Improving the accuracy of forest aboveground biomass using Landsat 8 OLI images by quantile regression neural network for Pinus densata forests in southwestern China

It is a challenge to reduce the uncertainties of the underestimation and overestimation of forest aboveground biomass (AGB) which is common in optical remote sensing imagery. In this study, four models, namely, the linear stepwise regression (LSR), artificial neural network (ANN), quantile regressio...

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Detaylı Bibliyografya

Asıl Yazarlar:	Xiaoli Zhang, Lu Li, Yanfeng Liu, Yong Wu, Jing Tang, Weiheng Xu, Leiguang Wang, Guanglong Ou
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Frontiers Media S.A. 2023-04-01
Seri Bilgileri:	Frontiers in Forests and Global Change
Konular:	uncertainty quantile regression (QR) quantile regression neural network (QRNN) Landsat 8 imagery Pinus densata forests
Online Erişim:	https://www.frontiersin.org/articles/10.3389/ffgc.2023.1162291/full
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