Birgit E. Peterson

Estimating aboveground biomass in interior Alaska with Landsat data and field measurements

a b s t r a c t Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field measurements. Tree, shrub, and herbaceous AGB data in both live and dead forms were collected in summers and autumns of 2009 and 2010. Using the Landsat-derived spectral variables and the field AGB data, we generated a regression model and applied this model to map AGB for the ecoregion. A 3-fold cross-validation indicated that the AGB estimates had a mean absolute error of 21.8 Mg/ha and a mean bias error of 5.2 Mg/ha. Additionally, we validated the mapping results using an airborne lidar dataset acquired for a portion of the ecoregion. We found a significant relationship between the lidar-derived canopy height and the Landsat-derived AGB (R 2 = 0.40). The AGB map showed that 90% of the ecoregion had AGB values ranging from 10 Mg/ha to 134 Mg/ha. Vegetation types and fires were the primary factors controlling the spatial AGB patterns in this ecoregion. Published by Elsevier B.V.

Towards Integration of GLAS into a National Fuel Mapping Program

Comprehensive canopy structure and fuel data are critical for understanding and modeling wildland fire. The LANDFIRE project produces such data nationwide based on a collection of field observations, Landsat imagery, and other geospatial data. Where field data are not available, alternate strategies are being investigated. In this study, vegetation structure data available from GLAS were used to fill this data gap for the Yukon Flats Ecoregion of interior Alaska. The GLAS-derived structure and fuel layers and the original LANDFIRE layers were subsequently used as inputs into a fire behavior model to determine what effect the revised inputs would have on the model outputs. The outputs showed that inclusion of the GLAS data enabled better landscape-level characterization of vegetation structure and therefore enabled a broader wildland fire modeling capability. The results of this work underscore how GLAS data can be incorporated into LANDFIRE canopy structure and fuel mapping.

Automated integration of lidar into the LANDFIRE product suite

Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are increasingly available, they have rarely been applied to wildland fuels mapping efforts, mostly due to two issues. First, the Landscape Fire and Resource Planning Tools (LANDFIRE) program, which has become the default source of large-scale fire behaviour modelling inputs for the US, does not currently incorporate lidar data into the vegetation and fuel mapping process because spatially continuous lidar data are not available at the national scale. Second, while lidar data are available for many land management units across the US, these data are underutilized for fire behaviour applications. This is partly due to a lack of local personnel trained to process and analyse lidar data. This investigation addresses these issues by developing the Creating Hybrid Structure from LANDFIRE/lidar Combinations (CHISLIC) tool. CHISLIC allows individuals to automatically generate a suite of vegetation structure and wildland fuel parameters from lidar data and infuse them into existing LANDFIRE data sets. CHISLIC will become available for wider distribution to the public through a partnership with the U.S. Forest Service’s Wildland Fire Assessment System (WFAS) and may be incorporated into the Wildland Fire Decision Support System (WFDSS) with additional design and testing. WFAS and WFDSS are the primary systems used to support tactical and strategic wildland fire management decisions.

Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska

Quantification of aboveground biomass (AGB) in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year (2008–2010) composite of top-of-atmosphere reflectance for six bands as well as the brightness temperature (BT). We constructed a multiple regression model using field-observed AGB and Landsat-derived reflectance, BT, and vegetation indices. A basin-wide boreal forest AGB map at 30 m resolution was generated by applying the regression model to the Landsat composite. The fivefold cross-validation with field measurements had a mean absolute error (MAE) of 25.7 Mg ha−1 (relative MAE 47.5%) and a mean bias error (MBE) of 4.3 Mg ha−1 (relative MBE 7.9%). The boreal forest AGB product was compared with lidar-based vegetation height data; the comparison indicated that there was a significant correlation between the two data sets.

Mapping forest height in Alaska using GLAS, Landsat composites, and airborne LiDAR

Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data.