Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux

Abstract

Lidar transforms how we map ecosystems, but its prospect for measuring ecosystem dynamics is limited by practical factors, such as variation in lidar acquisition and lack of ground data. To address practical use of multitemporal lidar for forest and carbon monitoring, we conducted airborne lidar surveys four times from 2002 to 2012 over a region in Scotland, and combined the repeat lidar data with field inventories to map tree growth, biomass dynamics, and carbon change. Our analyses emphasized both individual tree detection and area-based, grid-level approaches. Lidar-detected heights of individual trees correlated well with field values, but with noticeable underestimation biases (r=0.94, bias=−1.5m, n=598) due to the increased probability of missing treetops as pulse density decreases. If not corrected for such biases, lidar provided unrealistic or wrong estimates of tree growth unless laser sampling rates were high enough (e.g., >7points/m2). Upon correction, lidar could detect sub-annual tree growth (p-value<0.05). At grid levels, forest biomass density was reliably estimated from area-based lidar metrics by both Random Forests (RF) and a linear functional model (r>0.86, RMSEcv<21Mg/ha), irrespective of laser sampling rates. But RF constantly overfit the data, often with poorer predictions. The better generality of the linear model was further confirmed by its transferability—fitted for one year but applicable to other years—a strength not possessed by RF but desired to alleviate the reliance on ground biomass data for model calibration. Resultant lidar maps of forest structure captured canopy dynamics and carbon flux at fine scales, consistent with growth histories and known disturbances. The entire 20-km2 study area sequestered carbon at a rate of 0.59±0.4MgC/ha/year. Overall, our study describes robust techniques well suited for multitemporal lidar analysis and affirms the utility and potential of repeat lidar data for resource monitoring and carbon management; however, the full potential cannot be attained without the support of accompanying field surveys or modeling efforts in enhancing stakeholders' trustworthiness of lidar-based inference.