In cooperation with the USDA Forest Service Southern Research Station, an algorithm has been developed to replace the current aerial-photography-derived FIA Phase 1 estimates of forest/non-forest with a Landsat Thematic Mapper-based forest area estimation. Corrected area estimates were obtained using a new hybrid classifier called Iterative Guided Spectral Class Rejection (IGSCR) for portions of three physiographic regions of Virginia. Corrected area estimates were also derived using the Landsat Thematic Mapper-based Multi-Resolution Land Characteristic Interagency Consortium (MRLC) cover maps. Both satellite-based corrected area estimates were tested against the traditional photo-based estimates. Forest area estimates were not significantly different (at the 95% level) between the traditional FIA, IGSCR, and MRLC methods, although the precision of the satellite-based estimates was lower. The estimated percent forest area and the standard error (respectively) of the estimates for each region and method are as follows; Coastal Plain- Phase 1 66.06% and 1.08%, IGSCR 68.88% and 2.93%, MRLC 69.84% and 3.08%. Piedmont- Phase 1 63.87% and 1.91%, IGSCR 65.52% and 3.50%, MRLC 59.19% and 3.83%. Ridge and Valley- Phase 1 69.74% and 1.22%, IGSCR 70.02%, and 2.43%, MRLC 70.53% and 2.52%. Map accuracies were not significantly different (at the 95% level) between the IGSCR method and the MRLC method. Overall accuracies ranged from 80% to 89% using FIA definitions of forest and non-forest land use. Given standardization of the image rectification process and training data properties, the IGSCR methodology is objective and repeatable across users, regions, and time and outperforms the MRLC for FIA applications. / Master of Science

In order to produce a more current inventory of forest estimates along with change estimates, the Forest Inventory Analysis (FIA) program has moved to an annual system in which 20% of the permanent plots in a state are surveyed. The previous system sampled permanent plots in 10-year intervals by sampling states sequentially in a cycle (Wayman 2001, USDA FIA). The move to an annual assessment has introduced the use satellite technology to produce forest estimates. Wayman et al (2001) researched the effectiveness of satellite technology in relation to aerial photo-interpretation, finding the satellite method to do an adequate job, but reporting over-estimations of forest area. This research extends the satellite method a step further, introducing the use of ancillary data in post-classification. The US Forest Service has well-defined definitions of forest and nonforest land-use in its (FIA) program. Using these definitions as parameters, post-classification techniques were developed to improve forest area estimates from the initial spectral classification. A goal of the study was to determine the accuracy of using readily available ancillary data. US Census data, TIGER street files, and local tax parcel data were used. An Urban Mask was created based on population density to mask out Forested pixels in a classified image. Logistic Regression was used to see if population density, street density, and land value were good predictors of forest/nonforest pixels. Research was also conducted on accuracy when using contiguity filters. The current filter used by the Virginia Department of Forestry (VDoF) was compared to functions available in ERDAS Imagine. These filters were applied as part of the post-classification techniques. Results show there was no significant difference in map accuracies at the 95% confidence interval using the ancillary data with filters in a post-classification sort. However, the use of ancillary data had liabilities depending on the resolution of the data and its application in overlay. / Master of Science

The goal of this project was to develop an operational Landsat TM image classification protocol for FIA forest area estimation. A hybrid classifier known as Iterative Guided Spectral Class Rejection (IGSCR) was automated using the ERDAS C Toolkit and ERDAS Macro Language. The resulting program was tested on 4 Landsat ETM+ images using training data collected via region-growing at 200 random points within each image. The classified images were spatially post-processed using variations on a 3x3 majority filter and a clump and eliminate technique. The accuracy of the images was assessed using the center land use of all plots, and subsets containing plots with 50, 75 and 100% homogeneity. The overall classification accuracies ranged from 81.9-95.4%. The forest area estimates derived from all image, filter and accuracy set combinations met the USDA Forest Service precision requirement of less than 3% per million acres timberland. There were no consistently significant filtering effects at the 95% level; however, the 3x3 majority filter significantly improved the accuracy of the most fragmented image and did not decrease the accuracy of the other images. Overall accuracy increased with homogeneity of the plots used in the validation set and decreased with fragmentation (estimated by % edge; R2 = 0.932). We conclude that the use of random points to initiate training data collection via region-growing may be an acceptable and repeatable addition to the IGSCR protocol, if the training data are representative of the spectral characteristics of the image. We recommend 3x3 majority filtering for all images, and, if it would not bias the sample, the selection of validation data using a plot homogeneity requirement rather than plot center land use only. These protocol refinements, along with the automation of IGSCR, make IGSCR suitable for use by the USDA Forest Service in the operational classification of Landsat imagery for forest area estimation. / Master of Science