Estimation of Tropical Rain Forest Aboveground Biomass with Small-Footprint Lidar and Hyperspectral Sensors. Clark, M. L., Roberts, D. A., Ewel, J. J., & Clark, D. B. 115(11):2931–2942. ![Estimation of Tropical Rain Forest Aboveground Biomass with Small-Footprint Lidar and Hyperspectral Sensors [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Tropical forests are an important component of the global carbon balance, yet there is considerable uncertainty in estimates of their carbon stocks and fluxes, which are typically estimated through analysis of aboveground biomass in field plots. Remote sensing technology is critical for assessing fine-scale spatial variability of tropical forest biomass over broad spatial extents. The goal of our study was to evaluate relatively new technology, small-footprint, discrete-return lidar and hyperspectral sensors, for the estimation of aboveground biomass in a Costa Rican tropical rain forest landscape. We derived a suite of predictive metrics for field plots: lidar metrics were calculated from plot vertical height profiles and hyperspectral metrics included fraction of spectral mixing endmembers and narrowband indices that respond to photosynthetic vegetation, structure, senescence, health and water and lignin content. We used single- and two-variable linear regression analyses to relate lidar and hyperspectral metrics to aboveground biomass of plantation, managed parkland and old-growth forest plots. The best model using all 83 biomass plots included two lidar metrics, plot-level mean height and maximum height, with an r2 of 0.90 and root-mean-square error (RMSE) of 38.3~Mg/ha. When the analysis was constrained to plantation plots, which had the most accurate field data, the r2 of the model increased to 0.96, with RMSE of 10.8~Mg/ha (n~=~32). Hyperspectral metrics provided lower accuracy in estimating biomass than lidar metrics, and models with a single lidar and hyperspectral metric were no better than the best model using two lidar metrics. These results should be viewed as an initial assessment of using these combined sensors to estimate tropical forest biomass; hyperspectral data were reduced to nine indices and three spectral mixture fractions, lidar data were limited to first-return canopy height, sensors were flown only once at different seasons, and we explored only linear regression for modeling. However, this study does support conclusions from studies at this and other climate zones that lidar is a premier instrument for mapping biomass (i.e., carbon stocks) across broad spatial scales. ⺠We evaluate lidar and hyperspectral data for estimating tropical rain forest biomass. ⺠The best model included two lidar metrics with an r2 of 0.90 and RMSE of 38.3 Mg/ha. ⺠Models with hyperspectral metrics did not improve biomass estimates. ⺠Lidar is a premier sensor for broad-scale mapping of tropical rain forest biomass. ⺠Results are important in advancing new methods to assess carbon within REDD.
@article{clarkEstimationTropicalRain2011,
title = {Estimation of Tropical Rain Forest Aboveground Biomass with Small-Footprint Lidar and Hyperspectral Sensors},
author = {Clark, Matthew L. and Roberts, Dar A. and Ewel, John J. and Clark, David B.},
date = {2011-11},
journaltitle = {Remote Sensing of Environment},
volume = {115},
pages = {2931--2942},
issn = {0034-4257},
doi = {10.1016/j.rse.2010.08.029},
url = {https://doi.org/10.1016/j.rse.2010.08.029},
abstract = {Tropical forests are an important component of the global carbon balance, yet there is considerable uncertainty in estimates of their carbon stocks and fluxes, which are typically estimated through analysis of aboveground biomass in field plots. Remote sensing technology is critical for assessing fine-scale spatial variability of tropical forest biomass over broad spatial extents. The goal of our study was to evaluate relatively new technology, small-footprint, discrete-return lidar and hyperspectral sensors, for the estimation of aboveground biomass in a Costa Rican tropical rain forest landscape. We derived a suite of predictive metrics for field plots: lidar metrics were calculated from plot vertical height profiles and hyperspectral metrics included fraction of spectral mixing endmembers and narrowband indices that respond to photosynthetic vegetation, structure, senescence, health and water and lignin content. We used single- and two-variable linear regression analyses to relate lidar and hyperspectral metrics to aboveground biomass of plantation, managed parkland and old-growth forest plots. The best model using all 83 biomass plots included two lidar metrics, plot-level mean height and maximum height, with an r2 of 0.90 and root-mean-square error (RMSE) of 38.3~Mg/ha. When the analysis was constrained to plantation plots, which had the most accurate field data, the r2 of the model increased to 0.96, with RMSE of 10.8~Mg/ha (n~=~32). Hyperspectral metrics provided lower accuracy in estimating biomass than lidar metrics, and models with a single lidar and hyperspectral metric were no better than the best model using two lidar metrics. These results should be viewed as an initial assessment of using these combined sensors to estimate tropical forest biomass; hyperspectral data were reduced to nine indices and three spectral mixture fractions, lidar data were limited to first-return canopy height, sensors were flown only once at different seasons, and we explored only linear regression for modeling. However, this study does support conclusions from studies at this and other climate zones that lidar is a premier instrument for mapping biomass (i.e., carbon stocks) across broad spatial scales. ⺠We evaluate lidar and hyperspectral data for estimating tropical rain forest biomass. ⺠The best model included two lidar metrics with an r2 of 0.90 and RMSE of 38.3 Mg/ha. ⺠Models with hyperspectral metrics did not improve biomass estimates. ⺠Lidar is a premier sensor for broad-scale mapping of tropical rain forest biomass. ⺠Results are important in advancing new methods to assess carbon within REDD.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-9278302,biomass,empirical-equation,forest-resources,modelling,regression,remote-sensing,tropical-forests},
number = {11}
}
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