Large-scale estimation of change in aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data

As a non-Annex 1 Party to the United Nations Framework Convention on Climate Change (UNFCCC), Tanzania has committed to submit the results of the measurement, reporting, and verification of greenhouse gas (GHG) emissions reductions and removals by sinks under the Biennial Update Reports of national GHG inventory and mitigation actions. Thus, implementation of a robust and cost-efficient carbon accounting system is one of the prerequisites for receiving financial benefits under the REDD + mechanism. For estimation targeting large areas (i.e., country, region, and sub-regional), remote sensing techniques like airborne laser scanning (ALS) enable precise and timely estimation of biomass stocks and biomass changes. The main objective of the study was to compare alternative sampling strategies using ALS for estimating aboveground biomass (AGB) changes in miombo woodlands that would be suitable components of the National Forestry Resources Monitoring and Assessment (NAFORMA) program of Tanzania and of the Biennial Update Reports under UNFCCC. NAFORMA is the national forest inventory of Tanzania. The study area of 15,867 km2 was located in the administrative district of Liwale, south-eastern Tanzania and dominated by miombo woodlands. A longitudinal survey was carried out during two temporally consistent field and ALS data acquisition campaigns in 2012 and 2014. The field data were collected on permanent plots of the NAFORMA program. ALS measurements were acquired along 32 parallel strips spaced 5 km apart and covering approximately 25% of the study area. Estimation methods (1) solely based on the ground inventory data provided by NAFORMA and (2) by incorporating field observations and auxiliary variables derived from ALS data were pursued for assessing AGB change as the difference between estimated AGB in 2012 and 2014, together with the uncertainties related to sampling variability. The AGB estimates varied from 58.48 to 59.55 Mg ha− 1 in 2012 and between 58.20 and 59.29 Mg ha− 1 in 2014. The sampling variability (in terms of standard errors) was between 1.37 and 4.79 Mg ha− 1 in 2012, and 1.77 and 5.03 Mg ha− 1 in 2014. The mean AGB change varied from 0.11 to 0.35 Mg ha− 1 with standard errors of 0.31 to 0.85 Mg ha− 1. Compared to direct estimation based on field data only, the efficiency gain under ALS-assisted (design-based, model-assisted) estimation was 5-22%, and approximately 66% when using ALS data in a model-dependent estimation. The results provide statistically significant evidence of change in AGB over the 2-yr period spanned by the study only under model-dependent estimation at 50% confidence level, illustrating the great challenges of change estimation over such short time periods as requested for UNFCCC reporting.