Using Remote Sensing and Geographic Information Systems to Study the Change Detection in Temperature and Surface Area of Hamrin Lake

Main Article Content

Ahmed Bahjat Khalaf
https://orcid.org/0000-0002-2506-4604

Abstract

This study was conducted on Lake Hamrin situated in Diyala governorate, focal Iraq, between latitudes 44º 53ʹ 26.16 '- 45º 07 ʹ 28.03ʺ and 34º 04ʹ 24.75ʺ ــ 34º 19ʹ 12.74ʺ . As in this study, the surface area of Hamrin Lake was calculated from satellite images during the period from October 2019 to September 2020, with an average satellite image for each month, furthermore,by utilizing the Normalized Differences Water Index (NDWI), the largest surface area was 264,617 km2 for October and the lowest surface area 140.202 km2 for September. The surface temperature of the lake water was also calculated from satellite images of the Landsat 8 satellite, based on bands 10 (Thermal Infrared 1) and 11 (Thermal Infrared 2) that are sensitive to thermal radiation, as the highest surface temperature reached in June 45.49°C degrees Celsius due to the high temperatures for this month and the lowest in February 3.09°C degrees Celsius, which is one of the months in which temperatures drop to the lowest level. The utilization of remote sensing and GIS innovations has helped a lot in checking changes, whether in surface area or temperature, which saves effort, time and cost. The results of this study put decision makers in taking the necessary precautions for the seasons of water scarcity and drought to meet the community’s water needs in the areas of multiple human consumptions and at the same time take advantage of rainy seasons and water abundance to develop long-term strategic plans to maintain a sustainable water balance.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Khalaf AB. Using Remote Sensing and Geographic Information Systems to Study the Change Detection in Temperature and Surface Area of Hamrin Lake. Baghdad Sci.J [Internet]. 2022 Oct. 1 [cited 2022 Nov. 30];19(5):1130. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6420
Section
article

References

Al-Qaisi KA. Calculating surface evaporation and change in the surface area of Lake Habbaniyah - Iraq using remote sensing and geographic information systems. M Sc. Thesis. College of Graduate Studies, Mutah University, Jordan, 2018; 1- 76.

Husam NM, Muwafaq AR, Bayan MH. Characterization of the Groundwater within Regional Aquifers and Suitability Assessment for Various Uses and Purposes-Western Iraq. Baghdad Sci.J .2021; 18(1), 670-686.

Abhijit S, Panhalkar S, Bansode S. Impact of land use land cover change on land surface temperature using geoinformatics tachniques. Int J Res Ana Rev.2018; 5(4),550-559.

Aziz F, (Kusratmoko E, Mandini D. (Estimation of changes in the lake water level and area using remote sensing techniques ((Case study: Lake Toba, North Sumatra)). IOP Conf. Series: Earth Environ Sci. 561 2020; doi:10.1088/1755-1315/561/1/012022.

Javad A, Davood K, Esmaeil F, Khaled Z. Forecasting Surface Area Fluctuations of Urmia Lake by Image Processing Technique. J Appl Res. water wastewater. 2015; 2 (2),183-187.

Ruhakana A. The Estimation of Lake Naivasha Area Changes Using of Hydro-Geospatial Technologies.

Rwanda J Series. 2016; 1(2), 144-157.

David) (P, Zina) M, Nektrarios C, Michael A.2017. Online) Global Land Surface Temperature Estimation from Landsat. Remote Sens. (2017; 9(12), 1208. https://doi.org/10.3390/rs9121208.

Garegin T, (Vahagn M,) Azatuhi H, Lilit M, Shushanik A. A Landsat 8 OLI Satellite Data-Based Assessment of Spatio-Temporal Variations of Lake Sevan Phytoplankton Biomass. Geogr Ser. 2017; 17(1), 83-89.

Xiangchen MJ, Shaohua Z, Sihan L, Yunjun Y. Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm. Remote Sens. 2019. 11(2). https://doi.org/10.3390/rs11020155.

Guha S, Govil H, Diwan P. 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. J Appl Remote Sens. 2019; 13 (2), 24-38.

Himanshu G, Subhanil G, Anindita D, Neetu G. Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city. Heliyon, 2019; 5(6), 123-1134.

Jimenez-Munoz JA, Sobrino D, Skokovic C, Cristóbal J. Land Surface Temperature Retrieval Methods from Landsat-8 Thermal Infrared Sensor Data. IEEE Geosci. Remote Sens Lett. 2014; 11(10), 1840-1843.

Yang K, Yu Z, Luo Y, Yang Y, Zhao L, Zhou X. Spatial and temporal variations in the relationship between lake water surface temperatures and water quality—A case study of Dianchi Lake. Sci Total Environ, 2017; 62(4), 859-871.

Lim J, Choi M. Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea. Environ Monit Assess. 2015; 187, 384 https://doi.org/10.1007/s10661-015-4616-1.

Matheus H, Augusto F, David M, Lucia H. Comparison of Methods to Estimate Lake-Surface-Water Temperature Using Landsat 7 ETM+ and MODIS Imagery: Case Study of a Large Shallow Subtropical Lake in Southern Brazil. Water. 2018; 11(1), 168; 1-21, doi:10.3390/w11010168.

Mohamed) A, Bastawesy b, Fikry I, Khalaf A, Sayed M. Arafat B. (The use of remote sensing and GIS for the estimation of water loss from Tushka lakes, southwestern desert. Egypt J Afr Earth Sci. 2008; 52(3), 73-80.)

Duan SB, Li L, Tang BH, Wu H, Tan R. Generation of a time-consistent land surface temperature product from MODIS data. Remote Sens Environ. 2014; 140, 339–349.

Arthur WS, Godfrey OM. Monitoring water depth, surface area and volume changes in Lake Victoria: integrating the bathymetry map and remote sensing data during 1993–2016. Model Earth Syst Environ. 2017; 3(2), 533-538.

Osman O, Semih E, Filiz D. Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area. Turkey Sci World J. 2014; 14(1):55-71.

Ugur A, Gordana J. Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. J Sens. 2016; 2,1-8.

Ignacio F, Jose P, Floris O, Willem R. Comparison of Surface Water Volume Estimation Methodologies that Couple Surface Reflectance. Water. 2019; 11(4), 780. https://doi.org/10.3390/w11040780

. Manikandan S. Assessment of surface water dynamics using multiplewater indices around adama woreda, Ethiopia. ISPRS Annals of the Photogrammetry, Remote Sens Spat Inf. Sci.2018 4(5),181-188.

Khalaf AB, Al-Jibouri A I J. Detection land cover changes of the Baquba city for the period 2014-2019 using spectral indices. Iraq J Agric Sci. 2020; 51(3), 805-815.

Thomas P, Elias S. Assessing land degradation and desertification using vegetation index data: current frameworks and future directions. Remote Sens. 2014; 6, 9552-9575.

Subhanil G, Himanshu G, Prabhat D. 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. J Appl Remote Sens. 2019; 13(2),1-17.

Sobrinoa J, Jimenez C, Leonardo P. Land surface temperature retrieval from Landsat TM 5. Remote Sens. Environ. 2004; 90(4), 434–440.

Sun JJ, Xu YH. 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. Environ Earth Sci. 2009; 59(5), 1047–1055.

Arthur WS, Godfrey OM. Monitoring water depth, surface area and volume changes in Lake Victoria: integrating the bathymetry map and remote sensing data during 1993–2016. Model. Earth Syst Environ.2017; 3(2), 1-6.