Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest
AbstractICESat-2 is the new generation of NASA’s ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean
Share & Cite This Article
Chen, B.; Pang, Y.; Li, Z.; North, P.; Rosette, J.; Sun, G.; Suárez, J.; Bye, I.; Lu, H. Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest. Remote Sens. 2019, 11, 856.
Chen B, Pang Y, Li Z, North P, Rosette J, Sun G, Suárez J, Bye I, Lu H. Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest. Remote Sensing. 2019; 11(7):856.Chicago/Turabian Style
Chen, Bowei; Pang, Yong; Li, Zengyuan; North, Peter; Rosette, Jacqueline; Sun, Guoqing; Suárez, Juan; Bye, Iain; Lu, Hao. 2019. "Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest." Remote Sens. 11, no. 7: 856.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.