用地基激光雷达提取单木结构参数——以白皮松为例

以白皮松(Pinus bungeana Zucc)为研究对象,针对地基激光雷达TLS扫描的3维点云数据在单株木垂直方向的分布特征,提出了一种基于体元化方法的树干覆盖度变化检测方法,获取单木枝下高;然后根据获取的枝下高引入2维凸包算法获取垂直方向分层树冠轮廓,并计算树冠体积和冠幅;同时获取的单木参数还有胸径与树高。结果表明:单木枝下高的估测精度较高,R2与RMSE分别为0.97 m和0.21 m;胸径估测结果的R2与RMSE分别为0.79 cm和1.07 cm;采用逐步线性回归方法建立单木树冠体积与其他单木参数的相关关系,模型变量包括冠幅、叶子填充树冠长度和胸径,样本数为20,模型的R2与RMSE分别是0.967 m3和2.64 m3。本文方法能较准确地估测枝下高,TLS数据具有对树冠结构3维建模的潜力。     

车载激光点云数据中行道树三维信息自动提取研究

城市行道树三维信息是城市智慧管理的重要基础信息之一,本文研究了一种基于车载激光点云数据的行道树三维信息自动提取方法。首先,根据行道树点云和周围地面点计算树高;然后,针对残缺树冠点云,应用点云不同方位距离对比计算冠幅;最后,根据树干扫描分层点云,运用RANSAC算子拟合圆模型,计算胸径。通过实际测量数据进行验证,本文方法提取出的行道树信息误差较小,精度较高。     

三维激光扫描数据的单木树冠体积精确计算

针对现存单木树冠体积计算方法不能剔除树冠外部较大空隙以及树冠边界提取粗糙的问题,该文在对生长算法改进的基础上,提出了基于过滤三角网的树冠边界精确提取算法,确定了树冠最优分层间距与过滤阈值,实现了树冠体积的准确计算。该方法在对扫描的单木树冠点云数据进行拼接和过滤后,进行等间隔分层处理获取其切片点云,然后采用过滤三角网算法生成符合树冠实际情况的边界,再通过计算的切片面积获取各层点云间的体积,最终累加各层点云体积得到树冠体积的精确值。对校内树冠三维激光扫描实测数据进行计算与分析,结果表明过滤三角网算法提取的树冠边界能顾及树冠外部存在的空隙现象,进而得到准确的树冠体积值;此外,过滤三角网算法对树冠点云数据的密度要求远低于体元法,具有较高的算法稳定性。     

基于地面激光扫描数据的单木特征因子提取与分析

本文利用三维激光扫描仪对树木进行扫描获取树点云数据,经过格式转换、分离、提取后,对树木各测量因子包括胸径、树高、树冠、材积量进行测定与测量方法与意义的分析。通过实验分析,可以得出:树冠测定因子通过测定树冠的叶面积指数来更精确地反映树冠的生理学意义;通过不规则三角网构建的多面体计算的树干体积较以平均断面积、中央断面积求树干材积更为准确与便捷。     

顾及林草覆盖的大比例尺坡度因子计算

本文针对四川省区域水土流失动态监测对坡度因子计算的新要求,从大比例尺DEM数据快速、准确提取林草和非林草覆盖区坡度因子,以VS2015为开发平台,采用C#开发语言,基于ArcObjects类库进行二次开发,解决了内存溢出问题和林草与非林草覆盖区坡度因子镶嵌问题,开发了新的坡度因子计算程序。本文选取都江堰为实验区,实现了坡度因子快速提取,共耗时2分43秒。本方法能克服DEM计算坡度产生的边缘效应,快速对大批量DEM数据进行坡度因子提取,可用于四川省常态化水土流失动态监测及消长评价工作。     

基于改进凸包算法的树冠轮廓点提取与体积计算

针对城市中树木数量多、树冠结构复杂、形态多变、难以测量等特点,为解决传统凸包算法在提取树冠轮廓时误差较大的问题,并实现树冠轮廓点提取和树冠体积自动计算,提出迭代渐进的凸包算法。以三维激光扫描仪获取的点云为原始数据,基于所提出的算法获取树冠轮廓点,结合格林公式及不规则台体体积法计算树冠体积。为验证算法的准确性,采用人工交互的方式提取树冠外轮廓点计算树冠体积,以此为参照对迭代渐进的凸包算法进行相关性与均方根误差的验证,并将该算法分别与几何体模型法、体元模拟法、Graham扫描线法计算的树冠体积进行对比分析,结果表明,迭代渐进的凸包算法计算出的树冠体积更接近于树冠的真实体积,为树冠信息的精细提取与建模提供一种新的方法。     

基于赋值技术的退耕还林实现--以旅顺地区为例

测绘部门的地形图数据是以AutoCAD数据格式存储,不能够实现查询、检索,更不能够实现坡度计算、分析.而旅顺地区国土修编中需要把高程在60m以上以及坡度大于25°的耕地退耕还林.文章通过MapInfo、Vertical Mapper 、MapBasic结合,实现栅格数据与矢量数据的互操作,提取需要退耕还林的耕地.     

单流向算法与多流向算法下土壤侵蚀因子的比较研究

本文分别论述了单流向算法与多流向算法下土壤侵蚀因子提取的基本原理、计算模型以及计算流程,并以陕北黄土丘陵区为实验区域,以30m分辨率DEM数据作为基础数据,对比分析了单流向算法与多流向算法下土壤侵蚀因子的空间结构特征与空间数据统计特征。实验结果表明,多流向算法具有与DEM相似的空间结构特征即表面光滑连续特性;空间数据统计分析表明,单流向算法与多流向算法对土壤侵蚀因子的计算影响,在土壤侵蚀因子计算值较小区域有显著性差异。     

无人机航测技术在森林蓄积量估测中的应用

无人机（UAV）航测技术是近年来发展起来的快速获取高分辨率影像的测绘新技术。森林蓄积量估算需要快速高效地获取森林遥感影像。虽然利用卫星和机载雷达同样可获取高分辨率遥感影像,但无人机航测技术与其相比具有飞行成本低、外业周期短、机动灵活等优点。本文利用无人机航测系统获取了案例地区DSM和DEM,采用最大邻域法提取了树高,采用分水岭算法分割了树冠信息,并以树高和冠幅作为解释变量的立木材积二元模型估算了森林蓄积量。结果表明,树高提取精度为83.73%,冠幅提取精度为86.98%,林分蓄积量估算精度为81.80%。     

SRTM(1″)DEM在流域水文分析中的适用性研究

高精度的数字高程模型(digital elevation model,DEM)数据是流域水文分析应用的基础。美国地质调查局新发布了全球高分辨率数字高程数据产品,其空间分辨率为1″(约为30 m)。为评价该数据在流域水文分析中的适用性,以鹤壁汤河流域为实验区,以机载LiDAR DEM数据为参考,统计了SRTM(1″)数据的高程误差,分析了坡度、坡向、地表覆盖等对误差的影响;在基于地形的水文分析中,统计分析了SRTM(1″)数据误差对地形湿度指数、坡度坡长因子以及汇流动力指数等地形指数计算的影响;最后选取流域汇水区面积、最长水流路径长度、形状系数、弯曲度系数等流域特征参数对两种DEM数据提取结果进行了对比。研究表明SRTM(1″)DEM数据具有较高的精度,原始数据均方根误差为5.98 m,在消除平面位移误差后减小为4.32 m。基于地形的水文分析表明SRTM DEM与LiDAR DEM计算结果具有一定的差异,地形湿度指数平均值略高,坡度坡长因子和汇流动力指数平均值偏低,离散度偏小,这与SRTM DEM在微地貌以及高坡度地形区存在失真相关。两种DEM数据提取流域特征参数差异较小。上述研究表明SRTM DEM(1″)数据在流域水文分析中具有较大的应用潜力。     

Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data

Abstract

Individual tree crown segmentation is important step for deriving various information for fine-scale analysis of ecological process. However, only several studies have applied tree crown segmentation in tropical forest ecosystems, especially in mixed peat swamp forests. In this study, hyperspectral data were used to detect changes in the biochemical and biophysical characteristics, which are important factors for tree crown segmentation. Principal Component Analysis method was performed to investigate its influence on crown segmentation. Visually Selected PCs, 160 PCs and 160 Spectral Bands image were used and two segmentation techniques; Watershed Transformation and Region Growing segmentation were applied on those images. The highest accuracy was achieved for the crown segmentation is using Region Growing segmentation, based on 1:1 measurement, D value and RMSE value. The results obtained from 160 PCs image using region growing algorithm shows better accuracy with D value of 0.2 (80% accuracy, 20% error) and RMSE of 9.9 m².     

无人机遥感影像林地单株立木信息提取

针对无人机遥感技术在提取单株立木信息的限制性问题，提出一种新的自动单株立木信息提取方法。对原始无人机影像进行光谱信息增强处理以突出局部细节特征；通过引入DBI指数自动化确定K-means聚类方法的最优聚类数目，进而对影像像素进行标记；通过利用高斯马尔可夫随机场模型进一步对影像进行分割；使用数学形态学算子等方法对分割结果进行后处理得到单株立木树冠信息，通过图像几何矩原理计算得到单株立木位置以作为其识别的依据。结果表明，应用该提取方法，油松林区和樟子松林区单株立木识别总体精度分别为89.52%和95.65%、单木树冠提取精度分别为81.90%和95.65%，均具有较好地适用性。该方法不需要大量的人工干预和先验知识的输入，大大提高提取方法的自动化程度。     

Change in habitat selection by Japanese macaques (Macaca fuscata) and habitat fragmentation analysis using temporal remotely sensed data in Niigata Prefecture,Japan

The aim of this study was to evaluate changes in macaque habitat selection during a 29-year period. We focused on the 1970s, when little crop damage was caused by Japanese macaques (Macaca fuscata), and the 2000s, when the damage became remarkable. Landsat/MSS from 1978 and ALOS/AVNIR-2 from 2007 were employed for land-cover mapping. For the 2007 land-cover classification, we applied an object-oriented image classification and a classification and regression tree. The Kappa coefficient of the 2007 land-cover map was 0.89. For the 1978 land-cover classification, change detection using principal component analysis and object-oriented image classification were applied to reduce resolution difference errors. The Kappa coefficient of the 1978 land-cover map was 0.84. We applied a Random Forest model for machine learning and data mining to predict the habitat selection of macaques. Several important environmental factors were identified for macaque habitat selection: the ratio of coniferous forest to farmland, distance to farmland, and maximum snow depth. The Random Forest model was extrapolated to the 1978 land-cover map. Over the 29-year period, coniferous forest changed to broad-leaved forest and/or mixed forest within the macaque habitat area. Coniferous forests were not selected as food resources by Japanese macaques. Furthermore, large-scale patches of farmland were used as food resources over the 29-year period. These changes indicated that habitat selection by Japanese macaques changed over the study period. The results show that the home range of macaques expanded, and macaques may now be distributed over a wider area as a result of changes in landscape configuration. Thus, forest planning, such as sustainable management of artificial conifer forests, is important for reducing crop damage.     

Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests

Estimates of forest aboveground biomass are fundamental for carbon monitoring and accounting; delivering information at very high spatial resolution is especially valuable for local management, conservation and selective logging purposes. In tropical areas, hosting large biomass and biodiversity resources which are often threatened by unsustainable anthropogenic pressures, frequent forest resources monitoring is needed. Lidar is a powerful tool to estimate aboveground biomass at fine resolution; however its application in tropical forests has been limited, with high variability in the accuracy of results. Lidar pulses scan the forest vertical profile, and can provide structure information which is also linked to biodiversity. In the last decade the remote sensing of biodiversity has received great attention, but few studies focused on the use of lidar for assessing tree species richness in tropical forests.This research aims at estimating aboveground biomass and tree species richness using discrete return airborne lidar in Ghana forests. We tested an advanced statistical technique, Multivariate Adaptive Regression Splines (MARS), which does not require assumptions on data distribution or on the relationships between variables, being suitable for studying ecological variables.We compared the MARS regression results with those obtained by multilinear regression and found that both algorithms were effective, but MARS provided higher accuracy either for biomass (R² = 0.72) and species richness (R² = 0.64). We also noted strong correlation between biodiversity and biomass field values. Even if the forest areas under analysis are limited in extent and represent peculiar ecosystems, the preliminary indications produced by our study suggest that instrument such as lidar, specifically useful for pinpointing forest structure, can also be exploited as a support for tree species richness assessment.     

基于多样性特征协同技术的飓风前后森林破坏遥感监测

对被飓风破坏的森林进行变化监测与灾害评估是遥感技术的一个重要应用,遥感影像的特征信息提取对森林遥感监测的效果至关重要。多样性特征结合可以有效提高对森林变化的监测精度。然而,当前的空间信息如纹理特征的获取算法依旧保留着传统的固定式计算模式,一直面临着特征数量和邻域参考范围之间难以均衡的问题。为了解决以上问题,本文提出了基于多样性特征协同技术的飓风前后森林破坏遥感监测方法,首先计算出森林遥感影像变化前后的归一化植被指数差值和增强植被指数差值,并提出了基于复合窗口技术的来提取纹理特征,然后建立了多样性特性结合模型;其次提出了一种基于特征分离的旋转森林改进算法,最终,实现了内泽尔森林在暴风前后的高精度变化监测;另外,还测试了新模型在不同训练样本数量下的分类性能。实验结果表明,相对传统的基于光谱特征和单纯的纹理特征的变化监测方法,本文所提出的方法的整体精度、对变化区域和未变化区域的检测精度至多分别提高了3.68%、6.53%和3.46%。本文的研究方法可以有效提高森林变化监测的性能,为森林灾害评估与森林资源保护提供参考依据。     

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains,California

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling. Airborne Laser Scanning (ALS) can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains. This study proposed an ALS-based framework to quantify tree growth and competition. Bi-temporal ALS data were used to quantify tree growth in height (ΔH), crown area (ΔA), crown volume (ΔV), and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests. We analyzed the correlations between tree growth attributes and controlling factors (i.e. tree sizes, competition, forest structure, and topographic parameters) at multiple levels. At the individual tree level, ΔH had no consistent correlations with controlling factors, ΔA and ΔV were positively related to original tree sizes (R?>?0.3) and negatively related to competition indices (R?R|?>?0.7), ΔV was positively related to original tree sizes (|R|?>?0.8). Multivariate regression models were simulated at individual tree level for ΔH, ΔA, and ΔV with the R² ranged from 0.1 to 0.43. The ALS-based tree height estimation and growth analysis results were consistent with field measurements.     

WorldView-2影像林地信息提取的研究与实现

林地信息的获取具有重要意义,可广泛应用于城市规划编制、林地资源分析、水土流失治理等领域。本文基于WorldView-2影像,在研究分割与对象特征的基础上,采用面向对象方法,实现了林地信息提取,其用户精度达到94%,制图精度达到97.2%。然后,将像元二分模型应用于林地类对象,计算其植被覆盖度。试验表明,面向对象方法能高效、准确、经济地提取林地信息;基于林地类对象计算的植被覆盖度能较好地表征植被覆盖情况。     

多种特征信息在高分辨率遥感影像目标识别中的应用研究

以Quickbird影像为研究对象,探讨了利用多种特征信息识别地物目标的技术方法.首先采用区域生长法将影像分割为若干个具有语义信息的对象,然后在此基础上提取对象的光谱、形状和纹理特征并进行描述,最后根据提取的特征参数,采用最近邻方法将影像分为建筑物、公路、铁路、水塘、耕地、林地和荒地7类地物目标,综合分类精度达到91.03%.研究表明,多种特征信息的综合利用,在目标分类与识别方面明显优于传统的基于单一光谱特征的方法,在一定程度内提升了遥感信息的智能化水平.     

Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data

The sustainable management and monitoring of urban forests is an important activity in the urbanized world, and operational approaches require information about the status of urban trees to determine the best strategy. One limitation in urban forest studies is the detection and discrimination of tree species using limited training data. Thus, this study focuses on developing generic rule sets from high-resolution WorldView-2 imagery in conjunction with spectral, spatial, colour and textural information for automated urban tree species detection. The object-based image analysis and its combination with statistical analysis of object features is utilized for this purpose. Results of attribute selection indicated that from 55 attributes, only 26 were useful to discriminate urban tree species, namely Messua ferrea L., Samanea saman and Casuarina sumatrana. Finally, the high overall accuracy, approximately 86.87% with kappa of 0.75 confirmed the transferability of the generic model.     

The 13th Asian conference on remote sensing,Mongolia

Abstract

Three spatial resolutions of airborne remote sensing imagery (60 cm, 1 m, and 2 m) collected over multi‐layer aspen, pine, spruce, and mixedwood forest stands in Alberta on July 18^th, 1998 were tested for their ability to provide a statistical stand discrimination based on spatial co‐occurrence texture analysis. As spatial resolution increased, classification accuracies increased. The highest classification accuracy of 86.7% was obtained using the highest image spatial resolution data (60 cm), with spatial co‐occurrence texture and spectral signatures combined, and a thirteen‐class multi‐layer stand stratification. The texture of the highest spatial resolution imagery (60 cm pixel resolution) was interpreted to contain information on the crown architecture of individual trees. In larger windows, the texture was interpreted to contain information on stand structure. Texture of lower spatial resolution imagery (1 m and 2 m pixel resolution) could not detect individual tree crown architecture and was determined to be related primarily to stand structure characteristics. The use of texture channels improved the per‐plot classification accuracies by 15.7%, compared to the use of the spectral data alone.