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

Hasan Bilgehan Makineci

Makale detayları

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

Yazarlar:

Hasan Bilgehan Makineci

Yayıncı:

Nigde Omer Halisdemir University

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 2, pp.626-636

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001287864

pISSN:

2564-6605

Anahtar kelimeler:

Özet:

 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-2As 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.

 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-2As 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.

 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-2As 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.

 Türkiyenin ve dünyanın en kalabalık şehirlerinden biri olan İstanbul, her zaman kitleler halinde insanları kendine çekmiştir. Arnavutköy, artan Yapılaşma Alanları YAlar ile İstanbul şehrinin kritik ilçelerinden biri haline gelmiştir. Bu ilçenin YAlarının 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 YAların çıkarılması konusunda, SAR uydu verilerinin atmosferik koşullardan etkilenmediği için optik uydu verilerine göre daha uygun olduğu belirlenmiştir.

Makale detayları

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

Yazarlar:

Hasan Bilgehan Makineci

Yayıncı:

Nigde Omer Halisdemir University

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 2, pp.626-636

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001287864

pISSN:

2564-6605

Built-Up Areas Extraction

Change Detection

NDBI

Sentinel-1A

Random Forest ClassificationYapılaşma Alanları Çıkarımı

Değişim Analizi

NDBI

Sentinel-1A

Rastgele Orman

Sayı

Makale detayları

Yazarlar: Hasan Bilgehan Makineci

Yayıncı: Nigde Omer Halisdemir University

Dergi: Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 2, pp.626-636

Belge Tipi: Makale

Yayın Tarihi: 4/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001287864

pISSN: 2564-6605

Sayı

Yazarlar:

Hasan Bilgehan Makineci

Yayıncı:

Nigde Omer Halisdemir University

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 2, pp.626-636

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001287864

pISSN:

2564-6605