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In recent years, increasing tree diebacks and mortality in some forests, particularly in forest parks, created a need amongst forest managers to find effective methods to gather information about the rate of dieback and mortality and their reasons. High-quality air and space-born remote sensing data has established as an alternative to field surveys for certain inventory tasks. This study used high-quality UltraCam-Xp and UAV drone images from 2016 and 2021 to map tree dieback and mortality in Daland Forest Park, Golestan Province, Iran. High-quality ortho mosaics and Digital Surface Models (DSMs) were generated from UltraCam (2016) and UAV (2021) images. The images were then classified through object-based classification by Nearest Neighbor (NN), Support Vector Machine (SVM), and Bayes algorithms using various input data sets including spectral bands, Canopy Height Model (CHM), vegetation indices, and texture analysis features. Our results indicate that the Bayes algorithm is more precise in mapping tree dieback for the two time steps compared to other algorithms. The best tree dieback map on UltraCam images was obtained using the spectral bands with CHM, texture analysis features, and vegetation indices. This combination resulted in an overall accuracy of 91.20% and a Kappa coefficient of 0.88. It was also found that combining the UAV main bands with CHM and texture features did produce a high-accuracy map with an overall accuracy of 88.46% and a Kappa coefficient of 0.84. Change detection analysis of tree dieback showed that between 2016 and 2021, the number of healthy trees decreased, and the number of gaps and open areas increased in the study area. We conclude that UltraCam and UAV photographs can serve to identify and map tree dieback and dead trees with good accuracies and can hence support forest health monitoring.

Analysis of forest tree dieback using UltraCam and UAV imagery