Prediction of forest attributes using airborne laser scanning-based models without new in-situ field measurements

Abstract

The era of airborne laser scanning (ALS) and the development of new forest inventory methods has reduced the need for field visits and overall inventory costs over the last two decades. Although the development of inventory methods has been considerable, some systematic field visits are usually always required. For example, the most common ALS inventory method, the area-based approach (ABA), leans on field sample plot measurements. Likewise in the ALS inventory, the ABA method can also be used in drone-based inventories with image point cloud (IPC) data. Due to the small areal coverage of the drones, local sample plot measurements in drone image point cloud (DIPC) inventories are not usually profitable. The objective of this thesis was to examine the performance of ALS-based forest attribute models in ALS- and DIPC-based ABA inventories without new in-situ field measurements. In this study, nationwide ALS models for three forest attributes (stem volume, above ground biomass and dominant height) were fitted for the whole of Finland, and regional-level error rates of the nationwide model predictions were assessed. As the nationwide models tended to exhibit systematic region-wise under- and over-predictions, different calibration methods were examined. First, calibration of nationwide models with a small number of new field measurements from the target area was simulated. Second, the nationwide stem volume model or its regional predictions was calibrated without new in-situ field measurements by three test scenarios: a) using additional calibration variables in the models to account for geographical and environmental conditions throughout the country, b) refitting of the models by using existing sample plots from nearby regions, and c) matching the regional-level predictions with national forest inventory data. The DICP-based forest inventory without new in-situ field measurements was evaluated by replacing the ALS metrics from the ALS-based models with DIPC metrics when the models were applied. In the DIPC inventory, the metrics used in the ALS models were selected carefully so that they would be similar to the corresponding DIPC metrics. The results showed that forest attributes can be predicted without new in-situ field measurements using nationwide ALS-based models with moderate error rates. The systematic errors associated with the nationwide models decreased when the models were fitted with additional calibration variables, such as degree days, precipitation, and tree species proportions. However, the measurement of a carefully selected set of sample plots (e.g., 20 plots) from the target area for the calibration of the nationwide model is recommended, in instances where it is economically feasible. Prediction of forest attributes using ALS-based models with DIPC metrics is possible provided the predictor variables describe the upper canopy layer. The lowest error rates in DIPC-based inventories were obtained when the ALS-based model was fitted in a nearby region and the inventory units were disaggregated to coniferous and deciduous dominated areas before the prediction.