The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass
Open accessAbstract
) 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
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Cited by
A. Ramezan, C., A. Warner, T., & E. Maxwell, A. (2019) · Remote Sensing · doi:10.3390/rs11020185 · Cited by 322
Shao, Z., & Zhang, L. (2016) · Sensors · doi:10.3390/s16060834 · Cited by 105
Metadata
- Authors
- Fassnacht, F. E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P., & Koch, B.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 154
- Pages
- 102-114
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2020520344
- Cited by
- 438
- References
- 61
- FWCI
- 16.39
- Type
- journal article
- Language
- en
- Open access
- green
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