A Systems Engineering Approach to Estimating Uncertainty in Above‐Ground Biomass (<scp>AGB</scp>) Derived from Remote‐Sensing Data

Weisbin, C.R.; Lincoln, William; Saatchi, Sassan

doi:10.1002/sys.21275

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…This analysis accounted for spatial autocorrelation of the AGB error at the pixel scale using the techniques described by McRoberts [] and Weisbin et al . [] (details are provided in the supporting information). As the biomass consumption estimate is the difference between two correlated AGB estimates, its variance was estimated following McRoberts et al .…”

Section: Methodsmentioning

confidence: 96%

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data

et al. 2017

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Quantifying biomass consumption and carbon release is critical to understanding the role of fires in the carbon cycle and air quality. We present a methodology to estimate the biomass consumed and the carbon released by the California Rim fire by integrating postfire airborne LiDAR and multitemporal Landsat Operational Land Imager (OLI) imagery. First, a support vector regression (SVR) model was trained to estimate the aboveground biomass (AGB) from LiDAR-derived metrics over the unburned area. The selected model estimated AGB with an R 2 of 0.82 and RMSE of 59.98 Mg/ha. Second, LiDAR-based biomass estimates were extrapolated to the entire area before and after the fire, using Landsat OLI reflectance bands, Normalized Difference Infrared Index, and the elevation derived from LiDAR data. The extrapolation was performed using SVR models that resulted in R 2 of 0.73 and 0.79and RMSE of 87.18 (Mg/ha) and 75.43 (Mg/ha) for the postfire and prefire images, respectively. After removing bias from the AGB extrapolations using a linear relationship between estimated and observed values, we estimated the biomass consumption from postfire LiDAR and prefire Landsat maps to be 6.58 ± 0.03 Tg (10 12 g), which translate into 12.06 ± 0.06 Tg CO2 e released to the atmosphere, equivalent to the annual emissions of 2.57 million cars.

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Section: Methodsmentioning

confidence: 96%

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data

et al. 2017

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“…This process included the quantification of error at each step of the process and the use of the Gaussian error propagation approach:where each of the terms are the relative errors at that pixel and represent the measurement errors of lidar for capturing the forest height, the error associated with the lidar aboveground C allometry model for each forest type, the error associated with sampling the 1-ha pixel with GLAS footprint size (~0.25 ha), and the MaxEnt prediction error. In evaluating the errors at the state and county level, we also included the spatial correlation of the prediction error from the MaxEnt approach [24]. …”

Section: Methodsmentioning

confidence: 99%

Attribution of net carbon change by disturbance type across forest lands of the conterminous United States

Harris

Hagen

Saatchi

et al. 2016

Carbon Balance Manage

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BackgroundLocating terrestrial sources and sinks of carbon (C) will be critical to developing strategies that contribute to the climate change mitigation goals of the Paris Agreement. Here we present spatially resolved estimates of net C change across United States (US) forest lands between 2006 and 2010 and attribute them to natural and anthropogenic processes.ResultsForests in the conterminous US sequestered −460 ± 48 Tg C year−1, while C losses from disturbance averaged 191 ± 10 Tg C year−1. Combining estimates of net C losses and gains results in net carbon change of −269 ± 49 Tg C year−1. New forests gained −8 ± 1 Tg C year−1, while deforestation resulted in losses of 6 ± 1 Tg C year−1. Forest land remaining forest land lost 185 ± 10 Tg C year−1 to various disturbances; these losses were compensated by net carbon gains of −452 ± 48 Tg C year−1. C loss in the southern US was highest (105 ± 6 Tg C year−1) with the highest fractional contributions from harvest (92%) and wind (5%). C loss in the western US (44 ± 3 Tg C year−1) was due predominantly to harvest (66%), fire (15%), and insect damage (13%). The northern US had the lowest C loss (41 ± 2 Tg C year−1) with the most significant proportional contributions from harvest (86%), insect damage (9%), and conversion (3%). Taken together, these disturbances reduced the estimated potential C sink of US forests by 42%.ConclusionThe framework presented here allows for the integration of ground and space observations to more fully inform US forest C policy and monitoring efforts.

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“…where AGB An uncertainty analysis of the AGB 2012 and AGB 2014 at landscape level for each pulse density target and DTM scenario was also performed by integrating the pixel level errors and accounting for spatial autocorrelation of the errors as follows [27][28][29]:…”

Section: Aboveground Biomass Change Estimation and Mappingmentioning

confidence: 99%

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

et al. 2017

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Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m −2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m −2 . For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha −1 when pulse density decreased from 12 to 0.2 pulses·m −2 . The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha −1 . The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

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A Systems Engineering Approach to Estimating Uncertainty in Above‐Ground Biomass (AGB) Derived from Remote‐Sensing Data

Cited by 11 publications

References 38 publications

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data

Attribution of net carbon change by disturbance type across forest lands of the conterminous United States

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

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