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Drought monitoring and assessment using Landsat TM/OLI data in the agricultural lands of Bandar-e-Turkmen and Gomishan cities, Iran

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Abstract

Investigating agricultural drought is very important in semiarid areas, and it has a significant impact on people’s livelihood, which depends on farming. This research tried to examine the capability of six Landsat surface reflectance bands, vegetation indices (VIs), and drought indices (DIs) for drought monitoring in the agricultural lands of Bandar-e-Turkmen and Gomishan cities in 1986 and 2015. Statistical analysis, Pearson correlation analysis, and correlation matrices between the VIs and DIs with land surface temperature (LST) were monitored separately for each date. In addition, the spatial and temporal drought extents using the Standardized Evapotranspiration Precipitation Index (SPEI) and the Vegetation Health Index (VHI) approaches were determined and compared for both study periods. The correlations between the LST and VIs were negative and significant in both study periods. The range of VI values decreased with increasing temperature in 2015 compared to 1986. The mean surface reflectance of the visible bands (blue, green, red), NIR (near-infrared), SWIR (shortwave infrared), and MWIR (mid-wave infrared) increased in 2015 compared to 1986. A higher spectral reflectance of the visible bands indicated a reduction in vegetation cover or increase in stress in 2015 compared to 1986. Meanwhile, the SWIR and MWIR bands showed that the average surface reflectance increased in 2015, while they showed a lower reflectance in 1986. The results of the Water Supplying Vegetation Index (WSVI) implied that the crop water stress in 2015 was more than that in 1986. The VHIs were separately compared with the NDVI and LST in each period of study. The maximum LST increased from 38 °C in 1986 to 44 °C in 2015. The moderate drought extent was 65% in 2015, while the study area did not experience any drought in 1986. The SPEI maps showed that the entire study area experienced moderate drought in 2015, while no drought was observed in 1986. The results showed that the DIs and VIs and the visible, SWIR and MWIR surface reflectance bands can be effectively used by sampling plots in future drought assessment studies. The present study also emphasizes that there is a similarity between traditional and remote sensing methods and indicates that remote sensing data can be effectively used instead of traditional approaches when there is no meteorological station. The results would be useful in food security and sustainable agriculture management plans.

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References

  • AghaKouchak, A., Farahmand, A., Melton, F., Teixeira, J., Anderson, M., Wardlow, B., et al. (2015). Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics, 53, 452–480.

    Google Scholar 

  • Alcaraz-segura, D., Chuvieco, E., Epstein, H. E., Kasischke, E. S., & Trishchenko, A. (2010). Debating the greening vs. browning of the North American boreal forest: Differences between satellite datasets. Global Change Biology, 16, 760–770.

    Google Scholar 

  • Alshaikh, A. Y. (2015). Space applications for drought assessment in Wadi-Dama (West Tabouk), KSA. The Egyptian Journal of Remote Sensing and Space Science, 18, S43–S53.

    Google Scholar 

  • Arekhi, M., Ylmaz, O. Y., Ylmaz, H., & Akyüz, Y. F. (2017). Can tree species diversity be assessed with Landsat data in a temperate forest? Environmental Monitoring and Assessment, 189, 586.

    Google Scholar 

  • Aulia, M. R., Setiawan, Y., Fatikhunnada, A., et al. (2016). Drought detection of West Java’s paddy field using MODIS EVI satellite images (case study: Rancaekek and Rancaekek Wetan). Procedia Environmental Sciences, 33, 646–653.

    Google Scholar 

  • Bajgain, R., Xiao, X., Wagle, P., Basara, J., & Zhou, Y. (2015). Sensitivity analysis of vegetation indices to drought over two tallgrass prairie sites. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 151–160.

    Google Scholar 

  • Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 161–173.

    Google Scholar 

  • Belal, A.-A., El-Ramady, H. R., Mohamed, E. S., & Saleh, A. M. (2014). Drought risk assessment using remote sensing and GIS techniques. Arabian Journal of Geosciences, 7, 35–53.

    Google Scholar 

  • Bernstein, L., Bosch, P., Canziani, O., Chen, Z., Christ, R., Davidson, O., Hare, W., Huq, S., Karoly, D., Kattsov, V., et al. (2007). IPCC, 2007: Climate change 2007: Synthesis report. Contribution of working groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change, Geneva. http://www.ipcc.ch/ipccreports/ar4-syr.htm.

  • Cai, G., Du, M., & Liu, Y. (2010). Regional drought monitoring and analyzing using MODIS data—A case study in Yunnan Province. In International conference on computer and computing technologies in agriculture (pp. 243–251). Springer.

  • Campbell, J. (2007). Introduction to remote sensing (4th ed.). New York: The Guilford Press.

    Google Scholar 

  • Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote sensing reviews, 9, 161–173.

    Google Scholar 

  • Carlson, T. N., Perry, E. M., & Schmugge, T. J. (1990). Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agricultural and Forest Meteorology, 52, 45–69.

    Google Scholar 

  • Cetin, M., & Sevik, H. (2016a). Evaluating the recreation potential of Ilgaz Mountain National Park in Turkey. Environmental Monitoring and Assessment, 188, 52.

    Google Scholar 

  • Cetin, M., & Sevik, H. (2016b). Assessing potential areas of ecotourism through a case study in Ilgaz Mountain National Park. InTech: Tourism-From Empirical Research Towards Practical Application.

    Google Scholar 

  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113, 893–903.

    Google Scholar 

  • Chavez, P. S. (1996). Image-based atmospheric corrections—revisited and improved. Photogrammetric engineering and remote sensing, 62, 1025–1035.

    Google Scholar 

  • Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., et al. (2015). System for automated geoscientific analyses (SAGA) v. 2.1. 4. Geoscientific Model Development, 8, 1991–2007.

    Google Scholar 

  • Cunha, A., Alvalá, R., Nobre, C., & Carvalho, M. (2015). Monitoring vegetative drought dynamics in the Brazilian semiarid region. Agricultural and Forest Meteorology, 214, 494–505.

    Google Scholar 

  • Deering, D., & Rouse, J. (1975). Measuring ‘forage production’ of grazing units from Landsat MSS data. In International symposium on remote sensing of environment, 10th, Ann Arbor, MI (pp. 1169–1178).

  • Ebrahimi, M., Matkan, A. A., & Darvishzadeh, R. (2010). Remote sensing for drought assessment in arid regions : a case study of central part of Iran, Shirkooh - Yazd. In W. Wagner & B. Szekely (Eds.), ISPRS 2010 : isprs 1910–2010 Centenary celebrations : 100 years of ISPRS, Advancing remote sensing science : Symposium technical commission VII, Vol. XXXVIII part 7B, 5–7 July 2010, Wien, Osterreich (pp. 199–203). Vienna, Austria: International Society for Photogrammetry and Remote Sensing (ISPRS).

  • Fensholt, R., Langanke, T., Rasmussen, K., Reenberg, A., Prince, S. D., Tucker, C., et al. (2012). Greenness in semi-arid areas across the globe 1981–2007—An earth observing satellite based analysis of trends and drivers. Remote Sensing of Environment, 121, 144–158.

    Google Scholar 

  • Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175, 205–214.

    Google Scholar 

  • Gao, Z., Gao, W., & Chang, N.-B. (2011). Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM + images. International Journal of Applied Earth Observation and Geoinformation, 13, 495–503.

    Google Scholar 

  • Ghaleb, F., Mario, M., & Sandra, A. N. (2015). Regional Landsat-based drought monitoring from 1982 to 2014. Climate, 3, 563–577.

    Google Scholar 

  • Gutman, G. G. (1990). Towards monitoring droughts from space. Journal of Climate, 3, 282–295.

    Google Scholar 

  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.

    Google Scholar 

  • Ji, L., & Peters, A. J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87, 85–98.

    Google Scholar 

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

    Google Scholar 

  • Karnieli, A., Bayasgalan, M., Bayarjargal, Y., Agam, N., Khudulmur, S., & Tucker, C. (2006). Comments on the use of the vegetation health index over Mongolia. International Journal of Remote Sensing, 27, 2017–2024.

    Google Scholar 

  • Khosravi, H., Haydari, E., Shekoohizadegan, S., & Zareie, S. (2017). Assessment the effect of drought on vegetation in desert area using Landsat Data. The Egyptian Journal of Remote Sensing and Space Science, 20, S3–S12.

    Google Scholar 

  • Kogan, F. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11, 1405–1419.

    Google Scholar 

  • Kogan, F. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91–100.

    Google Scholar 

  • Kogan, F. N. (1997). Global drought watch from space. Bulletin of the American Meteorological Society, 78, 621–636.

    Google Scholar 

  • Lenney, M. P., Woodcock, C. E., Collins, J. B., & Hamdi, H. (1996). The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM. Remote Sensing of Environment, 56, 8–20.

    Google Scholar 

  • Li, H., Jiang, J., Chen, B., Li, Y., Xu, Y., & Shen, W. (2016). Pattern of NDVI-based vegetation greening along an altitudinal gradient in the eastern Himalayas and its response to global warming. Environmental Monitoring and Assessment, 188, 1–10.

    Google Scholar 

  • Mancino, G., Nolè, A., Ripullone, F., & Ferrara, A. (2014). Landsat TM imagery and NDVI differencing to detect vegetation change: Assessing natural forest expansion in Basilicata, southern Italy. iForest-Biogeosciences and Forestry, 7, 75.

    Google Scholar 

  • Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 13(3), 245–259. https://doi.org/10.2307/1907187.

    Article  Google Scholar 

  • Moran, M., Clarke, T., Inoue, Y., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 49, 246–263.

    Google Scholar 

  • Musick, H. B., & Pelletier, R. E. (1988). Response to soil moisture of spectral indexes derived from bidirectional reflectance in thematic mapper wavebands. Remote Sensing of Environment, 25, 167–184.

    Google Scholar 

  • Nguyen, U., Glenn, E. P., Nagler, P. L., & Scott, R. L. (2015). Long-term decrease in satellite vegetation indices in response to environmental variables in an iconic desert riparian ecosystem: The Upper San Pedro, Arizona, United States. Ecohydrology, 8, 610–625.

    Google Scholar 

  • Nichol, J. E., & Abbas, S. (2015). Integration of remote sensing datasets for local scale assessment and prediction of drought. Science of the Total Environment, 505, 503–507.

    CAS  Google Scholar 

  • Pachauri, R., & Reisinger, A. (2007). IPCC fourth assessment report. Geneva: IPCC.

    Google Scholar 

  • Payab, A. H., & Türker, U. (2017). Analyzing temporal–spatial characteristics of drought events in the northern part of Cyprus. Environment, Development and Sustainability, 20, 1553–1574.

    Google Scholar 

  • Perry, C. R., & Lautenschlager, L. F. (1984). Functional equivalence of spectral vegetation indices. Remote Sensing of Environment, 14, 169–182.

    Google Scholar 

  • Pravalie, R., Sîrodoev, I., & Peptenatu, D. (2014). Detecting climate change effects on forest ecosystems in Southwestern Romania using Landsat TM NDVI data. Journal of Geographical Sciences, 24, 815–832.

    Google Scholar 

  • QGIS Development, T. (2015). Quantum GIS geographic information system. Open Source Geospatial Foundation Project.

  • R Development Team. (2012). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3–900051–900007–900050, The R project for statistical computing website. Retrieved December 30, 2013, from http://www.R-project.org/.

  • Rahimzadeh-Bajgiran, P., Omasa, K., & Shimizu, Y. (2012). Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 1–12.

    Google Scholar 

  • Ramos, M. R., Cordeiro, C., Simos, T., Psihoyios, G., & Tsitouras, C. (2013). Trend tests in time series with missing values: A case study with imputation. In AIP Conference Proceedings AIP (pp. 1909–1912).

  • Rhee, J., Im, J., & Carbone, G. J. (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114, 2875–2887.

    Google Scholar 

  • Rouse, J., Jr., Haas, R., Schell, J., & Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309.

    Google Scholar 

  • Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79, 213–224.

    Google Scholar 

  • Sawyer, P. S. (2015). Ecosystem response to recent climate change in alpine environments. UNLV Theses, Dissertations, Professional Papers, and Capstones. 2579. https://digitalscholarship.unlv.edu/thesesdissertations/2579.

  • Sawyer, P., & Stephen, H. (2012). Assessing ecological response of the Big Pine Creek watershed to climate change using time series analysis of Landsat surface reflectance data over a 28-year period. River Basin Management VII, 172, 375.

    Google Scholar 

  • Sawyer, P. S., & Stephen, H. (2014a). Comparison of surface reflectance values from the USGS Landsat 5 TM climate data record (CDR) with values generated using a simple dark object subtraction (DOS) method in an Alpine watershed. In Proceedings of the international conference on advances in bio-informatics, bio-technology and environmental engineering-ABBE 2014.

  • Sawyer, P., & Stephen, H. (2014b). The Big Pine Creek watershed and climate change: A trend analysis of Landsat surface reflectance and PRISM datasets over the last 3 decades. Advances in Space Research, 54, 37–48.

    Google Scholar 

  • Senay, G., Velpuri, N., Bohms, S., Budde, M., Young, C., Rowland, J., & Verdin, J. (2015). Drought monitoring and assessment: Remote sensing and modeling approaches for the Famine Early Warning Systems Network. In P. Paron, G. Di Baldassarre, J. F. Shroder, (Eds.), Hydro-meteorological hazards, risks and disasters (pp. 233–262).

  • Shahabfar, A., Ghulam, A., & Eitzinger, J. (2012). Drought monitoring in Iran using the perpendicular drought indices. International Journal of Applied Earth Observation and Geoinformation, 18, 119–127.

    Google Scholar 

  • Shamsipour, A., AlaviPanah, S., Mohammadi, H., Azizi, A., & Khoshakhlagh, F. (2008). An analysis of drought events for central plains of Iran through an employment of NOAA-AVHRR data. Desert, 13, 105–115.

    Google Scholar 

  • Sharafi, S., Ramroudi, M., Nasiri, M., Galavi, M., & Kamali, G. A. (2016). Role of early warning systems for sustainable agriculture in Iran. Arabian Journal of Geosciences, 9, 734.

    Google Scholar 

  • Sholihah, R. I., Trisasongko, B. H., Shiddiq, D., La Ode, S. I., Kusdaryanto, S., Panuju, D. R., et al. (2016). Identification of agricultural drought extent based on Vegetation Health Indices of Landsat Data: Case of Subang and Karawang, Indonesia. Procedia Environmental Sciences, 33, 14–20.

    Google Scholar 

  • 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.

    Google Scholar 

  • Tadesse, T., Demisse, G. B., Zaitchik, B., & Dinku, T. (2014). Satellite-based hybrid drought monitoring tool for prediction of vegetation condition in Eastern Africa: A case study for Ethiopia. Water Resources Research, 50, 2176–2190.

    Google Scholar 

  • Todd, S. W., & Hoffer, R. M. (1998). Responses of spectral indices to variations in vegetation cover and soil background. Photogrammetric Engineering and Remote Sensing, 64, 915–922.

    Google Scholar 

  • Trisasongko, B. H., Panuju, D. R., Shiddiq, D., La Ode, S. I., Sholihah, R. I., & Kusdaryanto, S. (2015). Constraints of VSWI in the estimation of drought extent using Landsat data: A case of Tuban, Indonesia. Procedia Environmental Sciences, 24, 25–28.

    Google Scholar 

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

    Google Scholar 

  • Vicente-Serrano, S. M. (2007). Evaluating the impact of drought using remote sensing in a Mediterranean, semi-arid region. Natural Hazards, 40, 173–208.

    Google Scholar 

  • Vicente-Serrano, S. M., Gouveia, C., Camarero, J. J., Beguera, S., Trigo, R., López-Moreno, J. I., et al. (2013). Response of vegetation to drought time-scales across global land biomes. Proceedings of the National Academy of Sciences, 110, 52–57.

    CAS  Google Scholar 

  • Vogelmann, J. E., Gallant, A. L., Shi, H., & Zhu, Z. (2016). Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sensing of Environment, 185, 258–270.

    Google Scholar 

  • Wan, Z., Zhang, Y., Zhang, Q., & Li, Z.-L. (2004). Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing, 25, 261–274.

    Google Scholar 

  • Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, 4.

    Google Scholar 

  • Wilson, J. H., Zhang, C., & Kovacs, J. M. (2014). Separating crop species in northeastern Ontario using hyperspectral data. Remote Sensing, 2014(6), 925–945.

    Google Scholar 

  • Xiao, X., He, L., Salas, W., Li, C., Moore Iii, B., Zhao, R., et al. (2002). Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields. International Journal of Remote Sensing, 23, 3595–3604.

    Google Scholar 

  • Yang, S., Xu, Y., & Gao, Y. (2013). Summer drought assessment based on Vegetation Supply Water Index in Chongqing. Advances in Intelligent Systems and Research. https://doi.org/10.2991/rsete.2013.231.

    Article  Google Scholar 

  • Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A., & Tucker, C. (2014). The use of the Normalized Difference Vegetation Index (NDVI) to assess land degradation at multiple scales: A review of the current status, future trends and practical considerations. Lund University Center for Sustainability Studies (LUCSUS), and the Scientific and Technical Advisory Panel of the Global Environment Facility (STAP/GEF).

  • Yigit, N., Sevik, H., Cetin, M., & Kaya, N. (2016). Determination of the effect of drought stress on the seed germination in some plant species. InTech: Water Stress in Plants.

    Google Scholar 

  • Zargar, A., Sadiq, R., Naser, B., & Khan, F. I. (2011). A review of drought indices. Environmental Reviews, 19, 333–349.

    Google Scholar 

  • Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., et al. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, 243–257.

    Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the US Geological Survey (USGS) for providing the Landsat data.

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Authors and Affiliations

  1. Institute of Science, Graduate Education Institute, Forest Engineering, Istanbul University-Cerrahpaşa, 34452, Istanbul, Turkey

    Maliheh Arekhi

  2. Department of Forest Management, Faculty of Forestry, Istanbul University-Cerrahpaşa, 34473, Istanbul, Turkey

    Serhun Saglam & Ulas Yunus Ozkan

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  1. Maliheh Arekhi
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Arekhi, M., Saglam, S. & Ozkan, U.Y. Drought monitoring and assessment using Landsat TM/OLI data in the agricultural lands of Bandar-e-Turkmen and Gomishan cities, Iran. Environ Dev Sustain 22, 6691–6708 (2020). https://doi.org/10.1007/s10668-019-00509-y

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