Urban green space vegetation height modeling and intelligent classification based on UAV multi-spectral and oblique high-resolution images

Urban green space (UGS) vegetation plays an important role in mitigating the urban heat island effect by improving the environment and quality of life. Hence, there is a dire necessity for urban planning and management to precisely obtain the spatial distribution and structural information employing high-resolution data. Nevertheless, the limitations of remote sensing (RS) data and the complexity of urban landscapes pose significant challenges, so this study aims to introduce a method to classify UGS vegetation more precisely by integrating high spatial resolution multi-spectral and oblique photography images captured by unmanned aerial vehicle (UAV). A novel canopy height model (CHM) method is proposed to generate UGS vegetation information for urban areas while addressing the errors associated with traditional approaches in estimating non-ground vegetation heights, achieving a total Mean Absolute Error (MAE) of 0.17 m and an overall accuracy of 95.03 %. The proposed UGS mapping method combines spectral features, canopy height information, vegetation indices (VIs), and texture features to evaluate the impact of various characteristics on classification accuracy. The obtained experimental results show that by incorporating canopy height information classification accuracy is significantly improved and achieve overall accuracy of 93.82 % and Kappa coefficient of 0.91. Moreover, the proposed method not only precisely reflects the structure and distribution of UGS vegetation by showing specific advantages in complex environments but also offers a new arena for UGS vegetation classification based on the integration of multiple features.