CAJ | 학술논문

　　为了获取黄土沟壑区切沟的高精度地面高程模型(DEM)，该文针对其植被特点，提出一种基于激光回光强度衰减模型的植被滤波方法。首先建立回光强度随距离的衰减模型，通过得到的衰减系数，对点云作回光强度补偿。其次提出融合回光强度的自适应分块拟合法，在Matlab7.6中对甘肃天水桥子沟切沟点云数据应用该方法进行滤波。滤波后植被点数为160517，占点云总数的34.04%。对比滤波前后切沟数据，地面点集的高程均方根误差由0.1430降到0.1324，滤波后等高线毛刺基本消除，地面特征保持良好。对比该文方法、回光强度分类法、曲面拟合法的均方根误差分别为0.1324、0.1398、0.1412，说明该文方法降低了切沟DEM的误差。通过计算滤波前后DEM高程差的累积概率，得到桥子沟试验区一条典型切沟的植被盖度分布，沟底植被平均盖度为0.274，左侧A区和右侧B区沟壁植被平均盖度分别为0.802、0.583，得出了右侧沟壁侵蚀速度明显大于左侧且沟头前进速度较快的结论，与2002-2012十年间该切沟实际调查资料对比，沟长增长213.6%，沟宽仅增长83%，一定程度说明植被降低了沟壁的侵蚀速度。试验表明该方法适用于黄土沟壑区切沟点云的植被滤波处理，为建立高精度切沟DEM、切沟的发育监测和水土流失治理提供科学依据。
　　위료획취황토구학구절구적고정도지면고정모형(DEM)，해문침대기식피특점，제출일충기우격광회광강도쇠감모형적식피려파방법。수선건립회광강도수거리적쇠감모형，통과득도적쇠감계수，대점운작회광강도보상。기차제출융합회광강도적자괄응분괴의합법，재Matlab7.6중대감숙천수교자구절구점운수거응용해방법진행려파。려파후식피점수위160517，점점운총수적34.04%。대비려파전후절구수거，지면점집적고정균방근오차유0.1430강도0.1324，려파후등고선모자기본소제，지면특정보지량호。대비해문방법、회광강도분류법、곡면의합법적균방근오차분별위0.1324、0.1398、0.1412，설명해문방법강저료절구DEM적오차。통과계산려파전후DEM고정차적루적개솔，득도교자구시험구일조전형절구적식피개도분포，구저식피평균개도위0.274，좌측A구화우측B구구벽식피평균개도분별위0.802、0.583，득출료우측구벽침식속도명현대우좌측차구두전진속도교쾌적결론，여2002-2012십년간해절구실제조사자료대비，구장증장213.6%，구관부증장83%，일정정도설명식피강저료구벽적침식속도。시험표명해방법괄용우황토구학구절구점운적식피려파처리，위건립고정도절구DEM、절구적발육감측화수토류실치리제공과학의거。
The point cloud data of a gully region in loess plateau via Terrestrial Laser Scan (TLS) was characterized by uneven distribution of laser footprints, rapid geomorphologic change, and high density of herbaceous vegetation. In order to improve the precision of gully DEM, this paper proposes a vegetation filtering method of TLS point cloud. We first use laser return intensity to make an applicable classification.It is significant to compensate intensity attenuation which is brought by distance, angle of incidence, and environment, and establish a unified relationship between object and return light intensity. Available data indicates that return intensity is represented by an inverse second-order-dependent function of distance and other parameters can be treated as a constant in one experiment. We built a distance attenuation model of return light intensity. We can calculate the attenuation factor based on it and then compensate for laser return intensity of the whole point cloud. In this study, the return intensities of six sphere targets are used to build an attenuation model, and we obtained the attenuation factor as 0.3173. With the unified return intensity, each point’s intensity deviation with intensity of the ground was used as a weight to enlarge the difference of non-ground points and ground points. Then we used segmentation and surface fitting method to calculate each point’s distance to the trend surface, and set the threshold to separate the ground points and vegetation points. In this study, we propose an adaptive mesh grid filtering method integrated with return light intensity. In this method, we updated the distance to the trend surface though each point’s intensity weight which has a linear relationship with its intensity deviation. Besides, the adaptive segmentation is more fast and effective than the K neighborhood search method. The method’s reliability was tested through a point cloud acquired from a typical gully in Qiaozi Valley, Tianshui City of Gansu Province. It was located in 105°43’2’’E, 34°36’59’’N. The section is a typical V shape, and 90%of the surface is covered with low vegetation like grass and leguminous plant. The gully was scanned with a Leica HDS6100 3D laser scanner with a precision of 1mm. The cloud data containd 1,498,191 points, return light intensity varied from-2048 to-2047, covered an area of 14.0975 m2 and the average point density was 3345.1 points/m2. We practiced the adaptive mesh grid-filtering algorithm in Matlab, and iterated three times to get filtering result. There were 160 517 vegetation points which were removed from data, and they were 34.04% of the whole point cloud. Comparing the filtered DEM with the original one, we proved that this filtering method can overcome negative influences of uneven terrain and high vegetation coverage and filter efficiently. It reduces the elevation root-mean-square error (RMSE) of point cloud from 0.1430 to 0.1324, and most rags of original contour lines decrease and ground characteristics are well preserved. In addition, we compared this method with the intensity-classification method and surface fitting method, and found that this paper’s filtering method performs better. Furthermore, we got a typical gully vegetation probability distribution of Qiaozi Valley by calculating the cumulative probability of filtered and original DEMs’ deviation. It can explain the gully morphologic change, and the change coincides with the observation data. It proved that vegetation is effective in sand binding and reduction and slope impaction. Consequently, this study not only provides a new approach to filter gully vegetation in point cloud and acquiring high-precision DEM, but also helps to set the stage for future research to monitor gully morphologic change and control soil erosion.