VOLUME 72, NUMBER 12
PHOTOGRAMMETRIC ENGINEERING & REMOTE
SENSING
JOURNAL OF THE AMERICAN SOCIETY FOR PHOTOGRAMMETRY
AND REMOTE SENSING
In this month’s cover image, the background
image and upper inset show the canopy height model (CHM)
of a patch of forest in Appomattox Buckingham State Forest,
Virginia. Vegetation is composed of various coniferous (Pinus
taeda, P. virginiana, P. echinata, and P. strobus), deciduous
(Quercus coccinea, Q. alba, and Liriodendron tulipifera), and
mixed forests. The CHM was derived from lidar data collected
by Spectrum Mapping, LLC in September, 2002 with the Digital
Airborne Topographic Imaging System II (DATIS II) sensor
(footprint = 0.46 m, average point distance = 0.75 m). The
images at the bottom show forest growth from 1999 to 2002.
The 1999 CHM (left bottom image) was derived from a lidar
acquisition in September, 1999 using the AeroScan (EarthData,
Inc.) sensor (footprint = 0.65 m, average point distance = 1.5
m). The 2002 CHM (middle bottom image) is the same CHM
as the background image. The growth image (right bottom
image) was generated by subtracting the 1999 CHM from the
2002 CHM. Positive growth in the 3-year period is shown in red; areas with no growth or with removals
are shown in green. Images courtesy of the Center for Environmental Applications of Remote Sensing at
Virginia Polytechnic Institute and State University, USA, cears.fw.vt.edu.
The individual tree height growth of Scots pine was estimated
from two laser surveys with three different techniques, and
the accuracy of the estimation was evaluated with sample
trees.
Spatial distributions of plant area, leaf area, tree positions,
canopy height, and terrain elevation were generated for a deciduous
forest in an exploratory application of high-resolution
ground-based laser imaging.
The characteristics associated with differing laser pulse emission
frequencies are found to vary the penetration of pulses
within conifer forest canopies.
A comparison of landscape segmentation techniques, including
using a combination laser based canopy height variance/percent canopy cover classification coupled with smoothing
decisions to improve automated stem detection using a variable
window-size algorithm.