A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests
Department of Natural Resources & the Environment, University of New Hampshire, 56 College Rd, Durham, NH 03824, USA
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Remote Sens. 2021, 13(13), 2631; https://doi.org/10.3390/rs13132631
Received: 4 June 2021
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Revised: 28 June 2021
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Accepted: 1 July 2021
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Published: 4 July 2021
(This article belongs to the Special Issue Estimating and Monitoring Forest Structure Using Remote Sensing Techniques)
Unmanned aerial systems (UASs) have recently become an affordable means to map forests at the species level, but research into the performance of different classification methodologies and sensors is necessary so users can make informed choices that maximize accuracy. This study investigated whether multi-temporal UAS data improved the classified accuracy of 14 species examined the optimal time-window for data collection, and compared the performance of a consumer-grade RGB sensor to that of a multispectral sensor. A time series of UAS data was collected from early spring to mid-summer and a sequence of mono-temporal and multi-temporal classifications were carried out. Kappa comparisons were conducted to ascertain whether the multi-temporal classifications significantly improved accuracy and whether there were significant differences between the RGB and multispectral classifications. The multi-temporal classification approach significantly improved accuracy; however, there was no significant benefit when more than three dates were used. Mid- to late spring imagery produced the highest accuracies, potentially due to high spectral heterogeneity between species and homogeneity within species during this time. The RGB sensor exhibited significantly higher accuracies, probably due to the blue band, which was found to be very important for classification accuracy and lacking in the multispectral sensor employed here.
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Keywords:
remote sensing; forests; New Hampshire; UAS; multi-temporal; species level; OBIA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Grybas, H.; Congalton, R.G. A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests. Remote Sens. 2021, 13, 2631. https://doi.org/10.3390/rs13132631
AMA Style
Grybas H, Congalton RG. A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests. Remote Sensing. 2021; 13(13):2631. https://doi.org/10.3390/rs13132631
Chicago/Turabian StyleGrybas, Heather, and Russell G. Congalton. 2021. "A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests" Remote Sensing 13, no. 13: 2631. https://doi.org/10.3390/rs13132631
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