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, w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2023.2215722 |
| _version_ | 1797681612169674752 |
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| author | Guimei Qi Zhihong Yu Zhenjie Hou Ying Guo Billiger Huth |
| author_facet | Guimei Qi Zhihong Yu Zhenjie Hou Ying Guo Billiger Huth |
| author_sort | Guimei Qi |
| collection | DOAJ |
| description | 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: This study provides practical guidance for grassland management and rodent infestation control. |
| first_indexed | 2024-03-11T23:47:26Z |
| format | Article |
| id | doaj.art-d7a54482bf5a4de18ca02804ca525da6 |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-11T23:47:26Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-d7a54482bf5a4de18ca02804ca525da62023-09-19T09:13:18ZengTaylor & Francis GroupGeocarto International1010-60491752-07622023-12-0138110.1080/10106049.2023.22157222215722Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imageryGuimei Qi0Zhihong Yu1Zhenjie Hou2Ying Guo3Billiger Huth4College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural UniversityCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural UniversitySchool of Computer Science and AI, Changzhou UniversitySchool of Information Engineering, Inner Mongolia University of Science & TechnologyForestry and Grassland Bureau of Otog BannerRapidly 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: This study provides practical guidance for grassland management and rodent infestation control.http://dx.doi.org/10.1080/10106049.2023.2215722effective gerbil holesuav imageryobiacfs |
| spellingShingle | Guimei Qi Zhihong Yu Zhenjie Hou Ying Guo Billiger Huth Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery Geocarto International effective gerbil holes uav imagery obia cfs |
| title | Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery |
| title_full | Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery |
| title_fullStr | Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery |
| title_full_unstemmed | Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery |
| title_short | Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery |
| title_sort | toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery |
| topic | effective gerbil holes uav imagery obia cfs |
| url | http://dx.doi.org/10.1080/10106049.2023.2215722 |
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