Please use this identifier to cite or link to this item:
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones
Author(s): Medeiros, Thaís Pereira de
Morellato, Leonor Patrícia Cerdeira
Silva, Thiago Sanna Freire
Contact Email:
Keywords: UAS
unmanned aerial system
machine learning
random forest
heterogeneous vegetation
rupestrian grassland
Issue Date: 2023
Date Deposited: 8-Mar-2023
Citation: Medeiros TPd, Morellato LPC & Silva TSF (2023) Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones. <i>Frontiers in Environmental Science</i>, 11, Art. No.: 1083328.
Abstract: Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland.
DOI Link: 10.3389/fenvs.2023.1083328
Rights: © 2023 Medeiros, Morellato and Silva. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Licence URL(s):

Files in This Item:
File Description SizeFormat 
fenvs-11-1083328.pdfFulltext - Published Version5.32 MBAdobe PDFView/Open

This item is protected by original copyright

A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.