Estimation of above-ground forest biomass using metrics based on Gaussian decomposition of waveform lidar data

Abstract

Large-footprint waveform light detection and ranging (lidar) data have been widely used in above-ground forest biomass estimation. Waveform metrics derived from basic statistics (e.g. percentile of energy) of the lidar waveform, such as canopy height and height of median energy, have been applied to biomass estimation in numerous studies. In this study, a set of metrics based on Gaussian decomposition (GD) results were developed and evaluated for forest above-ground biomass estimation using NASA's laser vegetation imaging sensor (LVIS) data. The GD metrics were designed to explicitly incorporate lidar intensity and vertical structures of canopy layers for biomass estimation. The proposed GD metrics used information related to the above-ground height of each Gaussian centroid and the Gaussian area index (GAI), where GAI is the area covered by a Gaussian function. Two types of novel GD metrics were developed: (1) percentileheight GAI metrics expressing the GAI summation of a subset of Gaussian centroids located within a certain percentile height range; and (2) height-weighted GAI metrics, a summation of GAIs of a waveform weighted by the corresponding heights of their Gaussian centroids. A biomass regression model was built by eight newly developed GD metrics using GAI information and five pre-existing GD-derived metrics that have not previously been used for biomass estimation. The performance of the regression model was then compared to another regression model using 12 previously published metrics (non-GD metrics). The Random Forests (RF) regression algorithm was employed for predicting biomass. The RF out-of-bag results indicated that above-ground biomass estimations using GD metrics achieved significantly better results than those derived from non-GD metrics for deciduous plots (19% lower root mean square error (RMSE), 25% higher coefficient of determination (R 2 ), and marginally better results in coniferous plots (4% lower RSME, 6% higher R 2 ). The combination of GD and non-GD metrics achieved comparable biomass estimation results to the model using exclusively GD metrics. GD metrics also showed strong correlation with forest attributes such as mean diameter at breast height (DBH) and stem density. This study contributes to the usage of GD results for accurate estimation of forest above-ground biomass in large-footprint lidar waveform data in temperate deciduous forests, because temperate deciduous forests have been proved challenging in regard to lidar-derived biomass estimations.

Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation

“…The LVIS and PALSAR sensors have the ability to obtain more information than Landsat regarding the forest structure. For the deciduous plots, the inputs proposed by Zhuang et al [2015], including the new GAI and existing GD metrics, were most important inputs. The RatioE (normalized GAIEHSum) was most important followed by the GAIEHSum, RH100, MeanHeight, and FirstCentroid.…”

“…Our study did not include any coniferous stands. There is a variety of deciduous species present within the stands [Zhuang et al, 2015]. Three sensors are examined, Landsat, LVIS, PALSAR to incorporate optical, LiDAR and Radar information, respectively.…”

“…Details on the field biomass estimation are provided in Zhuang et al [2015]. In total there were 24 deciduous plots that overlapped with data from all three sensors.…”

“…From the data, 13 metrics were calculated based on Gaussian decomposition (GD) and 12 non-GD metrics were based on height and statistics analysis. Further information on the LVIS inputs is provided in Zhuang et al [2015].…”

“…This was shown by Huang et al [2013], Sun et al [2011] while using radar synergies. Zhuang et al [2015] developed and evaluated a set of Gaussian Decomposition metrics. By explicitly incorporating both LiDAR intensity and vertical structures of the canopy layers the biomass estimation accuracy increased.…”