Monitoring individual tree‐based change with airborne lidar

Abstract

Understanding the carbon flux of forests is critical for constraining the global carbon cycle and managing forests to mitigate climate change. Monitoring forest growth and mortality rates is critical to this effort, but has been limited in the past, with estimates relying primarily on field surveys. Advances in remote sensing enable the potential to monitor tree growth and mortality across landscapes. This work presents an approach to measure tree growth and loss using multidate lidar campaigns in a high‐biomass forest in California, USA. Individual tree crowns were delineated in 2008 and again in 2013 using a 3D crown segmentation algorithm, with derived heights and crown radii extracted and used to estimate individual tree aboveground biomass. Tree growth, loss, and aboveground biomass were analyzed with respect to tree height and crown radius. Both tree growth and loss rates decrease with increasing tree height, following the expectation that trees slow in growth rate as they age. Additionally, our aboveground biomass analysis suggests that, while the system is a net source of aboveground carbon, these carbon dynamics are governed by size class with the largest sources coming from the loss of a relatively small number of large individuals. This study demonstrates that monitoring individual tree‐based growth and loss can be conducted with multidate airborne lidar, but these methods remain relatively immature. Disparities between lidar acquisitions were particularly difficult to overcome and decreased the sample of trees analyzed for growth rate in this study to 21% of the full number of delineated crowns. However, this study illuminates the potential of airborne remote sensing for ecologically meaningful forest monitoring at an individual tree level. As methods continue to improve, airborne multidate lidar will enable a richer understanding of the drivers of tree growth, loss, and aboveground carbon flux.

A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network

“…Relaxing each of these constraints opens areas of future improvement, especially once species information is available for each label ( Maschler et al, 2018 ). For example, Duncanson and Dubayah, 2018 showed that site-specific allometric functions can be effective at Teakettle Canyon (TEAK) in predicting tree location and measuring growth over time.…”

“…This may be due in part to the point densities of much of NEON’s LiDAR data. Most LiDAR based methods are evaluated on LiDAR data from a single forest type with point densities ranging from ~15 pts/m (e.g., Duncanson and Dubayah, 2018 ) to over 100 pts/m (e.g., Aubry-Kientz et al, 2019 ). In contrast, NEON’s current continental airborne LiDAR program produces ~6 pts/m and these densities can be highly variable, with large areas containing less than 4 pts/m.…”

UAS-GEOBIA Approach to Sapling Identification in Jack Pine Barrens after Fire

“…There is a critical need to establish standard protocols to produce reproducible, repeatable workflows. Individual tree detection (IDT) is one application of UAS that is imperative for forest management [6,7].…”

“…Results of this study indicate that sapling identification accuracies can top 90%, and that accuracy improves with the inclusion of red and near infrared spectral bands. Results also indicated that late season imagery performed best when discriminating between young (<5 years) jack pines and herbaceous ground cover in these environments.Drones 2018, 2, 40 2 of 15 as a valid and low-cost method to generate both orthomosaics and digital surface models (DSMs) derived from 2D image sequences [5,6]. In their study of SfM derived IDT, Reference [7] performed ITD using UAS-SfM derived canopy height models based on algorithms designed for LiDAR data processing.…”

“…Drones 2018, 2, 40 2 of 15 as a valid and low-cost method to generate both orthomosaics and digital surface models (DSMs) derived from 2D image sequences [5,6]. In their study of SfM derived IDT, Reference [7] performed ITD using UAS-SfM derived canopy height models based on algorithms designed for LiDAR data processing.…”