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

Abstract

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.

Impacts of Global Change on the Spatial Dynamics of Treeline in Venezuelan Andes

“…Therefore, monitoring vegetation (Peyre et al, 2015;Cuesta et al, 2017) and landscape transformation studies are significant in implementing those models. Third, the use of new remote sensing classification techniques to know the distribution of species at a large scale, based on drones and hyperspectral images (Garzón-López and Lasso, 2020). The potential of this technology will help research in the tropical high-altitude ecosystems, to improve treeline prediction, expand existing databases (Peyre et al, 2015), make changes of scale and models (Fulton et al, 2019) improve, monitor, and detect threats to for these ecosystems.…”

Drones, Deep Learning, and Endangered Plants: A Method for Population-Level Census Using Image Analysis

“…However, using deep learning to perform classification within the target species could also be used to look at and measure other types of information. This could make it possible to develop a method for large-scale monitoring and demographic studies, similar to our earlier demographic study for the dwarf bear poppy [15], much more efficiently over even larger areas. For example, with imagery taken during the flowering season, poppies could be sub-classified by flowering class (i.e., flowering, non-flowering) and size.…”

“…We emphasize our approach for building a deep learning model in an effort to provide a beginning road map to aid conservation researchers considering the use of AI for drone-based census of other plant species. Many studies have used drones along with deep learning models to collect data in agriculture [10][11][12][13], and there are publications describing this approach for a variety of wild organisms, including plant species in general [14][15][16][17][18][19] and especially invasive species [20][21][22][23]. Interest in using drone imagery as a tool in rare plant conservation is increasing [14], but to our knowledge no published studies to date have successfully applied a deep learning approach to drone-acquired imagery with the goal of enumerating individuals of a rare plant species.…”

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

“…Hyperspectral remote sensing image is characterized by high dimension, high resolution, and rich spectral and spatial information [1], which have been diffusely used in numerous real-world tasks, such as sea ice detection [2], ecosystem monitoring [3,4], vegetation species analysis [5] and classification tasks [6,7]. With the speedy progress of remote sensing technology and artificial intelligence (AI), a great proportion of new theories and methods in deep learning have been proposed to handle the challenges and problems faced by the field of hyperspectral image [8].…”