Ecological and climatic transferability of airborne lidar-driven aboveground biomass models in Piñon-Juniper woodlands

Abstract

ABSTRACT Piñon-juniper (PJ) woodlands are an expansive and dynamic dryland ecosystem in the US that encompass a wide range of spatial, temporal, and ecological diversity. The dynamism of PJ woodland extent and abundance over space and time is attributable to variability in species compositions, stand structures, climatic conditions, and disturbance patterns. PJ aboveground biomass (AGB) quantification is important for understanding its role in the global carbon cycle, for tracking climate change impacts, and for informing forest management practices. Quantifying AGB in PJ woodlands is challenging due to complex vegetation structure. Although airborne laser scanning (ALS) has proven effective in estimating PJ AGB in local scale studies, these efforts only capture a fraction of the overall PJ range leaving open an important question as to whether a broad-scale, ecosystem-wide effort to map PJ woodland AGB could be successful. This research seeks to determine whether AGB predictive models built in ecologically distinct portions of PJ’s range can be accurately applied to prediction of AGB in other portions of the range. Using a large database of 497 field plots compiled from eight sources and a random forest modeling framework, transferability between predictive models was evaluated by grouping field reference plots into two clustering categories based on PJ characteristics: one category generated with environmental variables (climate and topography) and the other using species compositions. For both clustering categories, models trained on one cluster were used to predict AGB in every other cluster within that category. Relatively distinct clusters, such as those characterized by notably higher temperatures and precipitation totals or dominated by a unique species, had high transferability when models were trained on the distinct cluster and applied to other clusters, but low transferability when other clusters were tested on the distinct cluster. High training transferability for distinct clusters is likely due to the inability of random forests to extrapolate beyond the range of training data, while the high testing transferability is likely due to ecological reasons. Therefore, it is important to capture the distinct environments and species compositions within the range of PJ woodlands when training a range-wide model to map PJ AGB. With the eventual goal of a range-wide PJ AGB map, we developed a preliminary range-wide model of PJ woodland AGB using our entire field reference dataset. The bootstrapped accuracy assessment of the range wide model (median R2 = 0.52; median rRMSE = 0.49) suggests promise for future comprehensive PJ AGB maps. However, high AGB values in the range-wide model were under-predicted, suggesting that gathering additional field plots in high AGB PJ woodlands has the potential to enhance the accuracy of the predictive model across the range of PJ AGB.