Accuracy Assessment of Land Use/Land Cover Indices for Al-Rusafa in Baghdad Governorate by Remote Sensing Technology and GIS

• Published in: IOP conference series. Earth and environmental science Vol. 1300; no. 1; pp. 12009 - 12022
• Main Authors: Haitham, Maram, Mustafa, Mustafa Tariq, Ismail, Alhassan H.
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2024
• Subjects: Accuracy; Algorithms; Arid lands; Arid zones; Barren lands; Classification; GIS; Global positioning systems; GPS; Ground cover; Indicators; Land cover; Land use; Land use/land cover; Landsat; Landsat satellites; Mapping; Mathematical analysis; Monitoring; Remote sensing; Satellite imagery; spectral indices; supervised classification; Support vector machines; Urban sprawl; Vegetation; Vegetation cover; Vegetation index; Baghdad Iraq; Iraq
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/1300/1/012009

Abstract The management and planning of natural and artificial resources depend on accurately monitoring land cover changes. Land cover change mapping and monitoring used to require expensive field surveys. Remote sensing is cheaper and more practical for mapping land use and cover changes. The Tigris River divides the Iraqi capital, Baghdad, into two parts: Karkh and Rusafa. Al-Rusafa was selected as a study area for current research, which has had rapid population and urban growth in recent decades. The current research applies the support vector machine technique to supervised LU/LC maps’ classification into barren regions, water bodies, vegetation cover and built-up regions. Spectral indicators were calculated: Enhanced Vegetation Index, Modified Normalized Difference Water Index, Normalized Built-Up Area Index, Dry Bareness Index in addition to calculating the accuracy assessment and Kappa coefficient. Using the Landsat 9 satellite image, ArcGIS 10.8 and Envi5.3 software were used to analyze and evaluate the results and field points observed by GPS devices. The results showed that the SVM classification algorithm accurately revealed the categories of LU/LC, where the classification accuracy reached 95%, and that the arid lands covered most of the study area 848.864 km 2 and water bodies 76.747 km 2 , the vegetation and the built-up regions 466.459 km 2 and 439.077 km 2 , respectively. The spectral indices showed slightly different areas of barren lands (DBSI 752.589 km 2 , 93% accuracy), vegetation (EVI 423.651 km 2 , 96% accuracy), and water bodies (MNDWI 73.187 km 2 , 98% accuracy) and built-up areas (NBAI 501,731 km 2 , 90%accuracy). The Support Vector Machine method outperforms other classification methods, and the spectral indicators employed in this work are useful and dependable for extracting each LU/LC category. In conclusion, Landsat 9 satellite data can reliably and swiftly detect ground cover.