Abstract
Automatic discrimination of tree species and identification of physiological stress imposed on forest trees by biotic factors from unmanned aerial systems (UAS) offers substantial advantages in forest management practices. In this study, we aimed to develop a novel workflow for facilitating tree species classification and the detection of healthy, unhealthy, and dead trees caused by bark beetle infestation using ultra-high resolution 5-band UAS bi-temporal aerial imagery in the Czech Republic. The study is divided into two steps. We initially classified the tree type, either as broadleaf or conifer, and we then classified trees according to the tree type and health status, and subgroups were created to further classify trees (detailed classification). Photogrammetric processed datasets achieved by the use of structure-from-motion (SfM) imaging technique, where resulting digital terrain models (DTMs), digital surface models (DSMs), and orthophotos with a resolution of 0.05 m were utilized as input for canopy spectral analysis, as well as texture analysis (TA). For the spectral analysis, nine vegetation indices (VIs) were applied to evaluate the amount of vegetation cover change of canopy surface between the two seasons, spring and summer of 2019. Moreover, 13 TA variables, including Mean, Variance, Entropy, Contrast, Heterogeneity, Homogeneity, Angular Second Moment, Correlation, Gray-level Difference Vector (GLDV) Angular Second Moment, GLDV Entropy, GLDV Mean, GLDV Contrast, and Inverse Difference, were estimated for the extraction of canopy surface texture. Further, we used the support vector machine (SVM) algorithm to conduct a detailed classification of tree species and health status. Our results highlighted the efficiency of the proposed method for tree species classification with an overall accuracy (OA) of 81.18% (Kappa: 0.70) and health status assessment with an OA of 84.71% (Kappa: 0.66). While SVM proved to be a good classifier, the results also showed that a combination of VI and TA layers increased the OA by 4.24%, providing a new dimension of information derived from UAS platforms. These methods could be used to quickly evaluate large areas that have been impacted by biological disturbance agents for mapping and detection, tree inventory, and evaluating habitat conditions at relatively low costs.
Highlights
Climate-related outbreaks of bark beetle species pose a serious threat to the temperate forests of Europe
While support vector machine (SVM) proved to be a good classifier, the results showed that a combination of vegetation indices (VIs) and texture analysis (TA) layers increased the overall accuracy (OA) by 4.24%, providing a new dimension of information derived from Unmanned aerial systems (UAS) platforms
It is obvious from the post-hoc analysis in Figure 7 that NDVI, SAVI, and MCARI2 performed the best in classifying the reference data from spring 2019, whereas SAVI, MCARI2, and plant senescence reflectance index (PSRI) had the best performance for the summer period (Figure 8)
Summary
Climate-related outbreaks of bark beetle species pose a serious threat to the temperate forests of Europe. Increasing forest disturbances, such as moths [1], pine sawflies [2], and bark beetles [3], gave rise to substantial economic loss and other values [4]. Forest managers need to be more responsive to disturbances and the automatic and cost-efficient acquisition of tree-level geospatial information from remotely-sensed data would be very beneficial. Collection of post-disturbance tree information is essential in many aspects: (i) monitoring and mapping of invasive species [6] (ii) assessment of non-timber values (e.g., biodiversity assessment) (iii) mapping of wildlife habitats [7] (iv) prediction of future yields [8] and (v) estimation of the monetary value of the forest, woody biomass, and growing stock [9]. Unmanned aerial systems (UAS) are modern and versatile tools, offering a huge range of capabilities in a wide range of geospatial surveys with increased accuracy when it comes to detailed forest management applications. As a low-altitude airborne platform, UAS can collect data in high and very-high resolutions (VHR), that can be used in numerous research activities, such as tree diameter, volume estimation [10,11], and forest health assessment [12,13]
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