空載光達生產數值高程模型及其精度評估

Abstract

數值地形模型(Digital Terrain Model, DTM)在地理資訊系統應用與分析上是重要的數據，應用空載雷射掃描技術(或稱為空載光達，Airborne Light Detection and Ranging, LIDAR)測量地形的數值高程數據，其相關技術發展迅速，已經達到應用階段。空載雷射掃描儀量測地表的反射回訊，獲得三維座標的量測值，量測點包括了地形面以及非地面的量測點 (如建物、樹木，車輛)，為了生產DTM，地物的雷射量測點需進一步過濾或分類出來，留下地形面的雷射量測點，進而組成DTM。關於過濾非地面點的處理，此課題吸引了多方研究的投入，是重要的研究方向。本研究主要目的，發展出應用多重方式的過濾處理程序，並探討數據的精度評估。 關於濾除空載光達數據中非地面量測點之研究，目前成果指出，處理崎嶇的山區地形或高密度植被覆蓋的地區，仍是挑戰性的課題，諸多演算法為了過濾山區的植被量測點，將地形特徵如地形山脊亦同時被過度平滑濾除，此問題的主要原因是地物點與地面構成的坡度幾何和背景的陡坡地形坡度幾何，二者的可區分特性低，本研究提出自適性的過濾演算法，有效的移除背景陡坡地形所干擾的效應，處理特色在於過濾處理並且能保留地形的特徵。測試區域分別試驗於都市區域，複雜地類覆蓋與建物的濾除，以及山區植被的濾除，本研究比較不同的過濾方法，成果顯示本文的方法與商業軟體所使用的自動製程比較，在山區測試數據組，本文過濾成果與檢核點比較，誤差絕對值的平均22.2 cm，優於自動化程序25.4 cm，本研究獲得良好的過濾成果。 本研究以山區測試區域(坡度平均26.6°)評估比較兩組光達數據組的特性，應用地面檢核點(906個檢核數據)分析數據精度，本文分析地形特徵包括地形坡度，坡向與土地覆蓋類型等因素對於光達高程精度的影響量。文中提出利用光達數據量化描述土地覆蓋類型的方法，包括植被體積量的估計，局部性地表粗糙度量測，到達地面的雷射測點平均相鄰點距離，以及植被覆蓋地平角度等評估指標，用以量化區別出不同的土地覆蓋類型。應用這些指標推導不同植被型態與光達高程數據的精度關係。分析成果顯示，光達數據高程精度與這幾個植被型態因子當中的「植被覆蓋地平角度」，「植被量的估計值」，「局部性地表粗糙度量測」，「到達地面的雷射測點平均相鄰點距離」等因子有相關聯性，高程精度隨者這四個因子的變化有統計顯著差異性。「植被覆蓋地平角度因子的三角正切值」與「到達地面的雷射測點平均相鄰點距離因子」兩個因子乘積，與高程精度具有高度的線性相關性(迴歸判定係數r2 > 0.9)，高程精度隨不同植被覆蓋型態而變化。關於地形因子與光達數據高程精度的關係，高程精度隨地形坡度角的變化有統計顯著差異性。「坡度角的三角正切值」與「到達地面的雷射測點平均相鄰點距離」兩個因子乘積，與高程精度亦具有高度的線性相關性(迴歸判定係數r2 = 0.9)，乘積的數值越大(地形越陡)，高程誤差越大。另外，有一組數據具有附加的交錯飛行掃描數據，以本文測試數據而言，交錯飛行，能降低坡向因子對於高程精度的影響量。

Airborne light detection and ranging (LIDAR) technology has become a leading method for producing digital terrain models (DTMs) that are important to many GIS-related analyses and applications. In generating a digital terrain model, removing non-terrain measurements from LIDAR datasets has proven to be an important task. In this dissertation, a series of filters are developed to remove non-terrain LIDAR measurements. It is difficult to accurately extract the terrain surface in areas of rugged relief or discontinuous topography. This research applies adaptive techniques to remove the effects of background relief. The terrain data are preserved, while non-terrain points are removed. The proposed method can discriminate effectively between terrain and non-terrain measurements, and has been tested for urban and mountainous areas. The filtered results from the proposed method are compared to traditional automatic techniques. The results indicate that the proposed method produces a better digital terrain model. An accuracy assessment of two LIDAR-derived elevation datasets was conducted in areas of rugged terrain (average slope 26.6°). Data from 906 ground checkpoints in various land-cover types were collected in situ as reference points. Analysis of the accuracy of LIDAR-derived elevation as a function of several factors including terrain slope, terrain aspect, and land-cover types were conducted. This paper attempts to characterize vegetation information derived from LIDAR data based on variables such as canopy volume, local roughness of point clouds, point spacing of LIDAR ground returns, and vegetation angle. This information was used to evaluate the accuracy of elevation as a function of vegetation type. The experimental results revealed that the accuracy of elevation was considerably correlated with five factors: terrain slope, vegetation angle, canopy volume, local roughness of point clouds, and point spacing of LIDAR ground returns. The results show a linear relationship between the elevation accuracy and the combination of vegetation angle and the point spacing of ground returns (r2 > 0.9). The combination of vegetation angle and point spacing of ground returns explains a significant amount of the variability in elevation accuracy. Elevation accuracy varied with different vegetation types. The elevation accuracy was also linearly correlated with the product of the point spacing of ground returns and the tangent of the slope (r2 = 0.9). A greater product value implies a greater elevation error. In addition, with regard to terrain aspect, one dense dataset with extra cross-flight data revealed a lesser impact of aspect on elevation accuracy.