Modeling Biomass and Timber Volume by Using an Allometric Growth Model from Landsat TM Images Qingmin Meng, Chris Cieszewski D. B. Warnell School of Forest Resources University of Georgia

Introduction Ground truthing vs. remote sensing data. Remote sensing. Direct exploration of the multispectral data. Using vegetation index, such as VI, TVI, or NDVI. Kth nearest neighbor estimation.

Introduction Uncertainty of the expansion from a pixel scale to a regional scale. Can we improve it ? A method provides information of pixels and possesses the space information.

Objectives Mixed-effects models will be employed and regional differences will be considered. Build new indices, surface area and volume of NDVI. Model selection. Analyze the spatial difference of forest biomass and timber volume according to the fitted models.

Methodology NDVI, NDVIsa, and NDVIvol. Allometric growth model

Methodology (cont’d) General equation of allometric growth law What is the general law of allometric growth?

Methodology (cont’d) Linear fixed-effects model Linear mixed-effects model

Methodology (cont’d) GIS and RS techniques Geometric correction, data transformation, mask, triangular irregular network function, 3-D model, and NDVIsa and NDVIvol extraction ( in Imagine and ArcView).

Study area and data Figure 1. Five study regions in GA.

Study area and data (cont’d) Image boundary County boundary Figure 2. Study areas covered by images.

Study area and data (cont’d) The 2001 data for six county-level dependent vaiables. biomass of all, all live merchantable biomass, volume of all live trees, volume of growthing stock, volume of sawtimber, and volume of the sawlog portion. NDVIsa and NDVIvol are extracted from Landsat TM images.

Results Table 1. Fixed vs. mixed effects models using NDVIvol as predictor

Results (cont’d) Table 2. Fixed vs. mixed effects model using NDVIsa as predictor

Results Table 3. The best models

Results (cont’d) Table 3. The best models (cont’d)

Conclusions (cont’d) The allometric growth model is suitable for the assessment of biomass and timber volume at a large scale. The linear mixed-effects models can more accurately estimate biomass and timber volume than the linear fixed-effects models. NDVIsa and NDVIvol both contain the pixel information and area information. NDVIvol is more suitable than NDVIsa in predictions.

Conclusions (cont’d) Regional characteristics of allometry of biomass and timber volume. In the ridge and valley region and the lower coastal plain region, the overall indices, biomass of all, et al. have negative allometric characteristics. In the mountain region and piedmont region, the overall indices of biomass and volume have positive allmoteric characteristics. In the upper coast plain region, however, the overall index of biomass and volume have neutral allometric characteristics

The end. Thank you.