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Open AccessArticle

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

1
Department of Geography, The Hebrew University of Jerusalem, 91905 Jerusalem, Israel
2
Israel Nature and Parks Authority, 3 Am Ve Olamo Street, 95463 Jerusalem, Israel
3
Department of Geography and Environment, Bar Ilan University, 52900 Ramat Gan, Israel
4
Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, 91905 Jerusalem, Israel
5
3P Labs, 9432526 Jerusalem, Israel
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(11), 1130; https://doi.org/10.3390/rs9111130
Received: 19 September 2017 / Revised: 20 October 2017 / Accepted: 1 November 2017 / Published: 6 November 2017
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Most recent studies relating to the classification of vegetation species on the individual level use cutting-edge sensors and follow a data-driven approach, aimed at maximizing classification accuracy within a relatively small allocated area of optimal conditions. However, this approach does not incorporate cost-benefit considerations or the ability of applying the chosen methodology for applied mapping over larger areas with higher natural heterogeneity. In this study, we present a phenology-based cost-effective approach for optimizing the number and timing of unmanned aerial vehicle (UAV) imagery acquisition, based on a priori near-surface observations. A ground-placed camera was used in order to generate annual time series of nine spectral indices and three color conversions (red, green and blue to hue, saturation and value) in four different East Mediterranean sites that represent different environmental conditions. After outliers’ removal, the time series dataset represented 1852 individuals of 12 common vegetation species and annual herbaceous patches. A feature selection process was used for identifying the optimal dates for species classification in every site. The feature selection can be designed for various objectives, e.g., optimization of overall classification, discrimination between two species, or discrimination of one species from all others. In order to evaluate the a priori findings, a UAV was flown for acquiring five overhead multiband orthomosaics (five bands in the visible-near infrared range based on the five optimal dates identified in the feature selection of the near-surface time series of the previous year. An object-based classification methodology was used for the discrimination of 976 individuals of nine species and annual herbaceous patches in the UAV imagery, and resulted in an average overall accuracy of 85% and an average Kappa coefficient of 0.82. This cost-effective approach has high potential for detailed vegetation mapping, regarding the accessibility of UAV-produced time series, compared to hyper-spectral imagery with high spatial resolution which is more expensive and involves great difficulties in implementation over large areas. View Full-Text
Keywords: vegetation species classification; near-surface observations; feature selection; unmanned aircraft vehicles; Mediterranean vegetation vegetation species classification; near-surface observations; feature selection; unmanned aircraft vehicles; Mediterranean vegetation
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MDPI and ACS Style

Weil, G.; Lensky, I.M.; Resheff, Y.S.; Levin, N. Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection. Remote Sens. 2017, 9, 1130.

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