The Normalized Difference Built-up Index (NDBI) uses the NIR and SWIR bands to emphasize manufactured built-up areas. It is ratio based to mitigate the effects of terrain illumination differences as well as atmospheric effects.

The NDBI highlights urban areas with higher reflectance in the shortwave-infrared (SWIR) region, compared to the Near Infra-red (NIR) region. Applications include watershed runoff predictions and land-use planning.

Jan 11, 2024 · The primary purpose of using the NDBI is to identify and map built-up areas in satellite imagery. It provides a quantitative measure of the presence and intensity of built-up features, such as buildings, roads, and infrastructure.

Sep 30, 2018 · For analysis of Normal Difference Vegetation Index (NDVI), Normal Difference Built-up Index (NDBI) and Normal Difference Water Index (NDWI), only four bands are used (Green, Red, NIR, SWIR)....

Jan 20, 2023 · NDBI values range from -1 to +1 where the negative values represent areas with no built-up structures and positive values represent highly built-up areas. This article focuses on the step-by-step process of computing NDBI using Sentinel 2 in Google Earth Engine.

NDBI map shown in Figure 4 a reveals that the highest NDBI value was 0.98, and the lowest NDBI value was ?0.77. High NDBI values were largely found in Liwan, Yuexiu, Haizhu, south Tianhe,...

Apr 13, 2014 · The Normalized Difference Built-up Index (NDBI) is a remote sensing index used to identify urban and built-up areas from satellite imagery. It leverages the difference between the reflectance values in the Near-Infrared (NIR) and Shortwave Infrared (SWIR) spectral bands, where built-up areas tend to have higher reflectance in the SWIR band and ...

In this tutorial, I will explain everything you need to know about Normalized Difference Built-up Index and how to calculate NDBI using ArcGIS.

NDWI (Normalized Difference Water Index): NDWI is used to identify and map water bodies such as rivers, lakes, and wetlands. It is calculated using the green and NIR bands of remote sensing data, and ranges from -1 to 1. Higher values indicate a higher density of water.

imagery into land cover map using the existing methods of manual interpretation and parametric image classification digitally. In this paper we propose a new method based on Normalized Difference Built-up Index (NDBI) to automate the process of mapping built-up areas. It takes advantage of the unique spectral