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1.
Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence. 相似文献
2.
Individual tree crown delineation is of great importance for forest inventory and management. The increasing availability of high-resolution airborne light detection and ranging (LiDAR) data makes it possible to delineate the crown structure of individual trees and deduce their geometric properties with high accuracy. In this study, we developed an automated segmentation method that is able to fully utilize high-resolution LiDAR data for detecting, extracting, and characterizing individual tree crowns with a multitude of geometric and topological properties. The proposed approach captures topological structure of forest and quantifies topological relationships of tree crowns by using a graph theory-based localized contour tree method, and finally segments individual tree crowns by analogy of recognizing hills from a topographic map. This approach consists of five key technical components: (1) derivation of canopy height model from airborne LiDAR data; (2) generation of contours based on the canopy height model; (3) extraction of hierarchical structures of tree crowns using the localized contour tree method; (4) delineation of individual tree crowns by segmenting hierarchical crown structure; and (5) calculation of geometric and topological properties of individual trees. We applied our new method to the Medicine Bow National Forest in the southwest of Laramie, Wyoming and the HJ Andrews Experimental Forest in the central portion of the Cascade Range of Oregon, U.S. The results reveal that the overall accuracy of individual tree crown delineation for the two study areas achieved 94.21% and 75.07%, respectively. Our method holds great potential for segmenting individual tree crowns under various forest conditions. Furthermore, the geometric and topological attributes derived from our method provide comprehensive and essential information for forest management. 相似文献
3.
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。 相似文献
4.
Integration of WorldView-2 satellite image with small footprint airborne LiDAR data for estimation of tree carbon at species level has been investigated in tropical forests of Nepal. This research aims to quantify and map carbon stock for dominant tree species in Chitwan district of central Nepal. Object based image analysis and supervised nearest neighbor classification methods were deployed for tree canopy retrieval and species level classification respectively. Initially, six dominant tree species (Shorea robusta, Schima wallichii, Lagerstroemia parviflora, Terminalia tomentosa, Mallotus philippinensis and Semecarpus anacardium) were able to be identified and mapped through image classification. The result showed a 76% accuracy of segmentation and 1970.99 as best average separability. Tree canopy height model (CHM) was extracted based on LiDAR’s first and last return from an entire study area. On average, a significant correlation coefficient (r) between canopy projection area (CPA) and carbon; height and carbon; and CPA and height were obtained as 0.73, 0.76 and 0.63, respectively for correctly detected trees. Carbon stock model validation results showed regression models being able to explain up to 94%, 78%, 76%, 84% and 78% of variations in carbon estimation for the following tree species: S. robusta, L. parviflora, T. tomentosa, S. wallichii and others (combination of rest tree species). 相似文献
5.
Manuel A. Aguilar Abderrahim Nemmaoui Fernando J. Aguilar Rongjun Qin 《地理信息系统科学与遥感》2019,56(1):109-129
Digital surface models (DSMs) extracted from very high resolution (VHR) satellite stereo images are becoming more and more important in a wide range of geoscience applications. The number of software packages available for generating DSMs has been increasing rapidly. The main goal of this work is to explore the capabilities of VHR satellite stereo pairs for DSMs generation over different land-cover objects such as agricultural plastic greenhouses, bare soil and urban areas by using two software packages: (i) OrthoEngine (PCI), based on a hierarchical subpixel mean normalized cross correlation matching method, and (ii) RPC Stereo Processor (RSP), with a modified hierarchical semi-global matching method. Two VHR satellite stereo pairs from WorldView-2 (WV2) and WorldView-3 (WV3) were used to extract the DSMs. A quality assessment on these DSMs on both vertical accuracy and completeness was carried out by considering the following factors: (i) type of sensor (i.e., WV2 or WV3), (ii) software package (i.e., PCI or RSP) and (iii) type of land-cover objects (plastic greenhouses, bare soil and urban areas). A highly accurate light detection and ranging (LiDAR) derived DSM was used as the ground truth for validation. By comparing both software packages, we concluded that regarding DSM completeness, RSP produced significantly (p < 0.05) better scores than PCI for all the sensors and type of land-cover objects. The percentage improvement in completeness by using RSP instead of PCI was approximately 2%, 18% and 26% for bare soil, greenhouses and urban areas respectively. Concerning the vertical accuracy in root mean square error (RMSE), the only factor clearly significant (p < 0.05) was the land cover. Overall, WV3 DSM showed slightly better (not significant) vertical accuracy values than WV2. Finally, both software packages achieved similar vertical accuracy for the different land-cover objects and tested sensors. 相似文献
6.
激光雷达森林参数反演研究进展 总被引：6，自引：0，他引：6
激光雷达通过发射激光能量和接收返回信号的方式,来获取高精度的森林空间结构和林下地形信息。全波形激光雷达通过记录返回信号的全部能量,得到亚米级植被垂直剖面;离散回波激光雷达记录的单个或多个回波,表示来自不同冠层的回波信号。星载激光雷达一般采用全波形或光子计数激光剖面系统,仅能获取卫星轨道下方的单波束或多波束数据,用于区域/全球范围的森林垂直结构及变化观测。机载激光雷达多采用离散回波或全波形激光扫描系统,能够获取飞行轨迹下方特定视场范围内的扫描数据,用于林分/区域范围的森林结构观测。地基激光雷达多采用离散回波激光扫描系统,获取以测站为中心的球形空间内扫描数据,用于单木/样地范围的森林结构观测。激光雷达单木因子估测方法可分为CHM单木法、NPC单木法和体元单木法3类。CHM单木法通过局部最大值识别树冠顶点,采用区域生长或图像分割算法识别树冠边界或树冠主方向,NPC单木法一般通过空间聚类或形态学算法识别单木,体元单木法在3维体元空间采用区域生长或空间聚类算法识别树冠。根据激光雷达冠层高度分布可以估测林分因子,冠层高度分布特征来自于离散点云或全波形。多时相激光雷达可用于森林生长量、生物量变化等监测,以及森林采伐、灾害等引起的结构变化监测。随着激光雷达技术的发展,它将在森林调查、生态环境建模等生产与科学研究领域中得到更为广泛的应用。 相似文献
7.
Due to the fast development of the urban environment, the need for efficient maintenance and updating of 3D building models is ever increasing. Change detection is an essential step to spot the changed area for data (map/3D models) updating and urban monitoring. Traditional methods based on 2D images are no longer suitable for change detection in building scale, owing to the increased spectral variability of the building roofs and larger perspective distortion of the very high resolution (VHR) imagery. Change detection in 3D is increasingly being investigated using airborne laser scanning data or matched Digital Surface Models (DSM), but rare study has been conducted regarding to change detection on 3D city models with VHR images, which is more informative but meanwhile more complicated. This is due to the fact that the 3D models are abstracted geometric representation of the urban reality, while the VHR images record everything. In this paper, a novel method is proposed to detect changes directly on LOD (Level of Detail) 2 building models with VHR spaceborne stereo images from a different date, with particular focus on addressing the special characteristics of the 3D models. In the first step, the 3D building models are projected onto a raster grid, encoded with building object, terrain object, and planar faces. The DSM is extracted from the stereo imagery by hierarchical semi-global matching (SGM). In the second step, a multi-channel change indicator is extracted between the 3D models and stereo images, considering the inherent geometric consistency (IGC), height difference, and texture similarity for each planar face. Each channel of the indicator is then clustered with the Self-organizing Map (SOM), with “change”, “non-change” and “uncertain change” status labeled through a voting strategy. The “uncertain changes” are then determined with a Markov Random Field (MRF) analysis considering the geometric relationship between faces. In the third step, buildings are extracted combining the multispectral images and the DSM by morphological operators, and the new buildings are determined by excluding the verified unchanged buildings from the second step. Both the synthetic experiment with Worldview-2 stereo imagery and the real experiment with IKONOS stereo imagery are carried out to demonstrate the effectiveness of the proposed method. It is shown that the proposed method can be applied as an effective way to monitoring the building changes, as well as updating 3D models from one epoch to the other. 相似文献
8.
Ali Ozgun Ok Asli Ozdarici-Ok Emre Baseski 《Journal of the Indian Society of Remote Sensing》2018,46(8):1203-1212
In this study, we assess the quality of the digital surface model (DSM) generated from Pleiades-1 tri-stereo images which can potentially contribute to the detection of citrus trees in terms of height information. The methods tested on stereo/tri-stereo images are; (a) local methods (correlation-based and least squares method), (b) semi-global method (semi-global matching (SGM)), and (c) global method (SIFT-flow). DSMs of three sub-regions of Mersin area have been presented for each stereo/tri-stereo matching method; whereas for the SIFT-flow method, we have only depicted the parallax results. Numerical results reveal that the SGM forward-backward stereo combination which has the largest intersection angle provided the best results in 2 out of 3 test areas. However, the results confirm that none of the methods could reach the desired level of performance for the height estimation of citrus trees that can potentially guide the detection step. 相似文献
9.
以浙江省海宁市4种代表行道树(广玉兰、无患子、悬铃木、香樟树)为研究对象,结合无人机(UAV)影像和三维激光扫描数据,利用ContextCapture、LiDAR360软件完成点云拼接、滤波、降噪和编辑,通过迭代最近点算法实现点云精细匹配,完成多平台点云数据融合,进而得到数字表面模型与数字高程模型,并制作冠层高度模型;采用分水岭分割算法对不同行道树树种的冠层高度模型进行单木分割,并综合局部最大值法实现单木树高、冠幅的参数提取。结果表明,本文方法进行行道树单木分割的精度高,树高、冠幅参数提取值的效果好,满足行道树几何参数调查要求。 相似文献
10.
This study examines the understorey information present in discrete-return LiDAR (Light Detection And Ranging) data acquired for temperate deciduous woodland in mid summer (leaf-on) and in early spring when the understorey had mostly leafed out, but the overstorey had only just begun budburst (referred to here as leaf-off). The woodland is ancient, semi-natural broadleaf and has a heterogeneous structure with a mostly closed canopy overstorey and a patchy understorey layer. In this study, the understorey was defined as suppressed trees and shrubs growing beneath an overstorey canopy. Forest mensuration data for the study site were examined to identify thresholds (taking the 95th percentile) for crown depth as a percentage of crown top height for the six overstorey tree species present. These data were used in association with a digital tree species map and leaf-on first return LiDAR data, to identify the possible depth of space available below the overstorey canopy in which an understorey layer could exist. The leaf-off last return LiDAR data were then examined to identify whether they contained information on where this space was occupied by suppressed trees or shrubs forming an understorey. Thus, understorey was mapped from the leaf-off last return data where the height was below the predicted crown depth. A height threshold of 1 m was applied to separate the ground vegetation layer from the understorey. The derived understorey model formed a discontinuous layer covering 46.4 ha (or 31% of the study site), with an average height of 2.64 m and a 77% correspondence with field data on the presence/absence of suppressed trees and shrubs (kappa 0.53). Because the first return data in leaf-on and leaf-off conditions were very similar (differing by an average of just 0.87 m), it was also possible to map the understorey layer using leaf-off data alone. The resultant understorey model covered 39.4 ha (or 26% of the study site), and had a 72% correspondence with field data on the presence/absence of suppressed trees and shrubs (kappa 0.45). This moderate reduction in the area of understorey mapped and associated accuracy came with a saving of half of all data acquisition and pre-processing costs. Whilst the understorey modelling presented here undoubtedly benefited from the specific timing of LiDAR data acquisition and from ancillary data available for the study site, the conclusions have resonance beyond this case study. Given that the understorey and overstorey canopies in lowland broadleaf woodland can merge into one another, the modelling of understorey information from discrete-return LiDAR data must consider overstorey canopy characteristics and laser penetration through the overstorey. It is not adequate in such circumstances to apply simple height thresholds to LiDAR height frequency distributions, as this is unlikely to distinguish whether a return has backscattered from the lower parts of the overstorey canopy or from near the surface of the understorey canopy. 相似文献
11.
Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (Hcanopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 Hcanopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 Hcanopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 Hcanopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted Hcanopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in Hcanopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools. 相似文献
12.
结合机载LiDAR数据,提出了一种改进的GLAS光斑点冠层高度地形校正模型,以校正后的GLAS光斑点作为输入样本,结合MODIS遥感影像,利用支持向量回归（SVR）的方法对研究区森林冠层高度进行分生态区估测,并利用野外调查数据和机载LiDAR冠层高度结果对估测结果进行验证。结果显示:研究区的坡度等级直接影响GLAS光斑点森林冠层高度估测精度,改进的地形校正模型可以较好的减小坡度对GLAS光斑点森林冠层高度估测的影响,模型精度RMSE稳定在3.25~3.48 m;不同生态分区的SVR模型估测精度较为稳定,其RMSE=6.41~7.56 m;与算数平均高相比,样地的Lorey's高与制图结果拟合最好,不同生态分区平均估测精度为80.3%。机载LiDAR冠层高度结果的验证平均精度为79.5%,和Lorey's高验证结果呈现较好的一致性。 相似文献
13.
顾及纹理特征的航空影像自适应密集匹配方法 总被引：1，自引：1，他引：0
半全局匹配实质是在视差连续性假设下的离散优化方法。为克服视差断裂带影响,依赖一组参数控制视差不一致性。若参数过小,在平面内难以保证视差连续性,产生明显噪声,导致凹凸不平现象;若参数过大,将致使物体表面过于平滑,难以保留视差断裂等重要特征。为克服上述问题,本文提出了一种顾及纹理特征的自适应密集匹配方法:首先,检测影像纹理特征并定量表达纹理丰富性程度;其次,依据纹理丰富程度与视差差异存在正相关的规则知识,实现匹配参数依据纹理信息的自适应选择;最后,采用上述参数进行自适应的半全局匹配。通过ISPRS基准数据集和国产SWDC-5获取的倾斜影像进行试验分析证明,本文方法能够有效减少低纹理区域匹配噪声,同时更有效保留边缘特征。 相似文献
14.
机载激光雷达平均树高提取研究 总被引：16，自引：3，他引：13
为了研究机载激光雷达(LiDAR)树高提取技术,以山东省泰安市徂徕山林场为实验区,于2005年5月进行了机载LiDAR数据获取和外业测量.通过对LiDAR点云数据的分类处理,分别得到了试验区的地面点云子集、植被点云子集和高程归一化的植被点云子集.基于高程归一化的植被点云子集计算了上四分位数处的高度,与实地测量的数据进行了比较,并结合中国森林调查规程进行了实用性分析.结果表明:对于较低密度的点云数据,使用分位数法可以较好地进行林分平均高的估计;机载激光雷达技术对树高估计是可行的,精度都高于87%,总体平均精度为90.59%,其中阔叶树的精度高于针叶树.该试验精度可以满足中国二类森林调查规程中平均树高因子的一般商品林和生态公益林的精度要求,对国有商品林小班的调查精度要求(5%)存在一点差距,需要在国有商品林区进一步开展验证工作.对本试验区而言,已经可以满足其作为森林公园生态公益林的调查要求. 相似文献
15.
激光雷达在森林参数反演中的应用 总被引：1，自引：0，他引：1
激光雷达是近年来国际上发展十分迅速的主动遥感技术,在森林参数的定量测量和反演上取得了成功的应用。在林业上,高采样密度激光雷达能够获取单株木3维结构特征,采用不同的数据处理方法,可以得到不同精度的单株木参数。利用激光雷达测量森林参数不仅节省了人力,还提高了工作效率,现在已经成为快速获取树木几何参数的一种有效方法。文中主要介绍了LiDAR工作原理、类型及特点、影响LiDAR数据质量的因素、国内外LiDAR的发展状况及应用领域,重点介绍了国内外利用LiDAR数据反演森林参数(树高、郁闭度、冠幅、林分密度、断面积和蓄积量等)的方法和研究进展,同时对今后LiDAR在森林参数反演方面的研究作了展望。 相似文献
16.
《International Journal of Digital Earth》2013,6(3):307-323
ABSTRACTForests of the Sierra Nevada (SN) mountain range are valuable natural heritages for the region and the country, and tree height is an important forest structure parameter for understanding the SN forest ecosystem. There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution. In this study, we presented a method to map wall-to-wall forest tree height (defined as Lorey’s height) across the SN at 70-m resolution by fusing multi-source datasets, including over 1600 in situ tree height measurements and over 1600?km2 airborne light detection and ranging (LiDAR) data. Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements, and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System (GLAS) footprints. Finally, the random forest algorithm was used to model the SN tree height from these GLAS tree heights, optical imagery, topographic data, and climate data. The results show that our fine-resolution SN tree height product has a good correspondence with field measurements. The coefficient of determination between them is 0.60, and the root-mean-squared error is 5.45?m. 相似文献
17.
机载激光雷达及高光谱的森林乔木物种多样性遥感监测 总被引：1，自引：0，他引：1
利用机载LiDAR和高光谱数据并结合37个地面调查样本数据,基于结构差异与光谱变异理论,通过相关分析法分别筛选了3个最优林冠结构参数和6个最优光谱指数,在单木尺度上利用自适应C均值模糊聚类算法,在神农架国家自然保护区开展森林乔木物种多样性监测,实现了森林乔木物种多样性的区域成图。研究结果表明,(1)基于结合形态学冠层控制的分水岭算法可以获得较高精度的单木分割结果(R~2=0.88,RMSE=13.17,P0.001);(2)基于LiDAR数据提取的9个结构参数中,95%百分位高度、冠层盖度和植被穿透率为最优结构参数,与Shannon-Wiener指数的相关性达到R~2=0.39—0.42(P0.01);(3)基于机载高光谱数据筛选的16个常用的植被指数中,CRI、OSAVI、Narrow band NDVI、SR、Vogelmann index1、PRI与Shannon-Wiener指数的相关性最高(R~2=0.37—0.45,P0.01);(4)在研究区,利用以30 m×30 m为窗口的自适应模糊C均值聚类算法可预测的最大森林乔木物种数为20,物种丰富度的预测精度为R~2=0.69,RMSE=3.11,Shannon-Wiener指数的预测精度为R~2=0.70,RMSE=0.32。该研究在亚热带森林开展乔木物种多样性监测,是在区域尺度上进行物种多样性成图的重要实践,可有效补充森林生物多样性本底数据的调查手段,有助于实现生物多样性的长期动态监测及科学分析森林物种多样性的现状和变化趋势。 相似文献
18.
Phil Wilkes Mathias Disney Matheus Boni Vicari Kim Calders Andrew Burt 《Carbon balance and management》2018,13(1):10
Background
Urban trees have long been valued for providing ecosystem services (mitigation of the “heat island” effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB.Results
Camden has an estimated median AGB density of 51.6 Mg ha–1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤?25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB.Conclusion
Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.19.
融合SIFT与SGM的倾斜航空影像密集匹配 总被引：2，自引：0，他引：2
针对倾斜航空摄影特点与匹配处理要求,提出了融合尺度不变特征转换SIFT与半全局匹配SGM的倾斜影像密集匹配方法,包括两个阶段:(1)引入局部二阶矩变换的SIFT倾斜影像稀疏匹配。利用与二阶矩特征值有关的Hessian-Gabor算子提取影像初始特征,通过窗口二阶矩变换及椭圆归一化处理去除特征邻域的仿射变化,使得改进SIFT的特征描述符仿射不变性大大增强,满足宽基线倾斜像对稳健匹配要求的同时并能获得较多的匹配特征作为后续SGM优化计算的路径约束条件;(2)路径受限优化下的SGM倾斜影像密集匹配。以倾斜影像SIFT成功匹配像素的互信息为约束,对SGM的动态规划路径进行分段纠正,减小错误匹配代价的传播并加速最优路径搜索过程;以匹配像素的离散视差信息为基础,基于TPS变换生成良好初始视差图,以提高SGM互信息计算的可靠性并提高计算效率;对理想水平像对下的摄影测量水平核线重排过程进行扩展,整体旋转摄影基线以消除分量BX,BY的影响并建立虚拟"水平"像空间辅助坐标系,从而利用相对定向参数生成沿扫描线方向的"水平"核线影像以满足SGM应用要求。倾斜影像密集匹配试验结果证明了算法的有效性,可为后续摄影测量DSM自动生成或3维快速重建工作提供可靠、逐像素的密集匹配点。 相似文献
20.
The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R2 = 0.87, RMSE = 0.0022 g/cm2, nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data. 相似文献