Object-based image analysis for coal fire-related land cover mapping in coal mining areas

Abstract

Coal fires worldwide are causing enormous economic loss and environmental pollution, prompting a need to develop the methodology to map, analyse, and monitor them. Coal fire-related land cover mapping yields the knowledge of the distribution of coal, which is crucial for the delineation of potential coal fires and coal fire risk areas. The conventional pixel-based method does not make use of spatial relationships, whereas object-based image analysis (OBIA) has the potential to consider not only objects' spectral but spatial information, in terms of size, shape, texture and relations to other image objects. This article reports on coal fire-related land cover mapping with OBIA using multi-resolution segmentation and hierarchical classification. Both spectral and spatial information were used in the classification. It yielded an overall accuracy of 12.3% higher than that by the pixel-based method (70.67%), and the difference is significant (p = 0.05) by McNemar's test.

Keywords:

• land cover classification
• pixel-based image analysis
• object-based image analysis
• hierarchical network classification
• coal mining area

Acknowledgements

The authors thank the International Institute for Geo-information Science and Earth Observation (ITC), The Netherlands, for funding the field work and providing remote sensing data, and they also thank the Beijing China Remote Sensing Centre (BRSC) for cooperation during the field work. Thanks also go to Msc José Antonio Navarrete Pacheco for elaborating Figures 1, 2 and 4.