Use of waveform lidar and hyperspectral sensors to assess selected spatial and structural patterns associated with recent and repeat disturbance and the abundance of sugar maple (<italic>Acer saccharum</italic> Marsh.) in a temperate mixed hardwood and conifer forest

Abstract

Waveform lidar imagery was acquired on September 26, 1999 over the Bartlett Experimental Forest (BEF) in New Hampshire (USA) using NASA's Laser Vegetation Imaging Sensor (LVIS). This flight occurred 20 months after an ice storm damaged millions of hectares of forestland in northeastern North America. Lidar measurements of the amplitude and intensity of ground energy returns appeared to readily detect areas of moderate to severe ice storm damage associated with the worst damage. Southern through eastern aspects on side slopes were particularly susceptible to higher levels of damage, in large part overlapping tracts of forest that had suffered the highest levels of wind damage from the 1938 hurricane and containing the highest levels of sugar maple basal area and biomass. The levels of sugar maple abundance were determined through analysis of the 1997 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) high resolution spectral imagery and inventory of USFS Northern Research Station field plots. We found a relationship between field measurements of stem volume losses and the LVIS metric of mean canopy height (r 2 = 0.66; root mean square errors = 5.7 m 3 /ha, p < 0.0001) in areas that had been subjected to moderate-to-severe ice storm damage, accurately documenting the short-term outcome of a single disturbance event.

Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences

“…Maps derived from repeat lidar studies are of great value for calibrating, validating, and constraining a variety of models at the plot and landscape scale, including hydrological, geomorphological, vegetation, and habitat models. For example, erosion and deposition mapped and quantified by multitemporal lidar has provided unprecedented details into the fundamental processes that underlie Earth flow kinematics (Daehne and Corsini, 2014), riverbank and postfire erosion (Grove et al, 2013;Rengers et al, 2016), rockfall activity (Lim et al, 2005;Heckmann et al, 2012), precursors to rock slope failures (Rosser et al, 2007;Royán et al, 2015), carbon pools and fluxes (Dubayah et al, 2010;Goetz et al, 2011;Hudak et al, 2012, Liang et al, 2012Anderson et al, 2013;Meyer et al, 2013;Srinivasan et al, 2014;Hoffmeister et al, 2015;Cao et al, 2016;Réjou-Méchain et al, 2016), vegetation disturbance (Anderson et al, 2011;Dolan et al, 2011;Huang et al, 2013) and snow accumulation patterns (Veitinger et al, 2014).…”

Artificial target detection with a hyperspectral LiDAR over 26-h measurement

“…[3][4][5][6][7][8][9] The results of these studies have shown a clear trend in improving overall classification accuracies over singlesensor results. 3,8,9 Additionally, forest studies have moved from overall forest type mapping to tree species level.…”

Terrain slope estimation within footprint from ICESat/GLAS waveform: model and method

“…5 In urban areas, more than two peaks can be found in the waveform backscattered by tree canopy as well as building edges. 11 Full waveform LiDAR is used for scanning and measurement in three different platforms: spaceborne, airborne, and terrestrial. 6 In order to model and fit the waveforms of raw LiDAR data, Gaussian function 7 or a mixture of several Gaussian distributions 8,9 were commonly used to fit the peak or peaks of a detected waveform.…”