Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass
By Fabian Ewald Fassnacht, Florian Hartig, Hooman Latifi, C. Berger, Jaime Hernández, P. Corvalán, Barbara Koch. 2014
413 citations recorded by the source corpus.
) and root mean squared error (RMSE) for bootstrapped estimates using k-fold cross-validation with a varying number of folds. Finally, Analysis of Variance (ANOVA) was used to quantify the influence of the factors on the predictive error of the biomass models. Our results confirm previous findings that predictor data (sensor) type is the most important factor for the accu- racy of biomass estimates, with LiDAR being preferable to hyperspectral data. In contrast to some previous studies, complementing LiDAR with hyperspectral data did not improve predictive accuracy. Also the prediction method had a substantial effect on accuracy and was generally more important than the sample size. In most cases, random forest performed best and stepwise linear models worst, judging from r 2 and RMSE under cross- validation.Additional results suggestedthat r 2 may deliverunrealisticallylarge values whenthehold-out sample
Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/9aef0b1e140209b9d055e91090d1d8affb69b318
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In Ecology
In Environmental Engineering
In Nature and Landscape Conservation
Citations in the corpus
- Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data (2018, background): “Additional applications include biomass estimation (Fassnacht et al., 2014) and disease mapping as for example caused by fungal infections (Iturritxa et al., 2014).”