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Abstract

The study area was classified into three categories (vegetation cover, water, and others)using four satellite images of the Landsat 8 satellite captured during March for the period 2019-2022 into. The results showed that there is a change in the climatic conditions (temperature and rainfall) for the years of the study. The average temperature increased from 12.29°C to 25.967°C from the year 2019 to 2022. The annual amount of precipitation was decreased from 469.43 mm for the year 2019 to 105.49 mm for the year 2022.this negatively changed affected the water and agricultural resources, as the amount of water storage for Lake Hamrin and Lake Al-Wand together reached to 2,314,584,000 m3 and 40,404,000 m3 for the years 2019 and 2022, respectively. This led to decrease in the vegetation area from 1587.29 km2 to 356.17 for the year 2019 km2 and 2022, respectively.

Keywords

Climate change LST NDVI NDWI Vegetation cover

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Khalaf, A. B. . (2024). Using Geospatial Techniques to Analysis the Impact of Climate Change on Water and Agriculture Resources: Case study Khanaqin District in Diyala, Iraq. Basrah Journal of Agricultural Sciences, 37(1), 55–70. https://doi.org/10.37077/25200860.2024.37.1.05
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References

  1. Ali, Z. R., & Muhaimeed A. S. (2016). The study of temporal changes on land cover/ land use prevailing in Baghdad governorate using RS & GIS. The Iraqi Journal of Agricultural Sciences, 47(3), 846-855. https://doi.org/10.36103/ijas.v47i3.576 (In Arabic).
  2. Al-Hathal, Y. M. A., & Hassoun, E. S. (2022). The effect of climate on the different levels of groundwater in the Khanaqin District. Journal of Educational and Human Sciences, 11, 100-123.
  3. https://doi.org/10.33193/JEAHS.11.2022.231. (In Arabic).
  4. Abdelazem, E. (2022). The final report of the project to change the urban climate footprint of the city of Greater Cairo. Academy of Scientific Research and Technology National Committee for Environmental Issues, Cairo, Egypt.
  5. https://www.academia.edu/86597139. (In Arabic).
  6. Cao, F., & Gao, T. (2019). Effect of climate change on the centennial drought over China using high-resolution NASA-NEX downscaled climate ensemble data. Theoretical and Applied Climatology, 138, 1189-1202.
  7. https://doi.org/10.1007/s00704-019-02895-9.
  8. Esetlili, M. T., Kurucu,Y., Cicek, O., & Demirtas, O. (2018). Remote sensing and geographic information systems in the management of agricultural risks related to climate change. Journal of Environmental Protection and Ecology, 19(3), 1293-1306.
  9. https://scibulcom.net/en/article/sh4ym4qSZwTEliTurhx5
  10. Ferraz, S. F. B., Vettorazzi, C. A., & Theobald, D. M. (2009) Using indicators of deforestation and land-use dynamics to support conservation strategies: A case study of central Rondonia, Brazil. Forest Ecology and Management, 257(7), 1586-1595.
  11. https://doi.org/10.1016/j.foreco.2009.01.013
  12. Firas, A. (2012). The use of RS and GIS techniques for studying the impact of drought on the cultivation and productivity of some rainfed crop. M. Sc. Thesis, Damascus University. College of Agricultural Engineering. Syria
  13. Higginbottom, T. P., & Symeonakis, E. (2014). Assessing land degradation and desertification using vegetation index data: Current frameworks and future directions. Remote Sensing, 6(10), 9552-9575. https://doi.org/10.3390/rs6109552
  14. Guha, S., Govil, H., & Diwan, P. (2019). Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. Journal Applications Remote Sensig, 13(2), 1- 17.
  15. https://doi.org/10.1117/1.JRS.13.024518
  16. Gebeyehu, M. N. (2019). Remote Sensing and GIS Application in Agriculture and Natural Resource Management. International Journal of Environmental Sciences & Natural Resources, 19(2), 556009.
  17. https://doi.org/10.19080/IJESNR.2019.19.556009
  18. IPCC. (2012). Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 582pp.
  19. IPCC. (2019). Special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystem.
  20. https://www.ipcc.ch/srccl/
  21. Khalaf, A. B. (2022). Using remote sensing and geographic information systems to study the change detection in temperature and surface area of Hamrin Lake. Baghdad Science Journal, 19(5), 1130-1139.
  22. http://doi.org/10.21123/bsj.2022.6420
  23. Khalaf, A. B., & Al-Jibouri, A. I. J. (2020). Detection land cover changes of the BBaquba city for the period 2014-2019 using spectral indices. Iraqi Journal of Agricultural Sciences, 51(3), 805-815.
  24. https://doi.org/10.36103/ijas.v51i3.1036
  25. Khalaf, A. B., & Al-Alaf, M. Y. (2021). Use of remote sensing and GIS techniques in monitoring of land use/ land cover changes: Case study of Kanaan area of Diyala Province, Central Iraq. Indian Journal of Ecology, 48(4), 990-994.
  26. Kriegler, F. J., Malila, W. A., Nalepka, R., & Richardson, W. (1969). Preprocessing transformations and their effects on multispectral recognition. In Proceedings of the Sixth International Symposium on Remote Sensing of Environment, University of Michigan, Ann Arbor, 97-131. https://www.scirp.org/reference/referencespapers?referenceid=788826
  27. Lal, R. (2009). Soils and food sufficiency. A review. Agronomy for Sustainable Development, 29, 113-133. https://doi.org/10.1051/agro:2008044
  28. Liu, W., Jia, B., Li, T., Zhang, Q., & Ma, J. (2022). Correlation analysis between urban green space and land surface temperature from the perspective of spatial heterogeneity: A case study within the sixth ring road of Beijing. Sustainability, 14(20), 13492.
  29. https://doi.org/10.3390/su142013492
  30. Mahal, S. H., Al-Lami, A. M., & Mashee, F. K. (2022). Assessment of the impact of urbanization growth on the climate of BBaghdad province using remote sensing. Iraqi Journal of Agricultural Sciences, 53(5), 1021-1034.
  31. https://doi.org/10.36103/ijas.v53i5.1616
  32. Mariye, M., Jianhua, L., & Maryo, M. (2022). Land use land cover change analysis and detection of its drivers using geospatial techniques: A case of south-central Ethiopia. All Earth, 34(1), 309-332, https://doi.org/10.1080/27669645.2022.2139023
  33. Omuto, C. T. (2011). A new approach for using time-series remote sensing images to detect changes in vegetation cover and composition in drylands: A case study of eastern Kenya. International Journal of Remote Sensing, 32(21), 6025-6045.
  34. https://doi.org/10.1080/01431161.2010.499384
  35. Politi, E., Cutler, M. E. J., & Rowan, J. S. (2012). Using the NOAA advanced very high-resolution radiometer to characterise temporal and spatial trends in water temperature of large European lakes. Remote Sensing of Environment, 126, 1-11.
  36. https://doi.org/10.1016/j.rse.2012.08.004
  37. Piao, S., Ciais, P., Huang, Y., Shen, Z., Peng, S., Li, J., Zhou, L., Liu, H., Ma, Y., Ding, Y., Friedlingstein, P., Liu, C., Tan, K., Yu, Y., Zhang, T., & Fang, J. (2010). The impacts of climate change on water resources and agriculture in China. Nature, 467, 43-51. https://doi.org/10.1038/nature09364
  38. Sobrino, J. A., Jimenez-Munoz, J.C., & Paolini, L. (2004). Land surface temperature retrieval from Landsat TM 5. Envir Remote Sensing of Environment, 90(4), 434–440.
  39. https://doi.org/10.1016/j.rse.2004.02.003
  40. Siyal, A. A. (2018). Climate change: Assessing impact of seawater intrusion on soil, water, and environment on Indus delta using GIS & remote sensing tools. US. Pakistan Center for Advanced Studies in Water (USPCAS-W), MUET, Jamshoro, Pakistan, World Wide Web electronic publication.
  41. Sichangi, A. W., & Makokha, G. O. (2017). Monitoring water depth, surface area and volume changes in Lake Victoria: integrating the bathymetry map and remote sensing data during 1993–2016. Modeling Earth Systems and Environment, 3, 533-538.
  42. https://doi.org/10.1007/s40808-017-0311-2
  43. Sun, Q., Tan, J., & Xu, Y. (2010). An ERDAS image processing method for retrieving LST and describing urban heat evolution: a case study in the Pearl River Delta Region in South China. Environmental Earth Sciences, 59, 1047-1055.
  44. https://doi.org/10.1007/s12665-009-0096-3
  45. Silva, V. S., Salami, G., Silva, M. I. O., Silva, E.A., Junior, J. J. M., & Alba, E. (2020). Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification. Geology, Ecology, and Landscapes, 4(2), 159-169.
  46. https://doi.org/10.1080/24749508.2019.1608409
  47. Yelwa, S. A., & Eniolorunda, N. B. (2012). Simulating the Movement of Desertification in Sokoto and its Environs, Nigeria using 1km SPOT-NDVI Data. Environmental Research Journal, 6(3), 175-181. http://doi.org/10.3923/erj.2012.175.181