ABSTRACT
BACKGROUND: Lidar height data collected by the Geosciences Laser Altimeter System (GLAS) from 2002 to 2008 has the potential to form the basis of a globally consistent sample-based inventory of forest biomass. GLAS lidar return data were collected globally in spatially discrete full waveform "shots," which have been shown to be strongly correlated with aboveground forest biomass. Relationships observed at spatially coincident field plots may be used to model biomass at all GLAS shots, and well-established methods of model-based inference may then be used to estimate biomass and variance for specific spatial domains. However, the spatial pattern of GLAS acquisition is neither random across the surface of the earth nor is it identifiable with any particular systematic design. Undefined sample properties therefore hinder the use of GLAS in global forest sampling. RESULTS: We propose a method of identifying a subset of the GLAS data which can justifiably be treated as a simple random sample in model-based biomass estimation. The relatively uniform spatial distribution and locally arbitrary positioning of the resulting sample is similar to the design used by the US national forest inventory (NFI). We demonstrated model-based estimation using a sample of GLAS data in the US state of California, where our estimate of biomass (211 Mg/hectare) was within the 1.4% standard error of the design-based estimate supplied by the US NFI. The standard error of the GLAS-based estimate was significantly higher than the NFI estimate, although the cost of the GLAS estimate (excluding costs for the satellite itself) was almost nothing, compared to at least US$ 10.5 million for the NFI estimate. CONCLUSIONS: Global application of model-based estimation using GLAS, while demanding significant consolidation of training data, would improve inter-comparability of international biomass estimates by imposing consistent methods and a globally coherent sample frame. The methods presented here constitute a globally extensible approach for generating a simple random sample from the global GLAS dataset, enabling its use in forest inventory activities.
ABSTRACT
Tropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) - remotely estimated from LiDAR - control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one-hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.
Subject(s)
Carbon/metabolism , Light , Models, Biological , Plant Leaves/metabolism , Trees/metabolism , EnvironmentABSTRACT
Sources and sinks of carbon associated with forests depend strongly on the management regime and spatial patterns in potential productivity. Satellite remote sensing can provide spatially explicit information on land cover, standage class, and harvesting. Carbon-cycle process models coupled to regional climate databases can provide information on potential rates of production and related rates of decomposition. The integration of remote sensing and modeling thus produces spatially explicit information on carbon storage and flux. This integrated approach was employed to compare carbon flux for the period 1992-1997 over two 165-km2 areas in western Oregon. The Coast Range study area was predominately private land managed for timber production, whereas the West Cascades study area was predominantly public land that was less productive but experienced little harvesting in the 1990s. In the Coast Range area, 17% of the land base was harvested between 1991 and 2000. Much of the area was in relatively young, productive-age classes that simulations indicate are a carbon sink. Mean annual harvest removals from the Coast Range were greater than mean annual net ecosystem production. On the West Cascades study area, a relatively small proportion (< 1%) of the land was harvested and the area as a whole was accumulating carbon. The spatially and temporally explicit nature of this approach permits identification of mechanisms underlying land base carbon flux.
