Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city     

Feilin Lai 《地理信息系统科学与遥感》2020,57(6):830-844

ABSTRACT

Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability.  相似文献   

美国出口管制下中国外贸风险的省域地理差异     

姜辉 《地理科学》2020,40(10):1618-1626

从总贸易、出口、进口、外资和人员5个视角构建对美风险系数，测度和比较各省区市对美外贸风险的地理差异。结果表明：上海、浙江和福建与美国的进出口贸易规模最大，对美国市场的依赖程度最高，面临的综合外贸风险最大；河南尤其需要防范对美国的出口风险；海南、新疆、天津和西藏等需要加强从美国进口的风险管理；重庆除了需要防范对美出口风险外，还因为实际利用美资最多且依赖程度最大，亟需加强外资风险管理。宁夏和山西虽然从美国的引资规模不大，但是对美资的依赖程度最高，亟需提升外资多元化水平。从人员跨境流动风险来看，上海、北京和浙江面临的风险最大。中国各省区市在应对美国出口管制时应该加强外贸风险的识别和管理，针对不同的风险采取差别化的防范和化解措施。  相似文献   

东北地区水稻扩张的海拔优势区间分析     

黄莹泽  邱炳文  何玉花  张珂  邹凤丽 《地理科学进展》2020,39(9):1557-1564

及时掌握水稻的时空分布信息,对调整和优化农业生产结构至关重要。论文利用综合考虑植被物候和地表水变化的水稻自动制图方法,结合海拔、地表水体因素开展2001—2017年东北地区水稻分布的时空演变研究。通过889个地面调研点位对水稻分类结果验证,总体精度达90.66%,Kappa系数为0.8128。研究表明:① 21世纪初,东北地区水稻种植面积呈先略减后持续增加的趋势,2017年水稻种植面积达2001年的2.13倍。其中,水稻扩张面积的60%分布在三江平原,30%分布在松嫩平原,下辽河平原仅占不足5%。水稻扩张的海拔优势区间在200 m范围内,随着海拔的上升水稻扩张与地表水关系越来越密切。② 三江平原内,水稻扩张幅度在海拔30~70 m范围内逐渐增加,使优势区间从相对高度70 m缩减至40 m内,也使得分布优势逐渐趋向于距地表水体较远的区域。而松嫩平原和下辽河平原水稻种植分布的海拔优势区间始终分别保持在相对高度100 m、40 m内。③ 三江平原水稻的集中分布和急剧扩张,使水稻分布优势逐渐趋向于距地表水体远的区域,这将对地下水带来更大的压力;而松嫩平原水稻分布受地表水体影响较大,分布优势随着距地表水体距离的增加而减小。研究可为农业部门评估水资源承载力、保障农业可持续发展提供数据支撑及理论参考。  相似文献   

地理大数据挖掘——目标、内涵与研究问题     

裴韬  宋辞  郭思慧  舒华  刘亚溪  杜云艳  马廷  周成虎 《地理学报(英文版)》2020,30(2):251-266

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An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering     

Xiaojing Wu  Raul Zurita-Milla  Changqing Song 《International journal of geographical information science》2020,34(9):1822-1848

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基于金矿规格单元数据的机器学习方法在成矿建模分析中的应用     

张野  李明超  韩帅  任秋兵  朱月琴 《大地构造与成矿学》2020,(2):183-191

现代金矿勘察主要是通过综合地球化学和地质测量等数字化方法对深部矿床进行研究,所需要的人力物力成本较高。而通过分析积累的金矿规格单元数据,可以建立金矿成矿情况与相关成矿元素含量之间的非线性关系,从已有的勘查数据中寻找金矿成矿的一般规律。本文基于与金矿相关的成矿元素含量数据,分别采用逻辑斯蒂回归、随机森林和决策树方法对原始数据和重采样数据进行训练,综合运用召回率、精确率和准确率对模型进行评价。通过对比发现,在训练和测试原始数据过程中,由于每组之间数据量的巨大差距,导致成矿数据被淹没;而在训练重采样数据过程中,随机森林在召回率和准确率方面均有较好的表现,分别达到了90.63%和70.78%;并最终分析了随机森林模型中不同分类边界对于金矿成矿情况预测结果的影响。利用不同的测量指标对模型进行评价分析,使模型更适用于金矿成矿预测,可有效地提高金矿勘察的效率。  相似文献   

城市地理研究中的单分形、多分形和自仿射分形   总被引：1，自引：0，他引：1  

陈彦光 《地理科学进展》2019,38(1):38-49

分形几何学在城市地理研究中具有广泛的应用,然而很多基本概念却让初学者感到迷惑。如何区分单分形、自仿射分形与多分形,是一个基本而重要的问题。简单分形容易理解,而真实的地理现象很少是单分形的。城市生长过程具有自仿射特征,而城市空间格局却具有多分形性质。作者发现,各种分形的共性在于三个方面：标度律、分数维和熵守恒。论文基于标度、分维和熵守恒公式,借助隐喻城市生长的规则分形来区分单分形、多分形和自仿射分形,讨论分形系统演化的机理、分形与空间自相关和空间异质性的联系,同时澄清一些在地理分形研究中的常见错误概念。最后以城市位序-规模分布为例,说明并对比单分形和多分形在城市地理研究中的建模与应用思路。  相似文献   

保持局域特征的多源海冰图像融合与分类     

余志强  王廷伟  张晰  张杰  任鹏 《海洋学报(英文版)》2019,38(7):129-136

本文提出一种保持局域特征的多源海冰图像融合方法，并在此基础上进行海冰分类。本文提出的多源海冰图像融合方法包括保持空间局域融合和保持特征局域融合两方面。首先，通过学习得到投影矩阵和相似矩阵。投影矩阵将多源像素进行投影变换，得到保留像素空间局域特性的融合向量。相似矩阵度量像素特征间的相似性，通过拉普拉斯特征分解，得到保留像素特征局域相似性的融合向量。然后，将空间融合向量和特征融合向量进行像素综合，得到融合图像。在此基础上，本文设计一种滑动集成分类方法进行融合图像像素分类。提出的分类方法利用滑动集成的特点，在分类时增强刻画了海冰局域特性。由于本文的保持局域融合框架不仅刻画了海冰在物理空间中的邻接关系，而且考虑不同海冰类型的特征关系，因此其在多源图像（多光谱和合成孔径雷达（SAR）图像）的海冰分类任务中表现优异。实验结果表明本文提出的基于保持局域特征融合的多源海冰图像分类方法有效提升了海冰分类精度。  相似文献   

桂林-阳朔地区DEM地形特征与岩性相关性分析及分类研究     

陈霄燕  潘军  邢立新  蒋立军  孙也涵  仲伟敬  范博文 《地球信息科学学报》2019,21(12):1867-1876

地形地貌是岩性解译的重要信息,地形因子作为描述DEM数字曲面几何特征的定量指标参数,可用来定量化表达不同岩性所在地区地形地貌特征。本文以桂林-阳朔地区为研究区,研究地形因子数学、地质意义,建立岩性与地形因子组合间的定量关联,进而实现岩石类型划分。本文基于ASTERGDEM提取坡度、起伏度等12个地形因子,在分析各个地形因子地质意义基础上,通过聚类分析及方差分析的多元统计分析方法,研究各岩性地形因子特性及其关联性,建立研究区岩性之间的定量差异;此外,利用因子分析方法研究岩性分类过程中的主导因素,确定适宜岩性分类方法以实现定量化岩性分类。实验结果表明：不同岩性、不同地形地貌的地形因子（组合）之间具有显著差异,基于因子分析得到的宏观地形复杂度指数（MTI）以及微观曲率指数（MCI）对岩石类型的分类精度达77.36%。研究表明,地形复杂度等地形因子可用于岩性分类,采用因子分析方法可获取反映地形地貌宏观、微观特征的定量指标,且岩性分类效果良好。  相似文献   

In-season crop classification using elements of the Kennaugh matrix derived from polarimetric RADARSAT-2 SAR data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.  相似文献