28 May 2013 Object-based locust habitat mapping using high-resolution multispectral satellite data in the southern Aral Sea basin
Peter Navratil, Hans Wilps
Author Affiliations +
Abstract
Three different object-based image classification techniques are applied to high- resolution satellite data for the mapping of the habitats of Asian migratory locust (Locusta migratoria migratoria) in the southern Aral Sea basin, Uzbekistan. A set of panchromatic and multispectral Système Pour l’Observation de la Terre-5 satellite images was spectrally enhanced by normalized difference vegetation index and tasseled cap transformation and segmented into image objects, which were then classified by three different classification approaches: a rule-based hierarchical fuzzy threshold (HFT) classification method was com- pared to a supervised nearest neighbor classifier and classification tree analysis by the quick, unbiased, efficient statistical trees algorithm. Special emphasis was laid on the discrimination of locust feeding and breeding habitats due to the significance of this discrimination for practical locust control. Field data on vegetation and land cover, collected at the time of satellite image acquisition, was used to evaluate classification accuracy. The results show that a robust HFT classifier outperformed the two automated procedures by 13% overall accuracy. The classification method allowed a reliable discrimination of locust feeding and breeding habitats, which is of significant importance for the application of the resulting data for an economically and environmentally sound control of locust pests because exact spatial knowledge on the habitat types allows a more effective surveying and use of pesticides.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2013/$25.00 © 2013 SPIE
Peter Navratil and Hans Wilps "Object-based locust habitat mapping using high-resolution multispectral satellite data in the southern Aral Sea basin," Journal of Applied Remote Sensing 7(1), 075097 (28 May 2013). https://doi.org/10.1117/1.JRS.7.075097
Published: 28 May 2013
Lens.org Logo
CITATIONS
Cited by 9 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
Back to Top