LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification

Orthophotos (or orthoimages if in digital form) have long been recognised as a supplement or alternative to standard maps. The increasing applications of orthoimages require efforts to ensure the accuracy of produced orthoimages. As digital photogrammetry technology has reached a stage of relative maturity and stability, the availability of high quality ground control points (GCPs) and digital elevation models (DEMs) becomes the central issue for successfully implementing an image orthorectification project. Concerns with the impacts of the quality of GCPs and DEMs on the quality of orthoimages inspire researchers to look for more reliable approaches to acquire high quality GCPs and DEMs for orthorectification. Light Detection and Ranging (LiDAR), an emerging technology, offers capability of capturing high density three dimensional points and generating high accuracy DEMs in a fast and cost-effective way. Nowadays, highly developed computer technologies enable rapid processing of huge volumes of LiDAR data. This leads to a great potential to use LiDAR data to get high quality GCPs and DEMs to improve the accuracy of orthoimages. This paper presents methods for utilizing LiDAR intensity images to collect high accuracy ground coordinates of GCPs and for utilizing LiDAR data to generate a high quality DEM for digital photogrammetry and orthorectification processes. A comparative analysis is also presented to assess the performance of proposed methods. The results demonstrated the feasibility of using LiDAR intensity image-based GCPs and the LiDAR-derived DEM to produce high quality orthoimages.     

Impact of DEM accuracy and resolution on topographic indices

Topography is an important land-surface characteristic that affects most aspects of the water balance in a catchment, including the generation of surface and sub-surface runoff; the flow paths followed by water as it moves down and through hillslopes and the rate of water movement. All of the spatially explicit fully distributed hydraulic and hydrological models use topography (represented by the DEM of the area modelled) to derive bathymetry. DEM is also used to derive some other key information critical in fully distributed hydraulic and hydrological models.With high-resolution DEMs such as LiDAR (Light Detection and Ranging) becoming more readily available and also with the advancements in computing facilities which can handle these large data sets, there is a need to quantify the impact of using different resolution DEMs (e.g. 1 m against 10 m or 25 m) on hydrologically important variables and the loss of accuracy and reliability of the results as we move from high resolution to coarser resolution.The results from statistical analysis carried out to compare field survey elevations with the LiDAR DEM-derived elevations, show that there are small differences between the two data sets but LiDAR DEM is a reasonably good representation of the actual ground surface compared to other commonly used DEMs derived from contour maps.The results from the analysis clearly show that the accuracy and resolution of the input DEM have serious implications on the values of the hydrologically important spatial indices derived from the DEM. The result also indicates that the loss of details by re-sampling the higher resolution DEM to coarser resolution are much less compared to the details captured in the commonly available coarse resolution DEM derived from contour maps. Topographic indices based on contour derived DEMs should be used with caution and where available, the higher resolution DEM should be used instead of the coarse resolution one.     

Geomorphological changes associated with underground coal mining in the Fushun area,northeast China revealed by multitemporal satellite remote sensing data

Fushun is a famous coal-mining city in northeastern China with more than 100 years of history. Long-term underground coal mining has caused serious surface subsidence in the eastern part of the city. In this study, multitemporal and multisource satellite remote sensing data were used to detect subsidence and geomorphological changes associated with underground coal mining over a 10-year period (1996–2006). A digital elevation model (DEM) was generated through Synthetic Aperture Radar (SAR) interferometry processing using data from a pair of European Remote Sensing Satellite (ERS) SAR images acquired in 1996. In addition, a Shuttle Radar Topography Mission (SRTM) DEM obtained from data in 2000 and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM from 2006 were used for this study. The multitemporal DEMs indicated that the maximum vertical displacement due to subsidence was around 13 m from 1996 to 2006. Multitemporal ASTER images showed that the flooded water area associated with subsidence had increased by 1.73 km² over the same time period. Field investigations and ground level measurements confirmed that the results obtained from the multitemporal remote sensing data agreed well with ground truth data. This study demonstrates that DEMs derived from multisource satellite remote sensing data can provide a powerful tool to map geomorphological changes associated with underground mining activities.     

Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations

We used LiDAR topographic data, AVIRIS hyperspectral data, and locally measured tidal fluctuations to characterize patterns of plant distribution within a southern California salt marsh (Carpinteria Salt Marsh (CSM)). LiDAR data required ground truthing and correction before they were suitable for use. Twenty to forty percent of the uncertainty associated with LiDAR was due to variance in the elevation of the target surface, the balance was attributed to error inherent in the LiDAR system. The incidence of LiDAR penetration of plant canopy cover (i.e., registration of ground elevation) was only three percent. The depth of LiDAR penetration into the plant canopy varied according to plant species composition; plant species-specific corrections significantly improved LiDAR accuracy (58% reduction in overall uncertainty) and with the use of ground-based surveys, reduced overall RMSE to an average of 6.3 cm in vegetated areas. A supervised classification of AVIRIS data was used to generate a vegetation map with six classification types; overall classification accuracy averaged 59% with a kappa coefficient of 0.40. The vegetation classification map was overlaid with a LiDAR-based digital elevation model (DEM) to compute elevation distributions and frequencies of tidal inundation. The average elevations of the dominant plant classifications found in CSM (e.g., Salicornia virginica, Jaumea carnosa, and salt-grass mix, a mixture of multiple marsh plant species) occurred within a 17 cm range, a vertical change that resulted in a 7% difference in the period of tidal inundation.     

Digital Elevation Model generation for historical landscape analysis based on LiDAR data,a case study in Flanders (Belgium)

This paper discusses the generation of a high precision DEM (Digital Elevation Model) based on high density airborne LiDAR (Light Detection and Ranging) data for an interdisciplinary landscape archaeological study concerning the settlement history and environment in Sandy Flanders, a region to the north of Ghent (Belgium). The objective was to create a detailed topographical surface free of artificial features and topographical artefacts, in the form of a DEM, visualizing the natural and current topography through the implementation of true ground points only. The semi-automatical removal of these features and artefacts was based on topographical vector data, visual interpretations and slope analysis. Ultimately two DEM’s were constructed (1) a TIN (Triangulated Irregular Network) model, whereby the inherent large file format restricts the usability to large scale and (2) a grid model which can be used for small-, medium- and large-scale applications. Both datasets were used as an image that is interpreted using ancillary data from historical sources. Its usefulness is illustrated in a case of field pattern and microfield topography. Starting from this DEM, the approach of this landscape historical study is mainly retrogressive, i.e. starting from the landscape structures and elements that are still present in the contemporary landscape and moving into the past.     

Object-based land cover classification using airborne LiDAR

Light Detection and Ranging (LiDAR) provides high resolution horizontal and vertical spatial point cloud data, and is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the data using mainly its three dimensional nature. LiDAR information potential is made even greater though, with its consideration of intensity.Elevation and intensity airborne LiDAR data are used in this study in order to classify forest and ground types quickly and efficiently without the need for manipulating multispectral image files, using a supervised object-orientated approach. LiDAR has the advantage of being able to create elevation surfaces that are in 3D, while also having information on LiDAR intensity values, thus it is a spatial and spectral segmentation tool. This classification method also uses point distribution frequency criteria to differentiate between land cover types. Classifications were performed using two methods, one that included the influence of the ground in heavily vegetated areas, and the other which eliminated the ground points before classification. The classification of three meanders of the Garonne and Allier rivers in France has demonstrated overall classification accuracies of 95% and 94% for the methods including and excluding the ground influence respectively. Five types of riparian forest were classified with accuracies between 66 and 98%. These forest types included planted and natural forest stands of different ages. Classifications of short vegetation and bare earth also produced high accuracies averaging above 90%.     

Performance evaluation of classification trees for building detection from aerial images and LiDAR data: a comparison of classification trees models

This study assesses the performance of three classification trees (CT) models (entropy, gain ratio and gini) for building detection by the fusion of airborne laser scanner data and multispectral aerial images. Data from four study areas with different sensors and scene characteristics were used to assess the performance of the models. The process of performance evaluation is based on four criteria: model validation and testing, classification accuracies, relative importance of input variables, as well as transferability of CT derived from one data set to another. The LiDAR point clouds were filtered to generate a digital terrain model (DTM) based on the orthogonal polynomials, and then a digital surface model (DSM) and the normalized digital surface model (nDSM) were generated. A set of 26 uncorrelated feature attributes were derived from the original aerial images, LiDAR intensity image, DSM and nDSM. Finally, the three CT models were used to classify buildings, trees, roads and ground from aerial images, LiDAR data and the generated attributes, with the most accurate average classifications of 95% being achieved. The entropy splitting algorithm proved to be a preferable algorithm for building detection from aerial images and LiDAR data.     

A complex study of Etna's volcanic plume from ground‐based,in situ and space‐borne observations

Two periods of transboundary transport of volcanic aerosols and debris following recent eruptions of Mount Etna, Italy, were examined using ground‐based and satellite spectrophotometric measurements together with Light Detection And Ranging (LiDAR) and aerosol filter observations in Athens and Thessaloniki, Greece. Independent columnar SO₂ measurements from ground and space identified peaks at Greek sites after the volcanic eruptions. LiDAR measurements of the aerosol extinction at Thessaloniki and Athens performed in July 2001 have shown the height of the volcanic plume to be about 3.5 km asl and the optical thickness of the dust layer to be of the order of 3×10^?3 at 532 nm. Strong ozone depletion observed at the volcano plume level by using ozonesonde ascents may be attributed to the in‐plume processes that generate reactive halogens, which in turn destroy ozone. The chemical and elemental composition of aerosol samples, taken at the Earth's surface, was analysed and confirmed the volcanic origin of the dust.     

基于机载LiDAR 技术的典型水库水量变化估算

针对传统外业测量水库库容估算方法易受地形、气象条件影响,存在危险性高、效率低等问题,以仑山水库为研究对象,采用机载 LiDAR 技术估算水库面积及水量变化。通过实地数据采集,利用 KD- 树算法剔除点云中的粗差点,并采用渐进加密不规则三角网(PTIND)滤波分离出地面点,完成 LiDAR 点云数据的预处理;根据点云数据建立精细化数字高程模型(DEM),提取不同水位的水库水面面积;最终利用积分和传统棱台体积估算方法对比分析不同水位水量差值变化。结果表明：与高程实测值相比,DEM 高程反演值满足高程精度要求,基于机载 LiDAR 的水量估算受库底坡度变化的影响,估算值低于传统估算方法的估算值,估算结果更为精确,可为监测水库水量变化提供科学依据。     

Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data

In even-aged, single species conifer plantations LiDAR height data can be modelled to provide accurate estimates of tree height and volume. However, it is apparent that growth models developed for single species stands are not directly transferable to a more general situation of mixed species plantations. This paper evaluates the ability of small footprint, dual-return, pulsed airborne LiDAR data to estimate the proportion of the productive species when mixed with a nurse crop in closed canopy plantations. A study area located in Galloway Forest District in Scotland is used as an example of Lodgepole pine and Sitka spruce mixed plantation; this area contains good examples of a wide range of pure and mixed species plantation types. Three species groups are studied: areas of pure Sitka spruce, areas of pure Lodgepole pine and areas where the two species have been planted together. Two approaches are assessed for detection of plantation mixtures: the first uses LiDAR intensity data to separate spruce and pine species and the second uses LiDAR-derived canopy density measures, coefficient of variation, skewness, percent of ground returns (which provides a measure of canopy openness) and the mean canopy height, which enables areas with height variations to be identified. From analysis of LiDAR data extracted from 54 study plots using logistic regression, the coefficient of variation and LiDAR intensity data provide the most useful predictors of the proportion of spruce in a pine/spruce mixture with coefficients of determination (R²) of 0.914 and 0.930 respectively. The method could be developed as a mapping tool, which in combination with existing inventory data should help to improve timber volume forecasting for mixed species even-aged plantations.     

伪点云修正增强激光雷达数据

目的 激光雷达在自动驾驶中具有重要意义,但其价格昂贵,且产生的激光线束数量仍然较少,造成采集的点云密度较稀疏。为了更好地感知周围环境,本文提出一种激光雷达数据增强算法,由双目图像生成伪点云并对伪点云进行坐标修正,进而实现激光雷达点云的稠密化处理,提高3D目标检测精度。此算法不针对特定的3D目标检测网络结构,是一种通用的点云稠密化方法。方法 首先利用双目RGB图像生成深度图像,根据先验的相机参数和深度信息计算出每个像素点在雷达坐标系下的粗略3维坐标,即伪点云。为了更好地分割地面,本文提出了循环RANSAC （random sample consensus）算法,引入了一个分离平面型非地面点云的暂存器,改进复杂场景下的地面分割效果。然后将原始点云进行地面分割后插入KDTree （k-dimensional tree）,以伪点云中的每个点为中心在KDTree中搜索若干近邻点,基于这些近邻点进行曲面重建。根据曲面重建结果,设计一种计算几何方法导出伪点云修正后的精确坐标。最后,将修正后的伪点云与原始激光雷达点云融合得到稠密化点云。结果 实验结果表明,稠密化的点云在视觉上具有较好的质量,物体具有更加完整的形状和轮廓,并且在KITTI （Karlsruhe Institute of Technology and Toyota Technological Institute）数据集上提升了3D目标检测精度。在使用该数据增强方法后,KITTI数据集下AVOD （aggregate view object detection）检测方法的AP_3D-Easy （average precision of 3D object detection on easy setting）提升了8.25%,AVOD-FPN （aggregate view object detection with feature pyramid network）检测方法的AP_BEV-Hard （average precision of bird’s eye view on hard setting）提升了7.14%。结论 本文提出的激光雷达数据增强算法,实现了点云的稠密化处理,并使3D目标检测结果更加精确。     

Large-scale mapping of the riverbanks,mud flats and salt marshes of the Scheldt basin,using airborne imaging spectroscopy and LiDAR

For maintaining the tidal waterways in the Scheldt basin, including the rivers Rupel and Durme and a large part of the Nete catchment, and for ecological monitoring of the mud flats, salt marshes and riverbank vegetation, the Flemish government needs detailed maps of these rivers and their bank structures. These maps indicate not only vegetation types, plant associations and sediment types but also hard structures, such as quays, locks, sluices and roads. Different remote sensing techniques were used to collect the data necessary to produce the required detailed maps. During the months of July and August 2007 an airborne flight campaign took place to collect hyperspectral and LiDAR data of the Scheldt basin and the Nete catchments. These rivers have a total length of about 240 km. The Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor acquired hyperspectral data in 32 spectral bands covering the visible/near-infrared (VIS/NIR) part of the electromagnetic spectrum with a ground resolution of 1 m. A multiple binary classification algorithm based on Fisher's linear discriminant analysis (LDA) was used to map the salt marshes and riverbank vegetation. Ground truth information, that is vegetation and sediment types, together with their geographical locations collected around the time of the flight campaign, was used to train the classifier in the later classification step. Laser scanning was performed using the Riegl LMS-Q560. The LiDAR dataset obtained had a resolution of at least 1 point per m² and was used to produce a digital elevation model (DEM) that contains all elements of the terrain. From this DEM a digital terrain model (DTM) was derived by applying appropriate filtering techniques. The elevation models were used primarily to derive information on the height, slope and aspect of the banks and dikes, but they also served as expert knowledge in the classification of the mud flats and bank vegetation.

Overall, this work illustrates how airborne hyperspectral and LiDAR data can be used to derive highly detailed maps of the sediments, vegetation and hard structures along tidal rivers in large river basins. It also shows how large datasets can be handled in an expert system, in combination with different classification techniques, to produce the required result and accuracy.     

基于机载LiDAR点云数据的复杂城市区域数字地面模型提取

LiDAR已成为获取数字地面模型（DTM）的重要手段。结合城市地形较平坦的特点,提出了一种针对LiDAR离散点云数据提取DTM的分步滤波方法,该方法主要通过高程频率直方图获取滤波主要参数,运用趋势面拟合的方法剔除剩余地物点,然后通过克里金法对地面点进行插值,最终得到DTM。将这种滤波方法应用在上海陆家嘴某区域,并与单纯趋势面拟合法的结果进行比较,实验数据表明,该滤波方法能有效的获取城市DTM。     

高分三号卫星在极地冰区导航中的应用评价

针对面向极地冰区船舶航行导航的需求,利用我国第一颗民用合成孔径雷达卫星高分三号(GF-3)数据,通过定量化统计分析,开展了GF-3号标准条带模式数据在极地冰区应用潜力的评估。结果表明,GF-3号标准条带数据能较好地反映冰面的纹理,HH极化在图像层次和信息量上要优于HV极化;在图像的层次表达方面,GF-3/HV极化数据优于Radarsat-2/HV数据,GF-3/HH极化数据不如Radarsat-2/HH极化数据;GF-3数据能较好地反映南极冰区的纹理和类型特征,对于冰山、冰裂隙等冰上障碍具有较好的识别能力,对极地冰区导航和冰上卸货具备较好的应用效果。     

基于机载LiDAR的高寒山区遥感高程数据精度评估

遥感高程数据是获取缺资料地区DEM（Digital elevation models）数据的重要手段。然而,由于高寒山区实地高程测量稀少,难以对多源遥感DEM数据进行统一验证。ICESat-2等新的遥感高程数据在高寒山区也缺乏相应的精度评估。针对此问题,以青藏高原东北缘的冰沟流域作为研究区,采用机载航空遥感获取的大范围LiDAR（Light Detection And Ranging）DEM数据对新产品ICESat-2 ATL06（Ice, Cloud, and Land Elevation Satellite-2, Land Ice Height）、ALOS DEM（12.5 m分辨率）以及新版本SRTM V3（SRTM Arc-Second Global 1 V003）、ASTER GDEM V3（ASTER Global DEM）进行验证,并分析地形因子与均方根误差RMSE的关系。研究结果表明：ICESat-2 ATL06数据在高寒山区的RMSE为0.747 m。由于其较高的精度,可用于验证缺资料地区的其他遥感高程数据。其他遥感高程数据的精度都相对较低,ALOS 12.5 m数据的RMSE为5.284 m;ASTER GDEM V3版本的RMSE为9.903 m。实验所采用的4种遥感高程数据与机载LiDAR DEM均具有较高的相关性,相关系数在0.998与1.000之间。实验还揭示了坡度是影响遥感DEM精度的主要因素。除ICESat-2 ATL06外,其他高程数据的RMSE均随坡度的增大先减小再增大,且都存在一个最佳坡度值。鉴于地形复杂多样的冰沟流域具有青藏高原高寒山区的典型特征,多源遥感DEM数据在该区域的验证结论具有较好的代表性,可为相似地区DEM数据的使用和评估提供重要的知识补充。     

Analysis of Airborne Visible/Infrared Imaging Spectrometer (AVTRIS) data of volcanic hot spots

Abstract

In July 1991, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was flown over Mount Etna and Stromboli, Italy. At the time, magma-filled vents, of subpixel dimensions, occupied the summit craters of both volcanoes. For pixels located over these hot spots, thermal radiation dominates the sensor response between about 1-4 and 2-4 μm, the latter figure being the upper limit of spectral coverage. One thermal source within the Northeast Crater of Mount Etna is evident at wavelengths as short as I 0 μm despite this intensity, therc is no saturation of the AVIRIS sensors because of their wide dynamic range. A curve-fitting algorithm is used to match the thermal spectra with Planck radiation models, in order to derive information on temperature distributions at the subpixel scale. Difficulties arise because the sequential readout of detector elements during scanning leads to interband spatial misregistration. Although the standard preprocessing of AVIRIS data includes a linear interpolation of adjacent pairs of cross-track pixels to compensate for the readout delay, this procedure only complicates further the extraction of subpixel resolution thermal information. Only by reconfiguring AVIRIS, such that all spectral channels arc recorded simultaneously, could this problem be completely overcome. Nevertheless, by examining geometrically unprocessed spectra, and selecting from thcm thermal measurements in a few adjacent channels, meaningful radiometric calculations are possible in some cases. In this manner, we estimate that one of the hot spots on Stromboli was at ≈600°C and occupied ≈ 15 m₂ of ground. The methodologies proposed here are applicable to any hyperspectral record of thermal emission of volcanic or other origin     

Radiation search operations using scene understanding with autonomous UAV and UGV

Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data are used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g., road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.     

航空影像辅助下的城区机载LiDAR汽车目标检测方法

机载激光雷达(LiDAR)技术的出现为地面汽车目标检测提供了新的途径。为了从机载LiDAR点云数据中提取汽车对象,根据不同地物的属性特征,提出了一种航空影像辅助下的城区机载LiDAR汽车目标检测方法。首先利用形态学开重建滤波完成地面和地物的分类,然后在地物点的基础上结合正射影像,通过归一化植被指数(NDVI)特征完成对植被和非植被地物的初步分类,最后在非植被地物的基础上,根据地物对象的形状特征及高程信息完成汽车和建筑物及阴影植被等非汽车对象的分类,从而完成汽车目标的提取工作。3个实验区的计算结果表明:该方法能有效从LiDAR点云中提取汽车目标,正确度和完整度的均值分别为95%和85%,满足实用性要求。     

NASA EOS Terra ASTER: Volcanic topographic mapping and capability

Up-to-date, accurate topographic data are a crucial resource for volcanic research and risk mitigation efforts, in particular, for modeling volcanic flow processes at a detailed spatial resolution. In this paper, we examine the utility of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument currently operating on the NASA Terra satellite, which provides near infrared (VNIR) stereo imaging from which topography can be derived. We wrote software to generate digital elevation models (DEMs) from the ASTER level 1A product, which employs an automated stereo matching technique to calculate the parallax offsets between the images acquired by the nadir- and aft-looking sensors. Comparison of ASTER DEMs with DEMs derived from other sources (digitized 1:50 K topographic maps and aerial interferometric radar) at Ruapehu volcano reveal an RMS error of about 10 m for the ASTER DEM, in the absence of significant atmospheric water vapor. A qualitative assessment of surface features showed that the ASTER DEM is superior to the interpolated 1:50 K map product but falls short of the detail provided by aerial interferometric radar, especially in terms of stream channel preservation. A second ASTER DEM was generated for Taranaki volcano, where previously only 1:50 K topographic map data were available. Although the 2000 Space Shuttle radar topography mission (SRTM) will largely remedy the previous global paucity of adequate topographic data at volcanoes, such as Taranaki, we anticipate the problem that at active volcanoes, the topography may change significantly following activity, rendering the SRTM data inaccurate. With the high temporal coverage of the dataset, ASTER not only provides a means to update significant (>10 m) topographic measurements at active volcanoes via a time-series of DEMs, but also provides a simultaneous means to map surface cover and localized land-use via the near infrared sensors. Thus we demonstrate the potential for up-to-date volcanic economic risk assessment using geographic information systems (GIS) analysis.     

机载X-波段双天线InSAR数据森林树高估测方法

短波长的干涉合成孔径雷达(InSAR)适用于数字表面模型(DSM)提取,但难以提取准确的林下地相位,在缺乏高精度数字高程模型(DEM)的森林区域,短波长InSAR数据估测树高的能力受到限制。针对这一问题,采用机载X-波段单极化(HH)双天线InSAR数据开展了森林树高估测方法研究。双天线InSAR可以忽略时间去相干的影响,并且X-波段波长较短,入射角较大(中心入射角45.77°),地表对干涉去相干的贡献可以忽略,因此可将干涉复相干作为体去相干,对体去相干模型中的结构函数进行勒让德展开,截取第0阶展开式得到了基于相干幅度的森林树高估测模型,利用均匀选取的LiDAR冠层高度模型(CHM)检验样本对估测结果进行严格的精度评价,并与差分法的树高估测结果进行对比。精度评价结果显示:相干幅度法与差分法都得到了较高的估测精度,两者的R~2、RMSE、总精度分别为0.81、0.86;1.20m、0.97m;86.4%、88.7%。研究结果表明:相干幅度与森林树高具有负相关关系,适用于估测树高,基于单极化相干幅度的估测模型也可以得到较高的估测精度,与差分法的估测结果相比,虽然估测精度略有降低,但此方法具有两方面的优势:一方面,估测结果不需要实测样地数据标定,对于没有实测样地数据的森林区域亦能进行高精度的树高估测;另一方面,相干幅度法不需要高精度的DEM,具有更强的实用性。