Applying Multi-Index approach from Sentinel-2 Imagery to Extract Urban Area in dry season (Semi-Arid Land in North East Algeria)

Abstract

<p>The mapping of urban areas mostly presents a big difficulty, particularly, in arid and semi-arid environments. For that reason, in this research, we expect to increase built up accuracy mapping for Bordj Bou Arreridj city in semi-arid regions (North-East Algeria) by focusing on the identification of appropriate combination of the remotely sensed spectral indices. The study applies the ‘k–means’ classifier. In this regard, four spectral indexes were selected, namely normalized difference tillage index (NDTI) for built-up, and both bare soil index (BSI) and dry bare-soil index (DBSI), which are related to bare soil, as well as the normalized difference vegetation index (NDVI). All previous spectral indices mentioned were derived from Sentinel-2 data acquired during the dry season. Two combinations of them were generated using layer stack process, keeping both of NDTI and NDVI index constant in both combinations so that the multi-index NDTI/BSI/NDVI was the first single dataset combination, and the multi-index NDTI/DBSI/NDVI as the second component. The results show that BSI index works better with NDTI index compared to the use of DBSI index. Therefore, BSI index provides improvements: bare soil classes and built-up were better discriminated, where the overall accuracy increased by 5.67% and the kappa coefficient increased by 12.05%. The use of k-means as unsupervised classifier provides an automatic and a rapid urban area detection. Therefore, the multi-index dataset NDTI/ BSI / NDVI was suitable for mapping the cities in dry climate, and could provide a better urban management and future remote sensing applications in semi-arid areas particularly.</p>

A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

“…ISODATA is an unsupervised classification algorithm that has also been successfully applied to classify grayscale images into ISA and non-ISA classes [39], [40]. We used ISODATA to classify the index images into several clusters and merge the derived clusters into pervious and impervious surfaces.…”

“…To generate an impervious surface thematic map for spatial and quantitative analysis, the calculated index images need to be reclassified as either impervious or pervious. Commonly used methods include thresholding methods [18], [37], [38] and unsupervised classification methods [39], [40]. It is widely acknowledged that the selection of binary algorithms may have an influence on the accuracy of ISA extraction [38], [41].…”

Analysis of changes of impervious surface area in Ho Chi Minh City and Ba Ria - Vung Tau province

“…ISODATA is an unsupervised classification algorithm that has also been well applied to classify into ISA and pervious surface area (PSA) classes [5], [6]. Impervious surface threshold falls in the interval [0.0913, 0.1289].…”

Actas del XIX Congreso de Tecnologías de la Información Geográfica. TIG al servicio de los ODS Zaragoza, 12 a 14 de septiembre de 2022

“…Con estos datos podríamos disponer de la superficie construida de la zona urbana, sin embargo, en el caso de Guanabacoa la única cartografía es la proporcionada por OpenStreetMap que, para esta zona urbana, únicamente dispone de los polígonos de algunos edificios, insuficientes para poder establecer el área edificada. Otra opción sería establecer el área construida mediante la clasificación de las imágenes de satélite (Bramhe et al, 2018;Rouibah y Belabbas, 2020), aunque con la resolución de las imágenes Sentinel-2 podría diferenciarse únicamente entre zona construida, vegetación y masas de agua del municipio. En Guanabacoa hemos utilizado la capa de datos con el trazado de las calles de OpenStreetMap y las ortoimágenes para generar un entramado urbano formado principalmente por las manzanas con algunas subdivisiones de las mismas cuando la tipología de los edificios lo ha requerido.…”