Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques

Hasan Aksoy; Alkan Günlü

doi:10.17475/kastorman.1787120

Research Article

Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques

Year 2025, Volume: 25 Issue: 2, 152 - 176, 30.09.2025

Hasan Aksoy , Alkan Günlü

https://doi.org/10.17475/kastorman.1787120

Abstract

Aim of study: Forests contribute significantly to the global climate by acting as carbon sinks and controlling energy and water flows. This study aimed to model the aboveground carbon (AGC) of pure Scots pine stands within the boundaries of the Sinop Regional Directorate of Forestry in Turkey, using data obtained from various sensor images, including Sentinel-1 (S1), Sentinel-2 (S2), Landsat 8 OLI (L8) and Unmanned Aerial Vehicle (UAV) images, with artificial neural network (ANN) and multiple linear regression (MLR) modeling techniques.
Area of study: The study was carried out within pure Scots pine stands located in Sinop Regional Directorate of Forestry.
Material and method: a total of 184 sample plots were taken and field measurements were made in these sample plots. 80% of the sample plots (150) were used to fit the models and 20% (34) were used to test the models. The AGC values of each sample plot were estimated with the allometric equation. Brightness values and backscatter values from S1, vegetation indices, reflectance and texture values obtained for different window sizes (3x3, 5x5, 7x7 and 11x11) and different orientations (0°, 45°, 90° and 135°) from L8 and S2, and vegetation indices, band reflectance and digital band obtained from UAV were used in the study.
Main results: The results indicated that the texture variables obtained for the 15x15 of the Sentinel-2 image for AGC estimation, together with the MLR modeling technique, were the most successful technique compared to other images and ANN analysis (R2=0.86).
Research highlights: The results have shown that AGC can be predicted at high success levels with ANN modeling technique with remote sensing data sets.

Keywords

Aboveground Stand Carbon , Natural forests , Modeling , Satellite Images

Thanks

This study was produced from a doctoral thesis prepared by Hasan AKSOY and supervised by Prof. Dr. Alkan GÜNLÜ for the Institute of Natural and Applied Science, Çankırı Karatekin University, Türkiye.

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Farklı Uzaktan Algılama Verileri ve Modelleme Teknikleri Kullanılarak Topraküstü Karbonun Tahmin Edilmesi

Year 2025, Volume: 25 Issue: 2, 152 - 176, 30.09.2025

Hasan Aksoy , Alkan Günlü

https://doi.org/10.17475/kastorman.1787120

Abstract

Çalışmanın amacı: Ormanlar, karbonun depolanması, enerji ve su döngülerinin düzenlenmesi gibi süreçlerde küresel iklimde önemli bir rol oynamaktadır. Bu çalışmada Sinop Orman Bölge Müdürlüğü sınırlarında yayılış gösteren saf sarıçam meşcerelerinde Sentinel-1 (S1), Sentinel-2 (S2), Landsat 8 OLI (L8) ve İnsansız Hava Aracı (İHA) gibi farklı uzaktan algılama görüntülerinden elde edilen veriler ile topraküstü karbon (TÜK) arasındaki ilişkiler çoğul doğrusal regresyon (ÇDR) ve yapay sinir ağı (YSA) teknikleri ile modellenmesi amaçlanmıştır.
Çalışma alanı: Çalışma, Sinop Orman Bölge Müdürlüğü'nde bulunan saf sarıçam meşcerelerinde gerçekleştirilmiştir.
Materyal ve yöntem: Çalışma kapsamında toplam 184 adet örnek alan alınmış ve bu örnek alanlarda yersel ölçümler yapılmıştır. Alınan örnek alanların %80'i (150 adet) modellerin oluşturulmasında, %20'si (34 adet) ise modellerin test edilmesinde kullanılmıştır. Her bir örnek alanın TÜK değerleri allometrik denklem ile tahmin edilmiştir. Çalışmada uzaktan algılama verisi olarak, S1 görüntüsünden geri saçılma ve bant parlaklık değerleri, S2 ve L8 uydu görüntüleri için farklı pencere boyutlarına (3x3, 5x5, 7x7 ve 11x11) ve farklı yönelimlere (0°, 45°, 90° ve 135°) göre yansıma değerleri, vejetasyon indeksleri ve doku özellikleri ile İHA görüntülerinden elde edilen dijital bant, bant reflektans ve vejetasyon indisleri kullanılmıştır. Yersel ölçümler ve uzaktan algılama verileri arasındaki ilişkiler ÇDR ve YSA teknikleri ile modellenmiştir.
Temel sonuçlar: Sonuçlar, TÜK tahmininde S2 görüntüsünün 15x15 pencere boyutu için elde edilen doku değişkenleri ÇDR modelleme tekniği ile birlikte diğer görüntülere ve YSA analizine kıyasla en başarılı teknik olduğunu göstermiştir (R2=0.86).
Araştırma vurguları: Sonuçlar, TÜK’ün uzaktan algılama veri setleri ile ÇDR modelleme tekniği ile yüksek başarı düzeylerinde tahmin edilebileceğini göstermiştir.

Keywords

Topraküstü Meşcere Karbonu , Doğal orman , Modelleme , Uydu Görüntüleri

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Details

Primary Language	English
Subjects	Forestry Sciences (Other)
Journal Section	Articles
Authors	Hasan Aksoy Alkan Günlü
Early Pub Date	September 22, 2025
Publication Date	September 30, 2025
Submission Date	July 22, 2024
Acceptance Date	December 26, 2024
Published in Issue	Year 2025 Volume: 25 Issue: 2

Cite

APA	Aksoy, H., & Günlü, A. (2025). Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques. Kastamonu University Journal of Forestry Faculty, 25(2), 152-176. https://doi.org/10.17475/kastorman.1787120
AMA	Aksoy H, Günlü A. Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques. Kastamonu University Journal of Forestry Faculty. September 2025;25(2):152-176. doi:10.17475/kastorman.1787120
Chicago	Aksoy, Hasan, and Alkan Günlü. “Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques”. Kastamonu University Journal of Forestry Faculty 25, no. 2 (September 2025): 152-76. https://doi.org/10.17475/kastorman.1787120.
EndNote	Aksoy H, Günlü A (September 1, 2025) Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques. Kastamonu University Journal of Forestry Faculty 25 2 152–176.
IEEE	H. Aksoy and A. Günlü, “Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques”, Kastamonu University Journal of Forestry Faculty, vol. 25, no. 2, pp. 152–176, 2025, doi: 10.17475/kastorman.1787120.
ISNAD	Aksoy, Hasan - Günlü, Alkan. “Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques”. Kastamonu University Journal of Forestry Faculty 25/2 (September2025), 152-176. https://doi.org/10.17475/kastorman.1787120.
JAMA	Aksoy H, Günlü A. Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques. Kastamonu University Journal of Forestry Faculty. 2025;25:152–176.
MLA	Aksoy, Hasan and Alkan Günlü. “Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques”. Kastamonu University Journal of Forestry Faculty, vol. 25, no. 2, 2025, pp. 152-76, doi:10.17475/kastorman.1787120.
Vancouver	Aksoy H, Günlü A. Estimation of Aboveground Carbon Using Different Remote Sensing Data and Modelling Techniques. Kastamonu University Journal of Forestry Faculty. 2025;25(2):152-76.

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