RETRIEVING FOREST STRUCTURE VARIABLES FROM VERY HIGH RESOLUTION SATELLITE IMAGES USING AN AUTOMATIC METHOD

8 citations as recorded by crossref.

1. Potential of Multi-scale Completed Local Binary Pattern for Object Based Classification of Very High Spatial Resolution Imagery R. Chairet et al. https://doi.org/10.1007/s12524-021-01311-y
2. Assessing climate-driven treeline dynamics via the habitat suitability index L. Li et al. https://doi.org/10.1016/j.jenvman.2026.130173
3. MLS-assisted validation of WorldView-2 panchromatic image for estimating Pinus sylvestris crown height Y. Lin et al. https://doi.org/10.1080/2150704X.2015.1011351
4. Structure Tensor Riemannian Statistical Models for CBIR and Classification of Remote Sensing Images R. Rosu et al. https://doi.org/10.1109/TGRS.2016.2604680
5. TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest Y. Lin et al. https://doi.org/10.1080/17538947.2016.1247473
6. Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery B. Beguet et al. https://doi.org/10.1016/j.isprsjprs.2014.07.008
7. Predicting old-growth tropical forest attributes from very high resolution (VHR)-derived surface metrics J. Solórzano et al. https://doi.org/10.1080/01431161.2016.1266108
8. The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture J. Gallardo-Cruz et al. https://doi.org/10.1155/2024/7178211