Quantifying Land Cover Changes Caused by Granite Quarries from 1973-2015 using Landsat Data

Environmental monitoring is an important aspect in sustainable development. The use of remote sensing in the mining industry has evolved significantly and allows for improved mapping and monitoring environmental impacts related to mining activities. The aim of this study was to measure land cover changes caused by granite quarrying activities located between Rustenburg and Brits towns, North West Province, South Africa using Landsat time series data. Landsat data used in the study were acquired in the years 1973, 1986, 1998 and 2015. Each image was classified using supervised classification and change detection was subsequently applied to measure land cover changes. Furthermore, the normalized difference vegetation index (NDVI) was used to highlight the dynamics in vegetation in the quarries. Accuracy assessment of the classification resulted in an overall accuracy and Kappa coefficient of 75% and 0.71, respectively. The results of post –classification change detection revealed a significant increase of 907.4 ha in granite quarries between 1973 and 2015. The expansion in granite quarries resulted in development of water bodies (2.07 ha) within the quarries. Correspondingly, there were significant losses in vegetation (782.1 ha) and bare land (119 ha). NDVI results showed variability in mean NDVI values within the digitized quarries. The overall mean NDVI values trends showed that most granite quarries had the highest vegetation in 1998, while the least vegetation cover was observed 1986.

[1]  Jan G. P. W. Clevers,et al.  Assessing the Accuracy of Remotely Sensed Data—Principles and Practices, Second edition, Russell G. Congalton, Kass Green. CRC Press, Taylor & Francis Group, Boca Raton, FL (2009), 183 pp., Price: $99.95, ISBN: 978-1-4200-5512-2 , 2009 .

[2]  Peter Deer.  Digital Change Detection Techniques in Remote Sensing , 1995 .

[3]  David T. Potere,et al.  Horizontal Positional Accuracy of Google Earth's High-Resolution Imagery Archive , 2008, Sensors.

[4]  R. Jackson,et al.  Interpreting vegetation indices , 1991 .

[5]  Jianyu Yang,et al.  Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method , 2013, Math. Comput. Model..

[6]  D. Lu,et al.  Change detection techniques , 2004 .

[7]  E. Charou,et al.  Using Remote Sensing to Assess Impact of Mining Activities on Land and Water Resources , 2010 .

[8]  Glenn Banks,et al.  Monitoring the Environmental Impact of Mining in Remote Locations through Remotely Sensed Data , 2006 .

[9]  Rasim Latifovic,et al.  Assessing land cover change resulting from large surface mining development , 2005 .

[10]  Jacob Cohen,et al.  The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability , 1973 .

[11]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[12]  Ioannis Z. Gitas,et al.  Assessment of the visual impact of marble quarry expansion (1984-2000) on the landscape of Thasos island, NE Greece. , 2008 .

[13]  J. Mas.  Monitoring land-cover changes: A comparison of change detection techniques , 1999 .

[14]  V. Desai,et al.  Post-classification corrections in improving the classification of Land Use/Land Cover of arid region using RS and GIS: The case of Arjuni watershed, Gujarat, India , 2017 .

[15]  Nathalie Pettorelli,et al.  The Normalized Difference Vegetation Index , 2014 .

[16]  D. Haruna,et al.  An Assessment of Mining Activities Impact on Vegetation in Bukuru Jos Plateau State Nigeria Using Normalized Differential Vegetation Index (NDVI) , 2011 .

[17]  Ahmed Abu Hanieh,et al.  Sustainable development of stone and marble sector in Palestine , 2014 .

[18]  R. Cawthorn.,et al.  The Bushveld Complex , 1996 .

[19]  J. Kinnaird,et al.  A new stratigraphy for the Main Zone of the Bushveld Complex, in the Rustenburg area , 1998 .

[20]  Nuray Demirel,et al.  Surface coal mine area monitoring using multi-temporal high-resolution satellite imagery , 2011 .

[21]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[22]  A. Viera,et al.  Understanding interobserver agreement: the kappa statistic. , 2005, Family medicine.

[23]  G. Meera Gandhi,et al.  Ndvi: Vegetation Change Detection Using Remote Sensing and Gis – A Case Study of Vellore District☆ , 2015 .

[24]  E. Duncan,et al.  Open Pit Mining and Land Use Changes: An Example from Bogosu‐Prestea Area, South West Ghana , 2009, Electron. J. Inf. Syst. Dev. Ctries..

[25]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[26]  James K. Lein,et al.  Toward a Remote Sensing Solution for Regional Sustainability Assessment and Monitoring , 2014 .

[27]  Zeki Karaca,et al.  Remote sensing in management of mining land and proximate habitat , 2012 .

[28]  Pierre Defourny,et al.  Revisiting Land-Cover Mapping Concepts , 2012 .

[29]  G. Lameed,et al.  Effect of quarrying activity on biodiversity: Case study of Ogbere site, Ogun State Nigeria , 2010 .

[30]  P. K. Jain.  Environmental Degradation Due to Open Cast Mining Activities in Bundelkhand and Gwalior Region of M . P . , India , 2015 .

[31]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[32]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .