Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales
"> Figure 1
<p>The Morley Nelson Snake River Birds of Prey National Conservation Area (NCA), located in southwestern Idaho, USA. This study area is located in the northwestern portion of the NCA where the 2012 and 2013 Lidar data were obtained.</p> "> Figure 2
<p>Schematic of the field sampling procedure. The nine squares represent the 1 m<sup>2</sup> subplots distributed in the 1 ha plots.</p> "> Figure 3
<p>Scatterplots between the observed AGB (field-measured biomass) and the AGB with Equations (2) and (3) for total (<b>A</b>) and shrub (<b>B</b>) biomass.</p> "> Figure 4
<p>Scatterplots between the observed AGB (field-measured biomass) and the predicted AGB with the RF regression model for total (<b>A</b>) and shrub (<b>B</b>) biomass.</p> "> Figure 5
<p>Imputed total AGB (<b>A</b>), standard deviation of the imputed total AGB (<b>B</b>) and coefficient of variation (CV) of the imputed total AGB (<b>C</b>) and imputed shrub AGB (<b>D</b>), standard deviation of the imputed shrub AGB (<b>E</b>) and coefficient of variation (CV) of the imputed shrub AGB (<b>F</b>), across a sub-area (middle portion) of the 2012 Lidar.</p> "> Figure 6
<p>Imputed total AGB (<b>A</b>), standard deviation of the imputed total AGB (<b>B</b>) and coefficient of variation (CV) of the imputed total AGB (<b>C</b>) and imputed shrub AGB (<b>D</b>), standard deviation of the imputed shrub AGB (<b>E</b>) and coefficient of variation (CV) of the imputed shrub AGB (<b>F</b>), across the coverage of the 2013 Lidar.</p> "> Figure 7
<p>Scatterplots of the imputed biomass values and the standard deviation for total AGB (<b>A</b>) and for shrub AGB (<b>B</b>) and scatterplots of the imputed biomass values and the coefficient of variation for total AGB (<b>C</b>) and for shrub AGB (<b>D</b>).</p> "> Figure 8
<p>Linear regression of observed total AGB (<b>A</b>) and shrub AGB (<b>B</b>) with standard deviation of heights (H<sub>std</sub>).</p> ">
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Field Sampling
2.3. Airborne Lidar Data Acquisitions
3. Methodology
3.1. Data Processing
3.2. Moldeing Plot-Scale Biomass
3.2.1. RF Regression Model
3.2.2. SMR Model
3.3. Imputation of Regional Biomass and Uncertainty
4. Results
4.1. Plot-Scale Biomass from Raster-Derived Vegetation Metrics
4.2. Plot-Scale Biomass from Point Cloud-Derived Vegetation Metrics
4.3. Comparison of RF Model and SMR Model
4.4. Analysis of Imputed Regional Biomass
5. Discussion
5.1. RF Biomass Regression Model
5.1.1. Uncertainty
5.1.2. RF Regression Model Variables
5.2. Model Performances of RF and SMR
5.3. Broader Application of the Imputed Shrub Biomass
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Herbaceous Cover (%) | Shrub Cover (%) | Herbaceous AGB (g/m2) | Shrub AGB (g/m2) | Total AGB (g/m2) | |
---|---|---|---|---|---|
Minimum | 23.4 | 0 | 31.1 | 0 | 36.8 |
Maximum | 98.6 | 46.9 | 489.4 | 954.4 | 1116.8 |
Mean ± Std. | 65 ± 20 | 12 ± 13 | 144 ± 87 | 208 ± 253 | 352 ± 281 |
Lidar Metric | Description |
---|---|
Hmin | The minimum of all height points within each pixel |
Hmax | The maximum of all height points within each pixel |
Hrange | The difference of maximum and minimum of all height points within each pixel |
Hmean | The average of all height points within each pixel |
HMAD | The Median Absolute Deviation from Median Height value (HMAD) of all height points within each pixel, where HMAD = 1.4826 × median (|height − median height|) |
HAAD | The Mean Absolute Deviation from Mean Height (HAAD) value of all height points within each pixel, where HAAD = mean (|height − mean height|) |
Hvar | The variance of all height points within each pixel |
Hstd | The standard deviation of all height points within each pixel |
Hskew | The skewness of all height points within each pixel |
Hkurt | The kurtosis of all height points within each pixel |
HIQR | The Interquartile Range (HIQR) of all height points within each pixel, where HIQR = Q75 − Q25, where Qx is xth percentile |
HCV | The coefficient of variation of all height points within each pixel |
H5, H10 etc. | The 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of all height points within each pixel |
nAll | The total number of all points within each pixel |
nV | The total number of all the points within each pixel that are above the specified Crown Threshold value (CT) |
nG | The total number of all the points within each pixel that are below the specified Ground Threshold value (GT) |
Veg_density | The percent ratio of vegetation returns and ground returns within each pixel |
Veg_cov | The percent ratio of vegetation returns and total returns within each pixel |
pG | Percent of points within each pixel that are below the specified Ground Threshold |
pH1, pH2.5 etc. | Percent of vegetation in height ranges 0–1 m, 1–2.5 m, 2.5–10 m, 10–20 m, 20–30 m, and >30 m within each pixel |
CRR | Canopy relief ratio of points within each pixel, where CRR = ((Hmean − Hmin))/((Hmax − Hmin)) |
Htext | Texture of height of points within each pixel, where Htext = St. Dev. (Height > GT and Height < CT) |
FHDall | Foliage arrangement in the vertical direction (Foliage Height Diversity), where FHDall = −∑pi *lnpi where pi is the proportion of horizontal foliage coverage in the i-th layer to the sum of the foliage coverage of all the layers |
FHDGT | FHD calculated only from the points above GT |
Scale (m) | Pseudo R2 | RMSE (g/m2) | Predictors | |
---|---|---|---|---|
Total AGB | 1 | 0.74 | 141 | Hstd, HAAD, H90, HSkew, Hvar, Htext |
7 | 0.70 | 152 | Htext, FHDGT, H95, HAAD | |
30 | 0.58 | 180 | FHDGT, nV, HAAD, H5 | |
100 | 0.52 | 188 | FHDGT, nV, H16, HAAD | |
Shrub AGB | 1 | 0.76 | 125 | Hstd, HAAD, HCV, Hrange, FHDall |
7 | 0.67 | 143 | Htext, FHDGT, HAAD | |
30 | 0.50 | 176 | FHDGT, HAAD, HCV | |
100 | 0.40 | 184 | Htext, H50, pG, nG |
Scale (m) | Pseudo R2 | RMSE (g/m2) | Predictors | |
---|---|---|---|---|
Total AGB | 1 | 0.71 | 147 | HMAD, HSkew, HIQR, HAAD, Hstd, Hkurt, H90, HCV |
7 | 0.71 | 148 | Htext, HIQR | |
30 | 0.70 | 151 | HAAD, H95, HIQR, pH1, pG | |
100 | 0.67 | 160 | H90, H95, Htext, Veg_density | |
Shrub AGB | 1 | 0.73 | 129 | HIQR, Hstd, HMAD, HCV |
7 | 0.72 | 132 | Htext, H90, HIQR, HCV | |
30 | 0.65 | 146 | H90, HIQR, Htext, pH1 | |
100 | 0.64 | 151 | H95, Htext, pH1, GIQR, FHDGT |
Scale (m) | Source | Pseudo R2 | RMSE (g/m2) | Predictors | |
---|---|---|---|---|---|
Herbaceous AGB | 1 | Raster | 0.20 | 6.86 | HSkew, Htext |
1 | Point Cloud | 0.19 | 7.54 | HCV, Htext, HSkew |
2012 Lidar | 2013 Lidar | ||||
---|---|---|---|---|---|
Total AGB | Shrub AGB | Total AGB | Shrub AGB | ||
Biomass (g/m2) | Minimum | 36.8 | 0 | 36.8 | 0 |
Maximum | 1116.8 | 954.4 | 1116.8 | 662.5 | |
Mean ± Std. | 263 ± 204 | 60 ± 149 | 210 ± 238 | 51 ± 126 | |
CV (% biomass per area) | Minimum | 34.9 | 23.9 | 46.0 | 31.4 |
Maximum | 389.2 | 499.9 | 347.9 | 495.0 | |
Mean ± Std. | 121 ± 48 | 148 ± 102 | 136 ± 58 | 190 ± 90 |
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Li, A.; Dhakal, S.; Glenn, N.F.; Spaete, L.P.; Shinneman, D.J.; Pilliod, D.S.; Arkle, R.S.; McIlroy, S.K. Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales. Remote Sens. 2017, 9, 903. https://doi.org/10.3390/rs9090903
Li A, Dhakal S, Glenn NF, Spaete LP, Shinneman DJ, Pilliod DS, Arkle RS, McIlroy SK. Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales. Remote Sensing. 2017; 9(9):903. https://doi.org/10.3390/rs9090903
Chicago/Turabian StyleLi, Aihua, Shital Dhakal, Nancy F. Glenn, Lucas P. Spaete, Douglas J. Shinneman, David S. Pilliod, Robert S. Arkle, and Susan K. McIlroy. 2017. "Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales" Remote Sensing 9, no. 9: 903. https://doi.org/10.3390/rs9090903