Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data

Abstract

Field measurement of sample plots is a major cost in forest remote sensing. This is also relevant in drone-based forest inventories where the target area is rather small compared to the area used in other remote sensing techniques. Implementation of forest inventories by remote sensing could be streamlined by using models fitted elsewhere in a similar type of forest. The main objective of this study was to investigate the accuracy of forest attribute predictions from drone-based image point clouds (DIPC) without locally fitted models. Instead, the models were fitted in 22 inventory areas across Finland using airborne laser scanning (ALS) data. These models were applied to predict dominant height and stem volume for a separate test area located in eastern Finland. In the test area, the predictors were computed from DIPC data for 15 m × 15 m sub-plots that were finally aggregated to full 30 m × 30 m plots. All dominant height models performed well with the test data: the relative root mean square error (RMSE) varied between 3 and 5% and the relative mean difference (MD) values ranged between 0 and 3%. In contrast, the stem volume models fitted in northern Finland performed poorly with the test data. These models produced RMSE values between 40 and 65%, whereas models fitted in other parts of the country produced RMSE values between 20 and 30%. Similarly, MD values associated with the stem volume models fitted in northern Finland ranged between 24 and 51%, whereas MD values associated with models fitted elsewhere in Finland ranged between 3 and 17%. Regional variations in forest structure are the main reason why models fitted in northern Finland did not perform as well as in the test area.

Keywords

• Airborne laser scanning
• Area-based approach
• Drone
• Forest inventory
• Remote sensing