MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Assessment of tree detection methods in multispectral aerial images

Author(s)
Pulido, Dagoberto; Salas, Joaquín; Rös, Matthias; Puettmann, Klaus; Karaman, Sertac
Thumbnail
Downloadremotesensing-12-02379-v2.pdf (26.65Mb)
Publisher with Creative Commons License

Publisher with Creative Commons License

Creative Commons Attribution

Terms of use
Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/
Metadata
Show full item record
Abstract
Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM.
Date issued
2020-07-24
URI
https://hdl.handle.net/1721.1/127684
Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Journal
Remote Sensing
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Pulido, Dagoberto et al. "Assessment of tree detection methods in multispectral aerial images." Remote Sensing 12, 15 (July 2020): 2379 ©2020 Author(s)
Version: Final published version
ISSN
2072-4292

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.