gesjGEOGRAPHY, ENVIRONMENT, SUSTAINABILITYGEOGRAPHY, ENVIRONMENT, SUSTAINABILITY2071-93882542-1565Russian Geographical Society10.24057/2071-9388-2021-067gesj-2498Research ArticleRESEARCH PAPERСтатьиChange Detection of Vegetation Cover Using Remote Sensing and GIS – A Case Study of the West Coast Region of South AfricaCoetzeeClive

Private Bag X2, Saldanha, 7396

clivecoetzee@sun.ac.zaFaculty of Military Science, University of StellenboschSouth Africa20222806202215291102Copyright © Coetzee C., 20222022Coetzee C.Coetzee C.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ges.rgo.ru/jour/article/view/2498

This article investigates the possible permanent vegetation cover (VC) change over an extended time for five municipal regions in South Africa by applying satellite-acquired remote sensed normalized difference vegetation index (NDVI) values within a geographic information system (GIS), spatial (West Coast District) and time (1981 to 2019 and 2000 to 2020) context. The NDVI index measures surface reflectance and give a quantitative estimation of vegetation growth and biomass. The study found relevance in its application since VC change detection has taken prominence over the past number of years in terms of sustainable development. Methods of analysis include image mapping, temporal image differencing, Moran I statistic, and the Mann-Kendall trend test. In the main areas that recorded significant changes in their NDVI values (plus or minus 0.4 difference on their original NDVI value) over time, in general, have experienced substantial and permanent VC change. These areas are also spatially clustered and concentrated within specific areas within the wider district. However, these areas constitute only a minority of areas (less than 20%), whereas most of the areas within the district did not experience such significant and permanent change in VC.  Instead, the changes that did occur in these majority of areas were related to seasonal variation, i.e., temporal changes.

NDVINOAAMODISvegetation coverchange detectionReferencesAnselin L. (1996). The Moran Scatterplot as an ESDA Tool to Assess Local Instability in Spatial Association, in: M. Fischer, H. J. Scholten, D. Unwin (Eds.), Spatial analytical perspectives on GIS. Taylor & Frances, London, England, p. 111–125.Anselin L. (1996). The Moran Scatterplot as an ESDA Tool to Assess Local Instability in Spatial Association, in: M. Fischer, H. J. Scholten, D. Unwin (Eds.), Spatial analytical perspectives on GIS. Taylor & Frances, London, England, p. 111–125.Aburas M.M., Abdullah S.H., Ramli M.F. & Ash’aari Z.H. (2015). Measuring land cover change in Seremban, Malaysia using NDVI index. Procedia Environmental Sciences. 30, 238 – 243.Aburas M.M., Abdullah S.H., Ramli M.F. & Ash’aari Z.H. (2015). Measuring land cover change in Seremban, Malaysia using NDVI index. Procedia Environmental Sciences. 30, 238 – 243.Alawamy J.S., Balasundram S.K., Mohd. Hanif A.H. & Sung C.T.B. (2020). Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017. Sustainability 2020, 12, 4490 DOI:10.3390/su12114490.Alawamy J.S., Balasundram S.K., Mohd. Hanif A.H. & Sung C.T.B. (2020). Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017. Sustainability 2020, 12, 4490 DOI:10.3390/su12114490.Ahmad I., Tang D., Wang T., Wang M. & Wagan B. (2015). Precipitation trends over time using Mann-Kendall and Spearman’s rho tests in Swat River basin, Pakistan. Adv. Meteorol.Ahmad I., Tang D., Wang T., Wang M. & Wagan B. (2015). Precipitation trends over time using Mann-Kendall and Spearman’s rho tests in Swat River basin, Pakistan. Adv. Meteorol.Ayele G.T., Tebeje A.M., Demissie S.S., Belete A.M., Jemberrie M.A., Teshome W.M., Mengistu D.T. & Teshale E.Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, 1–18.Ayele G.T., Tebeje A.M., Demissie S.S., Belete A.M., Jemberrie M.A., Teshome W.M., Mengistu D.T. & Teshale E.Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, 1–18.Cihlar J. (2000). Land cover mapping of large areas from satellites: status and research priorities. International Journal of Remote Sensing. 21(6), 1093–1114.Cihlar J. (2000). Land cover mapping of large areas from satellites: status and research priorities. International Journal of Remote Sensing. 21(6), 1093–1114.Campos J., Ericsson N.R. & Hendry D.F. (2005). General-to-specific Modeling: An Overview and Selected Bibliography. Board of Governors of the Federal Reserve System, International Finance Discussion Papers, Number 838.Campos J., Ericsson N.R. & Hendry D.F. (2005). General-to-specific Modeling: An Overview and Selected Bibliography. Board of Governors of the Federal Reserve System, International Finance Discussion Papers, Number 838.Chang Y. & Song W. (2002). Panel Unit Root Tests in the Presence of Cross-Sectional Dependency and Heterogeneity. Presentation at the 2002 ASSA Meeting held in Atlanta.Chang Y. & Song W. (2002). Panel Unit Root Tests in the Presence of Cross-Sectional Dependency and Heterogeneity. Presentation at the 2002 ASSA Meeting held in Atlanta.Das S. & Angadi D.P. (2020). Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: a micro-level study. GeoJournal, DOI: 10.1007/s10708-020-10359-1.Das S. & Angadi D.P. (2020). Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: a micro-level study. GeoJournal, DOI: 10.1007/s10708-020-10359-1.Deng J., Wang K., Deng Y. & Qi G. (2008). PCA-based landuse change detection and analysis using multispectral and multisensor satellite data. International Journal of Remote Sensing, 29(16), 4823–4838.Deng J., Wang K., Deng Y. & Qi G. (2008). PCA-based landuse change detection and analysis using multispectral and multisensor satellite data. International Journal of Remote Sensing, 29(16), 4823–4838.Di Gregorio A. & Jansen L.J.M. (2000). Land Cover Classification System (LCCS): Classification Concepts and User Manual. Food and Agriculture Organization of the United Nations, Rome, 1998.Di Gregorio A. & Jansen L.J.M. (2000). Land Cover Classification System (LCCS): Classification Concepts and User Manual. Food and Agriculture Organization of the United Nations, Rome, 1998.Drapela K. & Drapelova I. (2011). Application of Mann-Kendall test and the Sen’s slope estimates for trend detection in deposition data from Bílý Kříž (Beskydy Mts., the Czech Republic) 1997–2010. Beskdy Mendel University in Brno, 4 (2), 133–146.Drapela K. & Drapelova I. (2011). Application of Mann-Kendall test and the Sen’s slope estimates for trend detection in deposition data from Bílý Kříž (Beskydy Mts., the Czech Republic) 1997–2010. Beskdy Mendel University in Brno, 4 (2), 133–146.Firl G.J. & Carter F. (2011). Lesson 10: Calculating Vegetation Indices from Landsat 5 TM and Landsat 7 ETM+ Data. [online] Available at: http://ibis.colostate.edu/webcontent/ws/coloradoview/tutorialsdownloads/co_rs_tutorial10.pdf. [Accessed 15 May 2021]Firl G.J. & Carter F. (2011). Lesson 10: Calculating Vegetation Indices from Landsat 5 TM and Landsat 7 ETM+ Data. [online] Available at: http://ibis.colostate.edu/webcontent/ws/coloradoview/tutorialsdownloads/co_rs_tutorial10.pdf. [Accessed 15 May 2021]Google Earth Engine, Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., & Moore R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.Google Earth Engine, Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., & Moore R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment.Herwartz H. (2007). A note on model selection in (time series) regression models - General-to-specific or specific-to-general?. Economics Working Paper, 2007-09.Herwartz H. (2007). A note on model selection in (time series) regression models - General-to-specific or specific-to-general?. Economics Working Paper, 2007-09.De Jong R. & Sakarya H. (2016). The Econometrics of the Hodrick-Prescott Filter. The Review of Economics and Statistics. 98(2), 310–317.De Jong R. & Sakarya H. (2016). The Econometrics of the Hodrick-Prescott Filter. The Review of Economics and Statistics. 98(2), 310–317.Le Roux A., Cooper A.K. & Mans G. (2016). Land Use and Land Cover Change in the Western Cape Province: Quantification of Changes &Le Roux A., Cooper A.K. & Mans G. (2016). Land Use and Land Cover Change in the Western Cape Province: Quantification of Changes &Understanding of Driving Factors. Conference: 7th Planning Africa Conference 2016 – Making Sense of the Future: Disruption and Reinvention At: Johannesburg, South AfricaUnderstanding of Driving Factors. Conference: 7th Planning Africa Conference 2016 – Making Sense of the Future: Disruption and Reinvention At: Johannesburg, South AfricaMeals D.W., Spooner J., Dressing S.A. & Harcum J.B. (2011). Statistical analysis for monotonic trends. Tech Notes 6, November 2011.Meals D.W., Spooner J., Dressing S.A. & Harcum J.B. (2011). Statistical analysis for monotonic trends. Tech Notes 6, November 2011.Developed for U.S. Environmental Protection Agency by Tetra Tech, Inc., Fairfax, VA, 23.Developed for U.S. Environmental Protection Agency by Tetra Tech, Inc., Fairfax, VA, 23.Meneses-Tovar C. (2012). NDVI as indicator of degradation. Unasylva (FAO).Meneses-Tovar C. (2012). NDVI as indicator of degradation. Unasylva (FAO).MODIS (2021). Images and data from 2000 was retrieved on 2021/01/10 from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. 2018.MODIS (2021). Images and data from 2000 was retrieved on 2021/01/10 from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. 2018.Mucina L., Knevel I.C., Adams J.B. & Rutherford M.C. (2006). Coastal Vegetation of South Africa. Strelitzia 19(2006)Mucina L., Knevel I.C., Adams J.B. & Rutherford M.C. (2006). Coastal Vegetation of South Africa. Strelitzia 19(2006)Mbatha N. & Xulu S. (2018). Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought. Climate, 6, 95, DOI:10.3390/cli6040095.Mbatha N. & Xulu S. (2018). Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought. Climate, 6, 95, DOI:10.3390/cli6040095.NOAA (2021). NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), v5. [online] Available at: https://data.noaa.gov/dataset/dataset/noaa-climate-data-record-cdr-of-avhrr-normalized-difference-vegetation-index-ndvi-version-5. [Accessed 15 May 2021]NOAA (2021). NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), v5. [online] Available at: https://data.noaa.gov/dataset/dataset/noaa-climate-data-record-cdr-of-avhrr-normalized-difference-vegetation-index-ndvi-version-5. [Accessed 15 May 2021]Motiee H. & McBean E. (2009). An Assessment of Long Term Trends in Hydrologic Components and Implications for Water Levels in Lake Superior. Hydrology Research, 40(6), 564-579.Motiee H. & McBean E. (2009). An Assessment of Long Term Trends in Hydrologic Components and Implications for Water Levels in Lake Superior. Hydrology Research, 40(6), 564-579.Rogan J. & Chen D. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning. 61(4), 301-325.Rogan J. & Chen D. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning. 61(4), 301-325.Rouse J. W., Haas R. H., Schell J. A. & Deering D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA. Goddard Space Flight Center 3d ERTS-1 Symp., 1, Sect. ARouse J. W., Haas R. H., Schell J. A. & Deering D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA. Goddard Space Flight Center 3d ERTS-1 Symp., 1, Sect. ASahebjalal E. & Dashtekian K. (2013). Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods. African Journal of Agricultural Research, 8(37), 4614-4622.Sahebjalal E. & Dashtekian K. (2013). Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods. African Journal of Agricultural Research, 8(37), 4614-4622.Sowunmi F. A., Akinyosoye V. O., Okoruwa V.O. & Omonona B. T. (2012). The Landscape of Poverty in Nigeria: A Spatial Analysis Using Senatorial Districts- level Data. American Journal of Economics. 2(5), 61–74.Sowunmi F. A., Akinyosoye V. O., Okoruwa V.O. & Omonona B. T. (2012). The Landscape of Poverty in Nigeria: A Spatial Analysis Using Senatorial Districts- level Data. American Journal of Economics. 2(5), 61–74.Vermote E., Justice C., Csiszar I., Eidenshink J., Myneni R., Baret F., Masuoka E., Wolfe R., Claverie M. & NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), v4. [indicate subset used]. NOAA National Climatic Data Center. DOI:10.7289/V5PZ56R6.Vermote E., Justice C., Csiszar I., Eidenshink J., Myneni R., Baret F., Masuoka E., Wolfe R., Claverie M. & NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), v4. [indicate subset used]. NOAA National Climatic Data Center. DOI:10.7289/V5PZ56R6.Viton P. A. (2010). Notes on Spatial Econometric Models. City and Regional Planning, 870(3):2-17.Viton P. A. (2010). Notes on Spatial Econometric Models. City and Regional Planning, 870(3):2-17.Wade T.G., Wickham J.D., Nash M. & Neale A.C. (2003). A Comparison of Vector and Raster GIS Methods for Calculating Landscape Metrics Used in Environmental Assessments. Photogrammetric Engineering and Remote Sensing. 69(12), 1399–1405.Wade T.G., Wickham J.D., Nash M. & Neale A.C. (2003). A Comparison of Vector and Raster GIS Methods for Calculating Landscape Metrics Used in Environmental Assessments. Photogrammetric Engineering and Remote Sensing. 69(12), 1399–1405.Wu D.H., Zhao X., Liang S.L., Zhou T., Huang K.C., Tang B.J. & Zhao W.Q. (2015). Time-lag effects of global vegetation responses to climate change. Global Chang. Biol, DOI:10.1111/gcb.12945.Wu D.H., Zhao X., Liang S.L., Zhou T., Huang K.C., Tang B.J. & Zhao W.Q. (2015). Time-lag effects of global vegetation responses to climate change. Global Chang. Biol, DOI:10.1111/gcb.12945.Yue S. & Wang C. (2004). The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series. Water Resources Management, 18, 201–218.Yue S. & Wang C. (2004). The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series. Water Resources Management, 18, 201–218.Zhang B.Q., Wu P., Zhao X.N., Wang Y.B. & Gao X.D. (2013). Changes in vegetation condition in areas with different gradients (1980–2010) on the Loess Plateau, China. Environ. Earth Sci, 68, 2427–2438.Zhang B.Q., Wu P., Zhao X.N., Wang Y.B. & Gao X.D. (2013). Changes in vegetation condition in areas with different gradients (1980–2010) on the Loess Plateau, China. Environ. Earth Sci, 68, 2427–2438.Zhao G., Lin G., & Warner T. (2004). Thematic mapper data for change detection and sustainable use of cultivated land: A case study in the Yellow River delta. China. International Journal of Remote Sensing, 25(13), 2509–2522.Zhao G., Lin G., & Warner T. (2004). Thematic mapper data for change detection and sustainable use of cultivated land: A case study in the Yellow River delta. China. International Journal of Remote Sensing, 25(13), 2509–2522.

The authors declare that there are no conflicts of interest present.