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

Abstract

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.

Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

“…NIR bands had the greatest discriminating power of the all spectra analyzed and provided more information about different tree canopies. As stressed by Otsu et al [26], NIR band was more sensitive to defoliation than the red-edge band, as observed in Figure 7b. Regarding the remaining bands, the reflectance of the green band was generally higher than that of red band because of the high absorption related to chlorophyll content [84].…”

“…Spectral indices, whose calculation includes bands such as NDVI, also presented highly discriminating behavior. This VI is particularly used to assess defoliation, and damage at high spatial resolutions using multispectral and RGB cameras mounted on UAV [12,22,23,26,85,86].…”

“…The Otsu thresholding method is an unsupervised method used to select a threshold automatically from a gray level histogram [52]. This approach assumes that the image contains two classes of pixels following a bimodal histogram, and estimates a threshold value which splits the foreground and the background of an image [26,[52][53][54]. The threshold value is calculated aiming to minimize intra-class intensity variance [52].…”

“…In recent years the use of unmanned aerial vehicle (UAV) platforms has become widely employed for pests and disease detection and monitoring [12,[22][23][24][25][26][27][28]. The main attributes are very high resolution, suitability for multitemporal analysis, lower operational costs compared with airplanes and satellites, independence of cloud cover, and the ability to operate in specific phenological phases of plants or pest/disease outbreaks [12].…”

“…In New Zealand, Dash et al [12] used NDVI and red-edge NDVI to study the discoloration classes of Pinus radiata through the OBIA classification approach with a random forest (RF) algorithm. In Catalonia (Spain), Otsu et al [26] detected defoliation of pine trees affected by pine processionary and distinguished pine species at a pixel level using four spectral indices and NIR band. The authors estimated the threshold values using histogram analysis.…”