台灣地區地層下陷問題肇始於六十年代迄今已逾三十餘載，持續下陷面積已達1,194平方公里，最大累積下陷量達到三公尺以上。而目前地層下陷地表監測所採用的傳統水準測量耗費人力、時間較多，且不易獲得連續和全面性之地層下陷資料，目前國內水利單位限於人力時間，無法針對所有監測區域每年皆施測一次。近年來由於空載光達測量技術興起，其具有短時間內獲取大區域高密度、高精度高程資料的特性，因此本研究之目的在探討如何利用空載光達測量技術快速獲取高精度之三維點雲資訊，進行大區域的地層下陷監測及其成效。 研究方法係首先將監測區內掃瞄的光達點雲資料進行網格化分群；接著，計算網格區域內所有光達點擬合平面的中心高程；然後，以人工或自動方法萃取出平坦、穩固的網格區域做為監測面；最後，進行不同時期網格監測面高程差異之統計測試分析，以求出地層下陷量。 實驗結果顯示改善點雲高程精度至5公分以內後，經由網格監測面的精度、坡度、坡向、反射強度、道路範圍等為門檻值，可萃取出80%以上正確率的穩固監測面，且其高差成果與長期監測成果的平均值差異在1.3公分至2.9公分之間，由此成果可以說明本研究成果對建立一套省時省力的監測模式，進而達到地層下陷監測自動化的目的有相當幫助。 / The issue of land subsidence in Taiwan has been concerned for over 30 years since 1970. Land subsidence area has been already over 1194 km2, the maximum amount of accumulative subsidence is more than 3 meters. The conventional leveling for the land subsidence monitoring is labor-intensive and time-consuming, so that the Water Resources Agency could not monitor all the subsidence area every year. Airborne LIDAR technology was developed in recent years, it has the characteristics of collecting 3-D point data at the high density and high elevation accuracy in short time. The purpose of this study, therefore, is to discuss how to utilize the airborne LIDAR technology to monitor the land subsidence. The proposed approach, therefore, is first to divide the collecting DSM points in the monitor area into regular grids. Secondly, all the points in the regular grids are fitted to one set planar parameters by least squares principle and the centric elevation of each grid is calculated. Third, the flatness and well-defined planar grids are selected as the monitoring surfaces with the manual or automatic method. Finally, the difference of centric elevation in each monitoring surfaces at different period is calculated and analyzed with statistical approach. This study shows that after refining the elevation accuracy of point clouds within 5 cm, our approach can extract stable monitoring surfaces by limiting planar fitting accuracy, flatness, slope, intensity, or by using road information. The extracted correct rate can be more than 80%. The discrepancy of elevation difference between this study and long-term monitoring result is between 1.3 cm and 2.9 cm. It proves the proposed approach is helpful on constructing the monitoring model in timesaving and efficient way, and our proposed approach has the potential for developing automatic land subsidence monitoring method.