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Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis Program,Landsat, and LANDFIRE
Authors:Xuexia Chen  Shuguang Liu  Zhiliang Zhu  James Vogelmann  Zhengpeng Li  Donald Ohlen
Institution:2. U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA;3. Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD 57007, USA;4. U.S. Geological Survey (USGS), Reston, VA 20192, USA
Abstract:The concentrations of CO2 and other greenhouse gases in the atmosphere have been increasing and greatly affecting global climate and socio-economic systems. Actively growing forests are generally considered to be a major carbon sink, but forest wildfires lead to large releases of biomass carbon into the atmosphere. Aboveground forest biomass carbon (AFBC), an important ecological indicator, and fire-induced carbon emissions at regional scales are highly relevant to forest sustainable management and climate change. It is challenging to accurately estimate the spatial distribution of AFBC across large areas because of the spatial heterogeneity of forest cover types and canopy structure. In this study, Forest Inventory and Analysis (FIA) data, Landsat, and Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) data were integrated in a regression tree model for estimating AFBC at a 30-m resolution in the Utah High Plateaus. AFBC were calculated from 225 FIA field plots and used as the dependent variable in the model. Of these plots, 10% were held out for model evaluation with stratified random sampling, and the other 90% were used as training data to develop the regression tree model. Independent variable layers included Landsat imagery and the derived spectral indicators, digital elevation model (DEM) data and derivatives, biophysical gradient data, existing vegetation cover type and vegetation structure. The cross-validation correlation coefficient (r value) was 0.81 for the training model. Independent validation using withheld plot data was similar with r value of 0.82. This validated regression tree model was applied to map AFBC in the Utah High Plateaus and then combined with burn severity information to estimate loss of AFBC in the Longston fire of Zion National Park in 2001. The final dataset represented 24 forest cover types for a 4 million ha forested area. We estimated a total of 353 Tg AFBC with an average of 87 MgC/ha in the Utah High Plateaus. We also estimated that 8054 Mg AFBC were released from 2.24 km2 burned forest area in the Longston fire. These results demonstrate that an AFBC spatial map and estimated biomass carbon consumption can readily be generated using existing database. The methodology provides a consistent, practical, and inexpensive way for estimating AFBC at 30-m resolution over large areas throughout the United States.
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