Spatial Variation in CO_(2) Concentration Improves the Simulated Surface Air Temperature Increase in the Northern Hemisphere

The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air temperature biases remains highly unclear.By incorporating the spatial distribution of satellite-derived atmospheric CO_(2) concentration in the Beijing Normal University Earth System Model,this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere(NH) under historical conditions from 1976-2005.In comparison with the increase in surface temperature simulated using a uniform distribution of CO_(2),simulation with a nonuniform distribution of CO_(2)produced better agreement with the Climatic Research Unit(CRU) data in the NH under the historical condition relative to the baseline over the period 1901-30.Hemispheric June-July-August(JJA) surface air temperature increased by 1.28℃ ±0.29℃ in simulations with a uniform distribution of CO_(2),by 1.00℃±0.24℃ in simulations with a non-uniform distribution of CO_(2),and by 0.24℃ in the CRU data.The decrease in downward shortwave radiation in the non-uniform CO_(2) simulation was primarily attributable to reduced warming in Eurasia,combined with feedbacks resulting from increased leaf area index(LAI) and latent heat fluxes.These effects were more pronounced in the non-uniform CO_(2)simulation compared to the uniform CO_(2) simulation.Results indicate that consideration of the spatial distribution of CO_(2)concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.

Spatiotemporal Differentiation of Urban Spatial Form and Carbon Emissions in Poyang Lake City Group

In response to the inherent requirements of low-carbon land spatial planning in Jiangxi Province and the lack of existing research,this paper explored the mechanism of spatial form elements of Poyang Lake urban agglomeration on urban carbon emissions.Based on generalized linear regression and geographically weighted regression models,this paper analyzed the spatiotemporal distribution characteristics of carbon emissions,the spatiotemporal relationship between urban form index and carbon emissions,and the spatial differentiation of the intensity of dominant factors from 63 county-level administrative units in the Poyang Lake city group from 2005 to 2020.The results showed that:①The carbon emissions of urban agglomerations around Poyang Lake are generally increasing,and the spatial distribution of carbon emissions is characterized by high-value concentration in the middle and low-value agglomeration in pieces;②The main driving factor for the spatial heterogeneity of carbon emissions was the expansion of built-up area;③Improving urban compactness and optimizing urban form could effectively reduce urban carbon emissions.The results showcased the correlation between urban spatial landscape pattern and the spatiotemporal distribution of carbon emissions,which could make the low-carbon land spatial planning in the Poyang Lake city group more reasonable and practical.

基于RSEI的乌鲁木齐市生态环境质量时空演变研究

利用中分辨率成像光谱仪(MODIS)遥感影像数据,通过谷歌地球引擎(GEE)平台构建了乌鲁木齐市2003—2022年的遥感生态环境指数(RSEI),并采用泰尔-森(Theil-Sen)中位数法和曼-肯德尔(Mann-Kendall, MK)检验方法对RSEI的时空变化趋势进行了分析。结果表明:(1)乌鲁木齐市2003—2022年的RSEI平均值为0.37,整体生态环境质量处于中等偏下水平。RSEI表现出明显的空间差异,呈现出自南向北递减的空间变化趋势。(2)2003—2022年,乌鲁木齐市整体RSEI上升趋势值为0.02/10 a。具体来看,增长趋势主要集中在2003—2016年间,RSEI增长率为0.07/10 a。2003—2016年,RSEI呈现波动增长的趋势,但2016—2022年间,RSEI呈现显著下降趋势,下降率为0.21/10 a。(3)2003—2022年间,乌鲁木齐市RSEI下降区域主要分布在南部(乌鲁木齐县南缘)以及东部(达坂城东北部),RSEI增长区域主要分布在中部以及北部(米东区)。从2016—2022年RSEI的变化趋势来看,中部和北部的RSEI有下降趋势,后期应重点关注这2个区域的生态环境改善。

可编辑三维场景的动态3D Tiles构建方法

可编辑的大型三维WebGIS具有一定的应用场景需求,常见的3DTiles数据格式局限于静态场景渲染,在编辑的三维场景中,需要构建动态的3D Tile文件格式和多细节层次(level of detail,Lo D)渲染方法。本文从动态空间索引和多细节层次技术两方面研究Web在线编辑大型三维场景的构建方法,将动态松散八叉树和半边折叠LoD构建算法应用于3D Tiles构建流程中,实现支持可编辑场景的3D Tiles构建与更新。结果表明:(1)松散八叉树索引在计算量上能够实现动态更新的目标,且在视锥体剔除上有着良好的精度;(2)基于半边折叠法进行准实时LoD的构建,能够满足动态场景下实时计算LoD的需求;(3)所构建的3D Tiles场景,配合Three.js等成熟框架并应用WebWorker等技术,能够顺利完成3DTiles的高效调度和渲染。研究成果对城市三维模型在线编辑、动态地理三维场景的加载渲染等相关研究和应用具有一定的参考价值。

Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China:Based on Spatial Heterogeneity Perspectives

Public environmental concern(PEC)is an important bottom-up force in building an environmentally sustainable society.Guided by attitude theory,this paper innovatively constructed a PEC evaluation index system,while introducing entropy weighted-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)to realize the assessment of PEC.Exploratory spatial data analysis was used to portray the spatio-temporal evolution patterns of PEC in 362 Chinese cities at prefecture-level and above from 2011 to 2018.Furthermore,the Geodetector model was performed to identify the multi-dimensional determinants of PEC from the perspective of spatial heterogeneity.The results indicated that:1)PEC in China exhibited a fluctuating upward trend,consistent with the spatial distribution law of‘Heihe-Tengchong Line’and‘Bole-Taipei Line’;2)the driving effect of each factor varied dynamically,but in general,economic development level,population size,industrial wastewater,and education level were the dominant driving factors explaining the spatial variation of PEC;3)risk detection revealed that four factors,government environmental regulations,PM_(2.5),vegetation coverage,and natural resource endowment,had nonlinear effects on PEC;4)the interactions between factors all demonstrated an enhancement in explaining the spatial differentiation of PEC.PEC was driven by the comprehensive interaction of four-dimensional factors of economy,society,pollutant emissions,and ecology.Among them,population agglomeration accompanied by a high level of regional economy and information technology can explain the increase in PEC to the greatest extent.

HQ-Tree： A Distributed Spatial Index Based on Hadoop

In this paper,we propose a novel spatial data index based on Hadoop:HQ-Tree.In HQ-Tree,we use PR QuadTrec to solve the problem of poor efficiency in parallel processing,which is caused by data insertion order and space overlapping.For the problem that HDFS cannot support random write,we propose an updating mechanism,called "Copy Write",to support the index update.Additionally,HQ-Tree employs a two-level index caching mechanism to reduce the cost of network transferring and I/O operations.Finally,we develop MapReduce-based algorithms,which are able to significantly enhance the efficiency of index creation and query.Experimental results demonstrate the effectiveness of our methods.

ANALYSIS OF HABITAT PATTERN CHANGE OF RED-CROWNED CRANES IN THE LIAO HE DELTA USING SPATIAL DIVERSITY INDEX

Habitat pattern change of red-crowned cranes (Grus japonensis) in t he Liaohe Delta between 1988 and 1998 was analyzed with the help of Spatial Dive rsity Index based on remote sensing data and field investigation. The result sho wed that the influence from human activities on the wetland habitat of red-crow ned cranes was prominent with the development of oil and agricultural exploitati on, and the habitat pattern of red-crowned cranes had been obviously changed by the human disturbance during the ten years. The areas with high Spatial Diversi ty values (SD≥0.65) and that with mid-high values (0.5≤SD< 0.65), which const ituted the main part of suitable habitat of red-crowned cranes,had reduced to 9142ha and 5576ha respectively, with the shrinking of natural land cover, such a s reed and Suaeda community. The habitat pattern became more fragmented, which w as caused by roads and wells during oil exploration. It was indicated that the s uitability and quality of habitat for red-crowned cranes in the Liaohe Delta we re degraded in the last decade. The results also showed that diversity index cou ld reflect the habitat suitability of red-crowned cranes quantitatively and des cribe the spatial pattern of the habitat explicitly. This study will provide a s cientific basis for habitat protection of red-crowned cranes and other rare spe cies in wetlands.

Model-Driven Deep Learning for Massive Space-Domain Index Modulation MIMO Detection

In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.

2-Level R-tree Index Based on Spatial Grids and Hilbert R-tree

Multi-level spatial index techniques are always used in large spatial databases. After a general survey of R-tree relevant techniques, this paper presents a novel 2-level index structure, which is based on the schemas of spatial grids, Hilbert R-tree and common R-tree. This structure is named H2R-tree, and it is specifically suitable for the indexing highly skewed, distributed, and large spatial database. Algorithms and a sample are given subsequently.

A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i<sup style='margin-left:-7px;'>*</sup>Statistic

Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

面向大规模MIMO的分块空间调制技术研究

广义空间调制(Generalized Spatial Modulation,GSM)是一种基于多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的高效的数字调制技术,在传统调制符号之外同时通过激活天线模式组合发送信息比特。针对大规模MIMO系统中发送天线数量过多导致的激活天线模式组合的数量过多的问题,提出了一种分块空间调制算法,将每个发送天线子块的激活模式组合构成完整的激活天线模式组合,可有效降低大规模MIMO系统中发送端的调制复杂度。在接收端使用球形译码方法实现逐个子块的天线激活模式组合的解调,可以大幅降低接收端的计算复杂度。仿真分析表明,提出的球形译码算法可以在大幅降低计算复杂度的情况下,实现接近最大似然(Maximum Likelihood,ML)接收机算法的误比特率(Bit Error Rate,BER)性能,且可实现接收端检测性能和计算复杂度之间的最佳折衷。

A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge

In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.

基于Beer-Lambert方法的机载LiDAR草地叶面积指数估算研究

草地叶面积指数(Leaf area index,LAI)是天然草地的重要结构参数,能够用来监测草地的生长状况和生产力水平,对草地资源可持续利用和科学管理具有重要意义。以内蒙古锡林郭勒盟典型草原为研究对象,首先使用无人机激光雷达(Airborne light detection and ranging,Air-LiDAR)草地冠层观测数据,通过解析点云数据构建冠层高度模型(Canopy height model,CHM),随后进行研究区草地冠层间隙率计算,最后基于Beer-Lambert方法进行0.05 m、0.10 m、0.15 m、0.20 m 4个不同空间分辨率采样尺度下的LAI估算,并选择CHM低值、中值、高值3个不同子区域分别进行不同冠层高度下LAI的检验和分析。结果表明:(1)草地叶面积指数与冠层高度模型数值呈正相关、与冠层间隙率呈负相关。(2)机载LiDAR草地LAI估算的最优采样尺度为0.15 m,CHM不同高度子区域LAI结果检验R^(2)和RMSE分别为:低值区为0.66和0.04;中值区为0.54和0.34;高值区为0.54和1.17,表明无人机LiDAR可捕获草地冠层观测采样存在的异质性差异分布特征。(3)不同空间分辨率0.05~0.20 m间隔采样LAI结果表明,对于CHM低值、植被分布稀疏区域不同分辨率LAI无显著空间尺度变化差异,但CHM高值、较密植被分布群落LAI会随空间分辨率表现出尺度性差异。综上所述,本研究设计完成的无人机LiDAR草地LAI估算模型,参数机理具体、流程方法可操作性强,具有较好的数值检验精度,可为激光雷达在草地植被叶面积指数遥感反演及应用提供技术参考。

Prediction method for risks of coal and gas outbursts based on spatial chaos theory using gas desorption index of drill cuttings

Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. We investigated and verified the prediction method by a spatial series data of a gas desorption index of drill cuttings obtained from the 113112 coal roadway at the Shitai Mine. Our experimental results show that the spatial distribution of the gas desorption index of drill cuttings has some chaotic charac- teristics, which implies that the risk of coal and gas outbursts can be predicted by spatial chaos theory. We also found that a proper amount of sample data needs to be chosen in order to ensure the accuracy and practical maneuverability of prediction. The relative prediction error is small when the prediction pace is chosen carefully. In our experiments, it turned out that the optimum number of sample points is 80 and the optimum prediction pace 30. The corresponding advanced prediction pace basically meets the requirements of engineering applications.

基于标准化降水指数(SPI)的宁夏中部干旱带旱灾危险性时空演变特征

[目的]揭示宁夏中部干旱带旱灾危险性的时空特征,为宁夏中部干旱带农业生产、农业灾害防治、种植结构调整和农业灌溉指导提供科学依据。[方法]收集宁夏中部干旱带的沙坡头区、中宁县、同心县、盐池县、海原县5个气象站点的气象观测资料,采用标准化降水指数(SPI)分析了近39年(1981—2019年)干旱等级的时空变化,并结合GIS技术评价其旱灾危险性时空演变特征。[结果]SPI 12结果表明代际干旱趋势呈波动中加剧趋势(趋势线斜率范围为-0.004~0.003)。SPI 3结果表明季尺度旱灾危险性时空分布异质性明显,空间上呈现出南低北高的特征,而时间上则表现为连旱特征突出。春冬两季旱灾发生频率高于45%;夏、秋两季干旱现象不显著,干旱发生频率处于10.26%~43.59%之间;旱灾危险性时空演变特征评价结果表明代际旱灾危险性呈现先下降后上升的变化趋势。季尺度旱灾危险性时空差异明显,空间上表现为旱灾危险性由南向北升高,时间上表现为春冬两季旱灾危险性较高,而夏、秋两季旱灾危险性较低。[结论]宁夏中部干旱带季尺度旱灾危险性时空异质性明显,沙坡头区、中宁县旱灾危险性高,未来应加强春、秋、冬三季的旱灾防治。

Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index(AQI) of the Yangtze River Delta region(YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn'. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.

基于SPEI的黑龙江省干旱时空分布特征分析

为分析黑龙江省干旱时空分布特征,利用黑龙江省27个站点1961~2020年的气象数据,采用Penman-Monteith公式计算不同时间尺度的标准化降水蒸散指数(SPEI),从时间尺度和空间尺度分析黑龙江省干旱的分布特征。结果表明,黑龙江省近60年来干旱趋势在逐渐减弱,慢慢呈现湿润化趋势;春、夏、秋季呈不显著湿润化趋势(P>0.05),冬季呈显著湿润化趋势(P<0.05);在空间上其北部、西北部和东北部为干旱频发区,且干旱程度上多以轻旱和中旱为主。黑龙江省整体上干旱趋势减弱,干旱时空分布具有较明显的季节性和区域性。

基于SPEI的1992-2022年塔额盆地干旱时空变化特征

标准化降水蒸散指数(Standardized precipitation evapotranspiration index,SPEI)是反映区域干旱状况的重要参数,基于塔额盆地近31 a逐日气象资料,计算不同时间尺度下的SPEI,采用Mann-Kendall检验、相关性分析等方法,探索塔额盆地干旱演变的时空特征,讨论气温和降水量对SPEI的影响程度及相关性。结果表明:(1)塔额盆地近31 a来全年和四季SPEI均呈下降趋势,整体向干旱化发展。(2)年干旱在1994年开始发生突变,春、夏、秋、冬干旱分别在1995、1992、2009、2008年发生突变。(3)塔额盆地的干旱在空间分布上差异显著,总体上北部干旱比南部严重,南部托里县和铁厂沟地区出现不显著的湿润化趋势。(4)相比于气温,降水量对SPEI的影响更大,尤其是春季降水量。研究结果有助于了解塔额盆地干旱演变特征,为该区域旱灾的预警预防及防灾减灾工作提供科学依据。

Coupled effects of climate change and human activities on vegetation dynamics in the Southwestern Alpine Canyon Region of China

The driving effects of climate change and human activities on vegetation change have always been a focal point of research.However,the coupling mechanisms of these driving factors across different temporal and spatial scales remain controversial.The Southwestern Alpine Canyon Region of China(SACR),as an ecologically fragile area,is highly sensitive to the impacts of climate change and human activities.This study constructed a vegetation cover dataset for the SACR based on the Enhanced Vegetation Index(EVI)from 2000 to 2020.Spatial autocorrelation,Theil-Sen trend,and Mann-Kendall tests were used to analyze the spatiotemporal characteristics of vegetation cover changes.The main drivers of spatial heterogeneity in vegetation cover were identified using the optimal parameter geographic detector,and an improved residual analysis model was employed to quantify the relative contributions of climate change and human activities to interannual vegetation cover changes.The main findings are as follows:Spatially,vegetation cover exceeds 60%in most areas,especially in the southern part of the study area.However,the border area between Linzhi and Changdu exhibits lower vegetation cover.Climate factors are the primary drivers of spatial heterogeneity in vegetation cover,with temperature having the most significant influence,as indicated by its q-value,which far exceeds that of other factors.Additionally,the interaction q-value between the two factors significantly increases,showing a relationship of bivariate enhancement and nonlinear enhancement.In terms of temporal changes,vegetation cover shows an overall improving trend from 2000 to 2020,with significant increases observed in 68.93%of the study area.Among these,human activities are the main factors driving vegetation cover change,with a relative contribution rate of 41.31%,while climate change and residual factors contribute 35.66%and 23.53%,respectively.By thoroughly exploring the coupled mechanisms of vegetation change,this study provides important references for the sustainable management and conservation of the vegetation ecosystem in the SACR.

基于InVEST模型的渭河流域生境质量评估

基于生态系统服务评估与权衡模型和ArcGIS软件探索2000-2020年渭河流域生境质量的时空变化,利用地形分布指数分析生境质量的地形梯度效应.结果表明, 20 a间,研究区土地利用变化主要包括耕地向建设用地和草地的转移,以及草地向耕地和林地的转移,不同类型之间的转移是影响局部生境质量变化的主要因素;生境质量的空间分布以中等生境为主,占比约57%,主要分布在关中城市群外围和西北部.低等生境占比约5%,但面积增长最快,增幅达到105.80%,原因是关中城市群的大面积扩张;研究区生境质量空间分布存在一定的正相关性,且聚集程度增加1.15%,局部自相关类型以高-高和低-低集聚为主,高-高集聚分布在研究区东北部黄土高原和南部秦岭北坡,低-低集聚分布在关中城市群;2020年生境质量具有显著的地形梯度效应,表现在生境质量等级随着地形梯度级别的增加而增加.