Abstract
The accurate estimation of individual tree level aboveground biomass (AGB) is critical for understanding the carbon cycle, detecting potential biofuels and managing forest ecosystems. In this study, we assessed the capability of the metrics of point clouds, extracted from the full-waveform Airborne Laser Scanning (ALS) data, and of composite waveforms, calculated based on a voxel-based approach, for estimating tree level AGB individually and in combination, over a planted forest in the coastal region of east China. To do so, we investigated the importance of point cloud and waveform metrics for estimating tree-level AGB by all subsets models and relative weight indices. We also assessed the capability of the point cloud and waveform metrics based models and combo model (including the combination of both point cloud and waveform metrics) for tree-level AGB estimation and evaluated the accuracies of these models. The results demonstrated that most of the waveform metrics have relatively low correlation coefficients (<0.60) with other metrics. The combo models (Adjusted R2 = 0.78–0.89), including both point cloud and waveform metrics, have a relatively higher performance than the models fitted by point cloud metrics-only (Adjusted R2 = 0.74–0.86) and waveform metrics-only (Adjusted R2 = 0.72–0.84), with the mostly selected metrics of the 95th percentile height (H95), mean of height of median energy (HOMEμ) and mean of the height/median ratio (HTMRμ). Based on the relative weights (i.e., the percentage of contribution for R2) of the mostly selected metrics for all subsets, the metric of 95th percentile height (H95) has the highest relative importance for AGB estimation (19.23%), followed by 75th percentile height (H75) (18.02%) and coefficient of variation of heights (Hcv) (15.18%) in the point cloud metrics based models. For the waveform metrics based models, the metric of mean of height of median energy (HOMEμ) has the highest relative importance for AGB estimation (17.86%), followed by mean of the height/median ratio (HTMRμ) (16.23%) and standard deviation of height of median energy (HOMEσ) (14.78%). This study demonstrated benefits of using full-waveform ALS data for estimating biomass at tree level, for sustainable forest management and mitigating climate change by planted forest, as China has the largest area of planted forest in the world, and these forests contribute to a large amount of carbon sequestration in terrestrial ecosystems.
Highlights
Forests play a key role in global carbon (C) cycle as they contain 85%–90% of total living terrestrial biomass, and annually exchange up to 90% of total terrestrial ecosystem C with the atmosphereRemote Sens. 2016, 8, 729; doi:10.3390/rs8090729 www.mdpi.com/journal/remotesensingRemote Sens. 2016, 8, 729 through photosynthesis and respiration [1,2]
This study demonstrated benefits of using full-waveform Airborne Laser Scanning (ALS) data for estimating biomass at tree level, for sustainable forest management and mitigating climate change by planted forest, as China has the largest area of planted forest in the world, and these forests contribute to a large amount of carbon sequestration in terrestrial ecosystems
This study has shown that the point cloud and waveform metrics extracted from full-waveform ALS data have strong capabilities for tree level aboveground biomass (AGB) estimation in the coastal planted forests
Summary
Forests play a key role in global carbon (C) cycle as they contain 85%–90% of total living terrestrial biomass, and annually exchange up to 90% of total terrestrial ecosystem C with the atmosphereRemote Sens. 2016, 8, 729; doi:10.3390/rs8090729 www.mdpi.com/journal/remotesensingRemote Sens. 2016, 8, 729 through photosynthesis and respiration [1,2]. Forests play a key role in global carbon (C) cycle as they contain 85%–90% of total living terrestrial biomass, and annually exchange up to 90% of total terrestrial ecosystem C with the atmosphere. The increase of planted forest biomass in China contributed to approximately 30% of carbon sinks in China’s terrestrial ecosystems [4]. Previous studies indicated that these forests play an important role in C sequestration to mitigate anthropogenic C emission [4,5,6]. Accurately assessing forest biomass (i.e., C sinks) of the planted forests in China is important to maintain regional and global carbon balance and mitigate climate change. Previous studies have found ecological and biomechanical links between biomass and vertical structure, and as a result, a strong correlation between ALS metrics and biomass can be expected [9,10,11]. Methodological approaches for estimating forest biomass can be classified into two categories, i.e., area-based (ABA)
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