TY - GEN
T1 - Quantifying land cover changes caused by granite quarries from 1973-2015 using landsat data
AU - Moeletsi, Refilwe
AU - Tesfamichael, Solomon
N1 - Publisher Copyright:
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Granite Quarries
KW - Land Cover Changes
KW - Landsat
KW - Remote Sensing
KW - Supervised Classification
UR - https://www.scopus.com/pages/publications/85052338735
U2 - 10.5220/0006675901960204
DO - 10.5220/0006675901960204
M3 - Conference contribution
AN - SCOPUS:85052338735
T3 - GISTAM 2018 - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management
SP - 196
EP - 204
BT - GISTAM 2018 - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management
A2 - Grueau, Cedric
A2 - Laurini, Robert
A2 - Ragia, Lemonia
PB - SciTePress
T2 - 4th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2018
Y2 - 17 March 2018 through 19 March 2018
ER -