Predicting bilberry and cowberry yields using airborne laser scanning and other auxiliary data combined with National Forest Inventory field plot dataBohlin, Inka; Maltamo, Matti; Hedenås, Henrik; Lämås, Tomas; Dahlgren, Jonas; Mehtätalo, Lauri
The increasing availability of wall-to-wall remote sensing datasets in combination with accurate field data enables the mapping of different ecosystem services more accurately and over larger areas than before. The provision of wild berries is an essential ecosystem service, and berries are the most used non-wood forest products in Nordic countries. The aim of the study was to 1) develop general prediction models for bilberry and cowberry yield based on metrics derived from airborne laser scanning (ALS) data and other existing wall-to-wall data and 2) to identify laser-based structural features of forests that can be linked to locations of the highest berry yields. We used the indirect approach where the correlation between forest structure described by the ALS data and the berry yields are utilized. Berry data collected in the Swedish National Forest Inventory (NFI) 2007–2016 were used for training the models and ALS data from 2009 to 2014 from the national ALS campaign of Sweden. Berry yields were modelled using generalised linear mixed models (GLMMs), and forest structural differences were demonstrated in histograms of presence/absence data. The ALS-based canopy cover was an important variable both in bilberry and cowberry models. Other significant variables were ALS-based height variance, shrub cover, height above sea level, slope, soil wetness and terrain ruggedness, satellite-based species-specific volume and percentage, seasonality of temperature and precipitation and annual precipitation, inventory year, soil type and land use class. In addition, the time difference between the inventory day and the Julian day when berries were expected to be ripe showed a 1.5% decrease for bilberry and a 1.1% decrease for cowberry yield per day during the season. The highest bilberry yield was identified in forests with a canopy cover of 50% and the highest cowberry yield in forests with a canopy cover close to zero. The canopy height of 15 m reflected the highest bilberry yield, whereas a canopy height close to 0 m resulted in the highest cowberry yield. The shrub cover was close to zero both with highest bilberry and cowberry yields. This is the first study combining ALS metrics with other wall-to-wall variables and NFI field data to model bilberry and cowberry yields. Prediction models can be used to produce maps showing the most potential locations for berry picking. Further, the models may, in the future, be imported into forest planning systems to obtain stand-level prognoses of berry yield development under different forest management strategies.
KeywordsLidar; Vaccinium myrtillus; Vaccinium vitis-idaea L; Berry yield; Remote sensing; Forest structure; Mixed models; NFI
Published inForest Ecology and Management
2021, volume: 502, article number: 119737
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