Modelling Forest Understory with Lidar

canadianEvaluating habitat quality and understory composition across complex landscapes remains a challenge for forest and wildlife managers, but is essential for ensuring the stability of vulnerable species. In this  study a group Canadian researchers investigate whether forest stand structure, as measured by airborne laser scanning (ALS), can be used to predict the abundance and fruit production (fruit count) for Canada buffaloberry (Shepherdia canadensis), huckleberry (Vaccinium membranaceum), and saskatoon (Amelanchier alnifolia) shrubs in southwest Alberta, Canada. We combine ALS, climate, and terrain data to build random forest models of species abundance and fruit productivity, trained on data from 322 field plots.

ALS data was processed into a suite of stand structure variables, under the hypothesis that models incorporating stand structure will be more powerful than models without for describing understory shrub abundance and reproduction (fruit productivity). ALS data improved model fit for saskatoon and huckleberry abundance models, with total explained variance (r2) ranging from 37.6 to 59.4%. Inclusion of ALS data improved explained variance between 0% and 16%, suggesting that saskatoon and huckleberry in particular were associated with overstory vegetation structure.

Despite the importance of ALS in further improving explanation of shrub abundance and fruit production, terrain factors were the dominant factor affecting regional and local variation in species abundance and fruit production.


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