Abstract
Arid and semi-arid regions have different spectral characteristics from other climatic regions. Therefore, appropriate remotely sensed indicators of land use and land cover types need to be defined for arid and semi-arid lands, as indices developed for other climatic regions may not give plausible results in arid and semi-arid regions. For instance, the normalized difference built-up index (NDBI) and normalized difference bareness index (NDBaI) are unable to distinguish between built-up areas and bare and dry soil that surrounds many cities in dry climates. This paper proposes the application of two newly developed indices, the dry built-up index (DBI) and dry bare-soil index (DBSI) to map built-up and bare areas in a dry climate from Landsat 8. The developed DBI and DBSI were applied to map urban areas and bare soil in the city of Erbil, Iraq. The results show an overall classification accuracy of 93% (κ = 0.86) and 92% (κ = 0.84) for DBI and DBSI, respectively. The results indicate the suitability of the proposed indices to discriminate between urban areas and bare soil in arid and semi-arid climates.
Highlights
In recent decades, different remote sensing methods such as aerial photography and satellite imaging have become widely available as a source of data for mapping and monitoring land use and land cover
In 2014, Bhatti and Tripathi proposed a built-up area extraction method (BAEM), and classes that are inherent in other indices for mapping land use land cover classes in cities in dry Zhou et al used a built-up and bare-land index (BBI) to extract built-up and bare soil from climates in anticipation that this will provide better information than that obtainable from
Since indices as normalized difference built-up index (NDBI) and normalized difference bareness index (NDBaI) are unable to distinguish between built-up areas and bare land that often surrounds cities in dry climates, this study proposes the application of two new spectral indices, dry built-up index (DBI)
Summary
Different remote sensing methods such as aerial photography and satellite imaging have become widely available as a source of data for mapping and monitoring land use and land cover. To minimize atmospheric absorption features, the width of several OLI bands in Landsat 8 was regions derived from Landsat 8 data It addresses the difference between bare soil and built-up enhanced [19]. In 2014, Bhatti and Tripathi proposed a built-up area extraction method (BAEM), and classes that are inherent in other indices for mapping land use land cover classes in cities in dry Zhou et al used a built-up and bare-land index (BBI) to extract built-up and bare soil from climates in anticipation that this will provide better information than that obtainable from. In mapping built-up and bare soil areas in dry this study focusses on8.identifying appropriate remotely sensed indices for arid and semi-arid regions climate from. Winter grains are the most common form of land cultivation in the area and depend on rainfall; in the summer, the majority of croplands are dry [27,28]
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