Judul Artikel Anda

Nama Penulis; Rastika Widiastuti; Muhammad Sufwandika Wijaya; Azhari Al Kautsar; Inanditya Widiana Putri; Eko Kusratmoko

doi:DOI Artikel

Authors

Rastika Widiastuti Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Muhammad Sufwandika Wijaya Geospatial Information Agency, Indonesia
Azhari Al Kautsar Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Inanditya Widiana Putri Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Eko Kusratmoko Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia

DOI:

https://doi.org/10.24114/jg.v17i1.59320

Keywords:

NDVI, NDBI, Surabaya, Urban

Abstract

This study investigates urban landscape changes in Surabaya from 2014 to 2024 using NDVI and NDBI indices derived from Landsat 8 OLI imagery. The Earth Engine platform was employed to generate cloud-free composite images, enabling detailed analysis of vegetation and built-up area changes. The methodology included a bivariate geovisualization technique to display areas of change, comparing NDVI and NDBI values over a decade to assess changes at a granular level. Results indicate that the 'Vegetation Stable - Built-up Area Stable' category dominates, covering 2422 km², suggesting consistent land use in established areas. This dominance indicates well-established land use patterns across much of the city. Significant urbanization is observed in the 'Vegetation Decreased - Built-up Area Increased' (70 km²) and 'Vegetation Stable - Built-up Area Increased' (177 km²) categories, reflecting ongoing development pressures. These areas highlight zones of active development and environmental intervention. Additionally, a 75 km² increase in vegetation, particularly in coastal mangrove regions, highlights successful environmental management efforts. The study achieved an overall accuracy of 71%, demonstrating the effectiveness of NDVI and NDBI in capturing urban dynamics. While some classes require improved detection accuracy, particularly those involving decreased built-up areas, the model reliably identifies increases in vegetation and built-up areas.

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