Effectiveness of Normalized Difference Built-Up Index in Mapping Built-Up Features across Arid Rural Regions
Abstract
Normalized Difference Built-up Index (NDBI) is a widely used remote sensing method for detecting built-up areas. However, its effectiveness in distinguishing built-up land from open land in dry rural regions remains underexplored. This study aims to evaluate the performance of NDBI in identifying built-up areas in Bayat Sub-district, Klaten Regency, Central Java, a predominantly rural area with semi-arid land characteristics during October 2023. The analysis employed Landsat 8 OLI imagery acquired in 2023, which was processed to generate NDBI values. These values were classified into four built-up intensity levels using the natural breaks (Jenks) method: Very Low, Low, Medium, and High. Validation was conducted using 36 ground truth points representing land cover types such as vegetation, built-up land, open land, and water bodies. Classification accuracy was assessed through a confusion matrix. The results revealed a significant degree of misclassification. NDBI is computed from the difference in reflectance between the Shortwave Infrared (SWIR) and Near Infrared (NIR) bands, where built-up areas typically exhibit high SWIR and low NIR values. However, dry open land (e.g., bare soil or unvegetated areas) displays a similar spectral pattern, high SWIR reflectance due to dry surfaces, and low NIR reflectance from the absence of biomass. This similarity causes elevated NDBI values for dry open areas, making them difficult to distinguish from actual built-up regions. The confusion matrix yielded an overall accuracy of 75.00% and a Kappa coefficient of 0.628, indicating moderate agreement between the classification results and ground data. These findings highlight the limitations of NDBI in differentiating built-up land from non-vegetated open land in semi-arid rural settings.
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