Evaluating statistical and combine method to predict stand above-ground biomass using remotely sensed data
Yazarlar (3)
Sinan Bulut Türkiye
Prof. Dr. Fatih SİVRİKAYA Kastamonu Üniversitesi, Türkiye
Prof. Dr. Alkan Günlü Türkiye
| Makale Türü | Özgün Makale (Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale) | ||
| Dergi Adı | Arabian Journal of Geosciences | ||
| Dergi ISSN | 1866-7511 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | Japanese Science and Technology Agency (JST) | ||
| Makale Dili | Türkçe | Basım Tarihi | 04-2022 |
| Cilt / Sayı / Sayfa | 15 / 9 / 838–0 | DOI | 10.1007/s12517-022-10140-3 |
| Makale Linki | http://dx.doi.org/10.1007/s12517-022-10140-3 | ||
| UAK Araştırma Alanları |
Orman Hasılatı ve Amenajmanı
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| Özet |
| Above-ground biomass (AGB) is a key parameter as indicator of forest carbon content and ecosystem productivity. Producing highly predictive models and spatial distribution of AGB stocks support the development of forest management plans. In this research, AGB was modeled using multiple linear regression (MLR) analysis and regression kriging (RK) techniques. Reflectance and vegetation indices data derived from the Sentinel-2 satellite images were used as an auxiliary variable. The highest correlation coefficients between the AGB and remote sensing data were obtained in broadleaf stands. These values were 0.743 and 0.869 for reflectance values of B8 band and PSSR vegetation index values, respectively. The AGB contents of coniferous, broadleaf, and mixed stands were modeled separately with MLR method. Then, RK method performed with combining MLR and ordinary kriging methods. The R2 … |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 11 |