Name
Title | ||
|---|---|---|
Modelling LiDAR derived tree canopy height from Landsat TM, ETM+ and OLI satellite imagery - A machine learning approach. |
Abstract | ||
|---|---|---|
•Canopy height was predicted from Landsat imagery (RMSE values between 2.3 m and 4.1 m).•Random forest regression accounted for complex vegetation structural types.•The model was robust across a range of vegetation communities and Landsat platforms.•Canopy height was used to identify structural change through time (1987–2016). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jag.2018.08.013 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | Field | DocType |
00-01,99-00 | Tree canopy,Thematic Mapper,Vegetation,Cyclone,Satellite imagery,Lidar,Artificial intelligence,Predictive modelling,Geography,Machine learning,Canopy | Journal |
Volume | ISSN | Citations |
73 | 0303-2434 | 1 |
PageRank | References | Authors |
0.37 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. W. Staben | 1 | 2 | 0.75 |
| Arko Lucieer | 2 | 455 | 46.51 |
| Peter Scarth | 3 | 3 | 3.59 |