Using remote sensing imagery and geographic information systems for mapping vegetation indices in Iraq

Document Type : Research Paper

Author

Al- Esraa University College, Baghdad, Iraq

Abstract

One of the parts of land cover is the vegetation cover. The changes in land cover are due to man-made of natural with the time. The vegetation indices are used in remote sensing for long time to monitor changes in vegetation. Remotely sensed data is considered as an important source of information. For particular area, the vegetation can be considered as a source to collect information about soil, or water table, and to delineate potential zone of ground water. Landsat 7 image is used to identify the land cover and to monitor the vegetation indices in the area under investigation. There are many indices in remote sensing. In this paper I used NDVI and SAVI for the study region and I produce Maps for these two indices using ArcGIS 10.2.2 software.

Keywords

[1] T. Abd, Y. S. Mezaal, M. S. Shareef, S. K. Khaleel, H. H. Madhi and S. F. Abdulkareem. Iraqi e-government
and cloud computing development based on unified citizen identification., Periodicals of Engineering and Natural
Sciences, 7 (4) (2019) 1776-1793.
[2] W. Abdul, E. Salik and A. Karacabey, Application of Landsat 8 Satellite Image – NDVI Time Series for Crop
Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan, Journal of Graduate School of
Natural and Applied Sciences, 5 (1) (2019) 49-62.
[3] G. Chander, B. L. Markham and D. L. Helder, Summary of current radiometric calibration coefficients for Landsat
MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sensing of Environment, 113 (2009) 893-903.
[4] W. B. Cohen and S. N. Goward, Landsat’s role in ecological applications of remote sensing, Bioscience, 54 (6)
(2004) 535545.
[5] J. Environ, Assessing Spectral Indices for Detecting Vegetative Overgrowth of Reservoirs, 28 (6 )(2019), 4199-4211.
[6] J. Hill and B. Sturm, Radiometric Correction of Multitemporal Thematic Mapper Data for Use in Agricultural
Land-cover Classification and Vegetation Monitoring, International Journal of Remote Sensing 12(7) (1991) 1471-
1491.
[7] A. R. Huete, A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, 25 (1988) 295-309.
[8] Z.K. Hussein, H.J. Hadi, M.R. Abdul-Mutaleb and Y.S. Mezaal, Low cost smart weather station using Arduino
and ZigBee., Telkomnika , 18 (1) (2020)282-288.
[9] K. Lavanya, A. J Obaid, I. Sumaiya Thaseen, K. Abhishek, K. Saboo and R. Paturkar, Terrain Mapping of
LandSat8 Images using MNF and Classifying Soil Properties using Ensemble Modelling, International Journal of
Nonlinear Analysis and Applications, 11(2020) 527-541. doi: 10.22075/ijnaa.2020.4750.
[10] A.A.H. Mohamad, Y. S. Mezaal and S. F. Abdulkareem, Computerized power transformer monitoring based on
internet of things, International Journal of Engineering & Technology 7, (4) (2018) 2773-2778.
[11] A. NASA, Landsat 7 Science Data Users Handbook, (2003).
[12] E. Patil Manoj and A. Bhole Snehal, A Comparative Study of Vegetation Change Detection Methods NDVI and
LULC with use of Satellite Images, International Journal of Engineering and Innovative Technology (IJEIT), 2
(3) (2012).
[13] A. Sahrish, Vegetation Index Based Coefficients to Estimate Crop Evapotranspiration of Wheat in Punjab, Pakistan, International Journal of Academic and Applied Research (IJAAR), 2 (9) (2018) 8-13.
[14] D. Thi Loi, T. Chou and Y. Fang, Integration of GIS and Remote Sensing for Evaluating Forest Canopy Density
Index in Thai Nguyen Province, Vietnam, International Journal of Environmental Science and Development, 8
(8) (2017).
Volume 12, Special Issue
December 2021
Pages 1205-1211
  • Receive Date: 06 June 2021
  • Revise Date: 10 August 2021
  • Accept Date: 18 September 2021