A comparison of feature selection methods for multitemporal tree species classification

An Operational Workflow of Deciduous-Dominated Forest Species Classification: Crown Delineation, Gap Elimination, and Object-Based Classification

“…When using single-seasonal images, we found that the late-spring and mid-summer images produced equivalent classification accuracy (i.e., OA: 0.46 vs. 0.42), but much higher than the accuracy yielded by the early-fall image (i.e., OA: 0.35). This observation was in agreement with the classification result reported by Pipkins et al [30], concluding that the late-spring and mid-summer images were the better choices than the early-fall to differentiate spectral features between species. In addition to species differences in foliage and shift caused by view angles, this could also be explained by species differences in reproductive biology, including the timing of reproduction as well as the spectral differences between reproductive structures (flowers and seeds).…”

“…Similar to [30], we found that the late-spring and mid-summer images were effective for classifying the forests where coniferous and deciduous species coexist. By the combined use of these three-season images, the significant increase in classification accuracy provided evidence that multi-seasonal images have the capability to enhance inter-species separability for tree species identification [28][29][30]43,44]. However, separation of deciduous and coniferous trees as an extra step based on a late-fall image was not successful, probably due to the complex structure of Haliburton Forest.…”

“…These bands have proven useful for separating tree species especially when there is substantial spectral overlap in the traditional four bands [16,26,27]. In addition to using more spectral bands for tree species identification, the use of seasonal time series imagery has also proven useful [28][29][30][31]. Key et al [28] found that classification using four dates of four-band aerial images yielded the best accuracy when identifying four deciduous species, including popular, red oak, white oak, and maple.…”

“…To overcome this limitation, the authors demonstrated that a combination of early-spring, mid-summer, and late-fall images was able to achieve better accuracy of six overstorey species classification than any single-seasonal image even though the timing of image collection was suboptimal [29]. Pipkins et al [30] utilized ten phenological seasons of RapidEye images to identify seven coniferous and deciduous species and suggested that the first-spring, full-spring, and early-summer images played most important roles in accuracy improvement due to individual species differences in tree phenology. Their results were in agreement with the previous studies, suggesting that a multi-seasonal approach to tree species classification has the potential to achieve better species classification results.…”