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UAV-g 2019: Unmanned Aerial Vehicles in Geomatics
Open AccessArticle

Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

1
Centre for Ecological Research and Forestry Applications (CREAF), 08193 Cerdanyola del Vallès, Spain
2
InForest JRU (CTFC–CREAF), Carretera de Sant Llorenç de Morunys Km 2, Solsona, 25280 Lleida, Spain
3
Department of Crops and Forest Sciences, University of Lleida, Avenida Rovira Roure 191, 25198 Lleida, Spain
4
Spanish National Research Council (CSIC), 08193 Cerdanyola del Vallès, Spain
*
Authors to whom correspondence should be addressed.
Drones 2019, 3(4), 80; https://doi.org/10.3390/drones3040080
Received: 13 September 2019 / Revised: 14 October 2019 / Accepted: 25 October 2019 / Published: 29 October 2019
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)
Periodical outbreaks of Thaumetopoea pityocampa feeding on pine needles may pose a threat to Mediterranean coniferous forests by causing severe tree defoliation, growth reduction, and eventually mortality. To cost–effectively monitor the temporal and spatial damages in pine–oak mixed stands using unmanned aerial systems (UASs) for multispectral imagery, we aimed at developing a simple thresholding classification tool for forest practitioners as an alternative method to complex classifiers such as Random Forest. The UAS flights were performed during winter 2017–2018 over four study areas in Catalonia, northeastern Spain. To detect defoliation and further distinguish pine species, we conducted nested histogram thresholding analyses with four UAS-derived vegetation indices (VIs) and evaluated classification accuracy. The normalized difference vegetation index (NDVI) and NDVI red edge performed the best for detecting defoliation with an overall accuracy of 95% in the total study area. For discriminating pine species, accuracy results of 93–96% were only achievable with green NDVI in the partial study area, where the Random Forest classification combined for defoliation and tree species resulted in 91–93%. Finally, we achieved to estimate the average thresholds of VIs for detecting defoliation over the total area, which may be applicable across similar Mediterranean pine stands for monitoring regional forest health on a large scale. View Full-Text
Keywords: unmanned aerial systems (UAS); multispectral imagery; forest defoliation; Thaumetopoea pityocampa; vegetation index; thresholding analysis unmanned aerial systems (UAS); multispectral imagery; forest defoliation; Thaumetopoea pityocampa; vegetation index; thresholding analysis
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MDPI and ACS Style

Otsu, K.; Pla, M.; Duane, A.; Cardil, A.; Brotons, L. Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery. Drones 2019, 3, 80.

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