Evaluation of indices based on remote sensing in drought monitoring of Neyriz city

Subject Areas : Agriculture, rangeland, watershed and forestry

Mohammad Shabani ¹

1 - Assistant Professor, Department of Water Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

Received: 2022-01-30 Accepted : 2022-05-02 Published : 2022-10-23

Keywords: remote sensing, Standard precipitation index, drought, Neyriz,

Abstract :

Background and Objective Knowing the extent and severity of drought in a region and planning to reduce its effects is one of the most important principles of management in regional planning to combat drought. Drought monitoring and management in an area using remote sensing data and satellite imagery as a suitable tool in temporal and spatial monitoring of agricultural drought has always been the focus of regional managers. The purpose of this study is to investigate the efficiency of remote sensing data and satellite images in the zoning of agricultural drought in the years 2000 to 2021 in Neyriz city. For this purpose, three vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI) were extracted from MODIS satellite images for the desired time period. The results of these indices were compared with the values of the standard precipitation index (SPI) in time series of 1, 3, 6, 9, 12, 18, 24, and 48 months.Materials and Methods The study area in this study is Neyriz city located in the southeast of Fars province with an area of 10787 Km2 and is part of one of the watersheds of Bakhtegan Lake. The average altitude of the region is 1798 meters, the maximum altitude of the region is 3235 meters and the minimum altitude is 1476 meters above sea level. The average annual rainfall, temperature, and evapotranspiration of the basin are 204.8 mm, 19 °C, and 1058.3 mm, respectively. In this study, the rainfall data of Neyriz synoptic station during the statistical period of 22 years (2000-2021) were used to calculate the SPI index in time series of 1, 3, 6, 9, 12, 18, 24, and 48 months. Then, 3 indices based on satellite imagery including vegetation condition (VCI), temperature condition index (TCI), and plant health index (VHI) were extracted from Modis measured data for May month from 2008 to 2021 and with standard precipitation index (SPI) were compared in time series of 1, 3, 6, 9, 12, 18, 24 and 48 months based on the correlation coefficient. Finally, the most appropriate drought index based on satellite images was selected from the indices and the percentage of drought classes was determined based on the selected index in the study area.Results and Discussion The results of calculating the values of the SPI index using DIP software in time series of 1, 3, 6, 9, 12, 18, 24, and 48 months in the statistical period of 2000-2021 showed that the trend of curves in some years is decreasing, in some years it has been increasing and in most years it has been almost normal. On average, the incidence of droughts and wetlands according to the SPI index in different time series during the statistical period is 68% in normal conditions, 18% in wet conditions, and 16% in drought conditions. The results of calculating the SPI index in different ground series were analyzed based on data from synoptic stations and remote sensing data. For this purpose, the values obtained from all indices based on satellite images including VCI, TCI, and VHI are extracted and compared and their correlation coefficient with the ground SPI index in time series 1, 3, 6, 9, 12, 18, 24, and 48 became. VCI index values in 2000 have the lowest value (32.1%) and in 2020 have the highest value (41.3%) during May. Therefore, based on the value of the VCI index during the statistical period in 2008, severe drought conditions prevailed in the region, and in 2020, more favorable vegetation and wetting conditions prevailed in the region. The results obtained from the SPI index in different time series also confirm the fact that the most severe drought and wet season during the statistical period studied in the two years 2000 and 2020, respectively, in the region. In addition, the VCI index is most correlated with the SPI index in different series and the SPI relationship is significant with the all-time series. TCI index has no significant correlation with any of the time series and has a weak correlation with the SPI index in different time series. In addition, the VHI index has a significant correlation with time series of one, three, six, and twelve months only at the level of 5% and its correlation with the SPI index in different time series is much less than the VCI index. Spatial distribution of drought intensity based on the values of the studied indices in May 2008 showed that the eastern parts of the region, which is also located at low altitudes, have been more affected by drought. The study of the area affected by drought classes based on the TCI index in 2008 showed that there is no very severe drought in the study area, 11% of the area suffers from moderate drought, 22% of the area suffers from mild drought and 67% has no drought. According to the VCI index, the level of severe drought on the date is 0.14%, severe at 0.33%, moderate at 17%, mild at 77%, and no drought at 6%. Also, according to the VHI index, there is no severe or severe drought in the study area only 9% of the area suffers from moderate drought and 91% does not have a drought. Spatial distribution of drought severity based on the values of the studied indices in May 2020 shows that in the study area according to the TCI index there is no very severe drought on the target date and 5% of the area has moderate drought, 22% drought Mild and 73% lack drought. According to the VCI index on the target date, the percentage of drought is very severe 0.5%, severe 0.8%, moderate 5%, mild 31%, and no drought 62%. Also, according to the VHI index in May 1999, 0.2% of the area has a moderate drought, 30% has a mild drought and 69% has no drought. According to this index, there is no very severe drought in the region.Conclusion Drought is one of the most important natural disasters that affect millions of people and large parts of the world every year. This phenomenon, which starts slowly and has a creeping nature, can cause a lot of damage to agriculture, natural resources, and the environment. Knowing how to occur and preparing drought severity maps based on new methods has a very positive and serious impact on drought management in an area. One of the new and widely used methods in temporal and spatial monitoring of drought is the use of drought indices based on satellite images, which has recently been used in drought-related topics. The results of the SPI index analysis showed that in most time series, the most severe drought and wet season during the study period occurred in 2000 and 2020, respectively. The results also showed that the temperature condition index (TCI) has no significant correlation with any of the time series and has a weak correlation with the SPI index in different time series. The plant health index (VHI) with time series of one, three, six, and twelve months has a significant correlation at the level of 5% and its correlation with the SPI index in different time series is less than the vegetation condition index (VCI). The value of the VCI index in 2008 had the lowest value (32.1%) and in 2020 had the highest value (41.3%) during May, which is consistent with the results obtained from the SPI index in the region. A comparison of the results of this study with the results of other researchers shows the excellent accuracy of remote sensing indices in drought monitoring. Therefore, the use of remote sensing technology in drought monitoring in areas that do not have meteorological stations or have meteorological stations with low density or scattered is recommended.

References:

Arabi Z, Mohammadi Sh. 2022. Monitoring Spatio-temporal pattern of drought using multi-satellite data during the period 2000 - 2018 (Case study: Iran). Journal of Natural Environmental Hazards, 10(30): 82-104. .(In Persian).

Asadi Meyabadi A, Akhzari D. 2022. Zoning of Drought by Integrating Satellite Imagery and Ground–Based Climate Data (Case study: Malayer Plain). Journal of Environmental Science and Technology, 23(4): 86-96.

Askarizadeh D, Arzani H , Jafary M, Bazrafshan J . 2018. Surveying of the past, present, and future of vegetation changes in the central Alborz ranges in relation to climate change. journal of RS and GIS for Natural Resources (Journal of Applied RS and GIS Techniques in Natural Resource Science), 9(3): 1-18. (In Persian).

Bento V A, Célia M G, Carlos C D, Renata L, Isabel F T. 2020. The roles of NDVI and Land Surface Temperature when using the Vegetation Health Index over dry regions, Global and Planetary Change,190:103198.

Bento V A, Gouveia C M, DaCamara C C,Trigo L F. 2018. A climatological assessment of drought impact on vegetation health index, Agricultural and Forest Meteorology, 259: 286-295.

Elhag  K, Zhang W. 2018. Monitoring and Assessment of Drought Focused on Its Impact on orghum Yield over Sudan by Using Meteorological Drought Indices for the Period 2001–2011, Remote Sensing, 10(8):1231 p.

Gidey  E, Dikinya O, Sebego R, Segosebe E, Zenebe A. 2018. Using drought indices to model the statistical relationships between meteorological and agricultural drought in Raya and its environs, Northern Ethiopia. Earth Systems and Environment, 2(2): 265–279.

Guo X, Kapucum N. 2018. Examining the impacts of disaster resettlement from a livelihood perspective: a case study of Qinling Mountains. China, Disasters Journal, 42:(2): 251-274.

  Hamzeh S, Farahani Z,  Mahdavi S,  Chatrobgoun O, Gholamnia M. 2017. Spatio-temporal monitoring of agricultural drought using remotely sensed data (Case study of Markazi province of Iran). Journal of Spatial Analysis Environmental Hazards, 4(3): 53-70. (In Persian).

Kazempour Choursi S, Erfanian, Ebadi Nehari Z. 2019. Evaluation of Modis And Trmm Satellite Data For Drought Monitoring In The Urmia Lake Basin. Geography and Environmental Planning, 30(2):17-33. (In Persian).

Kchouk S, Melsen L, Walker D W, Pieter R. 2022. A geography of drought indices: mismatch between indicators of drought and its impacts on water and food securities. Natural Hazards and Earth System Sciences, 22: 323–344.

Kim Y, Lee S B, Yun H, Kim J, Park Y. 2017. A drought analysis method based on modis  satellite imagery and AWS data, In Geoscience and Remote Sensing Symposium (IGARSS), IEEE.

Kukunuri A N, Murugan D, Singh D. 2020. Variance based fusion of VCI and TCI for efficient classification of agriculture drought using MODIS data. Geocarto International,10: 1-22.

Liang L, Qiu S, Yan J, Shi Y, Geng D. 2021.VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China. International Journal of Environmental Research and Public Health, 18(7967): 1-14.

 Mirahsani M, Salman Mahini A, Soffianian A, Moddares R, Jafari R , Mohammadi J. 2018. Regional Drought Monitoring in Zayandeh-rud Basin Based on Time Series Variations of the SPI and Satellite-Based VCI Indices. Journal of Geography and Environmental Hazards, 6(4):1-22. (In Persian).

Mojaradi B, mirmiri J, Alizadeh H. 2020.  Assessment of Vegetation Condition Index Using Modified Standard Precipitation Index to Monitor and zoning Drought. Journal of Watershed Engineering and Management, 12(3): 725-736. (In Persian).

Navabi N, Moghaddasi M, Gangi N. 2021. Assessment of Agricultural Drought Monitoring Using Various Indices based on Ground-based and Remote Sensing Data (Case Study:Lake Urima Basin). Journal of Watershed Engineering and Management,13(1) 1-12. (In Persian).

Pei F, Wu C, Liu X, Li X, Yang K, Zhou Y, Wang K, Xu L, Xia G. 2018. Monitoring the vegetation activity in China using vegetation health indices. Agricultural and Forest Meteorology. 248: 215-227.

Rolbiecki R, Yücel A, Koci ̨ecka J, Atilgan A, Markovi ́c M, Liberacki D. 2022. Analysis of SPI as a Drought Indicator during the Maize Growing Period in the Çukurova Region (Turkey). Sustainability,14: 3697. 1-29.

Yildirim T, Asik S. 2018. Index-based assessment of agricultural drought using remote sensing in the semi-arid region of Western Turkey. Journal of Agricultural Sciences, 24(4): 510-516.