Accuracy analysis, dem generation and validation using russian tk-350 stereo-images

TK-350 stereo-scenes of the Zonguldak testfield in the north-west of Turkey have been analysed. The imagery had a base-to-height ratio of 0·52 and covered an area of 200 km × 300 km, with each pixel representing 10 m on the ground. Control points digitised from 1:25 000 scale topographic maps were used in the test. A bundle orientation was executed using the University of Hanover program BLUH and PCI Geomatica OrthoEngine AE software packages. Tests revealed that TK-350 stereo-images can yield 3D geopositioning to an accuracy of about 10 m in planimetry and 17 m in height. A 40 m resolution digital elevation model (DEM) was generated by the PCI system and compared against a reference DEM, which was derived from digitised contour lines provided by 1:25 000 scale topographic maps. This comparison showed that accuracy depends mainly on the surface structure and the slope of the local terrain. Root mean square errors in height were found to be about 27 and 39 m outside and inside forested areas, respectively. The matched DEM demonstrated a systematic shift against the reference DEM visible as an asymmetric shift in the frequency distribution. This is perhaps caused by the presence of vegetation and buildings.     

利用TM数据快速提取含矿蚀变带方法研究

热液蚀变岩类是在含矿热液作用下引起原岩化学成分和物理性质发生变化的结果，因此蚀变岩类往往与内生产矿产伴生，有时其本身就是含矿体。不同蚀变矿特级组合具有独特的光谱响应，但是光谱的反射和吸收产特征较弱称为遥感找矿弱信息标志。     

利用GEOSAT／ERM卫星测高数据计算中国近海海平面

本文利用一年（１９８６年１１月８日～１９８７年１１月１７日）２２个ＥＲＭ周期的ＧＥＯＳＡＴ卫星测高数据计算出中国近海海域（１０５°～１３５°Ｅ，０°～４０°Ｎ）相对于ＧＲＳ８０参考椭球面的１°×１°年平均海平面。该海平面我们称之为ＩＧＧＭＳＳ９５平均海平面。在数据处理过程中，由于采用了混合轨道误差模型及交叠平差技术，极大地消除了在误差中占主要成分的径向轨道误差影响，使得ＩＧＧＭＳＳ９５平均海平面的总体精度达到±２０～３０ｃｍ（ＲＭＳ）水平。与ＯＳＵ９１Ａ重力大地水准面模型相比较可以发现两者具有惊人的一致性。     

向斜构造煤层开采地表移动规律研究

现行的地表移动预计方法，都是建立在开采工作面主断面为线性分布的基础上，因此，对于非线性分布煤层的预计将会产生较大的误差。本文以随机介质理论为基础，利用曲线积分的方法建立了地表移动与变形预计公式，并且利用数值积分法和叠加原理解算地表移动与变形值，扩大了地表移动预计理论的应用范围，同时也大大提高了预计值的精度     

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.     

Atmospheric pollution assessment near potential source of natural aerosols in the South Gobi Desert region,China

ABSTRACT

Rapid economic growth, a high degree of urbanization and the proximity of a large number of desert and semidesert landscapes can have a significant impact on the atmosphere of adjacent territories, leading to high levels of atmospheric pollution. Therefore, identifying possible sources of atmospheric pollution is one of the main tasks. In this study, we carried out an analysis of spatial and temporal characteristics of five main atmospheric pollutants (PM_2.5, PM₁₀, SO₂, NO₂, and CO) near potential source of natural aerosols, affecting seven cities (Wuhai, Alashan, Wuzhong, Zhongwei, Wuwei, Jinchang, Zhangye), located in immediate proximity to the South Gobi deserts. The results, obtained for the period from 1 January 2016 to 31 December 2018, demonstrate total concentrations of PM_2.5 and PM₁₀ are 38.2 ± 19.5 and 101 ± 80.7 μg/m³ exceeding the same established by the Chinese National Ambient Air Quality Standard (CNAAQS), being 35 and 70 μg/m³, respectively. Based on the data from Moderate Resolution Imaging Spectroradiometer (MODIS) for the whole period, Clean Сontinental (71.49%) and Mixed (22.29%) types of aerosols prevail in the region. In the spring and winter seasons maximum concentrations of pollutants and high values of Aerosol Optical Depth (AOD) in the region atmosphere are observed. PM_2.5 and PM₁₀ ratio shows the presence of coarse aerosols in the total content with value 0.43. The highest concentrations of pollutants were in the period of dust storms activity, when PM_2.5 and PM₁₀ content exceeded 200 and 1000 µg/m³, and AOD value exceeded 1. UV Aerosol Index (UVAI), Aerosol Absorbing Optical Depth (AAOD), and Single Scattering Albedo (SSA), obtained from Ozone Monitoring Instrument (OMI), demonstrate the high content of dust aerosols in the period of sandstorms. Analysis of backward trajectories shows that dust air masses moved from North to Northwest China, affecting large deserts such as Taklamakan, Gurbantunggut, Badain Jaran, Tengger, and Ulan Buh deserts.     

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。     

基于深度学习理论的无人机影像分类

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。