Comparison of upscaling cropland and non-cropland map using uncertainty weighted majority rule-based and the majority rule-based aggregation methods     

Peijun Sun  Jinshui Zhang 《国际地球制图》2019,34(2):149-163

Aggregation method is seriously impacted by the landscape characteristics, which has been emphasized due to proportional errors. This research proposed an uncertainty weighted majority rule-based aggregation method (UWMRB) to upscale the cropland/non-cropland map. The Cropland Data Layer for 2016 at 30m resolution, with its corresponding confidence level data, were collected to conduct the experiment using UWMRB and majority rule-based aggregation method. Proportional errors of crop/non-crop were used to assess the accuracy of the two methods. Ordinal logistic regression was used to obtain the probability of an error occurring to predict the uncertainty of both methods. The results show that UWMRB can achieve the lower proportional errors with lower uncertainty. Also, it can reduce the influence of complexity and fragmentation of landscape on aggregation performance. Additionally, the examination of UWMRB provides an important view of application of uncertainty information for upscaling land cover maps in an efficient way.  相似文献   

北斗二代卫星导航系统定位精度分析方法研究   总被引：5，自引：0，他引：5       下载免费PDF全文

范龙  柴洪洲 《海洋测绘》2009,29(1):25-27

卫星导航系统的定位精度主要受观测量的精度和卫星的空间几何分布两方面的影响,GPS等相同轨道分布的卫星导航系统一般采用几何精度因子（GDOP）来分析定位精度。我国的北斗二代卫星导航系统是由三类异质卫星组成的混合星座导航系统,不同轨道卫星定轨误差不同,用户所得到的观测量精度也不相同,因此精密定位精度计算和分析时必须要考虑这种差异。引入了加权几何精度因子（WGDOP）,利用模拟观测数据对北斗二代卫星导航系统的定位精度进行了分析。外部检核计算结果表明,精密定位计算时顾及观测量精度差异可进一步提高定位精度。  相似文献   

利用少量地面重力数据估计航空重力数据的系统偏差     

翟振和  孙中苗 《测绘科学》2009,34(4)

航空重力数据与地面重力数据之间存在的系统偏差,是亟待解决的一个问题,为了能够在缺乏地面重力点的山区有效地利用航空重力数据,本文利用少量地面点的控制分别利用最小二乘配置法和加权平均法对航空重力数据的系统偏差进行了估计及改正,通过实测数据计算表明,两种方法都较好地估计出了航空重力数据存在的系统偏差,数据精度也有所提高,同时也验证了两种地面数据布设方案的有效性,通过分析得出加权平均法适合于同类重力数据的联合处理,而最小二乘配置则更适合于不同重力数据处理。  相似文献   

Reconstructing Holocene sea level using salt‐marsh foraminifera and transfer functions: lessons from New Jersey,USA     

RICHARD J. TELFORD  BENJAMIN P. HORTON  SHIMON C. ANISFELD  CHRISTOPHER K. SOMMERFIELD 《第四纪科学杂志》2013,28(6):617-629

  相似文献   

Computational improvements to multi-scale geographically weighted regression     

Ziqi Li  A. Stewart Fotheringham 《International journal of geographical information science》2020,34(7):1378-1397

ABSTRACT

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download.  相似文献   

Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity     

Zhenhong Du  Zhongyi Wang  Sensen Wu  Feng Zhang  Renyi Liu 《International journal of geographical information science》2020,34(7):1353-1377

ABSTRACT

Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes.  相似文献   

花岗岩巴西劈裂试验的矩张量反演研究     

任义  高永涛  吴顺川  李栋栋  甘一雄 《岩土力学》2022,43(1):235-245

为了研究岩石张拉破坏的过程和机制,首先通过模拟计算,验证了加权反演法在进行巴西劈裂试验矩张量求解方面的优越性;其次基于花岗岩的巴西劈裂试验,进行声发射监测、事件定位及矩张量反演,并分析标准反演法和加权反演法对应矩张量结果的差别;使用聚类分析K均值算法对矩张量结果进行分簇研究。研究结果表明：模拟计算中加权反演法可以降低巴西劈裂试验矩张量反演误差;室内花岗岩巴西劈裂试验中,加权反演法较大程度地优化了声发射事件的剪切成分以及压/拉应力轴的分布,使得反演结果更加合理。针对加权反演结果,所有事件可分成3簇,同簇内事件的破裂类型、应力状态、破裂机制、辐射模式等均相近;不同簇中的事件在时空上交叉分布,试验后期在圆盘东?西方向上具有较为明显张拉特性的事件骤增,于圆盘中心附近聚集并占据主导,最终导致试样的宏观破裂。研究结果解释了花岗岩巴西劈裂试验的破裂过程和破裂机制,为岩石力学行为研究提供进一步指导。  相似文献   

湖底深基坑围护结构变形与受力特性研究     

张文涛  祁长青  周志芳  甘飞飞  韩辉 《中国煤炭地质》2022,(1)

基坑围护结构是基坑安全的重要保证。通过三维有限单元模拟,考虑地下水渗流的影响及结构与土体之间的相互作用,对某湖底明挖隧道基坑支护结构在施工过程中的变形和受力变化规律进行研究,并与现场监测结果进行对比验证。结果表明:围护结构的不同部位对基坑开挖的响应规律并不一致;水平向支护结构受开挖影响,向坑内倾倒,竖直向桩体随开挖应力重分布影响中段发生弯曲变形,总体稳定。模拟变形结果与监测结果较为一致,验证了基坑围护结构的有效性和模拟计算的准确性。研究结果为基坑后续施工提供了参考依据。  相似文献   

一种加权Logistic-Richards组合模型的地表沉降预测方法     

龙四春  彭强  黄两宜  张立亚 《大地测量与地球动力学》2015,35(5):821-824

利用平移反幂法特征值求权方法,对两种地表沉降预测模型(Logistic模型和Richards模型)进行加权组合。以深圳地铁人民南车站、鹿丹村车站和长沙地铁东四线车站为例,运用新的加权Logistic-Richards组合模型对地铁施工期间地表沉降进行拟合和预测,对比验证了该组合模型的优越性。  相似文献   

Measures of spatio-temporal accuracy for time series land cover data     

《International Journal of Applied Earth Observation and Geoinformation》2015

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.  相似文献