Dergiler / International Journal of Environment and Geoinformatics / 2019 / Cilt: 6 - Sayı: 2

Monitoring Drought and Its Impact on Agriculture Using Drought Indices and Geoinformatics Technology in Patan District, Gujarat

Sayfa
153–162
Yayın Tarihi
2022-10-19
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

During drought year of 2002 total precipitation was veryless resulting in Aridity Index of 0.09 which is classified as hyper arid for the Patan District.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Remote sensing (RS) and Geographic Information System (GIS) has played a major role in the study of different types of drought i.e.agricultural or hydrological drought.
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Temel Bulgular & Veri:The Landsat-7 TM and Landsat-8 OLI data of February-2002 and February-2018 covering Patan Districtwas analysed for Land use/Land Cover mapping and monitoring changes in agricultural area over the period of 16-years.
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Sonuç & Katkı:During drought year of 2002 total precipitation was veryless resulting in Aridity Index of 0.09 which is classified as hyper arid for the Patan District.
🔬Tür:Araştırma Makalesi
📍Kapsam:2002 Verileri
⚙️Değişkenler:Alan Parametreleri

Abstract

Remote sensing (RS) and Geographic Information System (GIS) has played a major role in the study of different types of drought i.e.agricultural or hydrological drought. The present study on agricultural Drought assessment was carried out in the Patan District ofNorth Gujarat, India. This study focuses on the assessment of climate change and its impact on Agriculture using Remote Sensing(RS) and GIS technology. The Landsat-7 TM and Landsat-8 OLI data of February-2002 and February-2018 covering Patan Districtwas analysed for Land use/Land Cover mapping and monitoring changes in agricultural area over the period of 16-years. TheSpectral vegetation index namely Normalized Difference Vegetation Index (NDVI) was computed using Landsat-7 TM and Landsat-8 OLI digital data for assessment of changes in agricultural vegetation during the drought and normal monsoon periods.Meteorological data like Minimum & maximum Temperatures and rainfall was collected from State Data Centre. Various Droughtindices like Standardized Precipitation Index (SPI), Potential Evapo-Transpiration (PET) and Aridity Index (AI) were computedusing meteorological data of past 16-years.The results indicate that total agricultural vegetation during drought season was only 21.9 per cent (2002) which has increased to 58.7per cent during the normal monsoon season of 2018. The rainfall deviations were computed for various years and it was observedthat high negative rainfall deviations (-40 to -70%) having low rainfall occurrence were associated with negative SPI values rangingfrom -0.5 to -1.1 in Patan district. These positive rainfall deviations were associated with positive SPI values of 1.08, 0.57 and 0.62of respective years indicating moderately wet and mildly wet conditions. During drought year of 2002 total precipitation was veryless resulting in Aridity Index of 0.09 which is classified as hyper arid for the Patan District.

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Bu Makaleye Atıf Yap

ACHARYA, M., SHUKLA, S. .., KALUBARME, M. H. (2022). Monitoring Drought and Its Impact on Agriculture Using Drought Indices and Geoinformatics Technology in Patan District, Gujarat. International Journal of Environment and Geoinformatics, Cilt: 6 - Sayı: 2, 153–162. https://ajindex.com/articles/541037

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Kaynakça (6)

  1. Chopra, P. (2006). Drought Risk Assessment using Remote Sensing and GIS: A case study of Gujarat (PhD thesis). IIRS Dehradun, India. Dadhich, G, Patel, P.R., Kalubarme, M.H. (2017). Agriculture land suitability evaluation for wheat cultivation using geomatics for Patan District, India, Int. J. Agricultural Resources, Governance and Ecology, 13(1). Elhag, KM., Zhang, W. (2018). Monitoring and Assessment of Drought Focused on Its Impact on Sorghum Yield over Sudan by Using Meteorological Drought Indices for the Period 2001–2011, Remote Sens. 10(8), 1231. Esetlili, MT., Bektaş Balçık, F., Balık Şanlı, f., Üstüner, M., Kalkan, K., Göksel, Ç., Gazioğlu, C., Kurucu, Y. (2018). Comparison of Object and Pixel-Based Classifications For Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey, International Journal of Environment and Geoinformatics (IJEGEO), 5(2), 231-243.
  2. Gandhi, Fenil R.,and Parekh, Falguni P. (2017). Assessment of Drought Using Standard Precipitation Index and Rainfall Deciles: A Case Study of Patan District. VIER Journal of Engineering Research, 1 - 7. Gorji, T., Yıldırım, A., Sertel, E., Tanık, A. (2019). Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes, International Journal of Environment and Geoinformatics (IJEGEO), 6(1), 33-49.
  3. Goumehei, E., Geravandi, Y., Yan, W. (2016). A GIS Based Study to Investigate Effect of Water Table Changes on DRASTIC Model: A Case Study of Kermanshah, Iran, International Journal of Environment and Geoinformatics (IJEGEO) 3 (2), 1- 10. İmamoğlu, MZ., Sertel, E. (2016). Analysis of Different Interpolation Methods for Soil Moisture Mapping Using Field Measurements and Remotely Sensed Data, International Journal of Environment and Geoinformatics (IJEGEO), 3(3) 111-25 IPCC, (2007). Climate change 2007: Impacts, adaptation and vulnerability. In M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden, & C. E. Hanson (Eds.), Contribution of working group II to the fourth assessment, report of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.
  4. McKee TB, Doesken NJ, Kleist J. (1993). The relationship of drought frequency and duration to time scales. Preprints, 8th Conference on Applied Climatology, 17–22
  5. January, Anaheim, CA, 179– 184. Min, S.K.; Zhang, X.; Zwiers, F.W.; Hegerl, G.C. (2011). Human contribution to more-intense precipitation extremes. Nature 470, 378–381.
  6. Naresh Kumar, M., Murthy, C.