Development of normalized soil area index for urban studies using remote sensing data

Pub Date : 2023-01-01 DOI:10.15233/gfz.2023.40.2
Akib Javed, Mardia, N. Sumari, Muhammad Nasar Ahmad, Nayyer Saleem, M. Ali, Md. Enamul Huq, I. Ara, Jiabin Wang, Zhuoyang Yu, Bin Bai, Z. Shao
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Abstract

This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.
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基于遥感数据的归一化土壤面积指数的城市研究
本文提出了两种新的光谱土壤面积指数来识别裸露土壤面积,并将其与城市不透水地表面积更准确地区分开来。本研究基于Landsat 8 OLI数据集的中空间分辨率遥感数据设计了这些指数。在多光谱遥感(RS)中提取裸露土壤或城市ISA比提取水体或植被更具挑战性。在多光谱传感器中,光秃秃的土壤和城市ISA区由于光谱相似性较大,经常被混用。本研究提出了典型多光谱波段归一化土壤面积指数1 (NSAI1)和归一化土壤面积指数2 (NSAI2)。实验表明,这两种指标的总体准确率在90%左右。光谱相似指数(SDI)表明,这两个指标在土壤面积和ISA之间具有较高的可分性。结果表明,百分位阈值能有效地对裸地进行背景分类。两种指标联合使用,测得研究区土壤面积超过71 km2。最重要的是,所提出的土壤指数可以在时空研究中提高城市ISA测量精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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