Aboveground Carbon Stock Estimation Using Airborne LiDAR in Forest Ecosystems

Yıl 2025, Cilt: 27 Sayı: 3, 445 - 463, 15.12.2025

Tufan Demirel

https://doi.org/10.24011/barofd.1798937

Öz

Global climate change, driven by the rapid increase of carbon dioxide (CO₂) and other greenhouse gases in the atmosphere, is causing profound and often irreversible shifts in ecosystems, with forest ecosystems playing a critical role as terrestrial carbon sinks. Forest carbon is a central component of REDD+ and other climate change mitigation programs, and accurate estimation of carbon sequestration is essential for the effectiveness of these strategies. Traditional field-based measurements are often costly, difficult to implement across large areas, and limited in capturing the heterogeneous structure of forests. Consequently, remote sensing technologies, particularly LiDAR (Light Detection and Ranging), have emerged as powerful tools for detailed three-dimensional characterization of forest structure. In this study, terrestrial measurements from 86 sample plots in the Istanbul University-Cerrahpaşa Faculty of Forestry Research Forest were used to estimate aboveground carbon (AGC). A total of 101 structural metrics derived from airborne laser scanning (ALS) data were analyzed, and highly correlated variables were removed, resulting in 25 key predictors. Random Forest regression, combined with leave-one-out cross-validation (LOOCV), was employed to predict AGC. The model achieved high accuracy with R² = 0.76, RMSE = 4.25 ton C/ha, and MAE = 3.48 ton C/ha. Variable importance analysis revealed that height percentiles and canopy relief ratio (CRR), representing vertical structural heterogeneity, were the most influential predictors for AGC estimation. The results demonstrate that ALS data provide a reliable tool for estimating AGC even in forests with diverse stand types and topographic variability. In particular, metrics representing canopy height, canopy cover, and vertical structural complexity are critical for accurate carbon stock modeling. These findings provide a reliable and practical approach for regional and national-scale carbon mapping and support the development of climate change mitigation strategies.

Anahtar Kelimeler

Climate Change , random forest , forest carbon

Teşekkür

The terrestrial inventory data used in this study were compiled from the dataset employed in the forest management planning project of the Istanbul University - Cerrahpaşa Faculty of Forestry Education and Research Forest. I would like to express my sincere gratitude to the faculty members of the Department of Forest Management at Istanbul University - Cerrahpaşa Faculty of Forestry for their valuable contributions.

Orman Ekosistemlerinde Hava Tabanlı LiDAR ile Toprak Üstü Karbon Stoklarının Tahmini

Öz

Küresel iklim değişikliği, atmosferdeki karbondioksit (CO₂) ve diğer sera gazlarının hızla artışıyla ekosistemlerde geri dönüşü zor değişimlere yol açmakta, orman ekosistemleri ise bu süreçte karasal karbon yutakları olarak kritik bir rol üstlenir. Orman karbonu, REDD+ ve diğer iklim değişikliğiyle mücadele programlarının temel bileşenini oluşturmakta ve karbon tutma miktarının doğru biçimde hesaplanması, iklim değişikliği stratejilerinin etkinliğini doğrudan etkilemektedir. Geleneksel arazi tabanlı ölçümler, geniş alanlarda uygulanması güç, maliyetli ve orman yapısındaki heterojenliği tam olarak temsil edememeleri nedeniyle sınırlı kalmaktadır. Bu nedenle, uzaktan algılama teknikleri, özellikle LiDAR (Light Detection and Ranging) sistemi, orman yapısını üç boyutlu olarak incelemede giderek daha fazla önem kazanmaktadır. Bu çalışmada, İstanbul Üniversitesi-Cerrahpaşa Orman Fakültesi Araştırma Ormanı’nda 86 örnek alanda yapılan arazi ölçümleri kullanılarak toprak üstü karbon (TÜK) tahmini gerçekleştirilmiştir. ALS (Airborne Laser Scanning) verilerinden türetilen 101 yapısal metrik incelenmiş, yüksek korelasyonlu değişkenler elenerek 25 belirleyici metrik seçilmiştir. Random Forest regresyon modeli ve leave-one-out cross-validation (LOOCV) yöntemi ile TÜK tahminleri yapılmıştır. Model, belirtme katsayısı (R²) = 0,76, kök ortalama kare hata (RMSE) = 4,25 ton C/ha ve ortalama mutlak hata (MAE) = 3,48 ton C/ha performans değerleri ile yüksek doğruluk göstermiştir. Değişken önem analizi, yükseklik yüzdelikleri ve canopy relief ratio (CRR) gibi dikey yapı heterojenliğini temsil eden metriklerin TÜK tahmininde en etkili değişkenler olduğunu ortaya koymuştur. Elde edilen sonuçlar, ALS verisinin farklı meşcere tipleri ve topoğrafik heterojenlik gösteren ormanlarda bile TÜK tahmininde güvenilir bir araç olduğunu doğrulamaktadır. Özellikle yükseklik, örtü kapanma oranı ve dikey yapı heterojenliğini temsil eden metrikler, karbon stoklarının doğru modellenmesinde kritik rol oynamaktadır. Elde edilen sonuçlar, havasal LiDAR verisinin bölgesel ve ulusal ölçekte karbon haritalaması ile iklim değişikliğiyle mücadele stratejilerinin geliştirilmesinde güvenilir bir yöntem sunduğunu göstermektedir.

Anahtar Kelimeler

İklim Değişikliği , random forest , orman karbonu

Teşekkür

Makalede kullanılan yersel envanter verisi, İstanbul Üniversitesi - Cerrahpaşa Orman Fakültesi Eğitim ve Araştırma Ormanı amenajman planı çalışmasında kullanılan veri setinden derlenmiştir. Bu vesileyle, İstanbul Üniversitesi - Cerrahpaşa Orman Fakültesi Orman Amenajmanı Anabilim Dalı öğretim üyelerine değerli katkılarından dolayı teşekkür ederim.

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