中低云云底高的地基红外遥感的初步分析

在辐射传输模拟结果分析的基础上,提出利用云底高与下行红外辐射之间的单调关系进行云底高遥感反演的算法,并结合激光测云仪云底高实测数据对算法进行了初步验证。结果表明,该算法对中低云云底高反演的平均误差为107 m,具有较高的准确性。     

The determination of cloud altitudes using SCIAMACHY onboard ENVISAT

This letter shows first results for the application of a recently developed semianalytical cloud retrieval algorithm for the determination of cloud top heights from space. The technique is based on the measurements of the top-of-atmosphere reflectance in the oxygen A-band. The depth of the band depends on the cloud top height and its geometrical thickness. The data obtained are compared to ground-based measurements of the cloud top height using a cloud-profiling radar.     

Deriving Marine-Boundary-Layer Lapse Rate from Collocated CALIPSO, MODIS, and AMSR-E Data to Study Global Low-Cloud Height Statistics

Global cloud-top height statistics of marine-boundary-layer clouds are derived from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 aerosol and cloud layer products. The boundary-layer lapse rate in the northeast region of the Pacific Ocean is investigated using sea surface temperature (SST) data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E), cloud-top temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and cloud-top height data from CALIPSO. Based on the lapse rate derived from the combined CALIPSO/MODIS/AMSR-E measurements, cloud-top heights in regions within CALIPSO tracks are derived from AMSR SST and MODIS cloud temperature to test the validity of this approach. For homogeneous low-level clouds, the results agree with the cloud-top height from the collocated CALIPSO cloud-top height measurements. These results suggest that the database of derived lapse rates from the combined measurements can be applied to study cloud-top height climate statistics using the MODIS and AMSR data when CALIPSO observations are not available.      

Tree species biomass and carbon stock measurement using ground based-LiDAR

This study scrutinises the use of terrestrial laser scanning (TLS) to measure diameter at breast height (DBH) and tree height at individual tree species level. LiDAR point cloud scans are collected from uniformly defined control points. The result of processed TLS data demonstrates the precise measurements of tree height and DBH by comparing it with field data (DBH, tree height, tree species and location). The average tree height and DBH obtained through TLS measurements were 9.44?m and 43.30?cm, respectively. A linear equation between TLS derived parameters and field measured values were established, which gave the coefficient of determination (r2) of 0.79 and 0.96 for tree height and DBH, respectively. Further, these parameters were used to calculate above ground biomass (AGB) for individual tree species by considering a non-destructive approach. The total AGB and carbon stock from 80 different trees are computed to be 49.601 and 22.320?tonnes, respectively.     

On the Use of CloudSat and MODIS Data for Estimating Hurricane Intensity

This letter presents preliminary results concerning the use of new observations from the A-Train Constellation for testing a new technique of remotely sensing hurricane intensity from space based on modeling a hurricane as a balanced, convectively neutral vortex. The key observational requirements are simultaneous, accurate measurements of cloud-top height, cloudtop temperature, and cloud profiling information across the center of the storm, although there are ways to bypass the need for cloud-top temperature. In this letter, the Moderate Resolution Imaging Spectroradiometer onboard Aqua provides an estimation of the cloud-top temperature, and the near-simultaneous CloudSat observations provide the essential cloud-top height and cloud profiling information. Initial results indicate that the new technique is a promising method for estimating storm intensity when compared post facto to the best track database. Potential uncertainties and room for further refinement of the technique are discussed.     

Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis

The aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A key issue of the alpha-shape algorithm is to define the α parameter, as it directly affects the crown delineation results. We propose to adjust this parameter based on a group of representative trees in an orchard for which the classical field measurements were performed. The best value of the α parameter is one whose correlation coefficient of dendrometric parameters between field measurements and estimated values is the highest. We determined crown diameters as principal components of ALS points representing a delineated crown. The method was applied to a test area of an olive orchard in Spain. The tree dendrometric parameters estimated from ALS data were compared with field measurements to assess the quality of the developed approach. We found the method to be equally good or even superior to previously investigated semi-automatic methods. The average error is 19% for tree height, 53% for crown base height, and 13% and 9% for the length of the longer diameter and perpendicular diameter, respectively.     

热红外与可见光数据的云边缘高度匹配

高分辨率数据中云高度的差异性突显,特别是边缘处高度在云阴影识别和地表辐射估算等方面成为需要考虑的重要因素。热红外数据获取云高度分辨率较低、缺乏细部差异性特征,为解决这一问题,首先将对应的热红外和可见光数据进行特征点配准,再将基于热红外数据计算的云高度重采样至高分辨率,然后以基于欧式距离变换的围线搜索方法及距离加权将热红外云边缘高度匹配至对应的可见光图像,最后根据云阴影的相似度匹配方法确定真实云高度。结果表明,算法在遵循热红外云高信息分布变化规律的同时,可以得到较准确的高分辨率云边缘高度,一定程度上解决了热红外技术获取云高在分辨率上的局限,扩展了其在云高反演方面的作用。     

电力巡线LiDAR点云电塔自动定位和提取算法

针对电力巡线机载激光雷达（LiDAR）激光点云电塔自动提取问题,提出了一种电塔自动定位和点云提取算法。首先,基于点云进行二维空间网格划分,利用网格点云高程偏差和方差特征提取潜在电塔网格;其次,基于电塔点云的高程连续特性完成电塔自动定位和点云粗提取;然后,利用点云分层密度信息和图像开运算,实现电塔精细提取;最后,利用轻小型无人机载激光雷达数据验证本文算法的有效性。试验结果表明,本文所提出的自动提取算法,能够有效解决LiDAR数据中电塔自动定位和点云提取问题,在LiDAR数据质量较差时仍能够取得良好效果,算法对于噪点数据具有较强的稳健性。本文所提出的电塔自动提取算法在LiDAR电力巡检数据处理中具有一定的应用价值。     

基于可变半径圆环和B样条拟合的机载LiDAR点云滤波

提出了一种LiDAR点云滤波方法,首先沿同一方向等间距逐行扫描点云,获取点序列构成的扫描线,针对每条扫描线,采取半径可变的圆环从面向地心一侧滚过,保留扫描线上被圆周滚过的点,从而滤除每条扫描线上的地物点;然后对滤波后每条扫描线上的地表点云数据等间隔采样,在此基础上采用均匀B样条曲面拟合地形表面,遍历每一个点,在拟合的B样条曲面上投影,根据投影点高程与实际高程的差判断其属性,保留地面点并滤除地物点。试验结果表明,与传统方法相比,本文方法的滤波精度提高1~5倍,可用于城市、山区和林地等各种地形,通用性好,其算法时间复杂度为O(n)。     

The cloud phase discrimination from a satellite

A new technique to identify mixed-phase clouds but also clouds with supercooled water droplets using satellite measurements is proposed. The technique is based on measurements of the backscattered solar light at wavelengths 1.55 and 1.67 /spl mu/m in combination with cloud brightness temperature measurements at 12 /spl mu/m. For the first time, the concept of the phase index-temperature correlation plot (the P-T diagram) is introduced in the cloud remote sensing. Retrievals of cloud temperature and cloud phase index are performed using data from the Advanced Along Track Scanning Radiometer (AATSR) and Scaning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) both onboard the Envisat platform.     

Height Assignment Improvement in Kalpana-1 Atmospheric Motion Vectors

The real-time operational use of atmospheric motion vectors (AMVs) at numerical weather prediction (NWP) centers in India are being adversely affected due to inaccurate height assignment of cloud tracers, especially in thin semi-transparent clouds. In India, the operational derivation of AMVs from the Indian geostationary satellite Kalpana-1 began few years ago. A statistical empirical method (SEM) of height assignment, based on a genetic algorithm, is currently used to estimate the height of the retrieved vectors from Kalpana-1. This method has many limitations. In this paper, attempts have been made to implement the widely used and well tested height assignment methods such as the infrared window (WIN) technique, the H₂O intercept, and the cloud base method in the Kalpana-1 AMV retrieval algorithm. The new height assignment algorithm significantly improves the statistics of the retrieved winds when compared to radiosondes, especially in high and mid levels winds.     

利用掩星资料分析东亚地区云厚的分布特征

利用2007年1月至2009年1月COSMIC掩星湿空气数据,基于相对湿度廓线出入云层时发生突变反演云边界高度的思想,统计分析了东亚地区云厚的分布特征。结果表明,掩星最低探测高度集中在3km以下,主要影响部分中云和低云云厚的反演。云发生概率沿纬度和经度方向呈现不同的分布特征,各类云概率随高度也有不同的变化规律。东亚地区所有云的平均厚度为1.78km,其中单层云和顶层云分布相似,双层云与其他云层的地理差异不大,单层云在春秋季分布相对均匀,而在夏冬季差异较大。夏季和冬季顶层云云顶高与云厚的二维频率分布各异,主要在于夏季有更多云体较厚且位于对流层上部的高云存在。     

地基激光雷达的玉兰林冠层叶面积密度反演

叶面积密度LAD(Leaf Area Density)是表征冠层内部叶面积垂直分布的重要参数,其分布廓线的准确反演对研究植被碳氮循环、初级生产力和生物量估算等具有重要意义。本文在电子科技大学校内建立实验样区,利用地基激光雷达Leica Scan Station C10和数码相机获取玉兰林高分辨率3维激光点云数据和真彩色影像。利用监督分类将真彩色影像中枝干等非光合组织与叶片分离,再将像素分类信息映射给点云数据,从而提取叶片点云。通过点云数据体元化,并引入2维凸包算法确定垂直方向分层树冠边界,获取激光接触冠层的频率;随机选择不同高度的多个叶片,利用特征值法进行叶片平面拟合,估算出叶倾角,并结合天顶角估算叶倾角校正因子;最后基于体元的冠层分析VCP(Voxel-based Canopy Profiling)方法实现树林冠层LAD反演。结果表明体元化的叶片点云数据能准确确定树林冠层边界和统计接触频率实现LAD反演;反演的LAD变化走势与区域林木冠层叶片垂直分布相吻合,在冠层中下部随着高度的增加叶面积密度也随之增加,在4 m高度处达到最大值1 m2/m3,之后随着高度的增加叶面积密度逐渐降低。根据LAD计算得到的累积叶面积指数LAI为3.20 m2/m2,与LAI-2200实测的叶面积指数相比,相对误差为1.26%。     

机载LiDAR点云数据降维与分类的随机森林方法

探索自动化的激光点云分类方法对于三维建模、城市土地分类、DEM制图等应用具有重要作用。考虑到现有的点云分类算法在提取依赖邻域结构的特征参数时面临邻域尺度的选择难、数据维度高、计算复杂,并且缺乏对分类特征参数的重要性评估和选择等问题,本文提出了基于随机森林的机载LiDAR点云数据降维与分类方法。在分析点云数据的高程、回波、强度等属性特征的基础上,提取归一化高度、高度统计量、表面特征、空间分布特征、回波特征及强度特征6大类特征参数,并构建多尺度特征参数,运用随机森林的特征选择算法对分类特征集进行优化,然后进行点云分类。试验结果表明,基于随机森林的特征选择方法可以有效地降低特征维度,并且使得总体分类精度达到94.3%（Kappa系数为0.922）,相比于使用全部特征分类和SVM分类方法而言,该方法的总体分类精度均有一定程度的提高;特征的重要性度量结果表明,归一化高度特征在点云分类中所起的作用最大。     

Assessing the sensitivity of site index models developed using bi-temporal airborne laser scanning data to different top height estimates and grid cell sizes

Site productivity and forest growth are critical inputs into projecting wood volume and biomass accumulation over time. Site productivity, which is determined most commonly using site index models is also the primary criterion to consider many forest management decisions. Most of the previous research utilizing the remote sensing data for assessment of site index with forest height are based on the existing site index models developed with traditional dendrometric methods. However, these traditional methods are both time-consuming and expensive. This study demonstrates how bi-temporal airborne laser scanning (ALS) data collected within the 8-year period can be used for the development of site index models for Scots pine. The accuracy of ALS-derived models was assessed by comparison to the reference site index model developed based on data from stem analysis of 174 felled Scots pine trees. We evaluated the effect of different height metrics and grid cell size on the trajectory of site index models developed from ALS-derived measurements. Four methods of estimating top height from ALS point clouds were evaluated: 95th, 99th and 100th percentiles of point clouds and an individual tree detection approach (ITD). The models were created for a range of grid cell sizes: 10 × 10 m, 30 × 30 m, and 50 × 50 m. The results indicate that bitemporal ALS data could substitute traditional methods that have been applied to date for stand growth modelling. It was found that top height increment can be estimated by using both ITD approach and the 100th percentile of point cloud giving an appropriate top height (TH) increment estimation. Observed growth curves of reference trees agreed best with the trajectories that were obtained based on TH calculated using ITD method (R² = 0.892) and 100th percentile (R² = 0.797). In case of TH obtained from 99th and 95th percentiles only weak correlation was found: R² = 0.358 and R² = 0.213, accordingly. The height growth models developed with 95th and 99th percentiles of point cloud were not compatible with the reference model. We also found that grid cell size did not affect the model height growth trajectories. Irrespective of the grid cell size, the obtained model trajectories for the given method of TH estimation are nearly identical for cells 10 × 10, 30 × 30 and 50 × 50 m.     

高分二号异轨立体数据的森林高度提取

森林植被碳储量的空间分布格局及其动态变化是陆地生态系统碳收支核算的基础。作为森林地上生物量的重要指示因子,森林高度的精确估算是提高森林植被碳储量估算精度的关键。现有研究已证明,由专业星载摄影测量系统获取的立体观测数据可用于森林高度提取,但光学遥感数据最大的问题是受云雨等天气因素的影响严重。区域森林地上生物量产品的生产需要充分挖掘潜在数据源。国产高分二号卫星(GF-2)虽然不是为获取立体观测数据而设计的专业星载摄影测量系统,但其获取的图像空间分辨率可达0.8 m,且具备±35°的的侧摆能力,在重复观测区域可构成异轨立体观测。本文以分别获取于2015年6月20日和2016年7月19的GF-2数据作为立体像对,其标称轨道侧摆角分别为0.00118°和20.4984°,以激光雷达数据获取的林下地形(DEM)和森林高度(CHM)为参考,对利用GF-2立体观测数据进行森林高度提取进行了研究。通过对立体处理得到的摄影测量点云的栅格化得到DSM,以激光雷达数据提供的DEM作为林下地形,得到了GF-2的CHM。结果表明GF-2提取的CHM与激光雷达CHM空间分布格局较为一致,两者之间存在明显的相关性,像素对像素的线性相关性(R2)达到0.51,均方根误差(RMSE)为3.6 m。研究结果表明,在林下地形已知的情况下,GF-2立体观测数据可用于森林高度估算。     

低矮植被的无人机激光雷达测高精度分析

针对无人机激光雷达估测低矮植被高度的精度大小,本文以3m以下低矮树木为研究对象,通过数值模拟方法得到不同航高、不同扫描角情况下激光脚点坐标和点云估测单一树木高度的最大测量误差值;对比实测树高,分析了激光点云估测树高的精度。结果表明,在航高为30m、扫描范围为(-50°,5°)的情况下,无人机激光雷达获取的激光脚点坐标误差和由激光点云估测低矮树高(3m以下)的误差均可以达到cm级;激光点云估测单一树木高度与实测高度的决定系数为0.977,均方根差为5cm,标准均方根差为4%。因此,应用无人机激光雷达数据可以快速、精确获取低矮植被高度信息,进而为反演植被生物量和植被长势信息监测提供重要依据。     

Cloud height determination using satellite stereoscopy from Along Track Scanning Radiometer onboard ERS-1 satellite: A simulation study

The first ERS-1 satellite launched in May 1991 promises to make a substantial contribution towards the cloud stereoscopy. One of its payload, Along Track Scanning Radiometer (hereafter ATSR) has IR split window channels and scans the earth conically leading to earth views at two view angles (0° and 55°) in two curved swaths registered with respect to each other in image frame. This enables us to construct stereoscopic image of a cloud thereby determining its height geometrically. The present paper describes the utility of ATSR onboard ERS-1 in retrieving cloud height by synthetic stereo imaging over NOAA-AVHRR visible and thermal band observations.     

Detecting pruning of individual stems using Airborne Laser Scanning data captured from an Unmanned Aerial Vehicle

Modern forest management involves implementing optimal pruning regimes. These regimes aim to achieve the highest quality timber in the shortest possible rotation period. Although a valuable addition to forest management activities, tracking the application of these treatments in the field to ensure best practice management is not economically viable. This paper describes the use of Airborne Laser Scanner (ALS) data to track the rate of pruning in a Eucalyptus globulus stand. Data is obtained from an Unmanned Aerial Vehicle (UAV) and we describe automated processing routines that provide a cost-effective alternative to field sampling. We manually prune a 500 m² plot to 2.5 m above the ground at rates of between 160 and 660 stems/ha. Utilising the high density ALS data, we first derived crown base height (CBH) with an RMSE of 0.60 m at each stage of pruning. Variability in the measurement of CBH resulted in both false positive (mean rate of 11%) and false negative detection (3.5%), however, detected rates of pruning of between 96% and 125% of the actual rate of pruning were achieved. The successful automated detection of pruning within this study highlights the suitability of UAV laser scanning as a cost-effective tool for monitoring forest management activities.     

A method for vertical adjustment of digital aerial photogrammetry data by using a high-quality digital terrain model

The accuracy of vertical position information can be degraded by various sources of error in digital aerial photogrammetry (DAP) based point clouds. To address this issue, we propose a relatively straightforward method for automated correction of such point clouds. This method can be used in conjunction with any 3D reconstruction method in which a point cloud is generated from a pair of aerial images. The crux of the method involves separately co-registering each DAP point cloud (formed by the overlap of two or more images) to a common airborne laser scanning (ALS) based digital terrain model. The proposed method has the following essential steps: (1) Ground surface patches are identified in the normalized DAP point clouds by selecting areas in which standard deviation of vertical height is low, (2) height differences between the DAP and ALS point clouds are calculated at these patches, and (3) a correction surface is interpolated from these height differences and is then used to rectify the entire DAP point cloud. The performance of the proposed method is verified using plot data (n = 250) from a forested study area in Eastern Finland. We observed that DAP data from the area corrected using our proposed method resulted in significant increases in prediction accuracy of key forest variables. Specifically, the root mean squared error (RMSE) values for dominant height predictions decreased by up to 23.2%, while the associated model R² values increased by 16.9%. As for stem volume, RMSEs dropped by 20.6%, while the model R² improved by 14.6%, respectively. Hence, prediction accuracies were almost as good as with ALS data. The results suggest that vertically misaligned DAP data, if rectified using an algorithm such as the one presented here, could deliver near ALS data quality at a fraction of the cost.