Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

真三维立体显示技术在不动产登记统一平台中的应用研究

随着国家不动产统一登记制度的实施,城市房屋、林地、草原、土地等不动产统一整合、调查和管理工作迫在眉睫。基于机载多角度倾斜摄影和激光雷达扫描技术的创新,真三维数据可为不动产登记提供真实统一、高精度、多层次的空间载体,保证了城市不动产登记应用创新和制度创新。     

车载移动测量系统应用于城市大比例尺地形图更新

将车载移动测量系统与城市大比例尺数字地形图测图相结合,成功地将车载移动测量系统运用于城市大比例尺测图更新应用中。通过在测区内布设靶标控制点,对比靶标的点云量算坐标与全站仪实测实际坐标,统计分析车载移动测量系统的测图精度。实验结果表明,车载移动测量系统能够满足城市大比例尺地形图测图要求。     

浅谈基于Android的移动地理信息采集系统建设

阐述了Android平台下调用GPS定位的方式方法,提出了PC端空间数据文件shapefile至Android平台可识别的SQLite数据库的转换方法和技术手段。通过Arc GIS SDK for Android移动GIS类库组件对SQLite数据库解析成Graphic图层,实现了地图属性编辑查看功能,并通过调用Android移动设备的拍照功能,将照片文件关联到属性信息,丰富地理信息数据的内容,实现地理信息数据的移动终端采集。     

移动测量系统宽幅影像的几何拼接与量测

研究了移动测量系统宽幅影像的拼接与量测算法。通过建立三幅影像柱面全景投影的几何关系,能够直接拼接完成宽幅影像。利用宽幅影像和原始影像间反投影变换关系以及三幅原始影像的同、异步立体像对,可建立宽幅影像与物方空间坐标间一一对应关系,从而实现宽幅影像目标的量测算法。宽幅影像的投影拼接和量测实验表明,利用柱面全景投影拼接得到宽幅影像的方法可应用于移动测量平台宽幅影像序列的拼接;在原始影像具备同步立体像对的条件下,宽幅影像量测的绝对精度可达1 m以内,相对精度可达0.2 m以内,满足城市环境实景目标量测的精度需求。     

Pes shape variation in an ornithopod dinosaur trackway (Lower Cretaceous,NW Spain): New evidence of an antalgic gait in the fossil track record

Trackways can provide unique insight to animals locomotion through quantitative analysis of variation in track morphology. Long trackways additionally permit the study of trackmaker foot anatomy, providing more insight on limb kinematics. In this paper we have restudied the extensive tracksite at Barranco de La Canal-1 (Lower Cretaceous, La Rioja, NW Spain) focussing on a 25-m-long dinosaur (ornithopod) trackway that was noted by an earlier study (Casanovas et al., 1995; Pérez-Lorente, 2003) to display an irregular pace pattern. This asymmetric gait has been quantified and photogrammetric models undertaken for each track, thus revealing distinct differences between the right and the left tracks, particularly in the relative position of the lateral digits II–IV with respect to the central digit III. Given that the substrate at this site is homogenous, the consistent repetition of the collected morphological data suggests that differences recorded between the right and the left tracks can be linked to the foot anatomy, but more interestingly, to an injury or pathology on left digit II. We suggest that the abnormal condition registered in digit II impression of the left pes can be linked to the statistically significant limping behaviour of the trackmaker. Furthermore, the abnormal condition registered did not affect the dinosaur's speed.     

Classification of forest land attributes using multi-source remotely sensed data

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.     

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R² = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.     

融合点、对象、关键点等3种基元的点云滤波方法

基元是影响点云滤波精度和效率的关键因素之一。本文提出了一种基于多基元的三角网渐进加密(MPTPD)滤波方法。它包括点云分割、对象关键点提取、基于关键点的对象类别判别3个主要阶段,且3个阶段的基元分别为点、对象、关键点。使用了4景机载激光雷达和摄影测量点云数据对MPTPD、三角网渐进加密(TPD)、基于对象的三角网渐进加密(OTPD)3种滤波方法进行了性能测试。试验表明,MPTPD方法具有整体上最优的性能:在精度方面,MPTPD与OTPD两种方法的精度相当,MPTPD方法的一类误差I、总误差T比TPD的相应误差分别低约22.07%和8.44%;在效率方面,多数情况下TPD、MPTPD、OTPD方法的效率依次降低,且MPTPD的平均耗时是OTPD平均耗时的57.93%。     

地理国情普查外业调绘核查系统实现

针对传统的基于纸质地图的外业调绘核查工作具有操作复杂、采集数据不精确、作业流程不规范等缺点,该文设计开发了基于iPad的地理国情普查外业调绘核查系统。在内业高精度遥感影像判读的基础上,采用全数字化的外业调绘核查技术,进行地理国情要素移动式、网络化外业调查和验证,快速采集和编辑调查数据,为外业工作提供了一套完整的解决方案。该系统在青岛市崂山区和城阳区的试点中已经得到广泛的应用,较好地验证了移动GIS技术用于外业信息采集的可行性。