# Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales

^{1}

^{2}

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## Abstract

**:**

^{2}of 0.76 and RMSE of 125 g/m

^{2}for shrub biomass and a pseudo R

^{2}of 0.74 and RMSE of 141 g/m

^{2}for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m

^{2}for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.

## 1. Introduction

^{2}, but the ecosystem is now one of the most imperiled on the continent [6,7]. An increase in invasive species, fire frequency, and other disturbances has resulted in a decrease in the extent of native shrub-steppe communities [7,8,9,10]. Indeed, the risk of permanent habitat loss from fire is so great, especially in the Great Basin, that in 2015, the secretary of the U.S. Department of Interior (DOI) released a secretarial order (SO3336; https://www.forestsandrangelands.gov/rangeland/index.shtml) that directed wildland fire prevention, suppression, and restoration in sagebrush-steppe ecosystems to protect the greater sage-grouse and other sagebrush-associated species. However, one limitation to the effective implementation of SO3336 is a lack of accurate and timely estimates of the distribution of AGB in sagebrush-steppe ecosystems, information that is critical for fuel management and fire risk planning at regional to landscape scales [11].

## 2. Study Area and Data

#### 2.1. Study Area

#### 2.2. Field Sampling

^{2}each, with 25 m spacing between subplots (Figure 2). The subplots were sampled to represent the 1-ha plot. Vegetation within each subplot was classified as either herbaceous or shrub, then clipped at ground level, bagged, and labeled. We oven-dried and weighed the harvested vegetation. If shrubs were too large to be harvested, a portion was collected for reference and the number of equivalent portions remaining in the quadrat was estimated. We calculated the biomass across each 1-ha plot as the average of the nine subplots for the herbaceous and shrub classes. We combined the data collected in 2012 and 2013 into one dataset (n = 46 plots) to compare with Lidar collected in the same years. We assumed negligible differences in shrub biomass between years due to the slow growth of shrubs in our study area (e.g., [16]). We estimated the herbaceous and shrub cover and biomass across the 46 field plots. Herbaceous and shrub cover ranged from 0 to 100% and 0 to 87%, respectively. The herbaceous class had a mean biomass of ~144 g/m

^{2}and the shrub class had a mean biomass of ~208 g/m

^{2}(Table 1).

#### 2.3. Airborne Lidar Data Acquisitions

^{2}. The Lidar system was ≥148 kHz and was flown at 1500 m above ground level, with a scan angle of 48° (±12°) from nadir (field of view). An opposing flight line side-lap of ≥50% (i.e., 100% overlap) was maintained to increase the point density. The absolute vertical accuracy was ~0.03 m and the relative accuracy was ~0.024 m. The vertical accuracy was primarily assessed from ground check points on open, bare earth surfaces with level slope (<20°) by the vendor.

## 3. Methodology

#### 3.1. Data Processing

#### 3.2. Moldeing Plot-Scale Biomass

#### 3.2.1. RF Regression Model

^{2}) (referred to as pseudo R

^{2}in RF) and lowest root-mean-square error (RMSE) estimated using “out-of-bag” (OOB) testing. The OOB error provided an internal leave-one-out cross-validation using the ‘boot’ package in R statistical software (R Development Core Team 2013) and has previously been used as an unbiased estimate of error [39,52,53]. The number of predictor variables in the models was kept as low as possible to maintain model parsimony. The variable selection was performed to reduce the number of predictor variables and to understand which predictor variables are most suitable to estimate biomass [54]. The analyses were performed for all four resolutions (i.e., 1 m, 7 m, 30 m, 100 m) for both raster and point cloud derived metrics.

#### 3.2.2. SMR Model

^{2}, the process ends. Based on results from the RF, the SMR model was used to model the relationship between the 35 Lidar derived metrics at a 1 m raster resolution and field AGB at the plot level (1 ha). A common problem with linear regression and its use in biomass estimation is multicollinearity between the independent variables, possibly leading to the violation of basic assumptions [55]. Hence, we used the SMR approach adopted by Lefsky et al. [56], which selects the two most important independent variables that were not collinear using the Pearson’s correlation coefficient.

#### 3.3. Imputation of Regional Biomass and Uncertainty

## 4. Results

#### 4.1. Plot-Scale Biomass from Raster-Derived Vegetation Metrics

_{AAD}and H

_{std}from the 1-m raster image, predicted total biomass with an R

^{2}of 0.74 and RMSE of 141 g/m

^{2}, whereas shrub biomass was predicted with an R

^{2}of 0.76 and RMSE of 152 g/m

^{2}(Table 3).

^{2}of 0.70, 0.58, and 0.52 at 7 m, 30 m, and 100 m, respectively, for total AGB. Similarly, the RMSE increased as the resolution decreased. We observed a similar trend for the shrub biomass.

#### 4.2. Plot-Scale Biomass from Point Cloud-Derived Vegetation Metrics

#### 4.3. Comparison of RF Model and SMR Model

_{std}as the variable with the highest correlation with total AGB (Pearson’s correlation r = 0.85) and shrub biomass (Pearson’s correlation r = 0.84). A regression analysis of total AGB with H

_{std}provided us with the following equation, with an R

^{2}of 0.72 and p-values < 0.001.

_{std}− 142.058

_{skew}was found to have the highest correlation (Pearson’s correlation r = 0.39). Hence H

_{skew}was added to the equation, resulting in an R

^{2}of 0.79, RMSE of 129 g/m

^{2}, and p-value < 0.001 (Figure 3).

_{std}+ 386 × H

_{skew}− 226.416

^{2}of 0.77, RMSE of 120 g/m

^{2}, and p-value < 0.001 (Figure 3).

_{std}− 19,052.4 × H

_{MAD}− 169.62627

^{2}using OOB testing with the R

^{2}from the linear regression model, we found the RF results to be slightly worse than the SMR models for both total and shrub AGB. We then used the optimal RF model (1 m raster scale) to estimate the predicted biomass for each observed (field) biomass. This resulted in the RF predicted total AGB of R

^{2}= 0.80 and shrub AGB of R

^{2}= 0.84 with RMSE values of 124 g/m

^{2}and 102 g/m

^{2}, respectively (Figure 4).

#### 4.4. Analysis of Imputed Regional Biomass

^{2}/RMSE of 0.74/141 g/m

^{2}and 0.71/147 g/m

^{2}, respectively. For shrub AGB, raster processing and point cloud processing had an R

^{2}/RMSE of 0.76/125 g/m

^{2}and 0.73/129 g/m

^{2}, respectively. There was no significant difference between the two data processing methods used (raster or point cloud). Based on these results and because raster processing is computationally more efficient, spatially-explicit, contiguous total and shrub aboveground biomass maps over the Lidar coverages were produced by imputation using predictors associated with the 1-m raster-derived metrics. Figure 5A,B and Figure 6A,B show that the shrub-dominant regions had higher biomass values in comparison to the sparse shrub and grass dominant areas. Note the crops depicted in the northeast corner of the 2013 Lidar were not masked as they had a small influence on the overall mean biomass values calculated for the study area. In this study area, the mean shrub biomass is 50–60 g/m

^{2}and the mean total biomass is 210–263 g/m

^{2}(Table 6). There are wide expanses of no shrub cover across the NCA (more discussion below) and in fact, the shrub biomass imputation represents large regions of 0–50 g/m

^{2}of biomass. These areas are likely representative of regions where the herbaceous class was present; this is confirmed by the total biomass imputations where biomass pixels in the ~0–200 g/m

^{2}are more abundant. The CV maps (Figure 5C,F and Figure 6C,F) illustrate the variation of the model estimates, represented as a percentage of the estimated biomass in each pixel. Larger biomass estimates had a higher standard deviation and lower CV (Figure 5, Figure 6 and Figure 7). Given the poor modeling results of the herbaceous cover class, and considering that the total biomass model includes both herbaceous and shrub components, the uncertainty in the total biomass imputation is higher than the shrub biomass imputation.

## 5. Discussion

#### 5.1. RF Biomass Regression Model

#### 5.1.1. Uncertainty

^{2}and RMSE, Table 3 and Table 4). However, 1-m scale point cloud and raster image processing provided nearly equivalent estimates of 1-ha plot average biomass. At the 1-m scale, the rasterization approach incorporates fewer points outside of the pixel boundary (and in close proximity). Furthermore, rasterization at 1 m had a greater probability of aligning with field plots and was less influenced by values from adjoining pixels in comparison to coarser pixel sizes. The similar RF regression model results indicate that the rasterization method preserves most of the 3D point cloud vegetation characteristics and thus is essentially equivalent to using point cloud data at the 1-m scale. At coarser raster scales, we attribute the declining results to boundary effects and alignment with field plots.

^{2}alone, at a 1-m resolution, the point cloud processing was not significantly different to raster data processing. The coarse-scale raster results may be more representative of expected results from large footprint Lidar than the point cloud analyses. This is because a large footprint Lidar is an integrated waveform (or photons in the case of ICESAT-2) of the canopy profile over the entire footprint.

#### 5.1.2. RF Regression Model Variables

_{std}, H

_{AAD}, and H

_{MAD}) scored higher than other predictors for both total and shrub biomass in all RF models, with high R

^{2}and low RMSE values. Considering the Lidar acquisition parameters in this study as equal to those in Li’s study [16], a higher number of Lidar returns from the vegetation canopy will occur in denser and larger shrubs (represented in the study in [16]) compared to the sparse canopies with smaller shrubs in our study. Vegetation Lidar returns are also more likely to be mixed with those of annual grasses, perennial bunchgrasses, litter, or bare ground in our study area. Hence, shrub height underestimation is likely more pronounced in this study due to constraints related to the laser pulse length [24,26,65,66]. Yet the variability of height may still be sufficiently captured by the Lidar to represent the spatial pattern of biomass with smaller shrub canopies in our study site.

_{std}, H

_{AAD}, H

_{CV}, H

_{range}, and FHD

_{all}) at the 1-m scale explained roughly 76% of the variability in shrub AGB (Table 3) in the optimal RF regression model. For the RF model for shrub biomass, the remaining 24% error may be credited to uncertainties associated with sparse vegetation distribution, the misclassification of canopy as ground, and the underestimation of the vegetation height [24,67]. Similar results were found by Estornell et al. [68] in a Mediterranean shrubland ecosystem. In their research, the median height, standard deviation of height, and percentile of height derived from airborne Lidar were the best predictors, explaining up to 78% and 84% of variability for biomass and volume, respectively. Greaves et al. [17] also reported a similar finding in an arctic shrubland, in which Lidar volume and canopy metrics coupled with vegetation indices from optical data explained roughly 71% of the variability of shrub biomass.

_{std}in the SMR and RF models, we further tested the ability of H

_{std}alone to estimate AGB biomass. Using univariate linear regression, we found that H

_{std}explained 73% and 71% of the variance of total and shrub AGB, respectively (Figure 8). While this relationship is likely oversimplified and the model fit is erroneous at low shrub biomass estimates, it is interesting to conceptualize that a vegetation roughness measure may coarsely approximate biomass. Notably, previous studies in this ecosystem have found vegetation roughness to be a proxy for classifying sagebrush [69] and sagebrush heights [24].

#### 5.2. Model Performances of RF and SMR

^{2}; and the two predictors in the best SMR model were included in the five important predictors in the best RF model. Yet, a high variable importance of an input variable (H

_{AAD}) in RF was not included in the SMR. This result may indicate that this variable represents interactions that are too complex to be captured by parametric regression models or simply because of correlation between the variables. If the former is true, RF’s non-linear model fit for biomass may be more appropriate as biomass is not controlled simply with one or two driving variables but a complex environment. Moreover, the RF model constrains predicted biomass within the range of the observed biomass (in comparison, SMR may represent invalid biomass values when the value of predictors is beyond the model range). Based on the results of this study, and understanding that advantages and disadvantages exist with most statistical representations, we recommend exploring a number of statistical approaches that may shed light on the behavior of the response variable, as well as the relative importance of predictor variables.

#### 5.3. Broader Application of the Imputed Shrub Biomass

^{2}and 60 ± 149 g/m

^{2}with 2013 Lidar and 2012 Lidar, respectively. While there are not many studies in similar xeric sagebrush-steppe ecosystems to compare these results to, our estimates are similar to those by Uresk et al. [72]. They estimated the total phytomass of big sagebrush in Eastern Washington to be 69 g/m

^{2}when they converted the individual sagebrush biomass to area based on density. As a comparison, Brown [73] estimated much higher shrub biomass values in Montana and Idaho, ranging from ~55 to 1490 g/m

^{2}, but their numbers are based on intact big sagebrush sites that included relatively mesic locations with mountain big sagebrush (A. t. vaseyana). Cleary et al. [74] estimated shrub biomass in Wyoming to be ~655 g/m

^{2}, also in mountain big sagebrush. They also converted their individual biomass estimates to mass per area based on density. It is important to note that our shrub biomass estimates (in a consistently arid landscape) included scattered shrub species other than big sagebrush.

^{2}may be used as a baseline for the larger NCA. However, additional field and Lidar data are necessary to develop models across larger areas representing more diverse growing conditions.

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Angell, R.F.; Svejcar, T.; Bates, J.; Saliendra, N.Z.; Johnson, D.A. Bowen ratio and closed chamber carbon dioxide flux measurements over sagebrush steppe vegetation. Agric. For. Meteorol.
**2001**, 108, 153–161. [Google Scholar] [CrossRef] - Shrestha, G.; Stahl, P.D. Carbon accumulation and storage in semi-arid sagebrush steppe: Effects of long-term grazing exclusion. Agric. Ecosyst. Environ.
**2008**, 125, 173–181. [Google Scholar] [CrossRef] - Rengsirikul, K.; Kanjanakuha, A.; Ishii, Y.; Kangvansaichol, K.; Sripichitt, P.; Punsuvon, V.; Vaithanomsat, P.; Nakamanee, G.; Tudsri, S. Potential forage and biomass production of newly introduced varieties of leucaena (Leucaena leucocephala (Lam.) de Wit.) in Thailand. Grassl. Sci.
**2011**, 57, 94–100. [Google Scholar] [CrossRef] - Perez-Quezada, J.F.; Delpiano, C.A.; Snyder, K.A.; Johnson, D.A.; Franck, N. Carbon pools in an arid shrubland in Chile under natural and afforested conditions. J. Arid Environ.
**2011**, 75, 29–37. [Google Scholar] [CrossRef] - Zandler, H.; Brenning, A.; Samimi, C. Quantifying dwarf shrub biomass in an arid environment: Comparing empirical methods in a high dimensional setting. Remote Sens. Environ.
**2015**, 158, 140–155. [Google Scholar] [CrossRef] - Barbour, M.G.; Billings, W.D. North American Terrestrial Vegetation; Cambridge University Press: Cambridge, UK, 2000; ISBN 0-521-55027-0. [Google Scholar]
- Miller, R.F.; Knick, S.T.; Pyke, D.A.; Meinke, C.W.; Hanser, S.E.; Wisdom, M.J.; Hild, A.L. Characteristics of sagebrush habitats and limitations to long-term conservation. Greater sage-grouse: Ecology and conservation of a landscape species and its habitats. Stud. Avian Biol.
**2011**, 38, 145–184. [Google Scholar] - Anderson, J.E.; Inouye, R.S. Landscape-scale changes in plant species abundance and biodiversity of a sagebrush steppe over 45 years. Ecol. Monogr.
**2011**, 71, 531–556. [Google Scholar] [CrossRef] - Creutzburg, M.K.; Halofsky, J.E.; Halofsky, J.S.; Christopher, T.A. Climate change and land management in the rangelands of central Oregon. Environ. Manag.
**2015**, 55, 43–55. [Google Scholar] [CrossRef] [PubMed] - Pyke, D.A.; Chambers, J.C.; Beck, J.L.; Brooks, M.L.; Mealor, B.A. Land uses, fire, and invasion: Exotic annual Bromus and human dimensions. In Exotic Brome-Grasses in Arid and Semiarid Ecosystems of the Western US: Causes, Consequences, and Management Implications; Germino, M.J., Chambers, J.C., Brown, C.S., Eds.; Springer International Publishing: Basel, Switzerland, 2016; pp. 307–336. ISBN 978-3-319-24928-5. [Google Scholar]
- Integrated Rangeland Fire Management Strategy Actionable Science Plan Team. The Integrated Rangeland Fire Management Strategy Actionable Science Plan; U.S. Department of the Interior: Washington, DC, USA, 2016; p. 128. Available online: https://www.fs.fed.us/rm/pubs_journals/2016/rmrs_2016_berg_k001.pdf (accessed on 29 August 2017).
- Sala, O.E.; Lauenroth, W.K. Small rainfall events: An ecological role in semiarid regions. Oecologia
**1982**, 53, 301–304. [Google Scholar] [CrossRef] [PubMed] - Clark, P.E.; Hardegree, S.P.; Moffet, C.A.; Pierson, F.B. Point sampling to stratify biomass variability in sagebrush steppe vegetation. Rangel. Ecol. Manag.
**2008**, 61, 614–622. [Google Scholar] [CrossRef] - Bonham, C.D. Measurements for Terrestrial Vegetation; John Wiley & Sons: Chichester, UK, 2013; ISBN 978-0-4709-7258-8. [Google Scholar]
- Waite, R.B. The application of visual estimation procedures for monitoring pasture yield and composition in exclosures and small plots. Trop. Grassl.
**1994**, 28, 38–42. [Google Scholar] - Li, A.; Glenn, N.F.; Olsoy, P.J.; Mitchell, J.J.; Shrestha, R. Aboveground biomass estimates of sagebrush using terrestrial and airborne Lidar data in a dryland ecosystem. Agric. For. Meteorol.
**2015**, 213, 138–147. [Google Scholar] [CrossRef] - Greaves, H.E.; Vierling, L.A.; Eitel, J.U.; Boelman, N.T.; Magney, T.S.; Prager, C.M.; Griffin, K.L. High-resolution mapping of aboveground shrub biomass in Arctic tundra using airborne Lidar and imagery. Remote Sens. Environ.
**2016**, 184, 361–373. [Google Scholar] [CrossRef] - Powell, S.L.; Cohen, W.B.; Healey, S.P.; Kennedy, R.E.; Moisen, G.G.; Pierce, K.B.; Ohmann, J.L. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sens. Environ.
**2010**, 114, 1053–1068. [Google Scholar] [CrossRef] - Lefsky, M.A.; Cohen, W.B.; Parker, G.G.; Harding, D.J. Lidar remote sensing for ecosystem studies. Bioscience
**2002**, 52, 19–30. [Google Scholar] [CrossRef] - Hall, S.A.; Burke, I.C.; Box, D.O.; Kaufmann, M.R.; Stoker, J.M. Estimating stand structure using discrete-return Lidar: An example from low density, fire prone ponderosa pine forests. For. Ecol. Manag.
**2005**, 208, 189–209. [Google Scholar] [CrossRef] - Ku, N.W.; Popescu, S.C.; Ansley, R.J.; Perotto-Baldivieso, H.L.; Filippi, A.M. Assessment of available rangeland woody plant biomass with a terrestrial LIDAR system. Photogramm. Eng. Remote Sens.
**2012**, 78, 349–361. [Google Scholar] [CrossRef] - Lin, Y.; Hyyppä, J.; Kukko, A.; Jaakkola, A.; Kaartinen, H. Tree height growth measurement with single-scan airborne, static terrestrial and mobile laser scanning. Sensors
**2012**, 12, 12798–12813. [Google Scholar] [CrossRef] [PubMed] - Zheng, G.; Moskal, L.M.; Kim, S.H. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning. IEEE Trans. Geosci. Remote Sens.
**2013**, 51, 777–786. [Google Scholar] [CrossRef] - Streutker, D.R.; Glenn, N.F. Lidar measurement of sagebrush steppe vegetation heights. Remote Sens. Environ.
**2006**, 102, 135–145. [Google Scholar] [CrossRef] - Su, J.G.; Bork, E.W. Characterization of diverse plant communities in Aspen Parkland rangeland using Lidar data. Appl. Veg. Sci.
**2007**, 10, 407–416. [Google Scholar] [CrossRef] - Glenn, N.F.; Spaete, L.P.; Sankey, T.T.; Derryberry, D.R.; Hardegree, S.P.; Mitchell, J.J. Errors in Lidar-derived shrub height and crown area on sloped terrain. J. Arid Environ.
**2011**, 75, 377–382. [Google Scholar] [CrossRef] - Bork, E.W.; Su, J.G. Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis. Remote Sens. Environ.
**2007**, 111, 11–24. [Google Scholar] [CrossRef] - García-Gutiérrez, J.; González-Ferreiro, E.; Mateos-García, D.; Riquelme-Santos, J.C.; Miranda, D. A comparative study between two regression methods on Lidar data: A case Study. In Hybrid Artificial Intelligent Systems HAIS 2011, Proceedings of the International Conference on Hybrid Artificial Intelligence Systems, Wrocław, Poland, 23–25 May 2011; Corchado, E., Kurzyński, M., Woźniak, M., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2011; Volume 6679. [Google Scholar]
- Laurin, G.V.; Chen, Q.; Lindsell, J.A.; Coomes, D.A.; Del Frate, F.; Guerriero, L.; Pirotti, F.; Valentini, R. Above ground biomass estimation in an African tropical forest with Lidar and hyperspectral data. ISPRS J. Photogramm. Remote Sens.
**2014**, 89, 49–58. [Google Scholar] [CrossRef] - Wilson, A.M.; Silander, J.A.; Gelfand, A.; Glenn, J.H. Scaling up: Linking field data and remote sensing with a hierarchical model. Int. J. Geogr. Inf. Sci.
**2011**, 25, 509–521. [Google Scholar] [CrossRef] - Hudak, A.T.; Crookston, N.L.; Evans, J.S.; Hall, D.E.; Falkowski, M.J. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from Lidar data. Remote Sens. Environ.
**2008**, 112, 2232–2245. [Google Scholar] [CrossRef] - Debouk, H.; Riera-Tatché, R.; Vega-García, C. Assessing post-fire regeneration in a Mediterranean mixed forest using Lidar data and artificial neural networks. Photogramm. Eng. Remote Sens.
**2013**, 79, 1121–1130. [Google Scholar] [CrossRef] - Breiman, L. Random Forests. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef] - Breidenbach, J.; Næsset, E.; Lien, V.; Gobakken, T.; Solberg, S. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sens. Environ.
**2010**, 114, 911–924. [Google Scholar] [CrossRef] - Vauhkonen, J.; Korpela, I.; Maltamo, M.; Tokola, T. Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics. Remote Sens. Environ.
**2010**, 114, 1263–1276. [Google Scholar] [CrossRef] - Guan, H.; Yu, J.; Li, J.; Luo, L. Random Forests-Based Feature Selection for Land-Use Classification Using LIDAR Data and Orthoimagery. International Archives of the Photogrammetry. Remote Sens. Spat. Inf. Sci.
**2012**, 39, B7. [Google Scholar] - Mitchell, J.J.; Shrestha, R.; Moore-Ellison, C.A.; Glenn, N.F. Single and multi-date Landsat classifications of basalt to support soil survey efforts. Remote Sens.
**2013**, 5, 4857–4876. [Google Scholar] [CrossRef] - Gleason, C.J.; Im, J. Forest biomass estimation from airborne Lidar data using machine learning approaches. Remote Sens. Environ.
**2012**, 125, 80–91. [Google Scholar] [CrossRef] - Prasad, A.M.; Iverson, L.R.; Liaw, A. Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems
**2006**, 9, 181–199. [Google Scholar] [CrossRef] - Mutanga, O.; Adam, E.; Cho, M.A. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Obs. Geoinf.
**2012**, 18, 399–406. [Google Scholar] [CrossRef] - Mitchell, J.J.; Shrestha, R.; Spaete, L.P.; Glenn, N.F. Combining airborne hyperspectral and Lidar data across local sites for upscaling shrubland structural information: Lessons for HyspIRI. Remote Sens. Environ.
**2015**, 167, 98–110. [Google Scholar] [CrossRef] - Passalacqua, P.; Belmont, P.; Staley, D.M.; Simley, J.D.; Arrowsmith, J.R.; Bode, C.A.; Crosby, C.; DeLong, S.B.; Glenn, N.F.; Kelly, S.A.; et al. Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review. Earth Sci. Rev.
**2015**, 148, 174–193. [Google Scholar] [CrossRef][Green Version] - El-Ashmawy, N.; Shaker, A. Raster vs. Point Cloud Lidar Data Classification. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2014**, 40, 79. [Google Scholar] [CrossRef] - Western Region Climate Center (WRCC). Swan Falls Power House, Idaho, Period of Record General Climate Summary. 2012. Available online: http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?id8928 (accessed on 1 June 2017).
- Anderson, K. Vegetation Measurement in Sagebrush Steppe Using Terrestrial Laser Scanning. Master’s Thesis, Idaho State University, Pocatello, ID, USA, 2014. [Google Scholar]
- U.S. Department of the Interior; Bureau of Land Management; Boise District Office. Snake River Birds of Prey National Conservation Area Proposed Resource Management Plan and Final Environmental Impact Statement. 2008. Available online: https://eplanning.blm.gov/epl-front-office/projects/lup/35553/41909/44409/SRBOPA_NCA_FEIS_V2_Appendices_508.pdf (accessed on 1 June 2017).
- Shinneman, D.J.; Arkle, R.; Pilliod, D.; Glenn, N.F. Quantifying and Predicting Fuels and the Effects of Reduction Treatments along Successional and Invasion Gradients in Sagebrush Habitats. Final Report to the Joint Fire Science Program; 2015; pp. 1–44. Available online: https://www.firescience.gov/projects/11-1-2-30/project/11-1-2-30_final_report.pdf (accessed on 1 June 2017).
- Pilliod, D.S.; Arkle, R.S. Performance of quantitative sampling methods across gradients of cover in Great Basin plant communities. Rangel. Ecol. Manag.
**2013**, 66, 634–647. [Google Scholar] [CrossRef] - Spaete, L.P.; Glenn, N.F.; Baun, C.W. 2013 Morley Nelson Snake River Birds of Prey National Conservation Area RapidEye 7 m Landcover Classification; Boise State University: Boise, ID, USA, 2016; Available online: http://dx.doi.org/10.18122/B21592 (accessed on 1 June 2017).
- Glenn, N.F.; Neuenschwander, A.; Vierling, L.A.; Spaete, L.; Li, A.; Shinneman, D.J.; McIlroy, S.K. Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass. Remote Sens. Environ.
**2016**, 185, 233–242. [Google Scholar] [CrossRef] - Painter, T.H.; Berisford, D.F.; Boardman, J.W.; Bormann, K.J.; Deems, J.S.; Gehrke, F.; Hedrick, A.; Joyce, M.; Laidlaw, R.; Marks, D.; et al. The Airborne Snow Observatory: Fusion of scanning Lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sens. Environ.
**2016**, 184, 139–152. [Google Scholar] [CrossRef] - Naidoo, L.; Cho, M.A.; Mathieu, R.; Asner, G. Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and Lidar data in a Random Forest data mining environment. ISPRS J. Photogramm. Remote Sens.
**2012**, 69, 167–179. [Google Scholar] [CrossRef] - Prinzie, A.; Van den Poel, D. Random forests for multiclass classification: Random multinomial logit. Expert Syst. Appl.
**2008**, 34, 1721–1732. [Google Scholar] [CrossRef] - Ismail, R.; Mutanga, O.; Kumar, L. Modeling the potential distribution of pine forests susceptible to sirex noctilio infestations in Mpumalanga, South Africa. Trans. GIS
**2010**, 14, 709–726. [Google Scholar] [CrossRef] - Montgomery, D.C.; Peck, E.A.; Vining, G.G. Introduction to Linear Regression Analysis; John Wiley & Sons: Hoboken, NJ, USA, 2015; ISBN 978-0-470-54281-1. [Google Scholar]
- Lefsky, M.A.; Cohen, W.B.; Harding, D.J.; Parker, G.G.; Acker, S.A.; Gower, S.T. Lidar remote sensing of above-ground biomass in three biomes. Glob. Ecol. Biogeogr.
**2002**, 11, 393–399. [Google Scholar] [CrossRef] - Hudak, A.; Evans, J.S.; Crookstone, N.L.; Falkowski, M.J.; Steigers, B.K.; Taylor, R.; Hemingway, H. Aggregating pixel-level basal area predictions derived from Lidar data to industrial forest stands in North-Central Idaho. In Proceedings of the Third Forest Vegetation Simulator Conference, Fort Collins, CO, USA, 13–15 February 2007; pp. 133–145. [Google Scholar]
- Eskelson, B.N.; Temesgen, H.; Lemay, V.; Barrett, T.M.; Crookston, N.L.; Hudak, A.T. The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases. Scand. J. For. Res.
**2009**, 24, 235–246. [Google Scholar] [CrossRef] - Crookston, N.L.; Finley, A.O. yaImpute: An R Package for kNN Imputation. J. Stat. Softw.
**2008**, 23, 1–16. [Google Scholar] [CrossRef] - Strobl, C.; Malley, J.; Tutz, G. An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods
**2009**, 14, 323–348. [Google Scholar] [CrossRef] [PubMed][Green Version] - Olsoy, P.J.; Glenn, N.F.; Clark, P.E.; Derryberry, D.R. Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning. ISPRS J. Photogramm. Remote Sens.
**2014**, 88, 166–173. [Google Scholar] [CrossRef] - Olsoy, P.J.; Glenn, N.F.; Clark, P.E. Estimating sagebrush biomass using terrestrial laser scanning. Rangel. Ecol. Manag.
**2014**, 67, 224–228. [Google Scholar] [CrossRef] - Greaves, H.E.; Vierling, L.A.; Eitel, J.U.; Boelman, N.T.; Magney, T.S.; Prager, C.M.; Griffin, K.L. Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial Lidar. Remote Sens. Environ.
**2015**, 164, 26–35. [Google Scholar] [CrossRef] - Ni-Meister, W.; Lee, S.; Strahler, A.H.; Woodcock, C.E.; Schaaf, C.; Yao, T.; Ranson, K.J.; Sun, G.; Blair, J.B. Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from Lidar remote sensing. J. Geophys. Res. Biogeosci.
**2010**, 115. [Google Scholar] [CrossRef] - Spaete, L.P.; Glenn, N.F.; Derryberry, D.R.; Sankey, T.T.; Mitchell, J.J.; Hardegree, S.P. Vegetation and slope effects on accuracy of a Lidar-derived DEM in the sagebrush steppe. Remote Sens. Lett.
**2011**, 2, 317–326. [Google Scholar] [CrossRef] - Mitchell, J.J.; Glenn, N.F.; Sankey, T.T.; Derryberry, D.R.; Anderson, M.O.; Hruska, R.C. Small-footprint Lidar estimations of sagebrush canopy characteristics. Photogramm. Eng. Remote Sens.
**2011**, 77, 521–530. [Google Scholar] [CrossRef] - Riaño, D.; Chuvieco, E.; Ustin, S.L.; Salas, J.; Rodríguez-Pérez, J.R.; Ribeiro, L.M.; Viegas, D.X.; Moreno, J.M.; Fernández, H. Estimation of shrub height for fuel-type mapping combining airborne Lidar and simultaneous color infrared ortho imaging. Int. J. Wildland Fire
**2007**, 16, 341–348. [Google Scholar] [CrossRef] - Estornell, J.; Ruiz, L.A.; Velázquez-Martí, B.; Hermosilla, T. Estimation of biomass and volume of shrub vegetation using Lidar and spectral data in a Mediterranean environment. Biomass Bioenergy
**2012**, 46, 710–721. [Google Scholar] [CrossRef] - Mundt, J.T.; Streutker, D.R.; Glenn, N.F. Mapping sagebrush distribution using fusion of hyperspectral and Lidar classifications. Photogramm. Eng. Remote Sens.
**2006**, 72, 47–54. [Google Scholar] [CrossRef] - Whittingham, M.J.; Stephens, P.A.; Bradbury, R.B.; Freckleton, R.P. Why do we still use stepwise modelling in ecology and behaviour? J. Anim. Ecol.
**2006**, 75, 1182–1189. [Google Scholar] [CrossRef] [PubMed] - Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. Random forests for classification in ecology. Ecology
**2007**, 88, 2783–2792. [Google Scholar] [CrossRef] [PubMed] - Uresk, D.W.; Gilbert, R.O.; Rickard, W.H. Sampling big sagebrush for phytomass. J. Range Manag.
**1977**, 30, 311–314. [Google Scholar] [CrossRef] - Brown, J.K. Fuel and Fire Behavior Prediction in Big Sagebrush. USDA Forest Service Research Paper INT (USA). 1982. Available online: https://www.fs.fed.us/rm/pubs_int/int_rp290.pdf (accessed on 1 June 2017).
- Cleary, M.B.; Pendall, E.; Ewers, B.E. Testing sagebrush allometric relationships across three fire chronosequences in Wyoming, USA. J. Arid Environ.
**2008**, 72, 285–301. [Google Scholar] [CrossRef] - Sankey, T.; Shrestha, R.; Sankey, J.B.; Hardegree, S.P.; Strand, E. Lidar-derived estimate and uncertainty of carbon sink in successional phases of woody encroachment. J. Geophys. Res. Biogeosci.
**2013**, 118, 1144–1155. [Google Scholar] [CrossRef] - Margolis, H.A.; Nelson, R.F.; Montesano, P.M.; Beaudoin, A.; Sun, G.; Andersen, H.E.; Wulder, M.A. Combining satellite Lidar, airborne Lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America. Can. J. For. Res.
**2015**, 45, 838–855. [Google Scholar] [CrossRef]

**Figure 1.**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.

**Figure 2.**Schematic of the field sampling procedure. The nine squares represent the 1 m

^{2}subplots distributed in the 1 ha plots.

**Figure 3.**Scatterplots between the observed AGB (field-measured biomass) and the AGB with Equations (2) and (3) for total (

**A**) and shrub (

**B**) biomass.

**Figure 4.**Scatterplots between the observed AGB (field-measured biomass) and the predicted AGB with the RF regression model for total (

**A**) and shrub (

**B**) biomass.

**Figure 5.**Imputed total AGB (

**A**), standard deviation of the imputed total AGB (

**B**) and coefficient of variation (CV) of the imputed total AGB (

**C**) and imputed shrub AGB (

**D**), standard deviation of the imputed shrub AGB (

**E**) and coefficient of variation (CV) of the imputed shrub AGB (

**F**), across a sub-area (middle portion) of the 2012 Lidar.

**Figure 6.**Imputed total AGB (

**A**), standard deviation of the imputed total AGB (

**B**) and coefficient of variation (CV) of the imputed total AGB (

**C**) and imputed shrub AGB (

**D**), standard deviation of the imputed shrub AGB (

**E**) and coefficient of variation (CV) of the imputed shrub AGB (

**F**), across the coverage of the 2013 Lidar.

**Figure 7.**Scatterplots of the imputed biomass values and the standard deviation for total AGB (

**A**) and for shrub AGB (

**B**) and scatterplots of the imputed biomass values and the coefficient of variation for total AGB (

**C**) and for shrub AGB (

**D**).

**Figure 8.**Linear regression of observed total AGB (

**A**) and shrub AGB (

**B**) with standard deviation of heights (H

_{std}).

Herbaceous Cover (%) | Shrub Cover (%) | Herbaceous AGB (g/m^{2}) | Shrub AGB (g/m^{2}) | Total AGB (g/m^{2}) | |
---|---|---|---|---|---|

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 |
---|---|

H_{min} | The minimum of all height points within each pixel |

H_{max} | The maximum of all height points within each pixel |

H_{range} | The difference of maximum and minimum of all height points within each pixel |

H_{mean} | The average of all height points within each pixel |

H_{MAD} | The Median Absolute Deviation from Median Height value (H_{MAD}) of all height points within each pixel, where H_{MAD} = 1.4826 × median (|height − median height|) |

H_{AAD} | The Mean Absolute Deviation from Mean Height (H_{AAD}) value of all height points within each pixel, where H_{AAD} = mean (|height − mean height|) |

H_{var} | The variance of all height points within each pixel |

H_{std} | The standard deviation of all height points within each pixel |

H_{skew} | The skewness of all height points within each pixel |

H_{kurt} | The kurtosis of all height points within each pixel |

H_{IQR} | The Interquartile Range (H_{IQR}) of all height points within each pixel, where H_{IQR} = Q_{75} − Q_{25}, where Q_{x} is xth percentile |

H_{CV} | The coefficient of variation of all height points within each pixel |

H_{5}, H_{10} 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 |

pH_{1}, pH_{2.5} 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 = ((H_{mean} − H_{min}))/((H_{max} − H_{min})) |

H_{text} | Texture of height of points within each pixel, where H_{text} = St. Dev. (Height > GT and Height < CT) |

FHD_{all} | Foliage arrangement in the vertical direction (Foliage Height Diversity), where FHD_{all} = −∑p_{i} *lnp_{i} where p_{i} is the proportion of horizontal foliage coverage in the i-th layer to the sum of the foliage coverage of all the layers |

FHD_{GT} | FHD calculated only from the points above GT |

**Table 3.**Results of the RF regression using raster data processing for total and shrub biomass at different resolutions representing 1-ha plots.

Scale (m) | Pseudo R^{2} | RMSE (g/m^{2}) | Predictors | |
---|---|---|---|---|

Total AGB | 1 | 0.74 | 141 | H_{std}, H_{AAD}, H_{90}, H_{Skew}, H_{var}, H_{text} |

7 | 0.70 | 152 | H_{text}, FHD_{GT}, H_{95}, H_{AAD} | |

30 | 0.58 | 180 | FHD_{GT}, nV, H_{AAD}, H_{5} | |

100 | 0.52 | 188 | FHD_{GT}, nV, H_{16}, H_{AAD} | |

Shrub AGB | 1 | 0.76 | 125 | H_{std}, H_{AAD}, H_{CV}, H_{range}, FHD_{all} |

7 | 0.67 | 143 | H_{text}, FHD_{GT}, H_{AAD} | |

30 | 0.50 | 176 | FHD_{GT}, H_{AAD}, H_{CV} | |

100 | 0.40 | 184 | H_{text}, H_{50}, pG, nG |

**Table 4.**Results of the RF regression using point cloud processing for total and shrub biomass at different resolutions representing 1-ha plots.

Scale (m) | Pseudo R^{2} | RMSE (g/m^{2}) | Predictors | |
---|---|---|---|---|

Total AGB | 1 | 0.71 | 147 | H_{MAD}, H_{Skew}, H_{IQR}, H_{AAD}, H_{std}, H_{kurt}, H_{90}, H_{CV} |

7 | 0.71 | 148 | H_{text}, H_{IQR} | |

30 | 0.70 | 151 | H_{AAD}, H_{95}, H_{IQR}, pH_{1}, pG | |

100 | 0.67 | 160 | H_{90}, H_{95}, H_{text}, Veg_density | |

Shrub AGB | 1 | 0.73 | 129 | H_{IQR}, H_{std}, H_{MAD}, H_{CV} |

7 | 0.72 | 132 | H_{text}, H_{90}, H_{IQR}, H_{CV} | |

30 | 0.65 | 146 | H_{90}, H_{IQR}, H_{text}, pH_{1} | |

100 | 0.64 | 151 | H_{95}, H_{text}, pH_{1}, G_{IQR}, FHD_{GT} |

Scale (m) | Source | Pseudo R^{2} | RMSE (g/m^{2}) | Predictors | |
---|---|---|---|---|---|

Herbaceous AGB | 1 | Raster | 0.20 | 6.86 | H_{Skew}, H_{text} |

1 | Point Cloud | 0.19 | 7.54 | H_{CV}, H_{text}, H_{Skew} |

2012 Lidar | 2013 Lidar | ||||
---|---|---|---|---|---|

Total AGB | Shrub AGB | Total AGB | Shrub AGB | ||

Biomass (g/m^{2}) | 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 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Li, 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