Evaluating the Potential for Estimating Age of Even-aged Loblolly Pine Stands Using Active and Passive Remote Sensing Data
Quirino, Valquiria Ferraz
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Data from an airborne laser scanner, a dual-band interferometric synthetic aperture radar (DBInSAR), and Landsat were evaluated for estimating ages of even-aged loblolly pine stands in Appomattox-Buckingham State Forest, Virginia, U.S.A. The DBInSAR data were acquired using the GeoSAR sensor in summer, 2008 in both the P- and X-bands. The LiDAR data were acquired in the same summer using a small-footprint laser scanner. Loblolly pine stand ages were assigned using the establishment year of loblolly pine stands provided by the Virginia Department of Forestry. Random circular plots were established in stands which varied in age from 5 to 71 years and in site index from 21 to 29 meters (base age 25 years). LiDAR- and GeoSAR-derived independent variables were calculated. The final selected LiDAR model used common logarithm of age as the dependent variable and the 99.5th percentile of height above ground as the independent variable (R2adj = 90.2%, RMSE = 4.4 years, n=45). The final selected GeoSAR models used the reciprocal of age as the dependent variable and had three independent variables: the sum of the X-band magnitude, the 25th percentile of X/P-band magnitudes, and the 90th percentile of the X-band height above ground (R2adj = 84.1%, RMSE = 7.9 years, n=46). The Vegetation Change Tracker (VCT) algorithm was run using a digital elevation layer, a land cover map, and a series of Landsat (5 and 7) images. A comparison was made between the loblolly pine stand ages obtained using the three methods and the reference data. The results show that: (1) although most of the time VCT and reference data ages were different, the differences were normally small, (2) all three remote sensing methods produced reliable age estimates, and (3) the Landsat-VCT algorithm produced the best estimates for younger stands (5 to 22 years old, RMSEVCT=2.2 years, RMSEGeoSAR=2.6 years, RMSELiDAR=2.6 years, n=35) and the model that used LiDAR-derived variables was better for older stands. Remote sensing can be used to estimate loblolly pine stand age, though prior knowledge of site index is required for active sensors that rely primarily on the relationship between age and height.
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