Assessment of tree detection methods in multispectral aerial images
Name
remotesensing-12-02379-v2.pdf
Size
26.66 MB
Format
Adobe PDF
Checksum (MD5)
7d2d1659e70c12c014bdde8ceed2070f
Author(s)
Pulido, Dagoberto
Salas, Joaquín
Rös, Matthias
Puettmann, Klaus
Karaman, Sertac
Date Issued
July 24, 2020
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/rs12152379
