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

Abstract

A method of predicting two forest stand structure attributes, basal area and aboveground biomass, from measurements of forest vertical structure was developed and tested using field and remotely sensed canopy structure measurements. Coincident estimates of the vertical distribution of canopy surface area (the canopy height profile), and field-measured stand structure attributes were acquired for two data sets. The chronosequence data set consists of 48 plots in stands distributed within 25 miles of Annapolis, MD, with canopy height profiles measured in the field using the optical-quadrat method. The stem-map data set consists of 75 plots subsetted from a single 32 ha stem-mapped stand, with measurements of their canopy height profiles made using the SLICER (Scanning Lidar Imager of Canopies by Echo Recovery) instrument, an airborne surface lidar system. Four height indices, maximum, median, mean, and quadratic mean canopy height (QMCH) were calculated from the canopy height profiles. Regressions between the indices and stand basal area and biomass were developed using the chronosequence data set. The regression equations developed from the chronosequence data set were then applied to height indices calculated from the remotely sensed canopy height profiles from the stem map data set, and the ability of the regression equations to predict the stem map plot’s stand structure attributes was then evaluated. The QMCH was found to explain the most variance in the chronosequence data set’s stand structure attributes, and to most accurately predict the values of the same attributes in the stem map data set. For the chronosequence data set, the QMCH predicted 70% of variance in stand basal area, and 80% of variance in aboveground biomass, and remained nonasymptotic with basal areas up to 50 m 2 ha −1, and aboveground biomass values up to 450 Mg ha −1. When applied to the stem-map data set, the regression equations resulted in basal areas that were, on average, underestimated by 2.1 m 2 ha −1, and biomass values were underestimated by 16 Mg ha −1, and explained 37% and 33% of variance, respectively. Differences in the magnitude of the coefficients of determination were due to the wider range of stand conditions found in the chronosequence data set; the standard deviation of residual values were lower in the stem map data set than on the chronosequence data sets. Stepwise multiple regression was performed to predict the two stand structure attributes using the canopy height profile data directly as independent variables, but they did not improve the accuracy of the estimates over the height index approach.