Title: Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon
Author: d'Oliveira, Marcus V.N.; Reutebuch, Stephen E.; McGaughey, Robert J.; Andersen, Hans-Erik.
Source: Remote Sensing of Environment. 124: 479-491
Publication Series: Journal/Magazine Article (JRNL)
Description: The objectives of this study were to estimate above ground forest biomass and identify areas disturbed by selective logging in a 1000 ha Brazilian tropical forest in the Antimary State Forest using airborne lidar data. The study area consisted of three management units, two of which were unlogged, while the third unit was selectively logged at a low intensity. A systematic random sample of fifty 0.25-ha ground plots were measured and used to construct lidar-based regression models for above ground biomass (AGB). A lidar model-assisted approach was used to estimate AGB for the logged and unlogged units (using both synthetic and model-assisted estimators). Two lidar explanatory variables, computed at a spatial resolution of 50 m x 50 m, were used in these predictions: 1) the first quartile height of all above ground returns (P25); and, 2) variance of the height above ground of all returns. In a second component of the analysis lidar metrics were also computed at 1 m x 1 m resolution to identify areas impacted by logging activities within the selectively harvested management unit. A high-resolution canopy relative density model (RDM) was used in GIS to identify and delineate roads, skidtrails, landings and harvested tree gaps. The area impacted by selective logging determined from the RDM was 58.4 ha or 15.4% of the total management unit. Using these two spatial resolutions of lidar analyses it was possible to identify differences in AGB in selectively logged areas that had relatively high levels of residual overstory canopy cover. The mean AGB obtained from the synthetic estimator was significantly lower in impacted areas than in undisturbed areas of the selectively logged management unit (p = 0.01 ).
Keywords: forest biomass, airborne laser scanning, selective logging, tropical forest monitoring, lidar, Amazon forest monitoring
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d'Oliveira, Marcus V.N.; Reutebuch, Stephen E.; McGaughey, Robert J.; Andersen, Hans-Erik. 2012. Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon. Remote Sensing of Environment. 124: 479-491.
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