Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul

Year 2023, , 626 - 636, 15.04.2023

Hasan Bilgehan Makineci

https://doi.org/10.28948/ngumuh.1203301

Cited By: 1

Abstract

As one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the crowd of people masses. Arnavutköy Town has become one of the critical points of Istanbul City with increasing built-up areas (BAs). The spatial-temporal change detection of the expansion of the BAs of this district is essential data on behalf of Istanbul City. This research aims to determine urban areas expansion zones, also defined as the BAs footprint, from Sentinel-1 radar data. The determination of Sentinel-1A data of the urban area change detection encountered in Arnavutköy Town between 2018-2021 with Random Forest (RF) classification machine learning algorithm is investigated in this study. Based on the changes in spatial-temporal data, the direction of urban development has been determined. In addition, to visually compare the Normalized Difference Built-up Index (NDBI) and optical Sentinel-2A's false color urban RGB composite, which is a distinct data format, the processes have been proved. As a result of the study, SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.

Keywords

Built-Up Areas Extraction, Change Detection, NDBI, Sentinel-1A, Random Forest Classification

Thanks

Many thanks to ESA for free SNAP software, Sentinel satellite data and all kinds of support, and the Republic of Turkey Ministry of Environment and Urbanization for their open-access Spatial Strategy Planning.

Sentinel-1 verilerine rastgele orman sınıflandırma yaparak İstanbul Arnavutköy için yapılaşma alanlarının konumsal ve zamansal değişiminin tespiti

Abstract

Türkiye'nin ve dünyanın en kalabalık şehirlerinden biri olan İstanbul, her zaman kitleler halinde insanları kendine çekmiştir. Arnavutköy, artan Yapılaşma Alanları (YA’lar) ile İstanbul şehrinin kritik ilçelerinden biri haline gelmiştir. Bu ilçenin YA’larının genişlemesinin konumsal ve zamansal değişiminin tespiti, İstanbul adına önemli bir ihtiyaçtır. Bu çalışma, Sentinel-1 radar verilerinden YA ayak izi olarak da tanımlanan kentsel alanların genişleme bölgelerini belirlemeyi amaçlamaktadır. Bu çalışmada, 2018-2021 yılları arasında Arnavutköy Kasabasında karşılaşılan kentsel alan değişim tespitinin Sentinel-1A verilerinden makine öğrenmesi algoritması olan Rastgele Orman (RO) sınıflandırıcısı ile belirlenmesi incelenmiştir. Konumsal-zamansal verilerde yaşanan değişimlerden yola çıkarak, kentsel gelişimin yönü belirlenmiştir. Ayrıca, Normalize Fark Yapı İndeksi (NDBI) ve Sentinel-2A yalancı renkli RGB kompoziti görsel olarak kullanılarak karşılaştırmalı olarak yapısal değişim kanıtlanmıştır. Çalışma sonucunda uzaktan algılanan verilerle YA'ların çıkarılması konusunda, SAR uydu verilerinin atmosferik koşullardan etkilenmediği için optik uydu verilerine göre daha uygun olduğu belirlenmiştir.

Keywords

Yapılaşma Alanları Çıkarımı, Değişim Analizi, NDBI, Sentinel-1A, Rastgele Orman

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