Predicting individual stem volumes of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR

This study investigated which predictor variables with respect to crown properties, derived from small-footprint airborne light detection and ranging (LiDAR) data, together with LiDAR-derived tree height, could be useful in regression models to predict individual stem volumes. Comparisons were also made of the sum of predicted stem volumes for LiDAR-detected trees using the best regression model with field-measured total stem volumes for all trees within stands. The study area was a 48-year-old sugi (Cryptomeria japonica D. Don) plantation in mountainous forest. The topographies of the three stands with different stand characteristics analyzed in this study were steep slope (mean slope ± SD; 37.6° ± 5.8°), gentle slope (15.6° ± 3.7°), and gentle yet rough terrain (16.8° ± 7.8°). In the regression analysis, field-measured stem volumes were regressed against each of the six LiDAR-derived predictor variables with respect to crown properties, such as crown area, volume, and form, together with LiDAR-derived tree height. The model with sunny crown mantle volume (SCV) had the smallest standard error of the estimate obtained from the regression model in each stand. The standard errors (m³) were 0.144, 0.171, and 0.181, corresponding to 23.9%, 21.0%, and 20.6% of the average field-measured stem volume for detected trees in each of these stands, respectively. Furthermore, the sum of the individual stem volumes, predicted by regression models with SCV for the detected trees, occupied 83%–91% of field-measured total stem volumes within each stand, although 69%–86% of the total number of trees were correctly detected by a segmentation procedure using LiDAR data.     

SV300测绘软件在地形图测绘中的应用

本文介绍了SV300测绘软件在地形图测绘中的应用，重点阐述了草图法的工作内容及步骤，并涉及到PDA测图精灵的使用。     

农田土地整理土方量计算方法比较研究

结合具体工作实践,分析了DTM法、方格网法、断面法、等高线法这4种土方计算方法的特点,并对结果进行比较。     

连续森林清查应用地理信息估计蓄积量的方法

提出了应用地理信息系统作为技术手段,以常规固定样地设置为抽样方式,以地形因子与蓄积量回归的估计方法来估计森林总体蓄积量的问题。讨论了可能性和可行性,给出和推导了总体无偏估计值和方差公式。     

基于人工智能技术的数字地面模型空间插值研究

本文针对数字地面模型(D igital Terrain Model)的建模问题,将高程作为插值变量,通过建立采样点的平面坐标(x,y)及位置因子x2 y2作为输入,对应点的地面高程作为输出的1种描述空间采样数据的BP模型,用人工智能技术研究了1个小区域的高程插值问题。结果表明:基于BP法的人工智能技术可用于DTM的最优空间插值,插值结果可直接用于DTM数字建模,为数字地面模型的建立提供了1种新方法。     

基于数字地面模型的公路纵断面设计

提出了用W=dx     

Arbitrary Curve-Based Methods in Ground Attributive Modelling

The basic concepts of ground attributes and Synthetic Digital Tarrain Model(SDTM) are introduced.An highly adaptable on_site realtime modeling method and on algorithms based arbitrary curves are suggested.The application of this method in high grade highway survey are introduced.     

数字地形模型及地形特征解析

道路和森林开发形式自动化设计其根本的一步,就是如何在选择的对象区域上制作数字地形模型。本文对建立数字地形模型的常用方法(方格网法)进行了解析和介绍。其中提出了依据不同比例尺的地形图来确定方格网密度的对应关系。这样建立的地形模型所表达的地面是诸多双曲抛物面的集合体。在此基础上利用数字地形模型对影响地形特征的几个因素:标高、倾斜、起伏量、谷密度进行定量分析,确定其具体计算方法,并找出他们同地形指数的相关性,据此选择适当的森林开发形式。     

The effectiveness of airborne LiDAR data in the recognition of channel-bed morphology

High-resolution topographic data have the potential to differentiate the main morphological features of a landscape. This paper analyses the capability of airborne LiDAR-derived data in the recognition of channel-bed morphology. For the purpose of this study, 0.5 m and 1 m resolution Digital Terrain Models (DTMs) were derived from the last pulse LiDAR data obtained by filtering the vegetation points. The analysis was carried out both at 1-D scale, i.e. along the longitudinal channel profile, and at 2-D scale, taking into account the whole extent of the channel bed. The 1-D approach analyzed the residuals of elevations orthogonal to the regression line drawn along the channel profile and the standard deviation of local slope. The 2-D analysis was based on two roughness indexes, consisting on the local variability of the elevation and slope of the channel bed. The study was conducted in a headwater catchment located in the Eastern Italian Alps. The results suggested a good capability of LiDAR data in the recognition of river morphology giving the potential to distinguish the riffle-pool and step-pool reaches.     

基于地形因子的土壤有机碳最优估算模型

基于数字地面模型(Digital Terrain Model,DTM),同时考虑因子组合和分辨率构建土壤有机碳(SOC)最优估算模型。在7 100 km2范围内,选取了71个分辨率和22个地形因子中不多于5个因子的所有可能组合,构造了2 514 820个模型。采样点随机分为两组,6 362个训练样点构造数据挖掘模型,其他2 208个为验证样点。根据模型相关系数r值大小从中选取了不同个数因子组合以及相应分辨率的最优模型,并根据这些模型生成对应的土壤有机碳图。结果表明:单个地形因子模型和栅格大小之间的关系表现出多样化,并不是分辨率越高模型结果越好。单因子模型r值的大小并不能决定其在因子组合模型中的重要性。不同的因子及其组合有其特定的最适分辨率,最佳分辨率范围约为60~150 m。综合数据的存储空间和计算量、模型复杂度、预测精度以及空间表达能力,该地区最优模型由相对坡位、高程、归一化高程及多尺度山谷平坦指数等4个变量组成,对应分辨率为121.6 m。同时与多种克里格空间插值方法生成的土壤有机碳空间分布图进行了对比分析,发现无论几个变量的组合,其空间预测能力均较克里格空间插值方法更能表达SOC的空间变化,预测精度也较高。