Abstract
The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree- or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO2 stocks and changes to comply with the UN-REDD policies.
Highlights
Measuring and monitoring aboveground biomass (AGB) has become an important research topic in the last decade as a result of its importance within the carbon cycle, as well as for its relevance to the international climate negotiations [1]
We show that state-of-the-art lidar processing techniques that apply to high-resolution point clouds are reliable tools to estimate aboveground biomass (AGB) with high accuracy when compared to field-derived AGB even over complex structures, such as multilayered Mediterranean forests
Our approach assesses AGB at the individual tree level for the understory layer and at the layer level for understory and ground vegetation. It applies the very same allometric equations commonly used by field-based techniques to convert forest metrics estimates into AGB
Summary
Measuring and monitoring aboveground biomass (AGB) has become an important research topic in the last decade as a result of its importance within the carbon cycle, as well as for its relevance to the international climate negotiations [1]. The paucity of field inventory plots in most of the unmanaged forests due to the difficulty of the access and cost of establishing large plots has been a source of uncertainty in the use of regression model techniques [9,10] Reducing such uncertainty would require the establishment of larger and more accurately located plots in global forests. A fair accounting of carbon sequestration would need sophisticated field inventory systems able to assess and to monitor AGB at a broad range of geographical sites, with frequent temporal visits and high accuracy requirements [12] Collecting such field measurements using traditional field techniques is not cost effective mainly in areas where there is little or no pre-existing inventory data, as well as in inaccessible areas and in areas that experience rapid changes in forest structure [3]
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