Abstract
Basra governorate represents one of the most important governorates in Iraq, where it is the only seaport for exporting oil and agriculture products. However, this governorate is facing serious land degradation problems results in major changes in land use/ cover (LULC) within the area. Accordingly, the main objective of this study was to evaluate LULC in Basra city using different techniques on remotely sensed data. For that purpose, Landsat 8, Sentinel 2A images were used in 2018 to study extent of urban areas, agricultural lands, water bodies and bare lands areas in Basra city center. The studied techniques include: the supervised classification in three methods (minimum distance, maximum likelihood, and Mahalanobis distance), spectral indices, manual digitizing of features and land surface temperature (LST). These methods were applied on both Sentinel 2A and Landsat data at three spatial resolutions 10, 15 and 30 m, respectively. The obtained results indicated that the minimum distance technique has the highest accuracy in identifying LULC when compared with the other classification methods. It was found that the higher the spatial resolution the higher the accuracy of the results. The spectral indices were more accurate than the classification methods in identifying agricultural areas and water bodies. There was a higher inclusion between urban areas and bare lands due to the similarity in their spectral reflectance. Accordingly, it is recommended to manually digitize urban areas than classifying it. The LST can be used as an indirect and fairly accurate method for evaluating LULC in Al-Basra city. In conclusion, remote sensing data and techniques could help in providing more accurate information about LULC in Basra City to be used in its future planning and sustainable development
Highlights
NOWADAYS, Basra governorate is facing a serious problem such as shortage of water resources and land degradation
The following are the results of LULC obtained for each spatial resolution: 1. Land use/ cover Classifications from Sentinel 2A (10 m) data: Table 4 shows the results of LULC classification by the three mention methods on Sentinel 2A data for 2018
It was observed that the LULC obtained by using the minimum distance classification method was higher in accuracy when compared with the two other methods. These results reveal that both agricultural land and water areas were classified with higher accuracies, whereas urban areas and bare land were classified at lower accuracies
Summary
NOWADAYS, Basra governorate is facing a serious problem such as shortage of water resources and land degradation. These problems cause major changes in land use/ cover. Remotely sensed (RS) data and techniques and geographical information system (GIS) are commonly used worldwide in monitoring environmental changes and evaluation in LU/LC [1]. It is at present the most fundamental elements that affect global environmental change and sustainability research is the change in LULC [2,3,4]
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