基于连续帧间差分的抛弃式探头三维下沉运动处理方法研究

抛弃式探头由无人机装载,能够在较远目标区域和危险海域开展海洋水文环境剖面参数的测量。通过安装不同传感器,可以实现对温度、盐度的剖面测量,其深度的测量采用数学方法计算得到。针对双摄像机水箱实验获得的5个不同攻角实验结果,分析了常用的运动目标的检测方法,最终选择基于连续帧间差分法,确定探头的三维坐标位置,进而得到探头下沉运动的三维运动轨迹和速度曲线等信息。探头从水面释放后攻角在下沉过程中不断调整,改变运动姿态,同时伴随自身的旋转,抵消水平方向阻力作用,初始攻角产生的深度测量误差主要体现在加速过程,探头达到匀速运动后测量误差不变,在不考虑横流的情况下,探头最后以匀速垂直下落运动。     

宁夏生态移民村空间剥夺测度及影响因素

生态移民村是一种具有扶贫脱贫与生态保护双重意义的特殊乡村类型,是乡村振兴不可或缺的重要对象。在空间重构过程中形成的空间剥夺现象,是当前生态移民村全面振兴面临的一个新问题。为探究生态移民村空间剥夺规律,以宁夏70个生态移民村2017年相关数据为基础,构建了以收入就业、教育培训、社会生活、居住环境及公共服务可达性为主要内容的空间剥夺指标体系并进行水平测度,进而运用地理加权回归和地理探测器对其影响因子进行探测。结果显示：① 宁夏生态移民村空间剥夺整体水平较低,但总指数均值相对于非生态移民村显著高出0.023;② 宁夏生态移民村空间剥夺时空分异特征明显,总指数均值在时间上呈现“先较低—后上升—再下降”趋势,在空间上呈现“北部低—中部高—南部次高”分布;③ 民族构成、地形地貌、移民时段、依托资源和经济区带是影响生态移民村空间剥夺的重要因子,前3项对空间剥夺总指数的解释力分别达到了22.4%、10.6%和14.0%。空间剥夺水平测度为生态移民生产生活评价和政策调整完善提供新的客观依据,相关部门可据此优化调节生态移民村空间资源配置,增加其获取资源的能力和机会,推动生态移民村全面振兴。     

Spatiotemporal distribution of fish eggs and larvae in the Huanghe(Yellow) River estuary,China in 2005–2016

Decreasing fish resources in estuaries is a subject of anthropogenic activities.Studies of the spatiotemporal distribution of fish eggs and larvae can help identify the status and processes underlying recruitment in a fishery.As the fifth largest river estuary in the world,the Huanghe(Yellow)River estuary(HRE)is a typical estuary that has been seriously affected by human activities.Annual surveys on ichthyoplankton and environmental factors were conducted in the months of May of 2005 and 2009-2016 in the HRE to investigate the spatiotemporal distribution of fish eggs and larvae and the associated influencing factors.A total of 23 and 20 species of eggs and larvae,respectively,were collected.The dominant orders were Perciformes(51.2%)and Clupeiformes(25.6%).The average number of fish species eggs and larvae were 6.0 and 4.1 in average abundance of 0.91 and 0.13 ind./m~3,respectively.The dominant species were mainly low-commercial-value small-sized fishes,such as Clupanodon punctatus,Hare,ngula zutnasi,and Acanthogobius,whereas certain traditional commercial fishes,such as Trichiurus lepturus,and Clupea pallasii,were not seen.Analysis of the fish egg and larval community revealed four temporal assemblages and two spatial assemblages.Salinity was the main factor on the spatial distribution of ichthyoplankton communities,the species number and Shannon-Weiner diversity index(H')of the fish egg and larval community near the river mouth with lower salinity were significantly lower than the community far away from the river mouth with higher salinity.In addition,increases of water temperature promoted the number and abundance of fish species eggs,and the areas of abundant prey tended to have a more diversified and abundant of ichthyoplankton species.In overall,overfishing,dam construction,and other human activities were the main drivers that led to the substantial decline in fishery resources in the HRE.     

基于卫星遥感数据的森林病虫害监测与评价

森林病虫害由于在森林资源中造成的重大破坏而被人们称为“不冒烟的火灾”,其对生态系统的研究具有重要意义。现有基于遥感数据的病虫害研究多集中在森林病虫害的监测、爆发原因以及发病区域内生产力的变化情况,而对于森林病虫害发生后森林中植被指数与叶面积指数之间的相关性的变化情况还相对较少,处于需要持续性深入探讨的阶段。以加拿大不列颠哥伦比亚地区2002—2012年森林病虫害数据为基础,分析不同严重程度的病虫害对叶面积指数（LAI）与归一化植被指数（NDVI）和增强型植被指数（EVI）的影响。结果表明：① 受病虫害感染的像元在轻度（Light）、中度（Moderate）和重度（Severe）三个严重级别中,NDVI与LAI之间的相关性由弱变强,又由强变弱;② EVI与LAI之间的相关性,在轻度（Light）、中度（Moderate）和重度（Severe）三个严重级别的像元中则依次变强。这一研究将为今后利用遥感数据识别病虫害、评价生态系统影响提供基础。     

规则池塘群的复杂直线模式识别

针对现有面要素直线模式识别方法难以解决规则池塘中破碎区域的问题,提出了一种规则池塘群的复杂直线模式识别方法。首先,分析了规则池塘群的空间特征和认知特点,提出了“主次关系→并列关系→直线模式→复杂直线模式”的多层次认知顺序;其次,设计了主次关系池塘组、并列关系池塘组、直线模式池塘群的识别方法;最后,构建了复杂直线模式池塘群的识别模型。试验表明:本文方法能够消除破碎区域对规则池塘群直线模式提取的不利影响,有效提升复杂直线模式识别的质量。     

一种潜艇装备作战能力比较研究方法

对潜艇装备作战能力评估问题进行了研究，发现运用定量评估方法的过程中存在指标评价所需数据无法完全获取的问题。结合美弗吉尼亚潜艇改进情况，分析了影响潜艇作战能力的一些关键因素，重点基于潜艇武器装载能力，对国外典型潜艇进行了作战能力比较分析，最后对分析结果进行了评价，并讨论了未来潜艇作战能力评估方法的发展方向。     

Land Resource Carrying Capacity in Xilin Gol Grassland Transects: A Perspective on Food Nutritional Demand

Food safety is an important issue for the development of the national economy and society. Studying regional food supply and demand from the perspective of land resource carrying capacity can provide new references for regional resource sustainability. This study uses the data from farmer and herdsmen household questionnaires, statistical data, land use data, and other sources to construct a land resource carrying capacity (LCC) assessment framework, targeting the food supply and demand of residents in representative areas, specifically the typical grassland pastoral areas, sandy pastoral areas and agro-pastoral areas on the Xilin Gol grassland transects. The three food nutritional indicators of calories, protein and fat were selected for analyzing the balance of land resource carrying capacity. We found that: 1) Along the Xilin Gol grassland, the main local food supply showed a shift from meat and milk to grains, vegetables and fruits. 2) From north to south along the grassland transects, the calorie intake increased gradually, while the intake of protein and fat was highest in pastoral areas and lowest in agricultural areas. 3) The overall land resource carrying capacity of the Xilin Gol grassland transects was in a surplus state, but the land carrying capacity of typical grassland pastoral area was higher than the two other types of areas. This study provides an empirical reference for the sustainable development of regional food nutrition.     

机载激光雷达测深技术研究与进展

当前包括内河、水库及海域在内的水上、水下地形测量任务需求不断增加,进行水域或海洋测绘领域研究已提升到新的国家战略性高度。首先介绍了当前机载激光雷达水深测量的基本原理;概括总结了激光测深中的波形处理、波浪潮汐改正、折射改正、航带拼接与数据融合等关键技术,对测深的精度及误差影响进行了分析。最后结合当前技术的发展及新技术的出现,对未来机载激光雷达测深技术的发展进行了展望。     

基于“DEM-NDVI-土地覆盖分类”的天山博格达自然遗产地山地垂直带提取与变化分析

本文基于Landsat影像数据获取天山博格达自然遗产地土地覆盖分类,结合归一化植被指数（NDVI）和数字高程模型（DEM）构建“DEM-NDVI-土地覆盖分类”散点图分析研究区植被受海拔和坡向的水热空间变化影响的分布特征,通过概率统计分析提取博格达遗产地山地垂直带,并结合研究区的气温、降水数据和NDVI变化特征分析垂直带变化的原因。研究结果表明：① 本文利用“DEM-NDVI-土地覆盖分类”散点图,揭示了研究区1989年和2016年的NDVI值和分类类别随着海拔上升的变化特征,其中NDVI值随着海拔上升呈现“倒U形”变化,而不同分类类别在一定的海拔区间内呈现出聚集效应,且不同分类类别有明显的高程界限。② 1989年和2016年博格达遗产地山地垂直带分带上限分别为：1278 m和1185 m（温带荒漠草原带）、1784 m和1759 m（山地草原带）、2706 m和2730 m（山地针叶林带）、3272 m和3293 m（高山草甸带）、3636 m和3690 m（高山垫状植被带）。③ 博格达遗产地1989年和2016年山地垂直带受区域气温升高和降雨增加的影响有较为明显的改变,其中温带荒漠草原带最为敏感,其上限变化最大,向下收缩93 m;山地针叶林带的分布范围则向两侧扩张49 m;山地草甸带带宽基本保持不变,但整体上移了约20 m;冰雪带则受到全球气候变暖的影响向上退缩54 m。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.