[Objective] Urban land carrying capacity in Liaoning Province was quantitatively analyzed and evaluated, and its obstacle factors were diagnosed in order to provide suggestions for the rational development of land use in Liaoning Province, and to produce new ideas for regional land carrying capacity evaluation. [Methods] Land use classification was carried out based on 2018 Landsat 8 OLI remote sensing images from 14 prefecture-level cities in Liaoning Province to obtain the land use area of various types. These data were combined with social and economic data to construct a land carrying evaluation index system. The AHP (analytic hierarchy process)-entropy weight TOPSIS (technique for order preference by similarity to an ideal solution) model was used to evaluate the carrying capacity of land resources. The obstacle degree model was used to evaluate the obstacle factors. [Results] The cities in Liaoning Province showed different bearing capacity states. The land carrying capacities of Panjin, Dalian, and Shenyang City were relatively high, but the social subsystem was the main obstacle factor restricting land carrying capacity. The carrying capacities of Anshan and Yingkou City were relatively low. The economic subsystem of Anshan City was the main factor limiting increases in carrying capacity. [Conclusion] The urban land carrying capacity of Liaoning Province is still far below its ideal state, and the coordinated development of the three subsystems of society, economy, and resources should be promoted to improve the land carrying capacity.

针对大连市海水入侵加剧地表变形等问题,文中通过时序InSAR技术对大连市主城区2018年11月8日—2020年10月28日的30景Sentinel-1A影像进行地表沉降分析,最终得到大连市两年内的平均沉降速率和时序形变量.同时将PS-InSAR技术和SBAS-InSAR技术获得的两种形变监测结果进行交叉验证,根据时序监测的沉降信息和沉降发育特征分析大连市主城区近两年地面沉降的主要原因.利用时序InS A R技术的优势在于能在大范围地表变形监测过程中克服一定程度的时间、空间以及大气等因素对监测结果的影响.结果表明,该地区沉降和地下水入侵及人类工程建设有关,且两种技术所获得的变形结果基本一致,证明两种方法在该地区监测的可靠性.

针对生态环境评价指数获取困难、评价标准各异的问题,本文提出了新生态环境指数(NEI),即以生物丰度指数、植被覆盖指数、干度指标、湿度指标及空气质量指数综合反映区域生态环境状况,并利用主成分分析法客观确定权重.结果表明,NEI的指标选取和结果均与《生态环境状况评价技术规范》中的生态环境指数(EI)具有较强可比性.但NEI指数具有结果量化、可视化及实时化的特点,对于评价区域生态环境状况具有较好的应用前景.

我国是世界上水土流失最严重的国家之一,土壤保持服务核算可为环境治理提供科学参考.本文采用通用土壤流失方程(USLE)模拟2010、2015年厦门市土壤保持服务功能,分析其时空动态变化,并对土壤保持服务价值量进行估算.结果表明:2015年厦门市土壤保持能力平均为2596.72 t/hm2,土壤保持总量为4.41×108 t.相比于2010年,2015年单位面积土壤保持能力增加了247.76 t/hm2,约增加了10.5％.厦门市各区土壤保持均值由大到小排序为:同安区>集美区>海沧区>翔安区>思明区>湖里区.各生态系统类型土壤保持均值由大到小排序为:灌木林地>森林>草地>农田>城市绿地.2015年厦门市土壤保持总价值约为4.37×1010元.

随着GPS全球定位系统的发展与壮大,越来越多的工程都在应用GPS来进行布设控制网.但是在实际工程中存在许多影响因素,我们需要进行优化设计来选取最优方案.在已经有很多学者研究控制网的优化设计并作出很多有益结论的情况下,结合GPS测量的特点以及控制网的特性,对以下对象进行了研究与分析:了解到控制网的优化设计指标,掌握各种优化设计的方法,并制订设计方案;根据接收机的标称精度通过相应的数学公式来进行基线向量的方差-协方差阵的计算估计;根据实际工程来进行优化设计,预估几种方案进行分析对比;选取最优的方案对6台接收机进行同步观测,结果满足布设控制点的要求.

针对由于岩移观测数据资料缺失或不准确而导致开采沉陷预计参数求取不精确的问题,本文提出一种基于模糊聚类的开采沉陷参数预计模型.首先,根据相似第三定理对地矿特征进行了分析简化;其次,利用方程分析法、量纲分析法进行特征提取,得到特征方程;然后,对原有模糊聚类方法进行改进,得到基于竞争合并策略的IWFCM_CCS算法的模糊聚类方法;最后,对岩移观测数据进行模糊聚类分析,得出观测站数据的隶属度矩阵和聚类中心,建立了基于隶属度权重的回归模型.通过与矿区实测数据和模型预计结果的对比分析,验证了所提参数预计模型的准确性和可行性.该模型减小了观测数据导致的预计参数求取误差,可为以后的预计参数求取提供参考.

现如今煤炭开采技术变得愈发的成熟,但是开采过程中依旧需要考虑到矿区滑坡、地表沉降以及地质坍塌等一系列灾害事故的发生,因此开采过程中对地表岩移规律的研究尤为重要.本文以晋煤集团长平煤矿5302首采工作面为例,对岩移观测站的布设、观测方法、岩层变形规律以及各种参数的计算方法进行了深入的研究,为煤矿的综合治理提供了保障.

利用倾斜摄影测量技术进行三维实景建模,是近年来国内的测绘热点高新技术之一.本文以辽宁工程技术大学北校区为实验场地,利用多旋翼无人机搭载索尼ILCE-5100相机获取倾斜影像数据,采用Smart3D软件来建立三维模型,利用DP-Modeler软件进行模型精细化处理,通过LocaSpace Viewer软件对三维实景模型进行空间量测及分析,实现了较好的建模效果,表明此方案的可行性.三维实景模型具有真实的几何和纹理信息,能够在满足用户可视化要求的同时也为校园规划分析等方面提供科学依据.

在遥感领域中,遥感图像分类是一项十分重要的内容,也是运用遥感技术手段提取地物类别信息的一个关键环节.本文以TM影像为研究对象,采用决策树分类方法进行研究分析,详细地论述了该分类方法的整个研究流程,并得到分类后的结果图,最后利用混淆矩阵和Kappa系数对分类后的结果进行精度分析.通过与最大似然分类方法进行比较发现,决策树分类方法的分类效果明显,分类精度较高,总体分类精度、kappa系数均达到90％以上,为遥感图像分类提供了广阔的发展前景.

针对传统的监测方法都是基于点位监测,耗时耗力且难以大面积监测,本文以唐山某矿1326工作面为研究区域,利用二轨法D-InSAR的方法,采用两景Sentine1-1A数据对该工作面进行监测与分析,将所求取的监测结果分别与水准测量数据、利用开采深陷预计方法所预计出的该工作面以及受到开采影响的周边建筑物的下沉值进行对比验证.实验结果表明,D-InSAR监测值与开采沉陷预计的值结果较为一致,能够较为精确有效地监测矿区地表形变以及开采范围内的建筑物沉降.

随着我国高速铁路网建设的迅速发展,高铁建设中的隧道沉降监测工作显得日益重要.针对传统水准监测方法空间分辨率低、成本较大的问题,本文利用短基线集时序InSAR技术对某隧道工程进行了时序形变监测,获取了2017/4—2017/10的平均沉降速率和时序形变量.根据时序监测结果重点分析了隧道工程中的地面沉降原因和规律,与实测水准结果相比表明:利用SBAS-InSAR技术进行形变监测可达到毫米级精度,两种方法所得监测结果与实际情况基本吻合.同时,证明了该技术在隧道工程沉降监测方面的可行性和可靠性,在今后具有一定的应用前景.

针对利用小波分析方法联合二次曲面模型改正合成孔径雷达干涉测量技术(InSAR)轨道误差时观测值的系统误差特性,该文在传统二次多项式的基础上建立了一种附加系统参数的轨道误差改正模型.利用小波分析方法可以有效提取干涉相位中的低频部分,二次多项式法计算简便,附加系统参数平差原理顾及了观测值中其它系统误差项的影响,结合3种方法更准确地求解出了改正模型参数,实现了对InSAR轨道误差的有效估计去除.基于伊朗巴姆地区的ENVISATASAR数据实验表明:在使用所提算法去除轨道误差相位后的干涉图中,远离形变区域位置的相位值基本趋近于0 rad,轨道残差相位也基本得到消除.

The subway station deep foundation pit engineering in subway design and construction is the key project. In recent years, the urbanization is very fast, urban subway construction is becoming more and more fast, so the subway station deep foundation pit en-gineering deformation monitoring work is particularly important. In this paper, a Hangzhou subway deep foundation pit is simulated by FLAC3D , get the biggest deformation and the curve of the deformation, and then compare with the test data. It was found that it can ef-fectively respond deformation law of subway station deep foundation pit by FLAC3D , and provide reference basis for the deformation monitoring of subway deep foundation pit projects in the future.

电离层延迟是GPS定位中主要误差源之一.本文针对中国区域的电离层延迟问题,首先使用区域电离层延迟模型理论和IGS提供的IONEX数据,分析了中国区域点120°E,30°N的不同时间尺度的TEC变化情况,总结出该地区的TEC变化规律.然后采用IGS公布的监测数据对中国区域的电离层延迟进行PPP解算,并利用Matlab对解算结果进行仿真,结果表明:球谐函数模型对于中国区域电离层电子含量解算的特征更具有区域代表性.

[目的]高分辨率遥感影像云检测技术一直是遥感影像处理亟待解决的难题,尤其是边缘薄云和散云的检测.针对高分辨率遥感影像的成像特点,利用形态学运算、多边形简化技术,实现含云影像的准确提取.[方法]首先对影像进行高斯低通滤波平滑,取得一致均匀的明暗效果;然后将影像分为多云、少云、无云3种情况,对多云影像采用Otsu阈值分割,对少云影像采用高斯混合模型进行阈值分割;最后对云区进行形态学处理得到最终云区.[结果]高分辨遥感影像云检测方法目视效果较好,可有效提高云检测精度,该方法准确率为98.60％,查全率在90％左右,错误率约为2.58％,可以较为准确地检测出厚云、薄云、散云,同时还可有效地减少对房屋、道路、裸地的误判.[结论]基于Otsu阈值分割和高斯混合模型的高分辨率遥感影像云检测技术算法复杂度适中,计算量小,运算速度快,检测精度高,适用性广.

In order to further improve the prediction accuracy of navigation satellite clock errors,the paper proposed an im-proved gray neural network model based on particle swarm algorithm combining the characteristics of particle swarm algorithm and gray neural network:particle swarm algorithm was used to optimize the weights and thresholds of gray neural network for increasing the prediction accuracy,and the comparative analysis among gray model,gray neural network model and the pro-posed model was carried out by selecting the precise clock error data provided by IGS.Experimental result showed that the pro-posed model could predict the precise clock errors of satellite efficiently with high prediction accuracy and good stability and re-liability.

In order to solve the problem that the iterative speed of classical iterative algorithm(iterative closest point,ICP)is poor,there is a mismatch between pairs of registration points and the robustness is not strong,the algorithm is improved by using distance constraint function,kd-tree accelerated iteration and CPC three constraint method.Firstly,using the foot-drop of the nearest point to the nearest point,the algorithm is improved by classification and constraint,and the false registration is eliminated when the point is out of the plane,so as to improve the iterative accuracy.Then,by using kd-tree algorithm accelerate the search point-to-point process and reduce the running time of the algorithm.Finally,by eliminating the error point pairs of the registration point set by the geometric constraints existing in the CPC,the robustness and anti-noise ability of the automatic registration technology is enhanced.The experimental results show that compared with the classical ICP algorithm,the improved ICP algorithm improves the registration accuracy and time,and can effectively eliminate part of mis-registration points and enhance the robustness of the algorithm(anti-noise ability).

In order to reduce influence of model error on FLAC simulation caused by mining activities, improve the reliability and ac-curacy of FLAC simulation, based on the 52304 working face of Daliuta mining area as an example, according to the height of the hill mining area due to the large complex problem of large error of In-SAR data, the establishment of complex surface contour based on FLAC model. And the simulation results were compared with the GPS data measured by the In-SAR data. The results shows as fol-lows:①the FLAC in the simulation of coal seam excavation, overlying the irregular surface model can produce is consistent with the actual situation of the subsidence basin, verify the reliability of the model of complex contour;②in the FLAC simulation, the complex contour model can reduce the model error;③the use of simulated data and GPS data calculation of subsidence angle and compared to verify the reliability of the simulation results;④through the comparison with the monitoring results of the In-SAR can be found, the FLAC model has the edge effect, the simulation results and the In-SAR data can improve the accuracy of complementary.

In order to overcome the problem of uncertainty in grey neural network model(GNNM),this paper proposed a prediction model of grey neural network optimized by modified fruit fly optimization algorithm.Fitness function was modified by the addition of jump coefficients,at the same time,the concept of three-dimensional space search was introduced to expand the scope of the fruit fly to improve the optimization algorithm,which could avoid the local extreme value and improve the searching ability of the algorithm.The improved algorithm was used to optimize the parameters of grey neural network,and the optimal value was obtained by training the grey neural network model.The prediction performance of the model was verified by the simulation of the actual engineering deformation data,and the results were compared with the grey neural network of the fruit fly optimization,the grey neural network and the grey neural network prediction model of the particle swarm optimization algorithm.The results showed that the prediction accuracy and fitting degree of improved fruit fly optimization algorithm of grey neural network were higher.

针对传统单一GPS高程拟合方法存在对数据和测区要求较高的问题,提出一种改进的灰色神经网络拟合方法:通过添加跳脱系数修正适应度函数扩大搜索范围,对果蝇优化算法进行改进,有效地提高算法的寻优能力;利用改进的果蝇算法优化灰色神经网络参数,训练灰色神经网络模型获得模型最佳参数,完成对GPS高程的拟合.实验结果表明该方法具有较高的拟合精度和较强的鲁棒性.