Surface Lidar Remote Sensing of Basal Area and Biomass in Deciduous Forests of Eastern Maryland, USA

A method of predicting two forest stand structure attrithe ability of the regression equations to predict the stem butes, basal area and aboveground biomass, from meamap plot’s stand structure attributes was then evaluated. surements of forest vertical structure was developed and The QMCH was found to explain the most variance in tested using field and remotely sensed canopy structure the chronosequence data set’s stand structure attributes, measurements. Coincident estimates of the vertical distriand to most accurately predict the values of the same bution of canopy surface area (the canopy height profile), attributes in the stem map data set. For the chronoand field-measured stand structure attributes were acsequence data set, the QMCH predicted 70% of variance quired for two data sets. The chronosequence data set in stand basal area, and 80% of variance in aboveground consists of 48 plots in stands distributed within 25 miles biomass, and remained nonasymptotic with basal areas of Annapolis, MD, with canopy height profiles measured up to 50 m2 ha21, and aboveground biomass values up to in the field using the optical-quadrat method. The stem450 Mg ha21. When applied to the stem-map data set, the map data set consists of 75 plots subsetted from a single regression equations resulted in basal areas that were, on 32 ha stem-mapped stand, with measurements of their average, underestimated by 2.1 m2 ha21, and biomass valcanopy height profiles made using the SLICER (Scanues were underestimated by 16 Mg ha21, and explained ning Lidar Imager of Canopies by Echo Recovery) in37% and 33% of variance, respectively. Differences in strument, an airborne surface lidar system. Four height the magnitude of the coefficients of determination were indices, maximum, median, mean, and quadratic mean due to the wider range of stand conditions found in the canopy height (QMCH) were calculated from the canopy chronosequence data set; the standard deviation of residheight profiles. Regressions between the indices and ual values were lower in the stem map data set than on stand basal area and biomass were developed using the the chronosequence data sets. Stepwise multiple regreschronosequence data set. The regression equations develsion was performed to predict the two stand structure oped from the chronosequence data set were then applied attributes using the canopy height profile data directly to height indices calculated from the remotely sensed as independent variables, but they did not improve the canopy height profiles from the stem map data set, and accuracy of the estimates over the height index approach. Published by Elsevier Science Inc.