Research Papers

Object-based classification of semi-arid vegetation to support mine rehabilitation and monitoring

[+] Author Affiliations
Nisha Bao

Northeast University, Institute for Geo-informatics and Digital Mine Research, Shenyang 110819, China

The University of Queensland, Sustainable Minerals Institute, Centre for Mined Land Rehabilitation, St. Lucia, Queensland 4072, Australia

Alex M. Lechner

The University of Queensland, Sustainable Minerals Institute, Centre for Mined Land Rehabilitation, St. Lucia, Queensland 4072, Australia

University of Tasmania, Centre for Environment, Hobart TAS 7005, Australia

Kasper Johansen

The University of Queensland, School of Geography, Planning and Environmental Management, Biophysical Remote Sensing Group, St. Lucia, Queensland 4072, Australia

Baoying Ye

China University of Geosciences, School of Land Science & Technology, Beijing 100083, China

J. Appl. Remote Sens. 8(1), 083564 (Aug 21, 2014). doi:10.1117/1.JRS.8.083564
History: Received February 5, 2014; Revised July 10, 2014; Accepted July 28, 2014
Text Size: A A A

Abstract.  Mining activities result in significantly modified landscapes that require rehabilitation to mitigate the negative environmental impacts and restore ecological function. The aim of this study was to develop a remote sensing method suitable for monitoring the vegetation cover at mine rehabilitation sites. We used object-based image analysis (OBIA) methods and high-spatial resolution SPOT-5 imagery to identify discrete land-cover patterns that occur at fine spatial scales. These patterns relate to spatial processes that are important drivers of successful restoration of mine sites. SPOT-5 imagery of the Kidston Gold mine tailing dam in semi-arid tropical north Queensland was acquired in July 2005, comprising four 10-m spectral bands and a 2.5-m panchromatic (PAN) band. The classification scheme used in this study was adapted to the spatial scale of SPOT-5 imagery from mine closure criteria cover requirements, according to a mine rehabilitation plan. Four land-cover classes were identified: tree cover, dense grass, sparse grass, and bare ground. First, textural layers (contrast, dissimilarity, and homogeneity) were derived for each vegetation class except for bare ground from the PAN and multispectral bands. Of all textural layer combinations, homogeneity and contrast in the PAN band were identified using a Z-test as the most useful for differentiating between multiple land-cover classes. Next, an optimal segmentation scale parameter of 15 was identified using an analysis of spatial autocorrelation. Finally, the SPOT-5 image bands, derived textural layers, and normalized difference vegetation index (NDVI) were used in an OBIA fuzzy membership classification approach to map vegetation land-cover classes. The classification results were assessed with the traditional error matrix approach and the object-based accuracy assessment method. The overall classification accuracy using the error matrix was 92.5% and 81% using the object-based method. The relatively high-classification accuracy demonstrates the potential of SPOT-5 imagery for monitoring mine rehabilitation. The complete spatial coverage associated with remote sensing data at fine spatial scales has the potential to complement field-based approaches commonly used in rehabilitation monitoring. Furthermore, SPOT-5 data along with OBIA can characterize vegetation spatial patterns at spatial scales appropriate for monitoring rehabilitated landscapes, providing an important tool for landscape function analysis.

Figures in this Article
© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Nisha Bao ; Alex M. Lechner ; Kasper Johansen and Baoying Ye
"Object-based classification of semi-arid vegetation to support mine rehabilitation and monitoring", J. Appl. Remote Sens. 8(1), 083564 (Aug 21, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083564


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.