Geofizika, Vol. 40 No. 1, 2023.
Izvorni znanstveni članak
https://doi.org/10.15233/gfz.2023.40.2
Development of normalized soil area index for urban studies using remote sensing data
Akib Javed
; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Zhenfeng Shao
; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Bin Bai
; Software R&D Center, Kunming Antai Software Co., LTD. Kunming, China
Zhuoyang Yu
; Software R&D Center, Kunming Antai Software Co., LTD. Kunming, China
Jiabing Wang
; Software R&D Center, Kunming Antai Software Co., LTD. Kunming, China
Iffat Ara
; Geography and Environment, Islamic University, Kushtia, Bangladesh ; Department of International Politics, Shandong University, Jinan, China
Md. Enamul Huq
; College of Environmental Science, Hohai University, Nanjing, China
Md. Yeamin Ali
; Caritas Bangladesh, Bandarban, Bangladesh
Nayyer Saleem
; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Muhammad Nasar Ahmad
; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Neema Simon Sumari
; Department of Informatics & Information Technology, Sokoine University of Agriculture, Morogoro, Tanzania
Mardia Mardia
; Geography and Environmental Studies, University of Rajshahi, Rajshahi, Bangladesh
Sažetak
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.
Ključne riječi
soil index; NSAI1; NSAI2; LULCC; Dhaka
Hrčak ID:
308539
URI
Datum izdavanja:
31.7.2023.
Posjeta: 160 *