Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest

Ryan Sheridan, Sorin Popescu, Demetrios Gatziolis, Cristine Morgan, Nian-Wei Ku
2014 Remote Sensing  
The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation's forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling
more » ... data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out OPEN ACCESS Remote Sens. 2015, 7 230 performed subplot-level and hectare-level models, producing R 2 values of 0.83 and 0.81 for AGBM and gV, utilizing mean height and the 90th height percentile as predictors, respectively. Similar results were found for multiple regression models, where plot-level analysis produced models with R 2 values of 0.87 and 0.88 for AGBM and gV, utilizing multiple height percentile metrics as predictor variables. Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements. have demonstrated that both large-footprint waveform and small-footprint discrete return ALS data, can be used to derive measurements (e.g., tree height, crown dimensions, tree location) at the stand level [5, 25, 30, 35] and plot level [8, 19, 36, 37] . Additionally, small-footprint LiDAR is also capable of deriving measurements at the individual tree level [10, 11, 21, 22, 28, [38] [39] [40] [41] [42] [43] [44] . These direct ALS measurements can then be used in conjunction with known allometric relationships or statistical analysis procedures to estimate parameters such as diameter at breast height (DBH), AGBM, or gross volume (gV). LiDAR research for forestry applications has largely focused on the development of methodologies to employ LiDAR data as a surrogate for various ground measurements. ALS data can be collected over larger areas with a reduced amount of effort compared to traditional field measurements. However, the high level of complexity present within many forests (e.g., large number of species and variable canopy densities) can complicate the retrieval of such measurements. In Norway, researchers have developed and implemented methods to produce measurements of interest for stand-based forest inventories, and were able to account for 84% to 89% of the variance when predicting stand volume [45] . A summary of stand-based variables of interest, study characteristics, and results from investigations by Scandinavian researchers are listed in [45] . Since ALS systems collect data looking down on the forest, forest measurements other than tree height or crown dimensions (e.g., diameter at breast height, biomass) are typically indirectly estimated. Popescu [21] , used regression analysis to estimate the DBH of individual trees, using the LiDAR-derived height and crown diameter measurements provided by TreeVaW (an individual tree detection software package) as independent variables in a regression analysis. Individual tree detection algorithms implemented in TreeVaW are described in Popescu and Wynne [46] . In traditional forestry, biomass estimation requires destructive sampling, or the use of species-specific [47], regional, or national [48] allometric equations. Allometric equations can also be applied to LiDAR data, if the required information is available. Popescu [21] outlined a method for obtaining individual tree AGBM estimates using allometric equations and estimates of individual tree DBH from ALS data. Examples of other studies that have also predicted AGBM using LiDAR data include [17, 20, 34, 49] . The United States Forest Service (USFS) Forest Inventory and Analysis (FIA) program provides forest inventory measurements used to assess the status of the nation's forests. Forest resource managers and researchers commonly use these measurements to estimate forest biophysical parameters such as, gV, AGBM, or Carbon stocks (C) at local, regional, and national scales. This direct link between data provider and end user makes the FIA Program the primary information provider for many of the gV estimates, AGBM budgets, and C budgets created in the United States. The collection of forest inventory data at a national level is a challenging and complex undertaking. Models relating ALS data to FIA parameters hold great potential to contribute to this task, by: (1) supplementing ground-based FIA measurements or biophysical parameter estimates with estimates produced from ALS data, especially in recently disturbed areas; (2) providing an increased amount of data for areas of interest that contain only a small number of FIA sample locations; or (3) aiding data collection in remote areas where challenging environmental or terrain conditions make ground-based measurements exceedingly dangerous, time consuming, and costly. The overall objective of this study is to model forest AGBM and gV utilizing LiDAR metrics from individual subplots, four clustered subplots (hereafter referred to as a plot), and hectare plots using Author Contributions Ryan Sheridan collected and analyzed data, interpreted results, prepared the manuscript, and coordinated revisions of the manuscript. Sorin Popescu secured funding for the project, assisted in data collection, assisted in the overall design of the study, assisted with interpretation of the results, and reviewed the manuscript. Demetrios Gatziolis contributed to project funding, assisted in data collection, provided access to and compilation of Pacific Northwest FIA data and LiDAR data, and reviewed the manuscript. Cristine Morgan assisted in reviewing the analysis methods and the manuscript. Nian-Wei Ku assisted with data collection.
doi:10.3390/rs70100229 fatcat:ckxng525wndeliw6trzgedki4y