Estimating boreal forest ground cover vegetation composition from nadir photographs using deep convolutional neural networks

Cameron, H.A. and Panda, P. and Barczyk, M. and et al, . (2022) Estimating boreal forest ground cover vegetation composition from nadir photographs using deep convolutional neural networks. Ecological Informatics, 69. pp. 1-13. ISSN 1574-9541

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Abstract

Ground cover and surface vegetation information are key inputs to wildfire propagation models and are important indicators of ecosystem health. Often these variables are approximated using visual estimation by trained professionals but the results are prone to bias and error. This study analyzed the viability of using nadir or downward photos from smartphones (iPhone 7) to provide quantitative ground cover and biomass loading estimates. Good correlations were found between field measured values and pixel counts from manually segmented photos delineating a pre-defined set of 10 discrete cover types. Although promising, segmenting photos manually was labor intensive and therefore costly. We explored the viability of using a trained deep convolutional neural network (DCNN) to perform image segmentation automatically. The DCNN was able to segment nadir images with 95% accuracy when compared with manually delineated photos. To validate the flexibility and robustness of the automated image segmentation algorithm, we applied it to an independent dataset of nadir photographs captured at a different study site with similar surface vegetation characteristics to the training site with promising results. Â© 2022 The Authors

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IITH Creators:

IITH Creators	ORCiD

Item Type:

Article

Additional Information:

This research was funded by Alberta Agriculture, Forestry and Rural Economic Development (AAFRED) through the Canadian Partnership for Wildland Fire Science, grant agreement number 18GRWMB06. We thank AAFRED field crews for assistance with data collection; A. Sharma for assistance manually processing nadir photographs; J. Randall for contributions to literature review; and B. Finch for computing systems support.

Subjects:

Physics > Mechanical and aerospace
Computer science

Divisions:

Department of Computer Science & Engineering
Department of Mechanical & Aerospace Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

22 Jun 2022 05:52

Last Modified:

22 Jun 2022 09:15

URI:

http://raiithold.iith.ac.in/id/eprint/9340