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.
Highlights
Climate change is predicted to continue increasing global temperatures over this century [1], which may lead to an alteration of forest disturbances including pest insects that are strongly dependent on climatic variables [2,3,4,5]
While the previous study in the Codo area [16] applied pixel-based unsupervised classification with normalized difference vegetation index (NDVI) to calculate the percentage of defoliation per tree crown area identified by individual tree delineation algorithm, in this study we focused on determining the threshold values of four vegetation indices (VIs) and their variations among four study areas as well as evaluating the performance of each VI
In each study area 100 pixels were randomly selected to assess the accuracy of final classification results by histogram thresholding analyses and Random Forest, separately, with predicted indices derived from the NIR imagery, in reference to ground observations based on the RGB orthomosaic images
Summary
Climate change is predicted to continue increasing global temperatures over this century [1], which may lead to an alteration of forest disturbances including pest insects that are strongly dependent on climatic variables [2,3,4,5] Such a combination of biotic and abiotic disturbance factors may accelerate forest damage as defoliation, growth reduction, and tree mortality in relation to global changes [6]. Since medium-high spatial resolution images from Sentinel-2 (10–20 m) became freely downloadable in 2015 [18], cost–effective monitoring of large areas is increasing With such further advancements in spaceborne technology, sensors’ spatial resolution continues to enhance temporal and spectral resolutions [19]. Airborne laser scanning (ALS) featuring point clouds complements the three-dimensional (3D) structure besides capturing two-dimensional (2D)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
