Temporal transferability of LiDAR-based imputation of forest inventory attributes

Abstract

Forest inventory and planning decisions are frequently informed by LiDAR data. Repeated LiDAR acquisitions offer an opportunity to update forest inventories and potentially improve forest inventory estimates through time. We leveraged repeated LiDAR and ground measures for a study area in northern Idaho, U.S.A., to predict (via imputation) — across both space and time — four forest inventory attributes: aboveground carbon (AGC), basal area (BA), stand density index (SDI), and total stem volume (Vol). Models were independently developed from 2003 and 2009 LiDAR datasets to spatially predict response variables at both times. Annual rates of change were calculated by comparing response variables between the two collections. Additionally, a pooled model was built by combining reference observations from both years to test if imputation can be performed across measurement dates. The R2 values for the pooled model were 0.87, 0.90, 0.89, and 0.87 for AGC, BA, SDI, and Vol, respectively. Mapping response variables at the landscape level demonstrates that the relationship between field data and LiDAR metrics holds true even though the data were collected in different years. Pooling data across time increases the number of reference observations available to resource managers and may ultimately improve inventory predictions.