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Evaluating statistical and combine method to predict stand above-ground biomass using remotely sensed data    
Yazarlar (3)
Sinan Bulut
Türkiye
Prof. Dr. Fatih SİVRİKAYA Prof. Dr. Fatih SİVRİKAYA
Kastamonu Üniversitesi, Türkiye
Alkan Günlü
Türkiye
Devamını Göster
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
Makale Türü Özgün Makale
Makale Alt Türü Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale
Dergi Adı Arabian Journal of Geosciences
Dergi ISSN 1866-7511 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler SCOPUS
Makale Dili Türkçe
Basım Tarihi 04-2022
Cilt No 15
Sayı 9
Doi Numarası 10.1007/s12517-022-10140-3
Makale Linki http://dx.doi.org/10.1007/s12517-022-10140-3