite images and LIDAR data, to predict canopy heights with a mean absolute error of 2.8 meters. The map tiles were downloaded from Amazon Web Services (AWS) S3 Bucket and processed using additional steps: merging and clipping tiles to cover the region of interest of Hacienda San Jose, vectorizing, filtering, and georeferencing individual trees to derive attributes like area, perimeter, equivalent diameter, and circularity representing the visual crown of each tree, and finally applying zonal statistics to assign tree height values. The resulting vector layer contains detailed tree metrics, supporting advanced canopy analysis. (2024-11) |
Subject
| Earth and Environmental Sciences; Agricultural Sciences |
Keyword
| remote sensing (AGROVOC) http://aims.fao.org/aos/agrovoc/c_6498
artificial intelligence (AGROVOC) http://aims.fao.org/aos/agrovoc/c_27064
Americas (Research Region)
Crops for Nutrition and Health (Research Lever) |
Topic Classification
| data collection (AGROVOC) http://aims.fao.org/aos/agrovoc/c_2128
trees (AGROVOC) http://aims.fao.org/aos/agrovoc/c_7887
agroforestry (AGROVOC) http://aims.fao.org/aos/agrovoc/c_207
silvopastoral systems (AGROVOC) http://aims.fao.org/aos/agrovoc/c_16097 |
Language
| English |
Producer
| International Center for Tropical Agriculture (CIAT) https://ciat.cgiar.org/ 
|
Production Date
| 2024 |
Funding Information
| USA-Bezos Earth Fund though Bioversity International USA, Inc .- Using genetic diversity to capture carbon through deep root systems in tropical soils (Research and Capacity development): A486 |
Distributor
| International Center for Tropical Agriculture (CIAT) https://ciat.cgiar.org/ 
|
Depositor
| Alliance Data Management |
Deposit Date
| 2024-11-18 |
Time Period
| Start Date: 2018; End Date: 2020 |
Date of Collection
| Start Date: 2018; End Date: 2020 |
Data Type
| Geospatial Data; Interpolated Data; Quantitative Data; Geographic Data |
Other Reference
| Tolan, J., Yang, H.-I., Nosarzewski, B., Couairon, G., Vo, H.V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., Moutakanni, T., Bojanowski, P., Johns, T., White, B., Tiecke, T., Couprie, C., 2024. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sensing of Environment 300, 113888. https://doi.org/10.1016/j.rse.2023.113888 |