Forest Biomass Estimation using Landsat Thematic Mapper Data in Southeast USA

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.

6. Plot Layout • GPS Data Collection • 16 plot-clusters • plots 30-meters (98.4 feet) apart • GPS’d 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 • plot basal areas ranged from 5 to 190 sq. ft. • understory condition ranged from very clean to dense sweetgum saplings

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. 10- and 28-meter Measured vs. Predicted Prediction Plot for 28-meter Buffer Group 200 180 160 140 Measured Basal Area 120 100 80 60 40 20 0 0 20 40 60 80 100 120 140 160 180 200 Estimated Basal Area • 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. Regression Analysis (28-Meter Buffer) 113 of 113 120 110 of 113 104 of 113 93 of 113 100 72 of 113 80 60 Number of Plots 47 of 113 40 29 of 113 20 0 5 10 15 20 25 30 35+ % Within Measured Basal Area • 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 (706) 542-1074 or via e-mail rcl7820@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.