共查询到19条相似文献，搜索用时 156 毫秒
1.
基于栅格面积成分数据的土地利用格局解释模型稳健估计 总被引：1，自引：0，他引：1
针对最小二乘估计不能应用于栅格尺度以面积成分表征的土地利用格局驱动机理分析的难题,本文提出了一种利用偏最小二乘回归法稳健估计该类土地利用格局解释模型的方法。利用该方法能在解释变量间存在多重共线性的情况下,获得基于栅格面积成分数据的土地利用格局解释模型的稳健估计。本文推导了应用偏最小二乘回归分析的数据处理和建模估计过程,并运用该方法开展了针对黄淮海地区耕地、建设用地分布格局及其驱动因子的建模分析,得到了拟合优度高的估计结果。研究表明,偏最小二乘回归分析方法在开展栅格尺度以面积成分表征的土地利用格局驱动机理分析时具备高效与稳健的特征,适宜在类似研究中推广应用。 相似文献
2.
标准生长曲线（Standardised Growth Curve，SGC）法的提出为高效快速地测定释光样品的等效剂量（De）值提供了可能。不同实验室的放射源剂量率、操作流程、仪器误差等的不同会导致SGC参数的不同。运用最小二乘法标准化实验剂量校正后的信号Lri/Tri*De得到一条本实验室的标准生长曲线，用来快速得到等效剂量值。通过R软件实现了最小二乘法标准化过程，建立了50、100、150、200、250℃激发温度下的钾长石的标准生长曲线，分析发现250℃下的SGC收敛性最好，由于此温度下的信号衰退率可忽略不计，因此，我们用250℃激发温度下的SGC方程来估计样品的等效剂量。比较250℃ SGC De和250℃ MET-pIRIR De的一致性，发现在0~500 Gy范围内用SGC法估计的De和MET-pIRIR法估计的De非常接近，表明了此范围内SGC法的可靠性，而在>600 Gy范围内出现较大差别。一方面，此范围内用于拟合SGC的数据点较少导致高剂量区域内拟合的方程参数不够准确；另一方面，600 Gy与SGC对应的饱和剂量水平值844.5 Gy已经接近，所以在>600 Gy区域测出的De很可能出现较大偏差。因此需在今后工作中逐步积累更多样品，完善SGC参数，使其也能胜任较老年龄样品。 相似文献
3.
黄土高原地区NDVI与气候因子空间尺度依存性及非平稳性研究 总被引：6，自引：3，他引：3
基于MODIS传感器的植被指数产品(MOD13Q1)及50年气候数据,通过地理加权回归与普通最小二乘回归模型对比,对中国黄土高原地区NDVI与气候因子间的空间尺度依存性及非平稳性进行研究,以期准确建立二者间关系.结果表明:① 研究区域内,NDVI与气候因子间存在很强的空间尺度依存关系,相同空间尺度下,年均降水较年均温对NDVI影响的波动性更大;② 与普通最小二乘回归模型相比,地理加权回归模型能够更准确地展现二者间关系;③气候因子对该地区NDVI的影响差异明显,降水存在直接正向影响,而温度的影响则较复杂;④ NDVI与气候因子间沿东北--西南的分布格局体现出区域内不同植被--气候区差异特征.二者间的异质情况还反映出除气候外,人类活动,地形等其他因素对NDVI的影响. 相似文献
4.
基于高光谱的民勤土壤盐分定量分析 总被引：2，自引：0，他引：2
土壤盐渍化是重要的生态环境问题,严重影响着干旱、半干旱区的农牧业及经济发展。高光谱遥感技术能够提供地物的连续光谱信息,易于分析细微差别,在定量研究土壤盐分含量方面具有较大优势。民勤县位于甘肃省石羊河流域下游,水力资源匮乏,盐渍化问题十分严峻。本研究基于实验室光谱数据,通过建立模型定量分析土壤盐分含量。首先对原始数据进行连续统去除（cn）预处理,然后分别建立了土壤盐分含量的高光谱指数模型（NDSI）、偏最小二乘回归模型（PLS）、间隔偏最小二乘法模型（iPLS）和反向间隔偏最小二乘法模型（BiPLS）,考察各种模型对土壤盐分的预测能力。对比分析发现,使用全部波段信息建模的PLS模型优于仅使用两个波段信息的NDSI模型,而iPLS和BiPLS模型通过选择特征波段进行建模,结果均好于全谱PLS模型。其中,BiPLS模型波段选择的能力优于iPLS模型,得出的模型结果最好,预测相对偏差RPD达到2.02,决定系数R2和模拟值与预测值线性回归的斜率分别为0.76和0.92,模型可以近似地预测土壤盐分含量。结果说明特征波段选择方法能够从大量数据中提取有效信息,简化模型,并获取比NDSI模型和全谱PLS模型更优的预测结果。这些研究对于使用高光谱数据定量分析土壤盐渍化有一定的意义。 相似文献
5.
通过序列影像间的互补信息改善影像失真和退化的超分辨率重建,其关键是精确获取序列影像间的运动信息。该文讨论了一种基于最小二乘影像匹配的高精度运动估计方法,根据最小二乘影像匹配的高精度同名像点,获取同名点在序列影像间的运动信息,从而进行低分辨率序列影像的子像素级运动信息精确估计,据此进行超分辨率重建。利用一组模拟低分辨率序列影像进行的超分辨率重建验证结果表明:基于最小二乘法运动估计精度较高,采用迭代反投影法重建影像具有较好的视觉效果,该方法尤其适用于存在平移运动的影像序列的超分辨率重建。 相似文献
6.
亚热带人工针叶林冠层叶氮浓度的星载高光谱遥感能力评估(英文) 总被引：1，自引：0，他引：1
冠层叶片氮浓度(CNC)是影响森林生态系统生产力的重要参数之一。本研究探讨了星载成像光谱遥感在估测亚热带红壤丘陵区人工针叶林CNC的表现。分析包括了星载成像光谱数据(Hyperion影像)覆盖的两条样带上的57个野外样方,并将其分为三个子集(A-C)。利用一元回归和偏最小二乘回归方法分析了CNC与成像光谱信息之间的关系。在A-C子集中,CNC与近红外反射率(NIR)之间的相关性一致都呈现显著的正相关关系(R2=0.29,0.33和0.36,P0.05或P0.01)。另外,我们利用归一化的氮指数(NDNI)估计森林CNC的变异。在3个子集中,NDNI与CNC都呈现极显著的正相关关系,但相关性不高(R2=0.38,0.20和0.17,P0.01)。然后利用偏最小二乘方法分析了CNC与整个成像光谱数据(反射率、对数变换和一阶导数变换)之间的相关性,对于各个子集相关性不同且相对微弱。在分析已有数据和对比前人文献基础上,文章分析了影响成像光谱遥感森林CNC的可能原因,并指出研究区人工针叶林单一的冠层结构可能减弱了该地区森林CNC与成像光谱信息之间的相关关系。 相似文献
7.
《热带地理》2017,(2)
基于2015年广州地区1 km空间分辨率的MOD13A 3月合成NDVI数据以及春夏秋冬4个季节的气象站点近地表气温,首先利用聚集密度计算方法计算NDVI的聚集密度,构建不同季节近地表气温与NDVI聚集密度的最小二乘线性回归模型(OLS)和地理加权回归模型(GWR),分析广州市近地表气温与NDVI聚集密度的相关关系,探讨不同季节NDVI聚集密度回归系数的空间分布,并利用AICc信息准则、拟合优度和Sigma指标对GWR与OLS的结果进行比较分析。结果表明:NDVI聚集密度较好地反映了研究区建设用地、植被和水体等下垫面的综合信息;与OLS模型相比,GWR模型的拟合效果更显著,最小的拟合度从0.02提高到0.464,GWR模型的拟合度最大值达到了0.724;GWR模型回归残差的Moran’s I显著减少,如1月份Moran’s I指数从0.383减少到0.022;NDVI聚集密度对气温的影响具有空间异质性,整体上,从广州北到南,GWR模型中NDVI聚集密度与气温的回归系数由负值逐渐增加到正值,表明NDVI聚集密度对气温有着从负到正的影响;下垫面以不透水面为主的区域,GWR模型拟合度较低,以植被为主要下垫面的区域,GWR模型拟合度较高。 相似文献
8.
基于高光谱数据的小麦叶绿素含量反演 总被引：18，自引：0，他引：18
近年来,遥感高光谱技术为获取农作物的某些生理化参数提供了丰富的数据来源。该文使用北京小汤山地区实验获取的小麦高光谱数据,应用偏最小二乘回归方法,建立了冬小麦冠层波谱与叶绿素含量的回归反演计算模型。研究结果显示:模型在350～1060nm波段具有较高的反演精度。本研究为应用高光谱数据反演冬小麦叶绿素含量提供了有效途径。 相似文献
9.
南京市三维生态足迹测算及驱动因子 总被引：5，自引：0，他引：5
基于三维生态足迹模型测度2001-2011年南京市三维生态足迹的动态变化,并利用偏最小二乘回归对南京市三维生态足迹的驱动因子进行分析。结果表明:1)2001-2011年南京市人均三维生态足迹总体上呈上升趋势,年均增长率为16.5%,人均生态承载力以年均0.79%的速率呈逐年下降趋势;2)偏最小二乘回归分析表明,城市生态建设、环境污染等是导致南京市生态足迹逐年上升的重要因素,而交流与贸易、环境治理和土地利用结构则有利于缓解生态压力扩大的态势;3)变量投影重要性分析显示人均公共绿地面积及工业废弃物排放量指标对南京市生态足迹的影响较大,与回归分析的综合评价结果较为一致。未来应通过优化产业结构、使用清洁能源、积极发展对外贸易与交流、合理规划城市土地利用等提高城市生态建设效率。 相似文献
10.
基于表观电导率与实测光谱的干旱区湿地土壤盐分监测 总被引：2，自引：0，他引：2
以新疆艾比湖滨盐渍化土壤为对象,利用磁感应电导仪和光谱仪测得的盐渍土表观电导率和可见光/近红外光谱数据,选取与EM38解译的土壤盐分相关性最好的光谱变换形式和特征波长,分别建立多元逐步回归、偏最小二乘回归和支持向量回归的土壤盐分监测模型。结果表明：（1）表观电导率两种模式相结合建立的盐分含量解译模型的拟合优度达到0.91,即在该区域内电磁感应技术可用于土壤盐分含量的间接监测。（2）一阶微分处理优于二阶微分,经一阶微分变换后的光谱可以较好地预测土壤盐分含量。（3）3种建模方法中,支持向量回归的建模精度最高,偏最小二乘回归和多元逐步回归次之。干旱区湖滨湿地土壤盐分含量的估测模型宜选取基于平滑后的原始一阶微分光谱数据建立的支持向量回归模型。 相似文献
11.
Xiaohua Tong Wenzhong Shi Dajie Liu 《International journal of geographical information science》2013,27(11):1413-1432
In this paper, a least‐squares based cadastral parcel area adjustment in geographic information systems (GIS) is developed based on (1) both the areas and coordinates being treated as observations with errors; and (2) scale parameters being introduced to take the systematic effect into account in the process of cadastral map digitization. The area condition equation for cadastral parcel considerations of scale parameters and geometric constraints is first constructed. The effects of the scale error on area adjustment results are then derived, and statistical hypothesis testing is presented to determine the significance of the scale error. Afterwards, Helmert's variance component estimation based on least‐squares adjustment using the condition equation with additional parameters is proposed to determine the weight between the coordinate and area measurements of the parcel. Practical tests are conducted to illustrate the implementation of the proposed methods. Four schemes for solving the inconsistencies between the registered areas and the digitized areas of the parcels are studied. The analysis of the results demonstrates that in the case of significant systematic errors in cadastral map digitization, the accuracies of the adjusted coordinates and areas are improved by introducing scale parameters to reduce the systematic error influence in the parcel area adjustment. Meanwhile, Helmert's variance component estimation method determines more accurate weights of the digitized coordinates and parcel areas, and the least‐squares adjustment solves the inconsistencies between the registered areas and the digitized areas of the parcels. 相似文献
12.
Xiaohua Tong Dan Liang Gusheng Xu Songlin Zhang 《International journal of geographical information science》2013,27(7):1147-1171
With the rapid development of geospatial data capture technologies such as the Global Positioning System, more and higher accuracy data are now readily available to upgrade existing spatial datasets having lower accuracy using positional accuracy improvement (PAI) methods. Such methods may not achieve survey-accurate spatial datasets but can contribute to significant improvements in positional accuracy in a cost-effective manner. This article addresses a comparative study on PAI methods with applications to improve the spatial accuracy of the digital cadastral for Shanghai. Four critical issues are investigated: (1) the choice of improvement model in PAI adjustment; five PAI models are presented, namely the translation, scale and translation, similarity, affine, and second-order polynomial models; (2) the choice of estimation method in PAI adjustment; three estimation methods in PAI adjustment are proposed, namely the classical least squares (LS) adjustment, which assumes that only the observation vector contains error, the general least squares (GLS) adjustment, which regards both the ground and map coordinates of control points as observations with errors, and the total least squares (TLS) adjustment, which takes the errors in both the observation vector and the design matrix into account; (3) the impact of the configuration of ground control points (GCPs) on the result of PAI adjustment; 12 scenarios of GCP configurations are tested, including different numbers and distributions of GCPs; and (4) the deformation of geometric shape by the above-mentioned transformation models is presented in terms of area and perimeter. The empirical experiment results for six test blocks in Shanghai demonstrated the following. (1) The translation model hardly improves the positional accuracy because it accounts only for the shift error within digital datasets. The other four models (i.e., the scale and translation, similarity, affine, and second-order polynomial models) significantly improve the positional accuracy, which is assessed at checkpoints (CKPs) by calculating the difference between the updated coordinates transformed from the map coordinates and the surveyed coordinates. On the basis of the refined Akaike information criterion, the two best optimal transformation models for PAI are determined as the scale and translation and affine transformation models. (2) The weighted sum of square errors obtained using the GLS and TLS methods are much less than those obtained using the classical least squares method. The result indicates that both the GLS and TLS estimation methods can achieve greater reliability and accuracy in PAI adjustment. (3) The configuration of GCPs has a considerable effect on the result of PAI adjustment. Thus, an optimal configuration scheme of GCPs is determined to obtain the highest positional accuracy in the study area. (4) Compared with the deformations of geometric shapes caused by the transformation models, the scale and translation model is found to be the best model for the study area. 相似文献
13.
积雪是我国西北干旱半干旱区重要的水资源，也是影响全球气候变化的重要因子之一。 目前光学影像反射率和雷达亮温数据是积雪遥感领域的主要数据，本文首次结合两类遥感数据估 算积雪深度，并比较偏最小二乘法和机器学习算法（人工神经网络、支持向量机和随机森林算法） 在积雪深度估算方面的表现。以锡林郭勒盟 2012—2015 年积雪深度数据为例，基于反射率和亮度 温度相结合的积雪深度估算精度优于单个数据源，且随机森林算法表现最好，均方根误差为 2.93 cm，满足实际应用的需求。研究结果对我国西北地区水资源分布、生态环境评估等研究具有重要 意义。 相似文献
14.
以香格里拉县2006年TM影像、2006年森林资源二类调查小班数据为信息源,结合研究区冷杉林地面实测标准地（30m×30m）数据,提取香格里拉县冷杉林TM影像分布信息及标准地纹理特征因子（共48个）,对各因子进行相关分析;利用主成分法对纹理特征因子进行因子分析,最终选出13个纹理特征因子利用偏最小二乘法进行回归建模并进行模型精度检验。根据回归估测模型以及提取出的冷杉林各波段纹理特征因子,进行研究区冷杉林郁闭度反演。结果表明,基于遥感影像纹理特征建立的郁闭度遥感估测模型,其RE=13.8%,RMSE=10.39,精度为83.3%。研究区冷杉林郁闭度反演可知冷杉林郁闭度多分布在0.6~0.7范围内,多为中度郁闭林地。 相似文献
15.
THE PERFORMANCE OF LEAST SQUARES AND ROBUST REGRESSION IN THE CALIBRATION OF ANALYTICAL METHODS UNDER NON-NORMAL NOISE DISTRIBUTIONS 总被引：1，自引：0，他引：1
By means of Monte Carlo simulations a comparison has been made between ordinary least squaresregression and robust regression. The robust regression procedure is based on the Huber estimate and iscomputed by means of the iteratively reweighted least squares algorithm. The performance of bothprocedures has been evaluated for estimation of the parameters of a calibration function and fordetermination of the concentration of unknown samples. The influence of the distributionalcharacteristics skewness and kurtosis has been studied, and the number of measurements used forconstructing the calibration curve has also been taken into account, Under certain conditions robustregression offers an advantage over least squares regression. 相似文献
16.
省域经济增长与电力消费的局域空间计量经济分析 总被引：11，自引：0，他引：11
中国各个地区经济发展对电力消费需求量大且存在地域差异,不同区域间的电力需求与经济增长之间的关系十分复杂,并非能由常系数的普通最小二乘回归分析所解释.采用电力消费模型,利用局域空间计量经济学模型方法--空间变系数的地理加权回归模型,对中国省域电力消费与经济增长之间的关系进行了局域空间计量经济分析.结果发现,中国大陆30个省域的电力消费和经济增长之间表现为一种非均衡的联动关系和局域性特征,制定差异化的区域电力消费调控政策是非常必要的. 相似文献
17.
Robust estimation of geomagnetic transfer functions 总被引：22，自引：0，他引：22
Summary. We show, through an examination of residuals, that all of the statistical assumptions usually used in estimating transfer functions for geomagnetic induction data fail at periods from 5 min to several hours at geomagnetic mid-latitudes. This failure can be traced to the finite spatial scale of many sources. In the past, workers have tried to deal with this problem by hand selecting data segments thought to be free of source effects. We propose an automatic robust analysis scheme which accounts for the systematic increase of errors with increasing power and which automatically downweights source contaminated outliers. We demonstrate that, in contrast to ordinary least squares, this automatic procedure consistently yields reliable transfer function estimates with realistic errors. 相似文献
18.
Shuqiang Xue Jiping Liu Jinzhong Mi Chun Dong Yingyan Cheng 《International journal of geographical information science》2016,30(11):2155-2170
The calculation of surface area is meaningful for a variety of space-filling phenomena, e.g., the packing of plants or animals within an area of land. With Digital Elevation Model (DEM) data we can calculate the surface area by using a continuous surface model, such as by the Triangulated Irregular Network (TIN). However, just as the triangle-based surface area discussed in this paper, the surface area is generally biased because it is a nonlinear mapping about the DEM data which contain measurement errors. To reduce the bias in the surface area, we propose a second-order bias correction by applying nonlinear error propagation to the triangle-based surface area. This process reveals that the random errors in the DEM data result in a bias in the triangle-based surface area while the systematic errors in the DEM data can be reduced by using the height differences. The bias is theoretically given by a probability integral which can be approximated by numerical approaches including the numerical integral and the Monte Carlo method; but these approaches need a theoretical distribution assumption about the DEM measurement errors, and have a very high computational cost. In most cases, we only have variance information on the measurement errors; thus, a bias estimation based on nonlinear error propagation is proposed. Based on the second-order bias estimation proposed, the variance of the surface area can be improved immediately by removing the bias from the original variance estimation. The main results are verified by the Monte Carlo method and by the numerical integral. They show that an unbiased surface area can be obtained by removing the proposed bias estimation from the triangle-based surface area originally calculated from the DEM data. 相似文献
19.
Huiguo Zhang 《International journal of geographical information science》2013,27(9):1467-1489
The geographically weighted regression (GWR) has been widely applied to many practical fields for exploring spatial non-stationarity of a regression relationship. However, this method is inherently not robust to outliers due to the least squares criterion in the process of estimation. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression relationship. Using the least absolute deviation criterion, we propose two robust scenarios of the GWR approaches to handle outliers. One is based on the basic GWR and the other is based on the local linear GWR (LGWR). The proposed methods can automatically reduce the impact of outliers on the estimates of the regression coefficients and can be easily implemented with modern computer software for dealing with the linear programming problems. We then conduct simulations to assess the performance of the proposed methods and the results demonstrate that the methods are quite robust to outliers and can retrieve the underlying coefficient surfaces satisfactorily even though the data are seriously contaminated or contain severe outliers. 相似文献