近地机载激光雷达棉花LAI提取方法研究

棉花叶面积指数（LAI）是描述其长势的重要指标,准确获取冠层结构参数是叶面积指数反演的必要条件。以ScoutB-100油动单旋翼无人机为飞行平台,搭载RIEGL VUX-1激光雷达,精确获取棉花高密度点云数据,得到研究区棉田数字表面模型（DSM）和数字高程模型（DEM）,通过差值运算获得其冠层高度模型（CHM）,进而提取有效的冠层结构参数。利用相关性分析法选取相关系数大于0.2的激光穿透力指数（LPI）、回波点云密度（D）、孔隙率（f_gap）、归一化高程值（V_nDSM）构建棉花LAI反演模型,并与实测叶面积指数进行精度验证与评价。实验结果表明：模型估算的LAI与实测LAI之间的决定系数为0.824,均方根误差为0.072,验证了模型的可靠性。     

基于Landsat8影像时间序列NDVI的作物种植结构提取

为提高内蒙古平原灌区作物种植结构遥感监测精度和效率，提出一种基于时序NDVI曲线的作物种植结构提取方法。以内蒙古土默特右旗平原区为研究区域，以2015年覆盖作物生育期的多时相Landsat影像为数据源，根据不同地物其NDVI值范围不同，将研究区地表分为植被覆盖地表，无植被覆盖地表和水体3类。在植被覆盖区域内，根据林地和荒草地时序NDVI曲线特征，提取林地和荒草地，其余区域即为农田。根据小麦、玉米、葵花和西葫芦的时间序列NDVI曲线特征差异构建分类决策树模型，在农田区域内提取上述作物的空间种植分布信息。研究区各类地物及作物遥感提取面积与实际统计面积接近，土地利用分类总体精度达到85.71%，作物分类总体精度达到82.69%。研究结果表明该方法提取作物种植信息的精度较高，能够实现区域作物种植信息的高效准确监测。     

近50年基于农作物种植结构的新疆绿洲农田蒸散发时空变化分析

基于农作物种植结构准确估算绿洲农田蒸散发,对中国西北干旱区水土资源优化配置和生态环境可持续发展有重要意义。利用1960-2010年县级农业统计数据和基于遥感提取的绿洲耕地分布数据,产生逐年的新疆绿洲三种农作物(棉花、小麦、玉米)种植比例时空分布数据集,并在此基础上利用Penman-Monteith单作物系数法估算农作物蒸散发。结果表明:1960-2010年全疆绿洲耕地面积持续增加,粮食作物占优势的单一种植结构类型已被经济作物替代,形成南疆棉花快速增加、北疆小麦快速减少的种植格局。近50年全疆三种农作物总蒸散发量增加了47.85%,棉花贡献率最高,其次为玉米,小麦最低。耕地面积和结构变化对绿洲农作物蒸散发时空分布和组成结构变化具有重要影响。     

黄河中游大尺度植被冠层截留降水模拟与分析

大尺度植被冠层截留降水定量模拟与分析是揭示气候变化和人类活动综合作用下区域水沙变化机制的重要研究内容。以黄河中游河口镇—潼关区间为研究区,耦合遥感等空间数据与植被冠层截留估算模型,利用地面监测站点降水数据、GLASS叶面积指数遥感数据和地理信息空间分析技术,定量模拟和分析了黄河中游20 世纪80 年代以来3 个典型年份的地表植被冠层截留降水及其时空变化特征。结果表明：（1） 20 世纪80 年代以来,特别是20 世纪末实施的生态修复政策,使得黄河中游叶面积指数显著提高,植被覆盖明显改善;（2） 20 世纪80 年代以来,黄河中游植被冠层截留降水发生了明显变化,1984 年、1995 年和2010 年研究区植被冠层截留降水量区域年平均值分别为19.57 mm、13.66 mm和22.68 mm,截留率分别为3.24%、3.32%和4.92%;（3） 黄河中游植被冠层截留降水特征及其变化受降水特征和地表植被状况共同影响,其中,降水量是决定植被冠层截留降水特征的控制性因素,而叶面积指数年际变化是影响植被冠层截留降水特征变化的主要因素。     

基于Hydrus-1d模型的农田SPAC系统水分通量估算——以山西省运城市董村农场为例

农田SPAC系统水分通量的研究对提高农业用水效率,缓解我国的水资源短缺现状具有重要作用.本文利用HydruS-1d模型,以山西省运城市董村农场为例,估算了研究区三种主要农作物(夏玉米、棉花和冬小麦)的农田水分通量.结果表明,在模拟期内,夏玉米、棉花和冬小麦的作物蒸腾占蒸散发(T/ET)的比值分别为84.7%、71.6%...     

绿洲-荒漠交错带防护林与自然植被的协同防风效能及优化模式探讨

以新疆准噶尔盆地南缘绿洲-荒漠交错带为研究区,研究了该区防护林与其外围自然植被的协同防风效能,进而构建了二者协同配置的优化模式。结果表明:①在有障碍物(防护林、自然植被)存在时,风表现出显著的规律性,即在0.5 m和1.5 m高度处,防护林与自然植被协同后对风速起到了减弱作用。对于疏透结构林带,风速的减弱程度为自然植被内部>自然植被与防护林之间>防护林外部;对于紧密结构林带,当风速较小时,风速的减弱程度为自然植被与防护林之间>自然植被内部>防护林外部。②防护林带的冠层体积(V)、单位长度林带段胸高断面积(S)、株行距(Q)与自然植被的平均高度(H)、覆盖度(C)协同后和两高度处的相对风速存在极显著相关关系,据此以编程运算得到最小相对风速下二者协同配置的优化模式,即在0.5 m高度处,当V=62.1476 m3、S=0.5756 m2/50m、Q=5.5645 m2、H=1.6889 m、C=0.2878时,二者的协同防风效能达到85.07%;在1.5 m高度处,当V=62.1476 m3、S=0.5237 m2/50m、Q=5.5645 m2、H=1.8154 m、C=0.3073时,二者的协同防风效能达到74.67%。对不同相对风速下二者协同配置的优化模式进行了预测。     

沙漠绿洲过渡带典型下垫面风速脉动特征——以塔克拉玛干沙漠南缘策勒绿洲为例

以塔克拉玛干沙漠南缘策勒绿洲西部沙漠-绿洲过渡带为研究区,选择植被盖度 < 5%裸沙样地、植被盖度10%～15%柽柳样地和植被盖度20%～25%骆驼刺样地,利用可移动梯度风测量系统,获取了系统性天气过程中距离地表20 cm、40 cm、60 cm、100 cm和200 cm高度的风速及200 cm高度的风向系列数据,对3个样地平均风速、风速脉动和风向脉动进行分析和研究。结果表明:(1)植被的存在大幅度消弱了平均风速,随着植物密度的增加平均风速逐渐降低,风速廓线显示平均风速发生明显降低的转折高度与植被冠层的平均高度相对应。(2)3个样地脉动风速均近似服从高斯分布,可见植被的存在并未改变风速脉动分布规律。各样地风速脉动强度均随指示风速的增大而增大,随距离地表高度的降低而下降。风速脉动相对值总体上呈现随植物密度增大而增大的趋势,植物群落结构的差异增加了相对风速脉动垂直分布的复杂性。(3)风向脉动幅度以裸沙样地最小、柽柳样地次之、骆驼刺样地最大,即随植物密度增加而增大。同一样地内风向脉动值不随指示风速的变化而发生明显变化。     

半干旱区退化草地和农田下垫面近地面层微气象特征——以吉林省通榆为例

利用2003-2005年"通榆长期观测实验站"的近地面层气象要素和湍流观测资料,分析了吉林省通榆县半干旱区退化草地和农田下垫面的近地面层风速、温度和湿度变化规律;分别用梯度法和湍流法估算了退化草地和农田的地表动力学粗糙度(z0m),退化草地的z0m全年变化不大,而农田的z0m随着作物的生长有增加趋势;全年平均的z0m值,退化草地为0.003 4 m;农田生长季为0.054 m,非生长季为0.001 7 m.农田和退化草地下垫面的近中性整体输送系数Cd分别为(4.9±2.1)×10-3和(2.6±1.4)×10-3,Cb分别为(3.6±1.9)×10-3和(2.6±1.6)×10-3.     

基于PROSAIL模型的广州市过渡带森林植被冠层可燃物含水率估算

基于PROSAIL模型,结合野外实测叶片等水分厚度、干物质重量、叶面积指数数据,构建一种基于归一化红外指数和归一化干物质指数的植被冠层可燃物含水率估算方法。首先,在PROSAIL模型输入实测参数模拟植被冠层光谱曲线,计算归一化红外指数、归一化干物质指数用于叶片等水分厚度、干物质重量的反演。结果表明：归一化红外指数与叶片等水分厚度、归一化干物质指数与干物质重量存在明显的线性关系,基于该关系建立叶片等水分厚度、干物质重量的经验估算模型,经验证估算结果精度较高;将该经验模型推广至利用Landsat 8数据拟合植被冠层可燃物含水率,并与实测数据进行验证,结果显示R²达到0.743,RMSE达到34.2%,具有较高的精度。文章提出的植被冠层可燃物含水率估算模型,可实现广州市过渡带森林大面积、较高精度植被冠层可燃物含水率监测,为预防森林火灾提供参考。     

泾河上游流域实际蒸散量及其各组分的估算

利用分布式生态水文模型SWIM,基于泾河上游(泾川测站以上) 植被、土壤、气象和水文数据对研究区进行了水文过程的模拟,从而估算了流域的实际蒸散量及其各组分。结果表明：SWIM模型能够较好的模拟泾河上游流域的水文过程,模拟的流域多年(1997-2003 年) 平均实际蒸散量为443 mm,其中土壤蒸发量为259 mm,植被蒸腾量为157 mm,冠层截持量为27 mm。石质山区的森林覆盖区和非森林地的年蒸散总量在整个流域分别具有最大值和最小值,为484 mm和418 mm;黄土区实际蒸散量介于二者之间,平均为447 mm。森林覆盖地区土壤蒸发明显小于其它区域,而蒸腾和冠层截留明显大于其它区域。年内蒸散量主要集中在5-8 月份,占全年总蒸散量的60%,且冠层蒸散比例较大达63%。整个流域湿润年份较干旱年份蒸散量增加了78 mm,其中土壤蒸发增加最多,其次是冠层蒸腾,冠层截留蒸发最小。     

Association of elevation error with surface type,vegetation class and data origin in discrete-returns airborne LiDAR

Airborne LiDAR (light detection and ranging) data are now commonly regarded as the most accurate source of elevation data for medium-scale topographical modelling applications. However, quoted LiDAR elevation error may not necessarily represent the actual errors occurring across all surfaces, potentially impacting the reliability of derived predictions in Geographical Information Systems (GIS). The extent to which LiDAR elevation error varies in association with land cover, vegetation class and LiDAR data source is quantified relative to dual-frequency global positioning system survey data captured in a 400-ha area in Ireland, where four separate classes of LiDAR point data overlap. Quoted elevation errors are found to correspond closely with the minimum requirement recommended by the American Society of Photogrammetry and Remote Sensing for the definition of 95% error in urban areas only. Global elevation errors are found to be up to 5 times the quoted error, and errors within vegetation areas are found to be even larger, with errors in individual vegetation classes reaching up to 15 times the quoted error. Furthermore, a strong skew is noted in vegetated areas within all the LiDAR data sets tested, pushing errors in some cases to more than 25 times the quoted error. The skew observed suggests that an assumption of a normal error distribution is inappropriate in vegetated areas. The physical parameters that were found to affect elevation error most fundamentally were canopy depth, canopy density and granularity. Other factors observed to affect the degree to which actual errors deviate from quoted error included the primary use for which the data were acquired and the processing applied by data suppliers to meet these requirements.     

基于正交多项式的平原城区机载LiDAR数据滤波算法

从机载LiDAR点云数据中分离地面点与非地面点生成城区DEM,是构建数字城市的首要工作。该文对机载LiDAR数据处理中的滤波关键算法进行了研究。首先,利用常用的区域增长方法对机载LiDAR点云数据进行了滤波处理,然后基于正交多项式分带滤波方法进行了点云滤波,比较了两种算法的运行效率,分析了二者的特点,并通过试验对比进行了阈值参数优选,试验表明该算法具有较好的滤波效果,在平原城区的数字城市三维建模中具有很好的应用价值。     

Errors in LiDAR-derived shrub height and crown area on sloped terrain

This study developed and tested four methods for shrub height measurements with airborne LiDAR data in a semiarid shrub-steppe in southwestern Idaho, USA. Unique to this study was the focus of sagebrush height measurements on sloped terrain. The study also developed one of the first methods towards estimating crown area of sagebrush from LiDAR. Both sagebrush height and crown area were underestimated by LiDAR. Sagebrush height was estimated to within ± 0.26-0.32 mm (two standard deviations of standard error). Crown area was underestimated by a mean of 49%. Further, hillslope had a relatively low impact on sagebrush height and crown area estimation. From a management perspective, estimation of individual shrubs over large geographic areas can be accomplished using a 0.5 m rasterized vegetation height derivative from LiDAR. While the underestimation of crown area is substantial, we suggest that this underestimation would improve with higher LiDAR point density (>4 points/m²). Further studies can estimate shrub biomass using LiDAR height and crown area derivatives.     

A fault scarp in an urban area identified by LiDAR survey: A Case study on the Itoigawa–Shizuoka Tectonic Line, central Japan

A light detection and ranging (LiDAR) survey was conducted in a densely built-up area to generate a high-resolution digital elevation model (DEM) to look for active faults. The urban district of Matsumoto City in central Japan is located in a 3-km² basin along the Itoigawa–Shizuoka Tectonic Line active fault system, one of Japanese onshore fault systems with the highest earthquake probability. A high-resolution DEM at a 0.5-m-grid interval was obtained after removing the effects of laser returns from buildings, clouds and vegetation. It revealed a continuous scarp, up to ~ 2 m in height. Borehole data and archaeological studies indicate the scarp was formed during the most recent faulting event associated with historical earthquakes. In addition, the fault scarp strongly supports that the urban district is in a pull-apart basin related to a fault step-over between two left-lateral strike-slip faults. Consequently, accurate interpretation of fault geometry is crucial to provide estimates of future surface deformation and to allow modeling of basin structure and strong ground motion. Thus, the LiDAR mapping survey in urban districts is effective for detailed active fault mapping in order to constrain basin structure and to forecast the exact location of surface rupturing associated with large earthquakes.     

Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network

Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods.     

基于森林冠层高度和异速生长方程的中国红树林地上生物量估算

红树林是一种高效率的滨海蓝碳生态系统,准确估算红树林地上生物量对研究碳循环和气候变化十分重要,获取中国红树林地上生物量将具有现实意义和应用价值。遥感技术便捷高效、观测范围广,能够服务于大尺度的生态系统监测。文章使用基于GEDI星载激光雷达反演的森林冠层高度数据和基于异速生长原理构建的红树林“树高-生物量”异速生长方程,估算2019年中国红树林地上生物量,进而分析其数量、空间分布特征及主要影响因素。结果显示,2019年中国红树林地上生物量总量和均值分别约为1 974 827 t和73.0 t/hm²;红树林分布的各省份(地区)的地上生物量均值在53.3～92.1 t/hm²,其中海南省的红树林地上生物量均值最高,达到92.1 t/hm²;中国红树林地上生物量的累积和分布受纬度和人为因素的影响。研究结果能够为后续红树林生态系统碳储量的核算提供数据基础和技术参考,也将有助于中国沿海红树林生态恢复和保护措施的制定,以及控制碳排政策的出台实施。     

Spatial structure analysis of a reptile community with airborne LiDAR data

The analysis of the spatial structure of animal communities requires spatial data to determine the distribution of individuals and their limiting factors. New technologies like very precise GPS as well as satellite imagery and aerial photographs of very high spatial resolution are now available. Data from airborne LiDAR (Light Detection and Ranging) sensors can provide digital models of ground and vegetation surfaces with pixel sizes of less than 1 m. We present the first study in terrestrial herpetology using LiDAR data. We aim to identify the spatial patterns of a community of four species of lizards (Lacerta schreiberi, Timon lepidus, Podarcis bocagei, and P. hispanica), and to determine how the habitat is influencing the distribution of the species spatially. The study area is located in Northern Portugal. The position of each lizard was recorded during 16 surveys of 1 h with a very precise GPS (error < 1 m). LiDAR data provided digital models of surface, terrain, and normalised height. From these data, we derived slope, ruggedness, orientation, and hill-shading variables. We applied spatial statistics to determine the spatial structure of the community. We computed Maxent ecological niche models to determine the importance of environmental variables. The community and its species presented a clustered distribution. We identified 14 clusters, composed of 1–3 species. Species records showed two distribution patterns, with clusters associated with steep and flat areas. Cluster outliers had the same patterns. Juveniles and subadults were associated with areas of low quality, while sexes used space in similar ways. Maxent models identified suitable habitats across the study area for two species and in the flat areas for the other two species. LiDAR allowed us to understand the local distributions of a lizard community. Remotely sensed data and LiDAR are giving new insights into the study of species ecology. Images of higher spatial resolutions are necessary to map important factors such as refuges.     

Improved visibility calculations with tree trunk obstruction modeling from aerial LiDAR

Viewshed and line-of-sight are spatial analysis functions used in applications ranging from urban design to archaeology to hydrology. Vegetation data, a difficult variable to effectively emulate in computer models, is typically omitted from visibility calculations or unrealistically simulated. In visibility analyzes performed on a small scale, where calculation distances are a few hundred meters or less, ineffective incorporation of vegetation can lead to significant modeling error. Using an aerial LiDAR (light detection and ranging) data set of a lodgepole pine (Pinus contorta) dominant ecosystem in Idaho, USA, tree obstruction metrics were derived and integrated into a short-range visibility model. A total of 15 visibility plots were set at a micro-scale level, with visibility modeled to a maximum of 50 m from an observation point. Digital photographs of a 1 m² target set at 5 m increments along three sightline paths for each visibility plot were used to establish control visibility values. Trunk obstructions, derived from mean vegetation height LiDAR data and processed through a series of tree structure algorithms, were factored into visibility calculations and compared to reference data. Results indicate the model calculated using trunk obstructions with LiDAR demonstrated a mean error of 8.8% underestimation of target visibility, while alternative methods using mean vegetation height and bare-earth models have an underestimation of 65.7% and overestimation of 31.1%, respectively.