Mapping individual tree and plot-level biomass using airborne and mobile lidar in pin?on-juniper woodlands

Pin similar to on-juniper (PJ) woodlands are a widespread dryland ecosystem in the US containing an immense but poorlyconstrained amount of aboveground biomass (AGB). Found at the dry end of the climatic range within which trees can persist, PJ faces an uncertain future in a changing climate, giving unique importance to mapping its AGB, past, present, and future. Lidar remote sensing offers great potential towards that end with research in a range of tree-dominant ecosystems demonstrating a strong capacity for mapping AGB. However, studies applying lidar to the task of mapping AGB in PJ are few. Given the unique structural characteristics of PJ trees, which tend to be short in stature (<10 m), possess multiple stems often obscured by low crown base heights, and irregularlyshaped canopies, there remains a high degree of uncertainty surrounding tree- and stand-level structural mapping in these woodlands. To resolve our limited understanding of how to best quantify AGB in PJ using lidar, we conducted a field-validated analysis comparing lidar platform (airborne laser scanner, ALS vs. mobile laser scanner, MLS) and analytical framework (area-based modeling vs. individual tree-based modeling). We used a random forest machine learning approach to predicting tree- and stand-level structure with leave-one-out crossvalidation to assess model performance. In an area-based context, MLS only marginally outperformed ALS, with both able to explain approximately 80 % of variance in AGB. However, in an individual tree-based context, MLS significantly outperforms ALS at several tasks, including: (1) delineating tree crowns (MLS delineated 100 % of trees; ALS delineated 69 %); (2) directly characterizing tree structural parameters including height (R2ALS = 0.52 vs. R2MLS = 0.74) and crown area (R2ALS = 0.26 vs. R2MLS = 0.68); and (3) modeling AGB (R2ALS = 0.38 vs. R2MLS = 0.62). We attribute this difference in performance to the vastly greater structural detail contained within the MLS point cloud data as compared to ALS, with the former possessing an average of approximately 30,000 pts/m2 and the latter 14 pts/m2. Based on our results, we make the following recommendation: if individual tree structure is needed, MLS is the far superior option; however, given the wider availability, ALS data are a suitable substitute when mapping PJ AGB, particularly under an area-based modeling analytical framework. This study represents the first time MLS data have been applied to tree structure mapping in PJ, and provides strong quantitative evidence towards best structural and AGB mapping practices in this unique, widespread, and important ecosystem. It serves as a solid foundation upon which future studies aiming to map AGB in PJ and other similar dryland woodland ecosystems can be built.