Identification of rodent hole patches in desert grasslands using UAV imagery and OBIA-CFS algorithms

Identifying rodent hole patches is ecologically crucial, yet it presents technical challenges. The combination of Unmanned Aerial Vehicle (UAV) imagery and object-oriented image analysis (OBIA) offers a promising approach to detect these patches, and explore the spatial relationships between gerbils and grass cover. However, the extensive feature space data obtained in OBIA often contains redundant information, which can reduce the efficiency and accuracy of rodent hole patch identification. This study proposes a framework integrating feature selection with OBIA to enhance rodent hole patch detection. The performance of three machine learning algorithms, support vector machine, random forest, and K-nearest neighbor, was evaluated. Our results demonstrate that the combination of feature selection and random forest algorithm achieved an overall accuracy of 91.74%, outperforming the support vector machine and K-nearest neighbor algorithms by 9.53% and 20.62%, respectively. These results highlight the effectiveness of feature selection in improving random forest’s performance while reducing the feature dimensionality. Furthermore, the use of support vector machine algorithm along with the optimal feature set exhibited the shortest processing time with an average runtime of 11.48 s per image. Additionally, our study revealed a quadratic relationship between the area of rodent hole patches and grass cover. These findings provide valuable insights for the development of data-driven rodent censuses in desert grassland.