Name
Abstract | ||
|---|---|---|
•We focus on stratification in remote sensing-assisted biomass models.•We used dataset based on hyperspectral and LiDAR predictors.•Benefits from stratification were assessed in a factorial design with other model choices.•The stratification of measurement units was marginally advantageous.•Input data type and statistical prediction showed to be most influential on model performances. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.jag.2015.01.016 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | Field | DocType |
LiDAR and hyperspectral remote sensing,Aboveground biomass,Statistical prediction,Post-stratification,Model performance,Factorial design | Biomass,Sampling design,Bootstrapping,Remote sensing,Mean squared error,Hyperspectral imaging,Data type,Lidar,Predictive modelling,Geography | Journal |
Volume | ISSN | Citations |
38 | 0303-2434 | 6 |
PageRank | References | Authors |
0.60 | 5 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hooman Latifi | 1 | 29 | 4.47 |
| Fabian Ewald Fassnacht | 2 | 94 | 9.85 |
| Florian Hartig | 3 | 6 | 0.60 |
| Christian Berger | 4 | 6 | 0.60 |
| Jaime Hernández | 5 | 16 | 2.36 |
| Patricio Corvalán | 6 | 11 | 1.14 |
| Barbara Koch | 7 | 87 | 8.38 |