Abstract
The goal of this study was to predict the need for commercial thinning using airborne lidar data (ALS) with random forest (RF) machine learning algorithm. Two test sites (with areas of 14,750 km2 and 12,630 km2) were used with a total of 1053 forest stands from southwestern Estonia and 951 forest stands from southeastern Estonia. The thinnings were predicted based on the ALS measurements in 2019 and 2017. The two most important ALS metrics for predicting the need for thinning were the 95th height percentile and the canopy cover. The prediction accuracy based on validation stands was 93.5% for southwestern Estonia and 85.7% for southeastern Estonia. For comparison, the general linear model prediction accuracy was less for both test sites—92.1% for southwest and 81.8% for southeast. The selected important predictive ALS metrics differed from those used in the RF algorithm. The cross-validation of the thinning necessity models of southeastern and southwestern Estonia showed a dependence on geographic regions.
Highlights
Forest management planning and decision making are mainly based on forest inventory (FI) data
Database were used— of 1053 forest stands from southwestern Estonia and 951 forest stands from southeastern Estonia (Figure 1)
Based on the random forest algorithm’s variable importance indicators mean decrease in Gini coefficient (MDG) and mean dein accuracy (MDA), the five most significant airborne laser scanning (ALS) metrics were independently selected for both test areas
Summary
Forest management planning and decision making are mainly based on forest inventory (FI) data. It is still common for FI data to be collected by specialized personnel via field survey, but today these data are increasingly obtained via remote sensing [1,2,3,4,5,6,7,8]. In addition to stereo- and orthophotos, airborne laser scanning (ALS; [9]) has gained a leading role as an FI data source, as it is the main basis for describing forest structure in remote-sensingbased inventories. With the large number of metrics and combinations of data sources, the need for machine learning algorithms to extract significant information from large datasets has increased [10,11]. During the fieldwork of an FI, expert suggestions for management (e.g., thinning, sanitary cutting) can be made; since large areas need to be covered, and the average revision cycle of stands is 5–10 years, management decisions are made based on somewhat outdated
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