Abstract
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.
Highlights
Detailed maps of forest composition are necessary for effective and efficient forest management [1,2]
There was a distinct leveling off in the OA as the number of dates included in the multi-temporal classification increased, reaching the peak
(2) compare the performance temporal and multi-temporal classifications of 14 different species were carried out both imagery collected via a consumer-grade to that of a multispectral camera
Summary
Detailed maps of forest composition are necessary for effective and efficient forest management [1,2]. Traditional remote sensing platforms, such as satellite or aerial imagery, are incapable of providing the temporal and/or spatial resolutions necessary for species level mapping at an affordable cost [12,13]. Thanks to recent technological advancements, unmanned aerial systems (UASs) have become an affordable alternative, capable of providing the flexibility and resolution necessary to accurately map forest species composition [2,14]. Many studies have employed UASs to map individual or small groupings of invasive plants [17,18,19,20], shrubs, grasses, and forbs [21,22,23,24,25], as well as wetlands [26,27]. All are taking advantage of the imagery’s high spatial resolution to distinguish and classify individual trees or small groupings of trees of the same species with positive results
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