Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery

Rapidly determining the density of effective gerbil holes is ecologically important but technically challenging. However, unmanned aerial vehicles (UAVs) offer new methods to identify effective gerbil holes and understand the spatial relationships between gerbils and grass coverage. In this study, we focused on Meriones unguiculatus, which live in desert grasslands of Ordos, and gathered UAV imagery to explore the performance of rule-based classification in object-based image analysis (OBIA) for identifying effective gerbil holes. To improve the classification accuracy and reduce the time cost of repeated 'trial and error', we propose a novel method to build rule sets. We adopted the estimation of scale parameter_2 (ESP2) method during the segmentation stage to enable fast and objective segmentation parameterization. For the classification stage, a correlation-based feature selection (CFS) algorithm was applied for feature selection and threshold prediction. Our analysis determined the following universally recommended rule sets for identifying effective gerbil holes using OBIA: scale parameter of 107, shape factor of 0.2, compactness value of 0.3, excess green value less than -5, brightness feature value in the 143-149 range and roundness feature less than 0.9. With these rule sets, OBIA exhibited higher classification accuracy than maximum likelihood (ML) classification, with an overall classification accuracy of 88.25%. We also found that the relationship between the area of effective gerbil holes (y) and the grass coverage (x) satisfied a quadratic function: y = - 1.981 x( 2) + 1.104 x + 0.416 . This study provides practical guidance for grassland management and rodent infestation control.

Automated summary

This study explores the performance of rule-based classification in object-based image analysis (OBIA) for identifying effective gerbil holes using UAV imagery. A novel method to build rule sets is proposed to improve classification accuracy and reduce the time cost of repeated 'trial and error'. OBIA demonstrated higher classification accuracy than maximum likelihood (ML) classification, with an overall accuracy of 88.25%. The study also provides insights into the relationship between the area of effective gerbil holes and grass coverage, which follows a quadratic function. Practical guidance for grassland management and rodent infestation control is provided.