Scientists & Staff
- University of Washington, Ph.D., 2003
- International Institute for
Aerial Survey and Earth Sciences (ITC), Professional Master Degree,
- University of Washington, M.S., 1997
- Williams College, B.A., 1994
My research consists of developing new techniques for using remote sensing
and other geospatial technologies within large-scale, multiobjective
resource inventory systems. The scale of the Alaskan landscape, and the
remoteness and lack of access to most of the forest and related ecosystems,
requires development and application of inventory and monitoring techniques
that largely depend on remotely acquired information, which must be integrated
with field-based information in innovative and complex statistical sampling
designs. I investigate how emerging geospatial/remote sensing technologies
and analytical techniques can best be applied to assess the extent, condition,
use, and trends associated with Alaska’s ecosystems and natural
Why This Research is Important
This research is important because adequate technology has not been
developed or applied to effectively, and comprehensively, inventory and
monitor the forested ecosystems of Alaska. Therefore, critical baseline
information on the extent, condition, trends, and uses of all Alaskan
forests is not available to meet the needs for management and protection
of these areas.
I plan on exploring how multitemporal remote sensing—collected over
a range of scales and resolutions--and field data can be used to investigate
complex interactions among climate, land use, disturbance, and condition
in the forested ecosystems of Alaska, with particular attention to rates
of carbon accumulation and loss within boreal forests
J., H. Temesgen, H.-E. Andersen, J. Flewelling, and L. Madsen. 2012.
Effects of lidar pulse density and sample size on a model-assisted
approach to estimate forest inventory variables. Canadian Journal of
Remote Sensing. 38(5): 644-654.
M.V.N., S. Reutebuch, R. McGaughey, and H.-E. Andersen. 2012. Estimating
forest biomass and identifying low-intensity logging
areas using airborne scanning lidar in Antimary State Forest, Acre State,
Western Brazilian Amazon. Remote Sensing of Environment. 124: 479-491.
J., S. Reutebuch, H.- E. Andersen, P. Gould, and R. McGaughey. 2012.
Model-Assisted Forest Yield Estimation with Light Detection and
Ranging. Western Journal of Applied Forestry. 27(2): 53-59.
H.-E., J. Stunk, H. Temesgen, D. Atwood, and K. Winterberger. 2011.
Using multi-level remote sensing and ground data to estimate forest
biomass resources in remote regions: A case study in the boreal forests
of interior Alaska. Canadian Journal of Remote Sensing. 37(6): 596-611.
H.-E., J. Strunk, and H. Temesgen. 2011. Using airborne light detection
and ranging as a sampling tool for estimating forest biomass
resources in the upper Tanana Valley of interior Alaska. Western
Journal of Applied Forestry. 26(4): 157-164.
A.; McGaughey, R.; Andersen, H.-E.; Schiess, P. 2009. Object-oriented
classification of forest structure from light detection and ranging
data for stand mapping. Western Journal of Applied Forestry. 24(4): 198-204.
Kim, S.; McGaughey, R.J.; Andersen, H.-E.; Schreuder, G. 2009. Tree
species differentiation using intensity data derived from leaf-on and
airborne laser scanner data. Remote Sensing of Environment. 113(8):
Andersen, H.-E.; Clarkin, T.; Winterberger, K.; Strunk. J.
2009. An accuracy assessment of geographic coordinates obtained using
GPS receivers across a range of forest conditions within the Tanana
Valley of interior Alaska. Western Journal of Applied Forestry. 24(3):
Andersen, H.-E. 2009. Using airborne LIDAR to characterize
forest stand condition on the Kenai Peninsula of Alaska. Western
Journal of Applied
Forestry. 24(2): 95-102.
Pang, Y.; Lefsky, M.; Andersen, H.-E.;
Miller, M.; Sherrill, K. 2008. Validation of the ICESat vegetation
product using crown-area-weighted
mean height derived using crown delineation with discrete return
LIDAR data. Canadian Journal of Remote Sensing. 34(Supplement
Li, Y.; Andersen, H.-E.; McGaughey. R.J. 2008. A comparison
of statistical methods for estimating forest biomass from light
detection and ranging
data. Western Journal of Applied Forestry. 23(4): 223-231.
J.; Kublin, E.; McGaughey, R.; Andersen, H.-E.; Reutebuch, S. 2008.
Mixed-effects models for estimating stand
volume by means
of small footprint airborne laser scanner data. The Photogrammetric
of Finland. 21(1): 4-15.
Andersen, H.-E.; McGaughey, R.J.; Reutebuch, S.E. 2008. Assessing
the influence of flight parameters, interferometric processing, slope,
canopy density on the accuracy of X-band IFSAR-derived forest canopy
height models. International Journal of Remote Sensing. 29(5): 1495-1510.
Gatziolis, D.; Andersen, H.-E. 2008. A
guide to LIDAR data acquisition and processing for the forests of the
Pacific Northwest. Gen. Tech. Rep.
PNW-GTR-768. Portland, OR: U.S. Department of Agriculture, Forest Service,
Pacific Northwest Research Station.
Andersen, H.-E.; Breidenbach, J. 2007. Statistical properties of mean
stand biomass estimators in a LIDAR-based double sampling forest survey
design. Proceedings of the ISPRS workshop on Laser Scanning. IAPRS.
XXXVI (Part 3 / W52): 8–13.
Andersen, H.-E.; Reutebuch, S.E.; McGaughey, R.J. 2006. Active
remote sensing. In: Shao, G.; Reynolds, K., eds. Computer applications in sustainable
forest management. Dordrecht: Springer-Verlag: 43-66. Chapter 3.
Andersen, H.-E.; Reutebuch, S.E.; McGaughey, R.J. 2006. A
rigorous assessment of tree height measurements obtained using airborne
LIDAR and conventional
field methods. Canadian Journal of Remote Sensing. 32(5): 355-366.
Reutebuch, S.E.; Andersen, H.-E.; McGaughey, R.J. 2005. LIDAR:
an emerging tool for multiple resource inventory. Journal of Forestry. 103(6):
Andersen, H.-E.; Reutebuch, S.E.; McGaughey, R.J. 2005. Accuracy
of an IFSAR-derived digital terrain model under a conifer forest canopy.
Journal of Remote Sensing. 31(4): 283-288.
Andersen, H.-E.; McGaughey, R.J.; Reutebuch, S.E. 2005. Estimating
forest canopy fuel parameters using LIDAR data. Remote Sensing
of Environment. 94: 441-449.
S.E., R.J. McGaughey, H.-E. Andersen, and W. Carson. 2003. Accuracy
of a high-resolution
under a conifer
forest canopy. Canadian Journal of
Remote Sensing 29(5): 1-9.