Estimation of coppice forest characteristics using spatial and non-spatial models and Landsat data. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Izadi, Somayeh; Sohrabi, Hormoz; Khaledi, Majid Jafari
• Is Part Of: Journal of spatial science. Volume 67:Issue 1(2022)
• Page Start: 143
• Page End: 156
• Publication Date: 2022-01-02
• Subjects: Spatial autocorrelation -- above-ground biomass -- stem density -- basal area -- remote sensing -- regression analysis; Geographic information systems -- Periodicals; Cartography -- Periodicals; Surveying -- Periodicals; Geodesy -- Periodicals; Photogrammetry -- Periodicals; Cartography; Geodesy; Geographic information systems; Photogrammetry; Surveying; Periodicals; 526.05
• DOI: 10.1080/14498596.2020.1734110
• Languages: English
• ISSNs: 1449-8596

Abstract

ABSTRACT: Accurate spatial modelling of forest characteristics is one of the most important challenges in remote sensing applications. In this study, we compared the ability of Multiple Linear Regression (MLR), Geographically weighted regression (GWR), and Random Forest (RF) to estimate different forest attributes based on field sample data and Landsat 8 image. CA was modelled with the highest accuracy compared to other variables using GWR. GWR outperformed other methods. The highest and the lowest values of RMSE were for BA using RF (31.0%) and CA using GWR (12.0%), respectively.