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...

詳細記述

書誌詳細

主要な著者:	Xiaoli Zhang, Lu Li, Yanfeng Liu, Yong Wu, Jing Tang, Weiheng Xu, Leiguang Wang, Guanglong Ou
フォーマット:	Artigo
言語:	Inglês
出版事項:	Frontiers Media S.A. 2023-04-01
シリーズ:	Frontiers in Forests and Global Change
主題:	uncertainty quantile regression (QR) quantile regression neural network (QRNN) Landsat 8 imagery Pinus densata forests
オンライン･アクセス:	https://www.frontiersin.org/articles/10.3389/ffgc.2023.1162291/full
タグ:	タグ追加 タグなし, このレコードへの初めてのタグを付けませんか!