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 m-250 m), permitting identification only general LULC classes, and limits 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 are 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.

SZANTOI Zoltan;  SMITH Scot;  STRONA Giovanni;  KOH Lian Pin;  WICH Serge; 

2017-01-27

TAYLOR & FRANCIS LTD

JRC94065

0143-1161,   

http://www.tandfonline.com/doi/full/10.1080/01431161.2017.1280638,    https://publications.jrc.ec.europa.eu/repository/handle/JRC94065,   

10.1080/01431161.2017.1280638,