Our recent work (and Yuri's 3rd PhD paper) on combining Lidar, SAR and very-high resolution multispectral imagery for upscaling forest attributes (tree number and density) is out in the Intl J of Applied Earth Observation and Geoinformation

Our latest work and Yuri's 3rd PhD paper "Multi-sensor airborne and satellite data for upscaling tree number information in a structurally complex forest", was published this week in the International Journal of Applied Earth Observation and Geoinfromation: http://www.sciencedirect.com/science/article/pii/S0303243418303155

Here are the highlights:

• We used ALS (LiDAR), WorldView 2 very-high resolution imagery, and SAR (radar) to upscale tree number information
• ALS dervied tree numbers were used for training a random forest regressor
• Tree numbers were upscaled with an R² of 0.61 and RMSE of 62%
• ALS and WorldView-2 predictors were the best for estimating tree numbers
• The SAR predictors alone were unable to estimate tree numbers reliably

Citation: I. Shendryk, M. Broich, M.G. Tulbure (2018). Multi-sensor airborne and satellite data for upscaling tree number information in a structurally complex eucalypt forest. International Journal of Applied Earth Observation and Geoinformation (73): 397-406.