Spotting East African mammals in open savannah from Space
Knowledge of population dynamics is essential for managing and conserving wildlife. Traditional methods of counting wild animals such as aerial survey or ground counts not only disturb animals, but also can be labour intensive and costly. New, commercially available very high-resolution satellite im...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2014
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| Acceso en línea: | https://hdl.handle.net/10568/56744 |
| _version_ | 1855527623057211392 |
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| author | Zheng Yang Tiejun Wang Skidmore, Andrew K. Leeuw, Jan de Said, Mohammed Yahya Freer, J. |
| author_browse | Freer, J. Leeuw, Jan de Said, Mohammed Yahya Skidmore, Andrew K. Tiejun Wang Zheng Yang |
| author_facet | Zheng Yang Tiejun Wang Skidmore, Andrew K. Leeuw, Jan de Said, Mohammed Yahya Freer, J. |
| author_sort | Zheng Yang |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Knowledge of population dynamics is essential for managing and conserving wildlife. Traditional methods of counting wild animals such as aerial survey or ground counts not only disturb animals, but also can be labour intensive and costly. New, commercially available very high-resolution satellite images offer great potential for accurate estimates of animal abundance over large open areas. However, little research has been conducted in the area of satellite-aided wildlife census, although computer processing speeds and image analysis algorithms have vastly improved. This paper explores the possibility of detecting large animals in the open savannah of Maasai Mara National Reserve, Kenya from very high-resolution GeoEye-1 satellite images. A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space, and therefore provide a complementary and alternative approach to the conventional wildlife survey techniques. |
| format | Journal Article |
| id | CGSpace56744 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace567442024-01-09T09:53:53Z Spotting East African mammals in open savannah from Space Zheng Yang Tiejun Wang Skidmore, Andrew K. Leeuw, Jan de Said, Mohammed Yahya Freer, J. Knowledge of population dynamics is essential for managing and conserving wildlife. Traditional methods of counting wild animals such as aerial survey or ground counts not only disturb animals, but also can be labour intensive and costly. New, commercially available very high-resolution satellite images offer great potential for accurate estimates of animal abundance over large open areas. However, little research has been conducted in the area of satellite-aided wildlife census, although computer processing speeds and image analysis algorithms have vastly improved. This paper explores the possibility of detecting large animals in the open savannah of Maasai Mara National Reserve, Kenya from very high-resolution GeoEye-1 satellite images. A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space, and therefore provide a complementary and alternative approach to the conventional wildlife survey techniques. 2014-12-31 2015-02-12T15:42:32Z 2015-02-12T15:42:32Z Journal Article https://hdl.handle.net/10568/56744 en Open Access Public Library of Science Zheng Yang, Tiejun Wang, Skidmore, A.K., Leeuw, J. de., Said M.Y. and Freer, J. 2014. Spotting East African mammals in open savannah from Space. PLoS ONE 9(12) |
| spellingShingle | Zheng Yang Tiejun Wang Skidmore, Andrew K. Leeuw, Jan de Said, Mohammed Yahya Freer, J. Spotting East African mammals in open savannah from Space |
| title | Spotting East African mammals in open savannah from Space |
| title_full | Spotting East African mammals in open savannah from Space |
| title_fullStr | Spotting East African mammals in open savannah from Space |
| title_full_unstemmed | Spotting East African mammals in open savannah from Space |
| title_short | Spotting East African mammals in open savannah from Space |
| title_sort | spotting east african mammals in open savannah from space |
| url | https://hdl.handle.net/10568/56744 |
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