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Drought conditions appraisal using geoinformatics and multi-influencing factors

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Abstract

The world including South Africa is faced with unprecedented environmental changes, which can be linked to climate-related disasters such as drought and extreme heat. Several studies have indicated that these changes are likely to accelerate in the future and cause an adverse impact on the environment. The Eastern Cape Province of South Africa, especially Amathole District Municipality (ADM), has recorded a high number of climate change–related disasters including prolonged drought conditions witnessed during the winter season of 2008, 2009, 2014 and 2015 among others. Consequently, this study aimed at exploring remote sensing information to assess and document drought occurrences in the ADM from 2007 to 2017. To accomplish the aim, the Normalized Difference Vegetation Index, Land Surface Temperature and Precipitation were utilised to access drought spatiotemporal variations. For the analysis, a total of 396 satellite imageries (MODIS and TRMM) were used. The results revealed that different correlations exist between the three variables. The significance of correlations differed from one season to another. Furthermore, it was revealed that the drought conditions in the district differed in the spatial distribution. The study accurately identified the drought episodes that occurred in the ADM in the years 2008, 2009, 2014, 2015 and 2016. The chosen methodology and variables proved to be suitable for analysing drought conditions offering space and temporal variation dimension, which is vital in monitoring drought events.

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Data availability

Datasets used in this study were acquired from United States Geological Survey (USGS) and NASA databases. Data are available upon reasonable request.

References

  • Adedeji, O., Olusola, A., James, G., Shaba, H. A., Orimoloye, I. R., Singh, S. K., & Adelabu, S. (2020). Early warning systems development for agricultural drought assessment in Nigeria. Environmental Monitoring and Assessment, 192(12), 1–21. https://doi.org/10.1007/s10661-020-08730-3

    Article  Google Scholar 

  • Adisa, O. M., Botai, J. O., Adeola, A. M., Botai, C. M., Hassen, A., Darkey, D., & Adisa, A. F. (2019). Analysis of drought conditions over major maize producing provinces of South Africa. Journal of Agricultural Meteorology, 75(4), 173–182.

    Article  Google Scholar 

  • AghaKouchak, A., Feldman, D., Hoerling, M., Huxman, T., & Lund, J. (2015). Water and climate: recognize anthropogenic drought. Nature, 524(7566), 409–411.

    Article  CAS  Google Scholar 

  • Ahmadi, B., & Moradkhani, H. (2019). Revisiting hydrological drought propagation and recovery considering water quantity and quality. Hydrological Processes, 33(10), 1492–1505.

    Article  CAS  Google Scholar 

  • Amathole Community Newsletter. (2017). Amathole Community News. Available from: http://www.amathole.gov.za/attachments/article/703/e%20AMATHOLE%20COMMUNITY%20NEWSLETTER%20REDONE%20copy.pdf (Accessed on 06 May 2020).

  • Amathole District Municipality Integrated Development Plan 2011/12. (2012). Amathole District Municipality Integrated Development Plan 2011/2012 – version 5 of IDP 2011-2012. [online] Available at: http://www.amathole.gov.za/index.php/library2/shortcodes/headings-2/707-2011-12-idp

  • Amathole District Municipality. (2017). Local action for biodiversity: wetland management in a changing climate [online] Available at: <http://cbc.iclei.org/wp-content/uploads/2017/07/10.-WSAP-Workshop-Report_LAB-Wetlands-SA_Amathole-1.pdf> [Accessed 20 June 2019].

  • Anyamba, A., & Tucker, C. J. (2005). Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of Arid Environments, 63(3), 596–614.

    Article  Google Scholar 

  • Arslan, M., Zahid, R., & Ghauri, B. (2016). Assessing the occurrence of drought based on NDVI, LST and rainfall pattern during 2010–2014. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4233-4236). IEEE.

  • Austin, W. D. (2008). Drought in South Africa: lessons lost and/or learnt from 1990 to 2005 (Doctoral dissertation, University of the Witwatersrand).

  • Bänziger, M., Edmeades, G. O., & Lafitte, H. R. (2002). Physiological mechanisms contributing to the increased N stress tolerance of tropical maize selected for drought tolerance. Field Crops Research, 75(2–3), 223–233.

    Article  Google Scholar 

  • Behrangi, A., Loikith, P. C., Fetzer, E. J., Nguyen, H. M., & Granger, S. L. (2015). Utilizing humidity and temperature data to advance monitoring and prediction of meteorological drought. Climate, 3(4), 999–1017.

    Article  Google Scholar 

  • Berger, K. A., Wang, Y., & Mather, T. N. (2013). MODIS-derived land surface moisture conditions for monitoring blacklegged tick habitat in southern New England. International Journal of Remote Sensing, 34(1), 73–85.

    Article  Google Scholar 

  • Brüntrup, M., & Tsegai, D. (2017). Drought adaptation and resilience in developing countries (No. 23/2017). Briefing Paper.

  • Chokngamwong, R., & Chiu, L. (2006). TRMM and Thailand daily gauge rainfall comparison. In Preprints, 20th Conf. on Hydrology, Atlanta, GA, Amer. Meteor. Soc. P (Vol. 1).

  • Chopra, P. (2006). Drought risk assessment using remote sensing and GIS: a case study of Gujarat. ITC.

  • Chu, D., Lu, L., & Zhang, T. (2007). Sensitivity of normalized difference vegetation index (NDVI) to seasonal and interannual climate conditions in the Lhasa area, Tibetan plateau, China. Arctic, Antarctic, and Alpine Research, 39(4), 635–641.

    Article  Google Scholar 

  • Csavina, J., Field, J., Félix, O., Corral-Avitia, A. Y., Sáez, A. E., & Betterton, E. A. (2014). Effect of wind speed and relative humidity on atmospheric dust concentrations in semi-arid climates. Science of the Total Environment, 487, 82–90.

    Article  CAS  Google Scholar 

  • Davis, C. (2010). Climate change handbook for north-eastern South Africa. Council for Scientific and Industrial Research (CSIR).

  • Davis, C. L., & Vincent, K. (2017). Climate risk and vulnerability: a handbook for Southern Africa.

  • Dharejo, F. A., Zhou, Y., Deeba, F., Jatoi, M. A., Du, Y., & Wang, X. (2021). A remote-sensing image enhancement algorithm based on patch-wise dark channel prior and histogram equalisation with colour correction. IET Image Processing, 15(1), 47–56.

    Article  Google Scholar 

  • Ekundayo, O. Y., Okogbue, E. C., Akinluyi, F. O., Kalumba, A. M., & Orimoloye, I. R. (2021). Geoinformatics Approach to Desertification EvaluationUsing Vegetation Cover Changes in the Sudano-Sahelian Region of Nigeria from 2000 to 2010. In Re-envisioning Remote Sensing Applications (pp. 261-270). CRC Press.

  • Gaveta, E. (2017). Crop yield responses to temperature and rainfall variability in Bolero, Malawi. International Journal of Climate Change: Impacts and Responses, 9(4), 43–54.

    Google Scholar 

  • Gaznayee, H. A., & Al-Quraishi, A. M. F. (2019). Analysis of agricultural drought’s severity and impacts in Erbil Province, the Iraqi Kurdistan Region based on Time Series NDVI and TCI Indices for 1998 through 2017. Jour of Adv Research in Dynamical & Control Systems11(11).

  • Ghoneim, E., Dorofeeva, A., Benedetti, M., Gamble, D., & Leonard, L. (2017). Vegetation drought analysis in Tunisia: a geospatial investigation. Journal of Atmospheric Earth and Science1(002).

  • Greyling, J. C. (2012). The role of the agricultural sector in the South African economy (Doctoral dissertation, Stellenbosch: Stellenbosch University).

  • Griscom, H. R., Miller, S. N., Gyedu-Ababio, T., & Sivanpillai, R. (2009). Mapping land cover change of the Luvuvhu catchment, South Africa for environmental modelling. GeoJournal, 75(2), 163–173.

    Article  Google Scholar 

  • Gu, Y., Brown, J. F., Verdin, J. P., & Wardlow, B. (2007). A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters34(6).

  • Gu, Z., Zeng, Z., Shi, X., Yu, D., Zheng, W., Zhang, Z., & Hu, Z. (2009). Estimating models of vegetation fractional coverage based on remote sensing images at different radiometric correction levels. Frontiers of Forestry in China, 4(4), 402.

    Article  Google Scholar 

  • Hao, F., Zhang, X., Ouyang, W., Skidmore, A. K., & Toxopeus, A. G. (2012). Vegetation NDVI linked to temperature and precipitation in the upper catchments of Yellow River. Environmental Modeling & Assessment, 17(4), 389–398.

    Article  Google Scholar 

  • International Federation of Red Cross. (2004). South Africa: Drought Information Bulletin No. 1. International Federation of Red Cross. Available from: http://www.ifrc.org/docs/appeals/rpts04/ZA040130.pdf (Accessed on 17 May 2017).

  • IPCC (2007). Impacts, adaptation and vulnerability. Contribution of Working.

  • Kakembo, V., & Ndou, N. (2019). Relating vegetation condition to grazing management systems in the central Keiskamma Catchment, Eastern Cape Province, South Africa. Land Degradation & Development, 30(9), 1052–1060.

    Article  Google Scholar 

  • Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: merits and limitations. Journal of Climate, 23(3), 618–633.

    Article  Google Scholar 

  • Leilei, L., Jianrong, F., & Yang, C. (2014). The relationship analysis of vegetation cover, rainfall and land surface temperature based on remote sensing in Tibet, China. In IOP Conference Series: Earth and Environmental Science, 17(1), 012034. IOP Publishing.

  • Li, X., Shen, H., Zhang, L., Zhang, H., Yuan, Q., & Yang, G. (2014). Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning. IEEE Transactions on Geoscience and Remote Sensing, 52(11), 7086–7098.

    Article  Google Scholar 

  • Mason, S. J., & Tyson, P. (2000). The occurrence and predictability of droughts over southern Africa.

  • Mberego, S., & Gwenzi, J. (2014). Temporal patterns of precipitation and vegetation variability over Zimbabwe during extreme dry and wet rainfall seasons. Journal of Applied Meteorology and Climatology, 53(12), 2790–2804.

    Article  Google Scholar 

  • Miettinen, J., Shi, C., & Liew, S. C. (2019). Towards automated 10–30 m resolution land cover mapping in insular South-East Asia. Geocarto International, 34(4), 443–457.

    Article  Google Scholar 

  • Milliken, K. L., & Curtis, M. E. (2016). Imaging pores in sedimentary rocks: foundation of porosity prediction. Marine and Petroleum Geology, 73, 590–608.

    Article  Google Scholar 

  • Mo, K. C., & Lettenmaier, D. P. (2016). Precipitation deficit flash droughts over the United States. Journal of Hydrometeorology, 17(4), 1169–1184.

    Article  Google Scholar 

  • Mokhtari, A., Mansor, S. B., Mahmud, A. R., & Helmi, Z. M. (2011). Monitoring the impacts of drought on land use/cover: a developed object-based algorithm for NOAA AVHRR time series data. Journal of Applied Sciences, 11(17), 3089–3103.

    Article  Google Scholar 

  • Nanzad, L., Zhang, J., Tuvdendorj, B., Nabil, M., Zhang, S., & Bai, Y. (2019). NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016. Journal of Arid Environments, 164, 69–77.

    Article  Google Scholar 

  • Naumann, G., Dutra, E., Barbosa, P., Pappenberger, F., Wetterhall, F., & Vogt, J. V. (2014). Comparison of drought indicators derived from multiple data sets over Africa. Hydrology and Earth System Sciences, 18(5), 1625–1640.

    Article  Google Scholar 

  • Ning, J., Gao, Z., & Chen, M. (2017, September). Analysis of relationships between NDVI and land surface temperature in coastal area. In Remote Sensing and Modeling of Ecosystems for Sustainability XIV (Vol. 10405, p. 104050K). International Society for Optics and Photonics.

  • Orimoloye, I. (2018). Assessment of the human health implications of climate variability in East London, Eastern Cape, South Africa (Doctoral dissertation, University of Fort Hare).

  • Orimoloye, I. R., & Adigun, A. I. (2017). Response of cassava and maize yield to varying spatial scales of rainfall and temperature scenarios in Port Harcourt. Research Journal of Environmental Sciences, 11, 137–142.

    Article  Google Scholar 

  • Orimoloye, I. R., Belle, J. A., Olusola, A. O., Busayo, E. T., & Ololade, O. O. (2020). Spatial assessment of drought disasters, vulnerability, severity and water shortages: a potential drought disaster mitigation strategy. Natural Hazards, 1–20.

  • Orimoloye, I. R., Mazinyo, S. P., Nel, W., & Iortyom, E. T. (2018). Assessing changes in climate variability observation and simulation of temperature and relative humidity: a case of east london, South Africa. Research Journal of Environmental Sciences, 12(1), 1–13.

    Article  Google Scholar 

  • Orimoloye, I. R., Ololade, O. O., Mazinyo, S. P., Kalumba, A. M., Ekundayo, O. Y., Busayo, E. T., & Nel, W. (2019). Spatial assessment of drought severity in Cape Town area, South Africa. Heliyon, 5(7), e02148.

    CAS  Google Scholar 

  • Orimoloye, I. R., & Ololade, O. O. (2020). Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology. International Journal of Environmental Science and Technology, 17, 4465–4480. https://doi.org/10.1007/s13762-020-02789-8

    Article  CAS  Google Scholar 

  • Orimoloye, I. R., Zhou, L., & Kalumba, A. M. (2021). Drought disaster risk adaptation through ecosystem services-based solutions: way forward for South Africa. Sustainability, 13(8), 4132. https://doi.org/10.3390/su13084132

    Article  Google Scholar 

  • Prakasam, C. (2010). Land use and land cover change detection through remote sensing approach: a case study of Kodaikanal taluk, Tamil nadu. International Journal of Geomatics and Geosciences, 1(2), 150.

    Google Scholar 

  • Ropo, O. I., Perez, M. S., Werner, N., & Enoch, T. I. (2017). Climate variability and heat stress index have increasing potential ill-health and environmental impacts in the East London, South Africa. International Journal of Applied Engineering Research, 12(17), 6910–6918.

    Google Scholar 

  • Rutherford, M. C., Mucina, L., & Powrie, L. W. (2012). The South African National Vegetation Database: history, development, applications, problems and future. South African Journal of Science, 108(1–2), 01–08.

    Google Scholar 

  • SA Statistics. (2011). Statistics South Africa Annual Report 2011/2012.

  • Sruthi, S., & Aslam, M. M. (2015). Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur district. Aquatic Procedia, 4, 1258–1264.

    Article  Google Scholar 

  • Su-ping, W., Jin-song, W., Zhang, Q., Li, Y. P., Zhi-lan, W., & Wang, J. (2016). Cumulative effect of precipitation deficit preceding severe droughts in southwestern and southern China. Discrete Dynamics in Nature and Society.

  • Swain, S., Wardlow, B. D., Narumalani, S., Tadesse, T., & Callahan, K. (2011). Assessment of vegetation response to drought in Nebraska using Terra-MODIS land surface temperature and normalized difference vegetation index. GIScience & Remote Sensing, 48(3), 432–455.

    Article  Google Scholar 

  • Sylla, M. B., Faye, A., Giorgi, F., Diedhiou, A., & Kunstmann, H. (2018). Projected heat stress under 1.5 C and 2 C global warming scenarios creates unprecedented discomfort for humans in West Africa. Earth's Future, 6(7), 1029–1044.

  • Wannous, C., & Velasquez, G. (2017). United nations office for disaster risk reduction (unisdr)—unisdr’s contribution to science and technology for disaster risk reduction and the role of the international consortium on landslides (icl). In Workshop on World Landslide Forum (pp. 109–115). Springer, Cham.

  • Water Research Commission. (2016). Background to current drought situation in South Africa. Water Research Commission, 11, 1.

    Google Scholar 

  • Ye, X., Zhang, Q., Liu, J., Li, X., & Xu, C. Y. (2013). Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China. Journal of Hydrology, 494, 83–95.

    Article  Google Scholar 

  • Zaitunah, A., Samsuri, A. A., & Safitri, R. A. (2018). Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia. In IOP Conference Series: Earth and Environmental Science (Vol. 126, No. 1, pp. 1–9).

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Risk & Vulnerability Science Centre, University of Fort Hare, Alice Campus, South Africa provided financial support.

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Correspondence to Masonwabe Dyosi or Israel R. Orimoloye.

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Dyosi, M., Kalumba, A.M., Magagula, H. et al. Drought conditions appraisal using geoinformatics and multi-influencing factors. Environ Monit Assess 193, 365 (2021). https://doi.org/10.1007/s10661-021-09126-7

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