| Summary: | 博士 === 國立成功大學 === 測量及空間資訊學系 === 107 === In subtropical forests, the penetration ability of airborne laser scanning (ALS) may be limited because of highly dense vegetation cover. However, in the typical planning of ALS surveys, the ability of laser pulses to penetrate forests is not considered. Nine round-trip flight lines covering the area of a subtropical forest on the northeast side of the Tsengwen Reservoir in Taiwan were designed in this study. Five flight lines flew at altitudes of 1525, 1830, 2135, 2440, and 2745 m, and the other four had pulse repetition frequencies (PRFs) of 100, 150, 200, and 250 kHz. The laser penetration index (LPI) is a quantitative index measuring the penetration ability of the ALS and consists of the ratio of the number of laser pulses reaching the forest floor to the total number of laser pulses. The LPI was used to represent the laser penetration rate and investigate the influence of flying altitude and PRF on the LPI. The results showed that as the flying altitude decreased by 1000 m, the average LPI increased by 10%, and as the PRF decreased by 50 kHz, the average LPI increased by 2%. The effect of the LPI on digital elevation models (DEMs) was confirmed by visual images obtained by DEMs at five altitudes. The DEM obtained at an altitude of 2745 m was coarsely textured, whereas that obtained at an altitude of 1525 m was finely textured. The LPI of a forest should be considered for ALS survey planning, especially when consistent DEM precision for large subtropical forest areas is paramount.
In addition, the forest canopy also affects the LPI of ALS. The denser the forest canopy, the lower the LPI, and vice versa. For an ALS survey project, with the goal to acquire sufficient ground surface points, the flight planning should be conducted according to the distribution forest canopy thickness. The information of forest canopy can be easily accessed via the satellite-derived vegetation index (VI). In this study, we examined the correction relationship between the satellite-derived VI and LPI. The two study areas were 21 square-kilometer located in Tsengwen reservoir and 36 square-kilometer located in Jinshan volcanic region. The ALS data were collected with an Optech HD400 instrument. The VIs were calculated from Formsat-2, SPOT-5, WorldView-2 and GeoEye, all of which were acquired near the time of ALS data acquisition. This study calculated four VIs, i.e., NDVI, RVI, PVI, and SAVI. The effect of atmospheric correction (conducted by ATCOR-3) were also discussed. The results show high linear correlation between LPI and VIs. It is suggested that in future ALS flight plans, we can refer to satellite VIs to understand the canopy density of forest in a given survey area, when those places with a high VI, decide whether we need to use a lower flight altitude or reduce the PRF.
Furthermore, the final aim of this study was using ALS data to estimate stand density in a subtropical forest. The forest-related statistics, including forest biomass, carbon sink, and the prevention of forest fires, can be obtained by estimating stand density. In this study, a dataset with the laser pulse density of 225.5 pulses/square-meter was obtained using airborne laser scanning in Xindian District of New Taipei City, Taiwan. Three digital surface models (DSMs) were generated using first-echo, last-echo, and highest first-echo data. Three canopy height models (CHMs) were obtained by deducting the DEM from the three DSMs. The cell sizes (Csizes) of the CHMs were 1, 0.5, and 0.2 m. In addition, stand density was estimated using CHM data and following the local maximum method. The stand density of 35 sample regions was acquired via in-situ measurement. The results indicated that the root-mean-square error (RMSE) ranged between 1.68 and 2.43 (trees/100 square-meter); the RMSE difference was only 0.78 (trees/100 square-meter), indicating that stand density was effectively estimated in both cases. Furthermore, regression models were used to correct the error in stand density estimations; the RMSE after correction was called RMSE'. A comparison of the RMSE and RMSE' showed that the average value decreased from 12.35 to 2.66 (trees/100 square-meter), meaning that the regression model could effectively reduce the error. Finally, a comparison of the effects of different laser pulse densities on the RMSE value showed that, in order to obtain the minimum RMSE for stand density, the laser pulse density must be greater than 10, 30, and 125 pulses/square-meter at Csizes of 1, 0.5, and 0.2 m, respectively.
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