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Dergiler / Türkiye Uzaktan Algılama Dergisi / 2026 / Volume: 8

Linking climate variability to forest canopy structure using multi-sensor remote sensing and machine learning

Nehir Uyar(ZONGULDAK BÜLENT ECEVİT ÜNİVERSİTESİ, ZONGULDAK MESLEK YÜKSEKOKULU)0000-0003-3358-3145
Azize Uyar(ONDOKUZ MAYIS UNIVERSITY)0000-0003-2625-3690
Sayfa
—
Yayın Tarihi
2026-07-03
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Future research should explore multi-source data fusion and advanced hybrid modeling approaches to improve forest monitoring and sustainable management.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Forests play a vital role in carbon sequestration, biodiversity conservation, and climate regulation.
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Temel Bulgular & Veri:However, changing environmental conditions, including variations in precipitation, temperature, and soil properties, significantly impact tree physiology and forest dynamics.
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Sonuç & Katkı:Future research should explore multi-source data fusion and advanced hybrid modeling approaches to improve forest monitoring and sustainable management.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Forest canopy height · Forest monitoring · LiDAR

Abstract

Forests play a vital role in carbon sequestration, biodiversity conservation, and climate regulation. However, changing environmental conditions, including variations in precipitation, temperature, and soil properties, significantly impact tree physiology and forest dynamics. This study aims to evaluate the potential of machine learning models in predicting canopy height, a key indicator of forest structure, using remote sensing data. Four models Random Forest, Support Vector Machine, and Gradient Boosting Trees (GBT) were tested under multiple scenarios incorporating Sentinel-2, Landsat 8, and Landsat 9 imagery. Predictor variables included Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), precipitation, land surface temperature (LST), soil moisture, population density, elevation, slope, aspect, albedo, and CO concentration. The study area, located south of Shaver Lake in Fresno, California, represents a dynamic forest ecosystem vulnerable to climatic variability. Results indicate that RF and GBT models achieved the highest predictive accuracy (R² = 0.66 and 0.63, respectively) with Sentinel-2 data, while CART and SVM performed less effectively, especially with Landsat 9 data. These findings emphasize the importance of selecting appropriate remote sensing datasets for tree physiology assessments. Future research should explore multi-source data fusion and advanced hybrid modeling approaches to improve forest monitoring and sustainable management.

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Bu Makaleye Atıf Yap

Uyar, N., Uyar, A. (2026). Linking climate variability to forest canopy structure using multi-sensor remote sensing and machine learning. Türkiye Uzaktan Algılama Dergisi, Volume: 8. https://doi.org/10.51489/tuzal.1909286

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