Geospatial technologies for Chang’e-3 and Chang’e-4 lunar rover missions

ABSTRACT

This paper presents a brief overview of the geospatial technologies developed and applied in Chang’e-3 and Chang’e-4 lunar rover missions. Photogrammetric mapping techniques were used to produce topographic products of the landing site with meter level resolution using orbital images before landing, and to produce centimeter-resolution topographic products in near real-time after landing. Visual positioning techniques were used to determine the locations of the two landers using descent images and orbital basemaps immediately after landing. During surface operations, visual-positioning-based rover localization was performed routinely at each waypoint using Navcam images. The topographic analysis and rover localization results directly supported waypoint-to-waypoint path planning, science target selection and scientific investigations. A GIS-based digital cartography system was also developed to support rover teleoperation.     

Influence of forest fire episodes on the distribution of gaseous air pollutants over Uttarakhand,India

ABSTRACT

Widespread forest fire events occurred in the foothills of North Western Himalaya during 24 April to 2 May 2016 (Event-1) and 20–30 May 2018 (Event-2). Their impacts were investigated on the distribution of pollutant gases ozone (O₃), carbon monoxide (CO), and oxides of nitrogen (NOx) over Uttarakhand using simulations of Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and in-situ observations of these gases over Dehradun, the capital of Uttarakhand. During Event-1, the observed CO mixing ratio over Dehradun increased from 25 April 2016 onwards, attained maximum (705.8 ± 258 ppbv) on 2 May 2016 and subsequently decreased. The rate of increase of daily baseline CO was 29 ppbv/day during HFAP (High Fire Activity Period). During Event-2, daily average concentrations of CO, O₃, and NOx showed systematic increase over Dehradun during HFAP period. The rate of increase of CO was 9 ppbv/day, while it was very small for NOx and O₃. To quantitatively estimate the influence of forest fire emissions, two WRF-Chem simulations were made: one with biomass burning (BB) emissions and other without BB emissions. These simulations showed 52% (34%) enhancement in CO, 52% (32%) enhancement in NOx, and 11% (9%) enhancement in O₃ during HFAP for Event-1 (Event-2). A clear positive correlation (r = 0.89 for Event-1, r = 0.69 for Event-2) was found between ?O₃ (O_{3with BB} minus O_{3without BB}) and ?CO (CO_{with BB} minus CO_{without BB}), indicating rapid production of ozone in the fire plumes. For both the events, the vertical distribution of ?O₃, ?CO, and ?NOx showed that forest fire emissions influenced the air quality upto 6.5 km altitude. Peaks in ?O₃, ?CO, and ?NOx during different days suggested the role of varying dispersion and horizontal mixing of fire plumes.     

A meta-analysis of global crop water productivity of three leading world crops (wheat,corn, and rice) in the irrigated areas over three decades

ABSTRACT

The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40–50° had much higher mean CWP (2.45?kg/m³) compared to mean CWP of corn grown in other latitudes such as 30–40° (1.67?kg/m³) or 20–30° (0.94?kg/m³). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (≤0.75?kg/m³), medium (>0.75 to <1.10?kg/m³), and high CWP (≥1.10?kg/m³). For corn the global CWP categories were low (≤1.25?kg/m³), medium (>1.25 to ≤1.75?kg/m³), and high (>1.75?kg/m³). For rice the global CWP categories were low (≤0.70?kg/m³), medium (>0.70 to ≤1.25?kg/m³), and high (>1.25?kg/m³). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India’s corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100?m³ in volume that leads to many benefits such as acting as ‘water banks’ for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%.     

基于深度残差网络的机载LiDAR点云分类

机载LiDAR点云的分类是利用其进行城市场景三维重建的关键步骤之一。为充分利用现有的图像领域性能较好的深度学习网络模型,提高点云分类精度,并降低训练时间和对训练样本数量的要求,本文提出一种基于深度残差网络的机载LiDAR点云分类方法。首先提取归一化高程、表面变化率、强度和归一化植被指数4种具有较高区分度的点云低层次特征;然后通过设置不同的邻域大小和视角,利用所提出的点云特征图生成策略,得到多尺度和多视角点云特征图;再将点云特征图输入到预训练的深度残差网络,提取多尺度和多视角深层次特征;最后构建并训练神经网络分类器,利用训练的模型对待分类点云进行预测,经后处理得到分类结果。利用ISPRS三维语义标记竞赛的公开标准数据集进行试验,结果表明,本文方法可有效区分建筑物、地面、车辆等8类地物,分类结果的总体精度为87.1%,可为城市场景三维重建提供可靠的信息。     

基于高分遥感影像的建筑物侧面信息提取及其高度计算

本文选取成都市某一区域建筑物A、B为研究对象,采用分辨率为0.61 m的Quick bird影像,运用图像分割法和LVQ神经网络算法,提取建筑物侧面信息,根据假设法原理,构建高度计算物理模型,求取建筑物高度。对比实测数据,结合可能影响实验结果的实地因素、遥感影像因素进行精度分析与评价,探讨基于高分遥感影像的建筑物侧面信息提取和高度计算的方法。结果表明,LVQ神经网络算法在建筑物侧面提取和高度计算中有更好的应用价值,精度高达94%。     

地下管网健康档案管理平台的设计与实现

针对地下管网隐患数据管理的应用需求,本文基于三维平台设计实现了地下管网健康档案管理平台,在三维场景加载地上精细模型和地下管网数据,以三维可视化的手段对管网各类隐患问题进行信息管理,结合二维GIS操作,实现了净距分析、覆土分析、占压分析等管线隐患分析功能,通过本平台建立的地下管网健康档案数据库,方便用户对各类管网隐患数据进行信息管理和统计,为管道病害修复、管网改造提升提供数据支撑,保证地下管网的安全运行。     

高精度导航电子地图的国家安全防控要点探究

近年来,汽车工业已进入智能时代,自动驾驶汽车必将引领时代风潮,与之而来的则是高精度导航电子地图产业的大发展。然而,高精度导航电子地图的出现,使导航电子地图由人读向机读转变。与之伴随的数据采集、更新、使用等方面的变化及可能产生的各种问题不容忽视,尤其是部分环节如果处理不当还将威胁国家安全。本文从高精度导航电子地图对国家安全可能造成危害的风险点切入,分析了高精度导航电子地图在数据采集、更新、使用等环节的国家安全风险,制定防控要点,为促进高精度导航电子地图行业的健康发展发挥相应作用。     

徕卡激光雷达技术在铁路全生命周期中的应用

为了将激光雷达技术应用到铁路全生命周期中,充分发挥激光雷达技术的优势,本文以徕卡公司相关激光雷达产品为例,系统分析了不同类型的激光雷达的特点和铁路工程不同阶段的测量要求,总结了激光技术在铁路全生命周期中的应用方法。通过典型工程应用分析表明,激光雷达技术可显著提高铁路工程测量的效率和质量,具有良好的应用前景。     

基于遥感技术的近30年中亚地区主要湖泊变化

在全球气候变暖的背景下,研究中亚干旱半干旱地区主要湖泊的变化不仅对内陆水资源管理和可持续发展有着重要意义,也为进一步研究湖泊变化对气候变化和人类活动的响应提供基础。获取1978年MSS、1989年TM、1998年TM及2010年ETM的同季相4期遥感图像数据,通过人工解译提取中亚地区7个湖泊信息,获得近30 a的湖泊面积变化;利用T/P和Envisat雷达高度计提取1992—2012年的湖泊水位信息;基于湖泊面积和水位的时空变化特征分析了湖泊变化的影响因素。结果表明:近30 a来,由于湖泊类型的不同,除萨雷卡梅什湖外的其他6个湖泊均出现不同程度的面积缩减;其中,平原尾闾湖面积变化最显著,高山封闭湖相对平稳,吞吐湖泊的面积变化相对复杂;湖泊的水位变化模式因流域和湖泊类型而异;高山封闭湖泊受气候的影响较大,而吞吐湖泊受人为因素影响显著。     

核密度估计法支持下的网络空间POI点可视化与分析

城市空间POI点的分布模式、分布密度在基础设施规划、城市空间分析中具有重要意义,表达该特征的核密度法(kernel density estimation)由于顾及了地理学第一定律的区位影响,比其他密度表达方法(如样方密度、基于Voronoi图密度)占优。然而,传统的核密度计算方法往往基于二维延展的欧氏空间,忽略了城市网络空间中设施点的服务功能及相互联系发生于网络路径距离而非欧氏距离的事实。本研究针对该缺陷,给出了网络空间核密度计算模型,分析了核密度方法在置入网络结构中受多种约束条件的扩展模式,讨论了衰减阈值及高度极值对核密度特征表达的影响。通过实际多种POI点分布模式(随机型、稀疏型、区域密集型、线状密集型)下的核密度分析试验,讨论了POI基础设施在城市区域中的分布特征、影响因素、服务功能。