Landsat Thematic Mapper (LTM) satellite data has been used by natural resource managers in many different ways. It has been successfully used to create landcover classifications, forest fire mapping, and wildlife habitat modeling , to name a few, at

1
R. LOWE, C. J. Cieszewski1, H. J-H Whiffen2,
M. Zasada3,4, B. E. Borders5 1Assistant
Professor 2Assistant Professor 3Postdoctoral
Fellow 5Professor Daniel B. Warnell School of
Forest Resources The University of
Georgia Athens, GA 30602, USA 3Assistant
Professor, Department of Forest
Productivity Faculty of Forestry Warsaw
Agricultural University Rakowiecka 26/30, 02-528
Warsaw, Poland

• Landsat Thematic Mapper (LTM) satellite data
  has been used by natural resource managers in
  many different ways. It has been successfully
  used to create landcover classifications, forest
  fire mapping, and wildlife habitat modeling , to
  name a few, at the stand-, county-, state-, and
  even national-scale. One aspect of natural
  resource management LTM data has seen little
  success in, especially in the southeast, is
  forest biomass modeling. Specifically,
  estimating standing volume and basal area.
• Using a unique sampling method and algorithm
  development, we were able to achieve high
  correlations between basal area and LTM
  variables. The poster will illustrate the
  sampling methodology and basal area estimation
  results.

2
How accurately, and at what scale can basal area
be estimated using Landsat Thematic Mapper
satellite data?
3
Background Information
Data Collection Processing
Results and Conclusions
Integration into TIP3 Project
References
Study Area
Biography
4

• The timber industry is one of the leading
  economic sectors in Georgia, contributing an
  estimated 19.5 billion to the economy annually.
  In addition to the economic impact, Georgia's
  forests provide hunting, fishing, camping, and
  other outdoor recreational opportunities, help
  maintain a clean water supply, conserve soil, and
  provide habitat for many fish and wildlife
  species. As Georgia becomes more populated, and
  forests are "lost" to urban/suburban expansion,
  it is imperative that we manage our forests to
  meet the needs of all - the forest industry,
  private citizens, and wildlife (to name only a
  few). The American Forest and Paper Association
  recognized this need for responsible management
  of our forests in the 1998 Second Blue Ribbon
  Panel on the Forest Inventory and Analysis
  Program when they acknowledged the importance of
  a consistent, timely, and accurate forest
  inventory system. Georgia expressed a
  willingness to plan for the future as well, when
  they initiated work on the Southern Annual Forest
  Inventory System (SAFIS) in partnership with the
  USDA Forest Service Forest Inventory Analysis
  (FIA) program.
• One question that must be answered before we
  can plan for future forests is "Are our forests
  being utilized on a sustainable basis?" The
  answer to this complex question can not be
  answered from simplistic comparisons of FIA
  timberland growth and removals, for timber growth
  is not linear, which is one of the assumptions in
  the simplistic comparisons, and growing
  stock-sized trees are not recorded until they
  reach a minimum size, which leaves out a large
  section of the timber population. To answer this
  question, one must conduct a much more complex
  analysis involving the proper modeling of changes
  over time that are nonlinear in nature. This
  includes using explicit assumptions concerning
  regeneration dynamics, clear assumptions
  regarding future land use changes, and also by
  taking into account supply and demand of forest
  products (Cieszewski).
• Directly related to the public's concern about
  our forest's sustainability, and possibly based
  on the aforementioned simple comparisons of FIA
  growth and removals, regulatory constraints are
  being imposed on both private and industry
  landowners in the name of "preservation and
  sustainability". Through our Traditional
  Industries in Pulp and Paper Production (TIP3)
  research project (Cieszewski, 2001) , we plan to
  provide a scientific basis for realistic analysis
  of the long-term considerations of the
  sustainability of natural resources in Georgia.
  To develop a responsible methodology for
  analyzing the effects of various regulations, we
  must include in the model locational constraints
  such as stream-side managemet zones (SMZs),
  maximum harvest areas, and other local and
  state-wide political rules, as well as biological
  constraints like "green-up". Most importantly,
  we must know the CURRENT landcover type and
  amount of standing timber.

5
(No Transcript)
6

• GPS Data Collection
• 16 plot-clusters
• plots 30-meters (98.4 feet) apart
• GPSd each corner plot
• manually located interior points
• Cruise Data Collection
• 10 BAF prism
• tree tally on all plots
• tree heights and diameters on every 4th and odd
  plots
• Satellite Images
• Landsat Thematic Mapper 5
• captured in January and June 1998
• Stand Characteristics
• natural and planted loblolly and (a small amount
  of)
• slash pine
• establishment dates range from 1960's to 1988

7
The 16-plot cluster minimizes the effects of
image mis-registration and captures variation in
the stand traditional cruises miss.

• If the image is off just a bit" due to
  mis-registration, the chain grids may miss that
  information all together, while the 16-plot
  cluster will most likely capture it.
• The 3-by-3 and 5-by-5 chain grids do not capture
  much of the variation the 16-plot cluster does.

8

• Buffer each cruise point by 10 meters
• Calculate average pixel value for each 10-meter
  buffer
• Calculate band ratios and vegetation indices

Represents, at most 4 LTM cells (0.89 acres).

• Locate midpoint of four cruise points in each
  quadrant
• Buffer midpoint by 28 meters
• Calculate average pixel value for each buffer
• Calculate band ratios and vegetation indices

Represents, at most represents 9 LTM cells (2
acres)
9

• At the 10-meter individual plot level, no
  correlation with Landsat 5 Thematic Mapper
  Satellite image variables and measured basal area
  higher than .4 (adjusted R2).
• At 28-meter averaged plot level, high
  correlations between the log of basal area,
  summer band 4, the ratio of summer band 5 and
  summer band 3, and ndvip_w.

10

• The LTM sensor is not sensitive enough to pick up
  the variation in basal area at the 10-meter
  resolution.
• Basal area can not be estimated at the 0.9 acre
  resolution
• At the 28-meter resolution, the LTM sensor can
  pick up the variation in basal area.
• Basal area can be estimated at the 2 acre
  resolution

11

• 82 of the samples were classified within 20 of
  the measured basal area

12

• If you would like further information about the
  basal area estimation or our current TIP3
  project, please feel free to contact me by phone
  at [phone] or via e-mail
  rcl7820_at_owl.forestry.uga.edu

Cieszewski, C. 2001. Long-term sustainability
analysis of forest resources in Georgia and
assessment of potential effects of riparian zones
and other regulatory and business constraints.
Traditional Industries in Pulp and Paper
Production 2001 Research Proposal.