Bioprospecting Model for a New Colombia Drug Discovery Initiative in the Pharmaceutical Industry

Bueno, Juan; Ritoré, Sebastian

doi:10.1007/978-3-030-13578-2_3

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Páramos are located across the highlands of Costa Rica and Panamá down to the northern Andes of Ecuador, Colombia, Venezuela, and Peru [1]. These highly diverse tropical alpine ecosystems provide various services to some of the main capital cities in Latin America, including clean water provision and carbon storage [2], and are an essential reservoir of species with high pharmaceutical potential [3]. Currently, this ecosystem is facing multiple threats that are reducing its surface area.…”

Section: Introductionmentioning

confidence: 99%

Species Classification in a Tropical Alpine Ecosystem Using UAV-Borne RGB and Hyperspectral Imagery

Garzón‐López

Lasso

2020

Drones

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Páramos host more than 3500 vascular plant species and are crucial water providers for millions of people in the northern Andes. Monitoring species distribution at large scales is an urgent conservation priority in the face of ongoing climatic changes and increasing anthropogenic pressure on this ecosystem. For the first time in this ecosystem, we explored the potential of unoccupied aerial vehicles (UAV)-borne red, green, and blue wavelengths (RGB) and hyperspectral imagery for páramo species classification by collecting both types of images in a 10-ha area, and ground vegetation cover data from 10 plots within this area. Five plots were used for calibration and the other five for validation. With the hyperspectral data, we tested our capacity to detect five representative páramo species with different growth forms using support vector machine (SVM) and random forest (RF) classifiers in combination with three feature selection methods and two class groups. Using RGB images, we could classify 21 species with an accuracy greater than 97%. From hyperspectral imaging, the highest accuracy (89%) was found using models built with RF or SVM classifiers combined with a binary grouping method and the sequential floating forward selection feature. Our results demonstrate that páramo species can be accurately mapped using both RGB and hyperspectral imagery.

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Section: Introductionmentioning

confidence: 99%