Modeling tree canopy height using machine learning over mixed vegetation landscapes

International Journal of Applied Earth Observations and Geoinformation

Travis Seaborn¹  Zhe Wang²  Timothy E. Link²  Christopher C. Caudill³  Hui Wang⁴ 

[1] Corresponding author at: Institute for Modeling Collaboration and Innovation, 875 Perimeter Drive, MS-1122, Moscow, Idaho 83844, United States.;Department of Fish and Wildlife Sciences, University of Idaho, Moscow, ID 83844, United States;Department of Geography, University of Idaho, Moscow, ID 83844, United States;Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States;

关键词: Tree canopy height;    LiDAR;    Random forest;    Spatial non-stationarity;    Landsat;   

DOI  :  

来源: DOAJ

【 摘 要 】

Although the random forest algorithm has been widely applied to remotely sensed data to predict characteristics of forests, such as tree canopy height, the effect of spatial non-stationarity in the modeling process is oftentimes neglected. Previous studies have proposed methods to address the spatial variance at local scales, but few have explored the spatial autocorrelation pattern of residuals in modeling tree canopy height or investigated the relationship between canopy height and model performance. By combining Light Detection and Ranging (LiDAR) and Landsat datasets, we used spatially-weighted geographical random forest (GRF) and traditional random forest (TRF) methods to predict tree canopy height in a mixed dry forest woodland in complex mountainous terrain. Comparisons between TRF and GRF models show that the latter can lower predefined extreme residuals, and thus make the model performance relatively stronger. Moreover, the relationship between model performance and degree of variation of true canopy height can vary considerably within different height quantiles. Both models are likely to present underestimates and overestimates when the corresponding tree canopy heights are high (>95% quantile) and low (

【 授权许可】

Unknown