Application and prospects of airborne LiDAR technology in landslide research in China
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摘要:
我国是世界上滑坡灾害最严重的国家之一。近年来发生的许多灾难性重大滑坡并不在国家地质灾害数据库内，这些滑坡具有高位发生、隐蔽性强、远程运动等特征，传统观测手段无法有效判识，给滑坡调查工作带来了极大困难。机载LiDAR是一种新型的主动式对地观测技术，具有全天候、高精度、真实三维等特点，在滑坡调查研究中发挥了重要作用。本文简要概述了机载LiDAR系统的技术原理、数据获取与处理方法，梳理总结了机载LiDAR在我国滑坡三维模型构建、滑坡识别与特征分析、滑坡形变监测等3个研究领域的一系列成果和进展，表明机载LiDAR技术能够有效获取去除植被后的真实地表三维模型，突出显示滑坡特征信息，进而准确提取滑坡形态和滑坡特征。讨论了机载LiDAR技术在滑坡研究领域需要解决的关键科学问题，目前尚未形成适用于不同类型滑坡的LiDAR点云分类技术标准，缺乏区域尺度的滑坡识别及其规律性研究。最后，总结展望了机载LiDAR技术在我国滑坡研究中的发展趋势。
Abstract:China is one of the countries mostly severely affected by landslide disasters in the world. Many catastrophic large landslides in recent years are not included in the national geo-hazard database, and these landslides are characterized by high-altitude occurrence, strong concealment, and long-distance movement. Traditional observation methods are ineffective in identifying them, posing great difficulties for landslide investigation work. Airborne LiDAR is a new type of active remote sensing technology with features of all-weather, high precision, and true three-dimensional representation, playing an important role in landslide investigation and research. This paper briefly summarizes the technical principles, data acquisition, and processing methods of airborne LiDAR systems, and summarizes a series of achievements and progress in three research fields in China, including landslide 3D model construction, landslide identification and feature analysis, and landslide deformation monitoring. It demonstrates that airborne LiDAR technology can effectively acquire true surface 3D model after vegetation removal, highlight landslide feature information, and accurately extract landslide morphology and characteristics. Key scientific issues in the field of landslide research using airborne LiDAR technology are discussed.The standard of LiDAR point cloud classification suitable for different landslide types has not yet been formed, and there is a lack of regional-scale landslide identification and development law research. Finally, the development trends of landslide research based on airborne LiDAR technology are summarized and prospected.
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0. 引言
随着人类工程活动对地质环境的破坏程度不断加大，以及全球气候变化等因素的影响不断加重，滑坡灾害已严重威胁人类生命财产安全[1]。我国是世界上滑坡灾害最严重的国家之一，根据自然资源部发布的《全国地质灾害通报》，我国在2009-2019年共发生滑坡灾害约95068处[2 − 3]。调查发现，近年来发生的许多重大灾难性滑坡并不在已有的滑坡隐患点数据库内，这些滑坡往往地处高山峡谷中，具有高位启滑、隐蔽性强、远程运动、灾害链效应等特征[4 − 6]，传统人工排查、卫星遥感等手段难以进行有效快速识别，需要采取新技术加强此类滑坡的调查判识。
遥感技术在滑坡研究中已经得到广泛应用[7 − 8]，遥感数据类型主要有光学影像数据、合成孔径雷达数据（SAR）和激光雷达数据（LiDAR）。光学卫星遥感技术是现阶段最常见的滑坡调查手段，通过获取多期、多尺度的光学影像，可以快速识别疑似滑坡区域，但是光学影像受天气影响较大，云雾或水汽会导致图像不准确或被遮盖。InSAR技术能够提取地表三维信息和高度变化信息，具有全天候、大范围的数据获取能力，能够远距离观测地表微小变形信息，在滑坡形变监测方面表现突出[9 − 10]，但它的数据精度同样受气象、地形和地表反射率等因素的影响。LiDAR技术是当今对地观测的最新技术之一[11]，根据搭载平台不同，主要分为星载、机载、地面和车载四种模式，其中机载LiDAR技术在滑坡研究中的应用最为广泛。机载LiDAR技术采用主动遥感成像方式，不受天气、光照等自然条件的限制，能够实现全天候、全天时的快速数据采集[12 − 13]。而且，激光的多回波特性使得LiDAR技术具有一定的植被穿透能力，能够有效剔除植被影响，获取高精度的真实地面信息[14 − 16]，具备其它遥感技术无法比肩的优势。
机载LiDAR技术正在逐渐应用到我国的滑坡研究中，并取得了一系列研究成果。本文首先简要介绍机载LiDAR技术基本原理，然后梳理总结LiDAR技术在滑坡三维模型构建、滑坡识别、滑坡动态监测三个方面的研究成果与最新发展动态，进而讨论LiDAR技术在滑坡研究领域的应用潜力、需要解决的问题，以期待更多学者认识并应用。
1. 机载LiDAR技术概述
1.1 机载LiDAR技术原理
机载LiDAR主要由无人机/有人机搭载平台和LiDAR系统组成。其中，有人机飞行高度高，采集范围广，但存在点云密度较低，作业人员安全隐患，成本高航等问题，无人机主要有旋翼无人机、固定翼无人机和垂直起降固定翼无人机等，适用于高山峡谷等人员难以到达的地区。LiDAR系统主要包括激光测距单元、惯性测量单元（IMU）、光学机械扫描单元和动态差分GPS定位系统（DGPS）[17 − 18]。LiDAR通过激光发射器主动发射激光脉冲，然后通过接收器接收地面目标反射的激光回波，并准确记录激光从发射到接收所经历的时间，进而确定激光发射器与目标之间的距离[19]，最后结合GPS和飞行姿态参数，解算出地面目标的三维坐标（图1）。
1.2 机载LiDAR数据获取与处理
机载LiDAR数据获取与处理过程主要有4部分（图2）：
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图 2 机载LiDAR数据采集与处理流程图Figure 2. Flow chart of airborne LiDAR data acquisition and processing（1）数据获取。在充分了解测区地理地质环境的基础上，规划符合数据需求的飞行航线，然后进行作业飞行和数据采集。机载LiDAR野外采集到的原始数据包括：原始激光点云数据、原始影像数据、机载GPS数据和IMU数据等。根据观测比例尺，点云数据密度应满足内插DEM数据的需求[20]，具体值见表1。
表 1 点云密度要求Table 1. Summary of Point cloud density requirements分幅比例尺 DEM格网间距/m 点云密度/（点/m2） 1：500 0.5 ≥16 1：1000 1.0 ≥4 1：2000 2.0 ≥1 1：5000 2.5 ≥1 1：10 000 5.0 ≥0.25 （2）点云数据预处理。联合解算地面GPS数据、机载GPS数据和IMU数据，得到具有激光扫描仪瞬时位置和姿态信息的飞行轨迹[21]，然后联合飞行轨迹数据和原始激光点云数据，加入系统误差改正参数和坐标投影参数等，解算出地表三维坐标（点云数据）。
（3）点云数据后处理。选择合适的点云滤波算法对机载LiDAR点云数据进行分类，重点分离地面点和非地面点，并根据不同需求进一步将非地面点分类为植被、建筑等。
（4）生成成果产品。根据需求，生成数字高程模型（DEM）和数字地表模型（DSM）等符合真实地形特点的模型产品。
2. 滑坡三维模型构建
传统遥感影像往往难以获取高山峡谷、植被发育地区的真实滑坡地表形态，无法准确分辨滑坡细微特征，导致实际或潜在的隐蔽性滑坡难以准确识别。机载LiDAR具有独特的技术优势，可以有效提取真实的滑坡地表数据，空间精度可达厘米级。机载LiDAR采用多次回波技术，通过发射离散型激光脉冲，接触到滑坡表面植被后，部分脉冲能量的反射信号被系统接受并记录，而剩余的脉冲能量继续“穿透”植被，直达地面[22 − 23]，经过处理后形成包含区域地物三维坐标信息的激光点云数据。点云数据的丰富程度关系到最终的成果精度，与点云密度呈正相关，研究发现，高密度植被覆盖区的激光点云密度应不低于50点/m2，才可达到较为精细的地表特征呈现效果[24 − 25]。
在滑坡研究中，通常情况下仅需要将点云分为地面点和非地面点两类，根据实地环境情况，建立点云滤波规则，从点云数据中分离出地面点，获取真实的滑坡地面三维坐标，进而构建真实滑坡三维模型。在庞大的点云数据集中准确提取地面点，是滑坡模型构建的重中之重，为此，国内外提出了许多点云滤波算法，可大致分为基于形态学的滤波算法、基于坡度的滤波算法、基于曲面拟合的滤波算法和基于不规则三角网的滤波算法[26]。渐进加密不规则三角网滤波算法能够有效提取高坡度区域的地面点，是目前滑坡研究领域中应用最多的点云滤波算法[26 − 27]，已在Terrasolid、LiDAR360等国内外LiDAR点云处理软件中实现应用。该方法首先将点云区域分割成网格，选择每个网格中高程值最低的点作为初始地面点，网格大小的划分是基于区域内存在的最大地物类型，如建筑物。然后根据初始地面点构建不规则三角网（TIN），并进行迭代加密，在迭代过程中设置阈值判断：（1）其余点到最近三角形顶点的夹角；（2）其余点到最近三角形的距离，将满足条件的点加入TIN，一直持续到没有点符合加入条件为止[28]。在构建TIN完成后，通过对LiDAR点云数据进行插值[29]，得到数据空洞点的高程，弥补数据空白区域，并辅以人工手动分类，表现更多的地表细节（冲沟、陡坎等），构建高精度DEM。图3是四川白玉格聂山滑坡光学影像和机载LiDAR DEM效果对比图。
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图 3 四川白玉格聂山滑坡光学遥感影像与机载LiDAR地形效果对比Figure 3. Comparison of optical remote sensing image and airborne LiDAR DEM in Genieshan landslide, Baiyu County, Sichuan Province国内对点云滤波算法的研究处于探索阶段。面对国内复杂多样的地形地貌，专家学者采用不同滤波算法对同一区域进行点云数据处理，评价滤波结果的精度，积累大量实用经验，在此基础上，进一步开展滤波算法改进研究。例如，张凡等[30]提出一种布料模拟滤波算法（CSF），生成的DEM能很好表征地区连续起伏的地表状态；崔浩等[31]结合CSF与TIN，通过连续迭代获取地面点，结果表明该算法在坡度较大区域优于传统算法。总而言之，当前的点云滤波算法研究处在实验阶段，还无法投入实际生产应用中。同时，面向滑坡等特殊地貌的专项滤波算法研究较少。
3. 滑坡识别
3.1 基于LiDAR的滑坡识别
滑坡识别是机载LiDAR技术在滑坡研究领域最为广泛的应用。常规滑坡识别方法是基于光学遥感影像，结合专业人员的先验知识，提取滑坡特征信息，建立滑坡遥感解译标志[32 − 34]，通过目视解译和自动解译相结合的方法，判识出滑坡隐患点，然后进行野外实地核查。但是，由于光学遥感影像的局限性，在气象条件较差、植被覆盖较高的地区，滑坡识别存在困难。基于LiDAR的滑坡识别方法在操作过程上与常规方法并无太大区别，但相比光学遥感影像，LiDAR生成的高精度DEM可以提供更丰富的地形地貌信息，清晰反映去除植被后的真实滑坡形态。
（1）定性的滑坡识别
定性滑坡识别是对去除植被的高精度DEM进行三维可视化处理，选取区域典型滑坡，根据专家经验和主观判断提取滑坡特征信息，构建该地区的滑坡解译标志集（图4），如滑坡平面形态（簸箕形、舌形、梨形等）、滑坡壁、滑坡台阶、滑坡舌等地貌形态，规模较大的可见拉张裂隙、冲沟和陡坎等[35 − 36]，最终通过判断与解译标志相似的地貌特征，目前最常见的定性识别手段是生成山体阴影图。
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机载LiDAR定性滑坡识别多应用于复杂地形地貌的快速滑坡灾害调查中。在汶川地震应急响应过程中，马洪超等[37]通过机载LiDAR快速采集震后地表特征，获取的有效数据面积达500 km2，解译出唐家山堰塞湖滑坡、马堰岭古滑坡和都-汶公路滑坡等大量滑坡，为减轻地震滑坡危害提供了可靠的技术支持。在植被较为茂密的地区，机载LiDAR能够充分发挥去除地表植被的优势，可以有效识别隐蔽性滑坡灾害，发现滑坡成灾的前兆信息。例如，通过高精度LiDAR DEM制作一系列不同太阳方位角的山体阴影图，能够清晰识别出滑坡滑动范围，准确确定滑坡后缘等滑坡要素信息[38]，精准呈现冲沟等滑坡局部地形地貌特征（图5）；潘星等[39]使用机载LiDAR DEM构建去除植被的滑坡解译标志，提取滑坡体形态与规模、表面特征和边界特征等，与现场复核后的数据对比发现，结果极为接近。
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山体阴影图容易受到太阳光照角等因素的影响，出现影像斜坡区阴影过重等情况，无法完整呈现机载LiDAR DEM包含的所有地表细节。为解决此类问题，王绚等[40]结合高分辨率LiDAR DEM和红色立体图（RRIM）的数据处理方法，去除光源、阴影等自然因素的干扰，采用渐变红色突出表达滑坡形态（图6）；郭晨等[41]采用天空视域因子（SVF）三维可视化方法，减弱了单一光源对DEM可视化影像的不利影响，较好的展示了滑坡微小地貌。
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（2）定量的滑坡识别
定量滑坡识别是在高精度LiDAR DEM上提取精细微地形地貌参数[42]，通过数字地形分析（DTA）区分滑坡体和周围地形在DEM上的差异化表现[13]，定量识别滑坡要素。根据对这些滑坡地形参数的详细解读，可判断区域滑坡分布情况与发育特征。例如，通过LiDAR DEM提取地表坡度、地表粗糙度和地表起伏度等地表特征（图7），发现在同一位置出现了明显的跳跃性变化，以此准确圈定了滑坡边界[34, 39]。赖自力等[44]提出一种结合高精度LiDAR DEM和分形理论的滑坡识别方法，采用最大曲率-面积（C-A）分形提取滑坡地形特征，经验证，识别的滑坡区域与实际滑坡位置一致。基于LiDAR DEM的精确数据值，选择高程、坡度等地形因子，统计因子分级中的滑坡数量，确定滑坡发育分布规律，为滑坡灾害防治与风险评价提供数据支撑[36, 42]。
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3.2 基于多源数据融合的滑坡识别
融合光学遥感影像、LiDAR数据和InSAR数据等多源遥感数据，从不同角度和尺度对滑坡特征进行提取和分析，可以有效提高滑坡识别精度和效率[45]。针对复杂地形环境，提出天-空-地一体化的滑坡隐患识别“三查”体系，由高精度的光学影像、InSAR数据、机载LiDAR和地面调查复核组成，机载LiDAR技术承担了区域滑坡普查和重大滑坡隐患的详查工作[4]。基于该体系，在四川丹巴地区和九寨沟地震地区开展了滑坡调查工作，证明了机载LiDAR技术可以清晰的界定植被覆盖下的滑坡轮廓、形态和微小形变等重要滑坡识别标志，相比其他光学遥感调查技术和人工调查具有突出优势[46 − 48]。在此基础上，孙涛[49]采用高清光学影像和LiDAR DEM构建了三维解译模型，并叠加InSAR形变监测数据等，极大地增强了LiDAR DEM的显示效果，提高了滑坡识别的效率和准确性。总而言之，机载LiDAR技术与其他遥感技术“优势互补”，在滑坡判识工作中逐步向着大区域、多层次和低成本的方向迈进。
3.3 滑坡智能识别
面对地表状态复杂的滑坡灾害，充分发挥机载LiDAR数据高精度的特点，许多学者采用机器学习等新技术，设计先进的地物识别方法，快速、准确的评估滑坡灾害范围。例如，康义凯[50]提出了一种基于Moravec算子的算法，根据高程变化特征在点云数据中提取变异点，采用变异点和原始点云分别构网的方法，分离滑坡体与原山体地形，从而自动判识滑坡体。寇蓉[51]联合LiDAR点云数据与高分辨率遥感影像作为样本，引入深度学习网络模型，融合了LiDAR点云局域空间结构的形状特征和遥感影像的光谱特征，提取更加精确的滑坡区域。
4. 滑坡动态监测
基于LiDAR技术的滑坡动态监测需要获取多期机载LiDAR数据，在完成滑坡调查的基础上，对比不同时期DEM，获取时间序列的滑坡变形、运动数据，如滑动位移、滑移速度、表面形态等[19]，分析滑坡变形发展和变形趋势，进而预测滑动时间，合理评估滑坡危险性。
我国机载LiDAR起步较晚，同一区域的多期LiDAR数据较少，目前仅完成了一些初步的探索性研究工作。例如，刘圣伟[52]采用间隔3年的2期滑坡机载LiDAR数据，根据控制点的精确坐标，分析滑坡特征点变形情况，发现多个特征点的坡度与地表粗糙度具有明显变化，推测滑坡的主体变动趋势是东西方向。魏恋欢[53]提出高精度LiDAR DEM辅助时序SAR数据的滑坡体变形监测方法，测得某特大滑坡2年间累计滑移量和随时间变化的滑移速度（图8）。针对高海拔峡谷地形地貌环境，杨耘[54]设计了一种光学影像点云辅助LiDAR三维激光点云的滑坡群DEM重建方案，在青海龙羊峡水电站的高陡边坡滑坡群开展实验，发现多个边坡发生了不同程度的土体滑动，形变高差达50多米，改善了高陡边坡DEM重建及形变监测的精度和完整性。另外，应用LiDAR提供的高精度地表三维坐标，能够准确的测定滑坡发育体积。倪培德[55]将滑坡发生前后的LiDAR DEM相减，高程差不为零的区域即为滑坡发生区域，然后将该区域前后时期的点云从原始数据中剥离出来，相互结合以构成滑坡体，从而较准确的计算出滑坡体的体积。
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5. 讨论
（1）我国西部高山峡谷区地形地貌极为复杂，人员难以临近现场进行滑坡调查，不能及时有效的提取地表特征，而机载LiDAR技术具有远程快速获取地表信息的能力，经过众多学者实验验证，能够有效的去除地表植被的影响，获取真实的地表三维数字模型，未来可以在此类复杂环境中投入更多应用。但到目前为止，尚未有学者提出面对滑坡地形地貌的LiDAR滤波参数标准，适用于不同类型滑坡的LiDAR点云分类技术方法的相关研究尚不成熟。
（2）高精度LiDAR DEM包含丰富的几何信息、纹理信息，利用多种数学方法开展滑坡地形的精细化参数特征研究，是当前我国滑坡判识的主要研究方向之一。但由于机载雷达的覆盖面积有限，目前的相关研究成果多针对典型滑坡案例或小区域滑坡识别，缺乏大区域尺度的滑坡识别及规律性研究。
（3）机载LiDAR技术在获取地表信息方面也具有局限性，一是受无人机等搭载平台续航限制，当前机载LiDAR技术难以满足大区域、大范围的数据采集作业，二是LiDAR产品质量受到固有的点云数据降噪、抽稀、内插和多期配准等问题的较大影响[56]。未来，可以通过融合LiDAR与光学遥感、SAR等多源、多平台数据，发挥每种数据源的自身优势，进一步提高滑坡三维模型精度。
（4）LiDAR高分辨率DEM可以提供丰富的学习样本，采用与深度学习结合的方法自动提取滑坡体，并基于学习结果对其他区域滑坡隐患进行自动识别，可极大的提高滑坡识别的工作效率和准确率，是滑坡识别自动化、智能化的趋势和发展方向。
6. 结论
梳理总结了机载LiDAR技术在我国滑坡研究中的应用现状，提出了需要解决的科学问题和下一步研究思路，期望推进机载LiDAR技术在滑坡研究的广泛应用。
（1）机载LiDAR技术受环境影响小，可以远程快速采集地面信息，有效去除植被、建筑物等非真实地表信息，获取真实的滑坡地表三维模型，为滑坡判识和特征分析提供了极为有效的技术支撑。
（2）机载LiDAR获取“裸地表”高精度DEM数据，通过数字地形分析，能够显著突出滑坡特征信息，构建准确的滑坡三维解译标志，计算滑坡完整形态参数，有助于深入分析滑坡形变过程和形成机理。
（3）相对于卫星平台，受搭载平台（无人机）影响，机载LiDAR的数据采集范围较小，在大区域、大范围的滑坡识别研究中的应用较少，更适用于小区域的滑坡灾害快速应急调查研究。LiDAR点云数据处理的专业性要求较高，需要进一步发展简便易用的数据处理软件。
（4）作为一种新型技术，机载LiDAR在滑坡研究领域还有很多发展空间。随着LiDAR技术向高精度、高密度和高分辨率发展的同时，提高设备性能，使其可以在高海拔、大范围区域发挥优势；设计适用于斜坡面的点云数据处理算法，加强LiDAR、光学遥感、SAR等多源遥感数据融合，提高滑坡参数计算精度和判识准确率。
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图 2 机载LiDAR数据采集与处理流程图
Figure 2. Flow chart of airborne LiDAR data acquisition and processing
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图 3 四川白玉格聂山滑坡光学遥感影像与机载LiDAR地形效果对比
Figure 3. Comparison of optical remote sensing image and airborne LiDAR DEM in Genieshan landslide, Baiyu County, Sichuan Province
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表 1 点云密度要求
Table 1 Summary of Point cloud density requirements
分幅比例尺 DEM格网间距/m 点云密度/（点/m2） 1：500 0.5 ≥16 1：1000 1.0 ≥4 1：2000 2.0 ≥1 1：5000 2.5 ≥1 1：10 000 5.0 ≥0.25 
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