Estimating Above-Ground Carbon of Taurus Cedar Stands Using Sentinel-2 Satellite Image: A Case Study of Elmalı Forest Enterprise
Yazarlar (3)
Arş. Gör. Döndü BULUR Kastamonu Üniversitesi, Türkiye
Prof. Dr. Alkan Günlü Türkiye
Doç. Dr. Oytun Emre Sakıcı Kastamonu Üniversitesi, Türkiye
| Bildiri Türü |
Tebliğ/Bildiri
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Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.61326/icelis.2023.45 | ||
| Kongre Adı | 3 RD INTERNATIONAL CONGRESS ON ENGINEERING AND LIFE SCIENCE ForesTR “The Balance of The life: Carbon” | ||
| Kongre Tarihi | 20-09-2023 / 22-09-2023 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Trabzon |
| Bildiri Linki | http://dx.doi.org/10.61326/icelis.2023.45 | ||
| UAK Araştırma Alanları |
Orman Hasılatı ve Amenajmanı
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| Özet |
| Most terrestrial carbon (C) is stored in forests, an important source of fiber and fuel for humans. Therefore, forests play an essential role in mitigating the effects of climate change by reducing the carbon level in the atmosphere. Field measurements and remote sensing techniques determine the stored above-ground carbon (AGC). This study used Sentinel-2 satellite image to estimate the amount of AGC in pure Taurus cedar (Cedrus libani A. Rich.) stands in Elmalı Forest Enterprise. Regression models were developed for AGC estimation with the reflectance and vegetation indices obtained from the Sentinel-2 satellite image. Within the scope of the study, the field measurement data obtained from 120 sample plots were used and AGCs of their corresponding stands were estimated with an allometric equation. The sample plots data was randomly divided into modeling (70%, 84 sample plots) and control data (30%, 36 sample plots) to fit the regression models and to test the accuracy of the models, respectively. Multiple linear regression analysis were conducted to develop the models, and three goodness-of-fit statistics (R2, RMSE and MAE) were used to compare the success of these models. When the achievements of the models were evaluated, it was revealed that the model containing the MSR vegetation indice gave more successful results (R2= 0.488). Consequently, it was determined that the developed models were moderately successful in estimating AGC. |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 3 |