Optimum Phenological Phases for Deciduous Species Recognition: A Case Study on Quercus acutissima and Robinia pseudoacacia in Mount Tai

Abstract

Tree species recognition is important for remote–sensing mapping and dynamic monitoring of forest resource. However, the complex phenological cycle poses a challenge to remote–sensing recognition of deciduous tree species in mountainous areas, and the selection of temporal phase is particularly important to improve recognition accuracy. Multispectral images of Ziyuan–1 02C (ZY–1 02C) and Ziyuan–3 (ZY–3) at three phenological phases of spring, autumn and winter (12 May, 29 September and 7 December, recorded as T5–12, T9–29 and T12–7) are selected to optimize sensitive spectral indices. Support vector machine (SVM) and maximum likelihood model (MLE) are constructed to explore the optimum phase of recognizing on Quercus acutissima (O. acutissima ) and Robinia pseudoacacia (R. pseudoacacia) in Mount Tai. The results showed the average spectral reflection intensity of O. acutissima was higher than that of R. pseudoacacia Compared to other phenological periods, the most significant spectral differences between O. acutissima and R. pseudoacacia were found in the spring (12 May), which was identified as the optimum phenological phase. Band 4 is the most sensitive band in all the three phases for the tree species recognition. Moreover, the overall recognition accuracy of deciduous tree species on 12 May reached 89.25%, which was significantly higher than the other two phases. On 12 May, the recognition accuracies of SVM based on sensitive spectral indices of up to 93.59% for O. acutissima and 85.44% for R. pseudoacacia, were higher overall than that of the MLE. Sensitive spectral indices introduced were shown to significantly improve the recognition accuracy for tree species over a single sensitive band. The study is expected to facilitate the precise recognition and forestry management on Mount Tai.

Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models

“…These methods characterised by mature application, simple implementation, a small amount of computation and fast speeds, but the disadvantage is that the recognition accuracy was insufficient, and only the samples with obvious features could be correctly recognized. SVM recently have been widely used for the accurate recognition of forest species and shown higher recognition performance than other methods 12 . Clouds are a new and readily visualizable concept of uncertainty.…”

“…Multispectral remote sensing has the characteristics of faster data acquisition, lower cost and longer timeliness, especially in high resolution images, which could elaborate on the details of tree species 8 , 9 . The relative completeness and consistency of spectral and geometric information in space and time strongly facilitates the rapid recognition of tree species and large-scale mapping 10 – 12 .…”