Analysis of Enhanced Built-up and Bare Land Index (EBBI) in the Urban Area of Yangon, Myanmar

Abstract

In planning and reviewing changes in the ground overview data, land distribution guidelines and identification of changes are critical. The availability of free global and historical satellite images offers a valuable resource for the built-up region to be continuously and accurately mapped and tracked year by year. For thirty years of data, this study uses Landsat images to obtain substantial and land spread data that is extremely useful for urban arrangement. This paper mainly focuses on the basic extraction of the built-up area for the urban planning area every five years from the satellite images of LANDSAT 5,7,8 and Sentinel 2A from USGS. The goal is to evaluate the year-by-year shift in the urban built-up area and to obtain the accuracy of the mapping of built-up and bare land areas in the study of the urban built-up trend from 1990 to 2020. In this research, GIS tools such as raster calculator and built-up region modeling are used to measure the indices that include the Enhanced Built-up and Bareness Index (EBBI), the Normalized Difference Built-up Index (NDBI) and the Urban Index (UI) or the Built-up Index (BUI). This study will therefore point out a variable approach to mapping traditional enhanced built-up and bare land changes (EBBI) automatically with simple indices and according to index outputs. The uncoordinated areas of land and population urbanization spread from areas and gradually the link between the expansion of urban land development and population growth has moved from weak positive to strong decoupling. The advantage of the method the enhanced built-up and bareness index (EBBI) can therefore be realized with the correlation of linear regression slightly expanded in 2020 over the last thirty years. The percentage of the outputs between the indexes and population rate was to use the entire spectral range of Landsat imageries which cause less spectral confusion between built-up area changes and higher accuracies compared to other indices. The modelling method was effective, quickly simple to implement, and can be used to find out the built-up area extraction.

Spatially Contextualizing Rural Land Transformation in Peri-Urban Area: A case of Jalandhar City, Punjab (India)

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Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery

“…𝑁𝐷𝐵𝐼 = 𝜌 𝑆𝑊𝐼𝑅 − 𝜌 𝑁𝐼𝑅 𝜌 𝑆𝑊𝐼𝑅 + 𝜌 𝑁𝐼𝑅 (10) [28] MSAVI is the improved algorithm of NDVI by reducing the reflectance factor of the soil background in order to produce a more accurate vegetation cover is as follows.…”