Deformation, Ecosystem Structure, And Dynamics Of Ice Research Articles

Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric

There have been few approaches developed for the estimation of height using waveform LiDAR data. Unlike any existing methods, we illustrate how the new Moment Distance (MD) framework can characterize the canopy height based on the geometry and return power of the LiDAR waveform without having to go through curve modeling processes. Our approach offers the possibilities of using the raw waveform data to capture vital information from the variety of complex waveform shapes in LiDAR. We assess the relationship of the MD metrics to the key waveform landmarks—such as locations of peaks, power of returns, canopy heights, and height metrics—using synthetic data and real Laser Vegetation Imaging Sensor (LVIS) data. In order to verify the utility of the new approach, we use field measurements obtained through the DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice) campaign. Our results reveal that the MDI can capture temporal dynamics of canopy and segregate generations of stands based on curve shapes. The method satisfactorily estimates the canopy height using the synthetic ( r 2 = 0.40) and the LVIS dataset ( r 2 = 0.74). The MDI is also comparable with existing RH75 (relative height at 75%) and RH50 (relative height at 50%) height metrics. Furthermore, the MDI shows better correlations with ground-based measurements than relative height metrics. The MDI performs well at any type of waveform shape. This opens the possibility of looking more closely at single-peaked waveforms that usually carries complex extremes.

Forest biomass mapping from lidar and radar synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R 2 of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R 2 of 0.63–0.71, RMSE of 32.0–28.2 Mg/ha up to biomass levels of 200–250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar–SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.

Linking models and data on vegetation structure

For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model‐data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model‐data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree‐level heterogeneity in forest structure globally.

Forest canopy height from the Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar

In this study retrievals of forest canopy height were obtained through adjustment of a simple geometric-optical (GO) model against red band surface bidirectional reflectance estimates from NASA's Multiangle Imaging SpectroRadiometer (MISR), mapped to a 250 m grid. The soil-understory background contribution was partly isolated prior to inversion using regression relationships with the isotropic, geometric, and volume scattering kernel weights of a Li-Ross kernel-driven bidirectional reflectance distribution function (BRDF) model. The height retrievals were assessed using discrete return lidar data acquired over sites in Colorado as part of the Cold Land Processes Experiment (CLPX) and used with fractional crown cover retrievals to obtain aboveground woody biomass estimates. For all model runs with reasonable backgrounds and initial b/r (vertical to horizontal crown radii) values < 2.0, root mean square error (RMSE) distributions were centered between 2.5 and 3.7 m while R 2 distributions were centered between 0.4 and 0.7. The MISR/GO aboveground biomass estimates predicted via regression on fractional cover and mean canopy height for the CLPX sites showed good agreement with U.S. Forest Service Interior West map data (adjusted R 2 = 0.84). The implication is that multiangle sensors such as MISR can provide spatially contiguous retrievals of forest canopy height, cover, and aboveground woody biomass that are potentially useful in mapping distributions of aboveground carbon stocks, tracking disturbance, and in initializing, constraining, and validating ecosystem models. This is important because the MISR record is spatially comprehensive and extends back to the year 2000 and the launch of the NASA Earth Observing System (EOS) Terra satellite; it might thus provide a ~ 10-year baseline record that would enhance exploitation of data from the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission, as well as furthering realization of synergies with active instruments.