This research assesses the accuracy of SRTM and AIRSAR DEMs acquired over the mountainous-hillands of Cameron Highlands with DEMs generated from Digital Aerial Photograph (DAP) with a fine (2 m) spatial resolution and height resolution of about 0.5 m. The ground control points used for generating stereo models from the DAP were acquired during field work using GPS which achieved accuracy better than 2 cm in most cases. To overcome the difficulty of overlaying the DEMs with the DAP DEM as no features can be easily identified on both the images, therefore a technique of using transects and contours generated from the DEMs were used to correct the horizontal displacement. For AIRSAR DEM, comparing the accompanying AIRSAR composite images was also employed. These then allowed an analysis of the height accuracy to be undertaken. The height of both the AIRSAR and SRTM DEMs were also corrected by applying Linear Regression Models. These models were produced by comparing pixels obtained from points, profiles and an area. Once again the corrected DEMs were assessed. Finally the extracted profiles and contours from the corrected SRTM and AIRSAR were compared with the reference DEM. From the comparisons, the horizontal errors were found to be about one and the half pixels (138.72 m: for SRTM) to the east and 1 pixel (10 m: for AIRSAR) to the south. The SD of height differences of the SRTM and AIRSAR DEMs using 90% data were 9.2 m and 5.2 m with profiles comparison; 10.4 m and 5.4 m with area comparison; 10.8 m and 2.4 m with GPS GCPs comparison respectively. From the three comparisons, the means of height differences are 5.2 m, 6.1 m and 15.2 m for SRTM and 8.1 m, 8.3 m and 7.9 m for AIRSAR DEM. The results suggest there is height offset in the AIRSAR DEM. When both heights of DEMs were corrected, the generated contours are close to each other and to reference contours. Using contour colours images and height modelling, the corrected DEM was found to have the potential to detect areas that prone to flash floods and mudslides.

This research assesses the accuracy of SRTM and AIRSAR DEMs acquired over the mountainous-hillands of Cameron Highlands with DEMs generated from Digital Aerial Photograph (DAP) with a fine (2 m) spatial resolution and height resolution of about 0.5 m. The ground control points used for generating stereo models from the DAP were acquired during field work using GPS which achieved accuracy better than 2 cm in most cases. To overcome the difficulty of overlaying the DEMs with the DAP DEM as no features can be easily identified on both the images, therefore a technique of using transects and contours generated from the DEMs were used to correct the horizontal displacement. For AIRSAR DEM, comparing the accompanying AIRSAR composite images was also employed. These then allowed an analysis of the height accuracy to be undertaken. The height of both the AIRSAR and SRTM DEMs were also corrected by applying Linear Regression Models. These models were produced by comparing pixels obtained from points, profiles and an area. Once again the corrected DEMs were assessed. Finally the extracted profiles and contours from the corrected SRTM and AIRSAR were compared with the reference DEM. From the comparisons, the horizontal errors were found to be about one and the half pixels (138.72 m: for SRTM) to the east and 1 pixel (10 m: for AIRSAR) to the south. The SD of height differences of the SRTM and AIRSAR DEMs using 90% data were 9.2 m and 5.2 m with profiles comparison; 10.4 m and 5.4 m with area comparison; 10.8 m and 2.4 m with GPS GCPs comparison respectively. From the three comparisons, the means of height differences are 5.2 m, 6.1 m and 15.2 m for SRTM and 8.1 m, 8.3 m and 7.9 m for AIRSAR DEM. The results suggest there is height offset in the AIRSAR DEM. When both heights of DEMs were corrected, the generated contours are close to each other and to reference contours. Using contour colours images and height modelling, the corrected DEM was found to have the potential to detect areas that prone to flash floods and mudslides.

Forests have been proposed as a means of reducing atmospheric carbon dioxide levels due to their ability to store carbon as biomass. To quantify the amount of atmospheric carbon sequestered by forests, biomass and density estimates are often needed. This study develops, implements, and tests an individual tree-based algorithm for obtaining forest density and biomass using orthophotographs and small footprint LiDAR imagery. It was designed to work with a range of forests and image types without modification, which is accomplished by using generic properties of trees found in many types of images. Multiple parameters are employed to determine how these generic properties are used. To set these parameters, training data is used in conjunction with an optimization algorithm (a modified Nelder-Mead simplex algorithm or a genetic algorithm). The training data consist of small images in which density and biomass are known. A first test of this technique was performed using 25 circular plots (radius = 15 m) placed in young pine plantations in central Virginia, together with false color othophotograph (spatial resolution = 0.5 m) or small footprint LiDAR (interpolated to 0.5 m) imagery. The highest density prediction accuracies (r2 up to 0.88, RMSE as low as 83 trees / ha) were found for runs where photointerpreted densities were used for training and testing. For tests run using density measurements made on the ground, accuracies were consistency higher for orthophotograph-based results than for LiDAR-based results, and were higher for trees with DBH ≥10cm than for trees with DBH ≥7 cm. Biomass estimates obtained by the algorithm using LiDAR imagery had a lower RMSE (as low as 15.6 t / ha) than most comparable studies. The correlations between the actual and predicted values (r2 up to 0.64) were lower than comparable studies, but were generally highly significant (p ≤ 0.05 or 0.01). In all runs there was no obvious relationship between accuracy and the amount of training data used, but the algorithm was sensitive to which training and testing data were selected. Methods were evaluated for combining predictions made using different parameter sets obtained after training using identical data. It was found that averaging the predictions produced improved results. After training using density estimates from the human photointerpreter, 89% of the trees located by the algorithm corresponded to trees found by the human photointerpreter. A comparison of the two optimization techniques found them to be comparable in speed and effectiveness. / Ph. D.

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