DOI: 10.1016/j.jag.2016.08.009
Scopus记录号: 2-s2.0-85003952953
论文题名: The accuracy of large-area forest canopy cover estimation using Landsat in boreal region
作者: Hadi, Korhonen L ; , Hovi A ; , Rönnholm P ; , Rautiainen M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 53 起始页码: 118
结束页码: 127
语种: 英语
英文关键词: Boreal
; Canopy cover
; Forest
; Landsat
; Tree cover
Scopus关键词: accuracy assessment
; boreal forest
; forest canopy
; forest cover
; Landsat
; satellite imagery
英文摘要: Large area prediction of continuous field of tree cover i.e., canopy cover (CC) using Earth observation data is of high interest in practical forestry, ecology, and climate change mitigation activities. We report the accuracy of using Landsat images for CC prediction in boreal forests validated with field reference plots (N = 250) covering large variation in latitude, forest structure, species composition, and site type. We tested two statistical models suitable for estimating CC: the beta regression (BetaReg) and random forest (RanFor). Landsat-based predictors utilized include individual bands, spectral vegetation indices (SVI), and Tasseled cap (Tass) features. Additionally, we tested an alternative model based on spectral mixture analysis (SMA). Finally, we carried out a first validation in boreal forests of the recently published Landsat Tree Cover Continuous (TCC) global product. Results showed simple BetaReg with red band reflectance provided the highest prediction accuracy (leave-site-out RMSECV 13.7%; R2 CV 0.59; biasCV 0.5%). Spectral transformations into SVI and Tass did not improve accuracy. Including additional predictors did not significantly improve accuracy either. Nonlinear model RanFor did not outperform BetaReg. The alternative SMA model did not outperform the empirical models. However, empirical models cannot resolve the underestimation of high cover and overestimation of low cover. SMA prediction errors appeared less dependent on forest structure, while there seemed to be a potential for improvement by accounting for endmember variability of different tree species. Finally, using temporally concurrent observations, we showed the reasonably good accuracy of Landsat TCC product in boreal forests (RMSE 13.0%; R2 0.53; bias −2.1%), however with a tendency to underestimate high cover. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80118
Appears in Collections: 气候变化事实与影响
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作者单位: Aalto University, School of Engineering, Department of Built Environment, PO Box 15800, Aalto, Finland; University of Eastern Finland, School of Forest Sciences, PO Box 111, Joensuu, Finland; Aalto University, School of Electrical Engineering, Department of Radio Science and Engineering, PO Box 13000, Aalto, Finland
Recommended Citation:
Hadi, Korhonen L,, Hovi A,, Rönnholm P,et al. The accuracy of large-area forest canopy cover estimation using Landsat in boreal region[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,53