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Remote Sens. 2017, 9(2), 154; doi:10.3390/rs9020154

LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon

1
Shoolf of Engineering Science, Lappeenranta University of Technology, P.O. Box 20, 53851 Lappeenranta, Finland
2
Arbonaut Ltd., Kaislakatu 2, 80130 Joensuu, Finland
3
Conservation Sciences Program, University of Minnesota, St. Paul, MN 55108, USA
4
World Wide Fund for Nature, Nepal, Kathmandu 44600, Nepal
*
Author to whom correspondence should be addressed.
Received: 2 September 2016 / Accepted: 4 February 2017 / Published: 14 February 2017
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Abstract

Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based measurement methods. For any remote-sensing based methods to be accurate, they must be validated against field data. We present a method that combines field measurements with two layers of remote sensing data: sampling of forests by airborne laser scanning (LiDAR) and Landsat imagery. The Bayesian model-based framework presented here is called Lidar-Assisted Multi-source Programme—or LAMP—for Above Ground Biomass estimation. The method has two variants: LAMP2 which splits the biomass estimation task into two separate stages: forest type stratification from Landsat imagery and mean biomass density estimation of each forest type by LiDAR models calibrated on field plots. LAMP3, on the other hand, estimates first the biomass on a LiDAR sample using models calibrated with field plots and then uses these LiDAR-based models to generate biomass density estimates on thousands of surrogate plots, with which a satellite image based model is calibrated and subsequently used to estimate biomass density on the entire forest area. Both LAMP methods have been applied to a 2 million hectare area in Southern Nepal, the Terai Arc Landscape or TAL to calculate the emission Reference Levels (RLs) that are required for the UN REDD+ program that was accepted as part of the Paris Climate Agreement. The uncertainty of these estimates is studied with error variance estimation, cross-validation and Monte Carlo simulation. The relative accuracy of activity data at pixel level was found to be 14 per cent at 95 per cent confidence level and the root mean squared error of biomass estimates to be between 35 and 39 per cent at 1 ha resolution. View Full-Text
Keywords: LiDAR; Above Ground Biomass; AGB; REDD+; Monitoring Reporting and Verification; MRV; Bayesian estimation; uncertainty quantification; forest carbon density LiDAR; Above Ground Biomass; AGB; REDD+; Monitoring Reporting and Verification; MRV; Bayesian estimation; uncertainty quantification; forest carbon density
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kauranne, T.; Joshi, A.; Gautam, B.; Manandhar, U.; Nepal, S.; Peuhkurinen, J.; Hämäläinen, J.; Junttila, V.; Gunia, K.; Latva-Käyrä, P.; Kolesnikov, A.; Tegel, K.; Leppänen, V. LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon. Remote Sens. 2017, 9, 154.

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