Tools
Tools
Szantoi, Z, Smith, SE, Strona, G, Koh, LP and Wich, SA (2017) Mapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photography. International Journal of Remote Sensing. ISSN 0143-1161
|
Text
Szantoi_etal_2017.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
Conservation of the Sumatran orangutans’ (Pongo abelii) habitat is threatened by change in land use/land cover (LULCC), due to the logging of its native primary forest habitat, and the primary forest conversion to oil palm, rubber tree, and coffee plantations. Frequent LULCC monitoring is vital to rapid conservation interventions. Due to the costs of high-resolution satellite imagery, researchers are forced to rely on cost-free sources (e.g. Landsat), those, however, provide images at a moderate-to-low resolution (e.g. 15–250 m), permitting identification only general LULC classes, and limit the detection of small-scale deforestation or degradation. Here, we combine Landsat imagery with very high-resolution imagery obtained from an unmanned aircraft system (UAS). The UAS imagery was used as ‘drone truthing’ data to train image classification algorithms. Our results show that UAS data can successfully be used to help discriminate similar land-cover/use classes (oil palm plantation vs. reforestation vs. logged forest) with consistently high identification of over 75% on the generated thematic map, where the oil palm detection rate was as high as 89%. Because UAS is employed increasingly in conservation projects, this approach can be used in a large variety of them to improve land-cover classification or aid-specific mapping needs.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 0406 Physical Geography And Environmental Geoscience, 0909 Geomatic Engineering |
| Subjects: | G Geography. Anthropology. Recreation > GB Physical geography G Geography. Anthropology. Recreation > GE Environmental Sciences |
| Divisions: | Natural Sciences & Psychology (closed 31 Aug 19) |
| Publisher: | Taylor & Francis |
| Date Deposited: | 31 Jan 2017 12:06 |
| Last Modified: | 04 Sep 2021 12:00 |
| DOI or ID number: | 10.1080/01431161.2017.1280638 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/5402 |
 |
View Item |