Analysis of Spatial Distribution of the Drought Hazard Index (DHI) by Integration AHP-GIS-Remote Sensing in Gorontalo Regency

  • Muhammad Ramdhan Olii Department of Civil Engineering, Universitas Gorontalo, Gorontalo, INDONESIA
  • Aleks Olii Department of Civil Engineering, Universitas Gorontalo, Gorontalo, INDONESIA
  • Ririn Pakaya Department of Public Health, Universitas Gorontalo, Gorontalo, INDONESIA
Keywords: Analytic Hierarchy Process, Geographic Information System, Remote Sensing, Drought Hazard Index, Gorontalo Regency


Several regions across the world are presently experiencing a continuous increase in water scarcity due to the rise in water consumption resulting from population development, agricultural and industrial expansion, climate change, and pollution. Droughts are increasing in recurrence, severity, duration, and spatial extent as a result of climate change. Drought will be one of the most serious threats posed by climate change, often in conjunction with other effects such as rising temperatures and shifting ecosystems. Therefore, this study analyzes the spatial distribution of the Drought Hazard Index (DHI) by integrating AHP-GIS-Remote Sensing in Gorontalo Regency. AHP was used to determine the significance of each map as an input parameter for the DHI, while GIS-Remote Sensing was utilized to supply and analyze all input maps and the study outcome. The DHI assessment consists of four criteria, namely with Normalized Difference Vegetation Index accounting for the highest proportion at 42.9%, followed by Land Surface Temperature (33.6%), Normalized Difference Moisture Index (16.8%), and Topographic Wetness Index (6.7%), with the consistency of the underlying expert opinion measured by the consistency ratio of 0.048. The results indicated that the general hazard of drought in the Gorontalo Regency area was low (43.53%), with 17.87% of the whole area experiencing high hazard. The high class of drought was discovered to be centered in the central region of Gorontalo Regency, which was mostly used for agricultural and economic purposes, thereby enabling policymakers to have evidence to develop management policies suitable for local conditions. Therefore, despite the limits of climatology data, this study established the value of satellite-derived data needed to support policymakers in guiding operational actions to drought hazards reduction.


Adams, H.R., Barnard, H.R. & Loomis, A.K., 2014. Topography alters tree growth-climate relationships in a semi-arid forested catchment. Ecosphere, 5(11), pp.1–16.

Akbar, T.A., Hassan, Q.K., Ishaq, S., Batool, M., Butt, H.J. & Jabbar, H., 2019. Investigative spatial distribution and modelling of existing and future urban land changes and its impact on urbanization and economy. Remote Sensing, 11(2).

Alavipanah, S.K., Mogaddam, M.K. & Firozjaei, M.K., 2017. Monitoring spatiotemporal changes of heat island in Babol City due to land use changes. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Tehran, Iran.pp.17–22.

Amalo, L.F., Ma’Rufah, U. and Permatasari, P.A., 2018. Monitoring 2015 drought in West Java using Normalized Difference Water Index (NDWI). IOP Conference Series: Earth and Environmental Science, 149(1), pp.1–7.

Arnfield, A.J., 2003. Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23(1), pp.1–26.

Bajgiran, P.R., Darvishsefat, A.A., Khalili, A. and Makhdoum, M.F., 2008. Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. Journal of Arid Environments, 72(6), pp.1086–1096.

Barsi, J.A., Schott, J.R., Hook, S.J., Raqueno, N.G., Markham, B.L. and Radocinski, R.G., 2014. Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), pp.11607–11626.

Belal, A.A., El-Ramady, H.R., Mohamed, E.S. and Saleh, A.M., 2012. Drought risk assessment using remote sensing and GIS techniques. Arabian Journal of Geosciences, 7(1), pp.35–53.

Bennie, J., Huntley, B., Wiltshire, A., Hill, M.O. and Baxter, R., 2008. Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecological Modelling, 216(1), pp.47–59.

Beven, K.J. and Kirkby, M.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24(1), pp.43–69.

Bhattacharya, S., Halder, S., Nag, S., Roy, P.K. and Roy, M.B., 2021. Assessment of Drought Using Multi-parameter Indices. In: P.K. Roy, M.B. Roy and S. Pal, eds. Advances in Water Resources Management for Sustainable Use, 1st ed. Singapore: Springer Singapore.pp.243–255.

Chakraborty, A. & Joshi, P.K., 2016. Mapping disaster vulnerability in India using analytical hierarchy process. Geomatics, Natural Hazards and Risk, [online] 7(1), pp.308–325. Available at: <>.

Chang, D.Y., 1996. Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), pp.649–655.

Chen, K., Blong, R. and Jacobson, C., 2003. Towards an Integrated Approach to Natural Hazards Risk Assessment Using GIS : With Reference to Bushfires. Environmental Management, 31(4), pp.546–560.

Cheng, J. & Tao, J.P., 2010. Fuzzy comprehensive evaluation of drought vulnerability based on the Analytic Hierarchy Process- An empirical study from Xiaogan City in Hubei Province. Agriculture and Agricultural Science Procedia, 1, pp.126–135.

Dutta, D., Kundu, A., Patel, N.R., Saha, S.K. and Siddiqui, A.R., 2015. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). Egyptian Journal of Remote Sensing and Space Science, [online] 18(1), pp.53–63.

Ekrami, M., Marj, A.F., Barkhordari, J. & Dashtakian, K., 2016. Drought vulnerability mapping using AHP method in arid and semiarid areas: a case study for Taft Township, Yazd Province, Iran. Environmental Earth Sciences, 75(12), pp.1–13.

Faridatul, M.I. & Ahmed, B., 2020. Assessing agricultural vulnerability to drought in a heterogeneous environment: A remote sensing-based approach. Remote Sensing, 12(20), pp.1–17.

Gao, B.C., 1996. NDWI A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. Remote Sensing of Environment, 58, pp.257–266.

Gebrehiwot, T., van der Veen, A. and Maathuis, B., 2011. Spatial and temporal assessment of drought in the Northern highlands of Ethiopia. International Journal of Applied Earth Observation and Geoinformation, [online] 13(3), pp.309–321. Available at: <>.

Goepel, K.D., 2013. Implementing the Analytic Hierarchy Process as a Standard Method for Multi-Criteria Decision Making in Corporate Enterprises – a New AHP Excel Template with Multiple Inputs. In: International Symposium on the Analytic Hierarchy Process.

Gulácsi, A. & Kovács, F., 2015. Drought Monitoring With Spectral Indices Calculated From Modis Satellite Images In Hungary. Journal of Environmental Geography, 8(3–4), pp.11–20.

Hais, M., Hellebrandová, K.N. & Šrámek, V., 2019. Potential of Landsat spectral indices in regard to the detection of forest health changes due to drought effects. Journal of Forest Science, 65(2), pp.70–78.

Haq, M., Akhtar, M., Muhammad, S., Paras, S. and Rahmatullah, J., 2012. Techniques of Remote Sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan. Egyptian Journal of Remote Sensing and Space Science, [online] 15(2), pp.135–141. Available at: <>.

Jin, M., Li, J., Wang, C. and Shang, R., 2015. A practical split-window algorithm for retrieving land surface temperature from Landsat-8 data and a case study of an urban area in China. Remote Sensing, 7(4), pp.4371–4390.

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

Lin, M.L., Chu, C.M. & Tsai, B.W., 2011. Drought risk assessment in western inner-mongolia. International Journal of Environmental Research, 5(1), pp.139–148.

Lin, M.L., Wang, Q., Sun, F., Chu, T.H. and Shiu, Y.S., 2010. Quick spatial assessment of drought information derived from MODIS imagery using amplitude analysis. World Academy of Science, Engineering and Technology, 43(7), pp.628–632.

Loon, A.F.V., Stahl, K., Baldassarre, D.G., Clark, J., Rangecroft, S., Wanders, N., Gleeson, T., Dijk, A.I.J.M.V., Tallaksen, L.M., Hannaford, J., Uijlenhoet, R., Teuling, A.J., Hannah, D.M., Sheffield, J., Svoboda, M., Verbeiren, B., Wagener, T. and Van Lanen, H.A.J., 2016. Drought in a human-modified world : reframing drought definitions , understanding , and analysis approaches. Hydrol. Earth Syst. Sci., 20, pp.3631–3650.

Malik, M.S., Shukla, J.P. and Mishra, S., 2019. Relationship of LST, NDBI and NDVI using landsat-8 data in Kandaihimmat watershed, Hoshangabad, India. Indian Journal of Geo-Marine Sciences, 48(1), pp.25–31.

McKee, T.B., Doesken, N.J. and Kleist, 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the Eighth Conference on Applied Climatology. Boston: American Meteorological Society.pp.179–184.

Moghari, S.M.H., Araghinejad, S.& Azarnivand, A., 2017. Fuzzy analytic hierarchy process approach in drought management: Case study of Gorganrood basin, Iran. Journal of Water Supply: Research and Technology - AQUA, 66(3), pp.207–218.

Muukkonen, P., Nevalainen, S., Lindgren, M. and Peltoniemi, M., 2015. Spatial occurrence of drought-associated damages in Finnish boreal forests: Results from forest condition monitoring and GIS analysis. Boreal Environment Research, 20(2), pp.172–180.

Olii, M.R., Olii, A. & Pakaya, R., 2021. The Integrated Spatial Assessment of The Flood Hazard Using AHP-GIS: The Case Study of Gorontalo Regency. Indonesian Journal of Geography, 53(1), pp.126–135.

Patel, D.P. & Prashant, S.K., 2013. Flood Hazards Mitigation Analysis Using Remote Sensing and GIS : Correspondence with Town Planning Scheme. Water Resources Management, 27, pp.2353–2368.

Prasad, A.S., Pandey, B.W., Leimgruber, W. and Kunwar, R.M., 2016. Mountain hazard susceptibility and livelihood security in the upper catchment area of the river Beas , Kullu Valley , Himachal Pradesh, India. Geoenvironmental Disasters, 3(3), pp.1–17.

Prasetya, T.A.E., Munawar, Taufik, M.R., Chesoh, S., Lim, A. and McNeil, D., 2020. Land Surface Temperature Assesment in Central Sumatra, Indonesia. Indonesian Journal of Geography, [online] 52(2), pp.239–245. Available at: <>.

Rahmati, O., Kalantari, Z., Samadi, M., Uuemaa, E., Moghaddam, D.D., Nalivan, O.A., Destouni, G. and Bui, D.T., 2019. GIS-based site selection for check dams in watersheds: Considering geomorphometric and topo-hydrological factors. Sustainability (Switzerland), 11(20).

Rousta, I., Olafsson, H., Moniruzzaman, M., Zhang, H., Liou, Y.A., Mushore, T.D. and Gupta, A., 2020. Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sensing, 12(15), pp.1–21.

Saaty, T.L., 1980. The Analytic Hierarchy Process. New York: McGraw Hill. International.

Saaty, T.L., 2008. Decision making with the analytic hierarchy process. International Journal Services Sciences, 1(1), pp.83–98.

Sheffield, J. & Eric, F.W., 2011. Drought : past problems and future scenarios. London, Washington D.C.: Eartscan.

Sholihah, R.I., Trisasongko, B.H., Shiddiq, D., Iman, L.S., Kusdaryanto, S., Manijo and Panuju, D.R., 2016.

Identification of Agricultural Drought Extent Based on Vegetation Health Indices of Landsat Data: Case of Subang and Karawang, Indonesia. Procedia Environmental Sciences, 33, pp.14–20.

Sruthi, S. X & Aslam, M.A.M., 2015. Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District. Aquatic Procedia, [online] 4, pp.1258–1264. Available at: <>.

Sun, D. & Pinker, R.T., 2003. Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8). Journal of Geophysical Research: Atmospheres, 108(11), pp.1–15.

Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), pp.127–150.

Tucker, C.J. & Choudhury, B.J., 1987. Satellite remote sensing of drought conditions. Remote Sensing of Environment, 23(2), pp.243–251.

USGS, 2019. Landsat 8 Data Users Handbook. 5th ed. [online] USGS, United State: USGS. Available at: <>.

Voogt, J.A. & Oke, T.R., 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), pp.370–384.

Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A. and Zhao, S., 2015. An improved mono-window algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data. Remote Sensing, 7(4), pp.4268–4289.

Weng, Q., 2009. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 64(4), pp.335–344. Available at: <>.

Wijitkosum, S., 2018. Fuzzy AHP for drought risk assessment in lam Ta Kong watershed, the north-eastern region of Thailand. Soil and Water Research, 13(4), pp.218–225.

Wijitkosum, S. and Sriburi, T., 2019. Fuzzy AHP integrated with GIS analyses for drought risk assessment: A case study from Upper Phetchaburi River Basin, Thailand. Water (Switzerland), 11(5).

Wu, J., Lin, X., Wang, M., Peng, J. and Tu, Y., 2017. Assessing agricultural drought vulnerability by a VSD Model: A case study in Yunnan Province, China. Sustainability, 9(6), pp.1–16.

Yang, L., Chen, L. and Wei, W., 2015. Effects of vegetation restoration on the spatial distribution of soil moisture at the hillslope scale in semi-arid regions. Catena, [online] 124, pp.138–146.

How to Cite
Olii, M. R., Aleks Olii, & Ririn Pakaya. (2021). Analysis of Spatial Distribution of the Drought Hazard Index (DHI) by Integration AHP-GIS-Remote Sensing in Gorontalo Regency. Journal of the Civil Engineering Forum, 8(1), 81-96.