Comparison and assessment of NDVI time series for seasonal wetland classification

Satellite-based wetland mapping faces challenges due to the high spatial heterogeneity and dynamic characteristics of seasonal wetlands. Although normalized difference vegetation index (NDVI) time series (NTS) shows great potential in land cover mapping and crop classification, the effectiveness of various NTS with different spatial and temporal resolution has not been evaluated for seasonal wetland classification. To address this issue, we conducted comparisons of those NTS, including the moderate-resolution imaging spectroradiometer (MODIS) NTS with 500?m resolution, NTS fused with MODIS and Landsat data (MOD_LC8-NTS), and HJ-1 NDVI compositions (HJ-1-NTS) with finer resolution, for wetland classification of Poyang Lake. Results showed the following: (1) the NTS with finer resolution was more effective in the classification of seasonal wetlands than that of the MODIS-NTS with 500-m resolution and (2) generally, the HJ-1-NTS performed better than that of the fused NTS, with an overall accuracy of 88.12% for HJ-1-NTS and 83.09% for the MOD_LC8-NTS. Future work should focus on the construction of satellite image time series oriented to highly dynamic characteristics of seasonal wetlands. This study will provide useful guidance for seasonal wetland classification, and benefit the improvements of spatiotemporal fusion models.     

Land-use and vegetation-cover mapping of an indigenous land area in the state of Mato Grosso (Brazil) based on spectral linear mixing model,segmentation and region classification

A spectral linear-mixing model using Landsat ETM+ imagery was undertaken to estimate fraction images of green vegetation, soil and shade in an indigenous land area in the state of Mato Grosso in the central-western region of Brazil. The fraction images were used to classify different types of land use and vegetation cover. The fraction images were classified by the following two methods: (a) application of a segmentation based on the region-growing technique; and (b) grouping of the regions segmented using the per-region unsupervised classifier named ISOSEG. Adopting a 75% threshold, ISOSEG generated 44 clusters that were grouped into eight land-use and vegetation-cover classes. The mapping achieved an average accuracy of 83%, showing that the methodology is efficient in mapping areas of great land-use and vegetation-cover diversity, such as that found in the Brazilian cerrado (savanna).     

鞍山区域电离层模型构建及GNSS应用研究

针对电离层延迟误差目前是GNSS导航定位精度最重要误差源的现状,通过GNSS参考站或跟踪站实测数据计算电子总含量值,建立区域电离层模型,监测区域电离层变化,进而找到削弱或消除电离层延迟误差影响方法。利用曲面拟合实现建模,在模型的建立过程中通过对不同的模型阶数进行设置,对比不同情况下的模型精度,从而确定特定区域内最佳数据采样间隔及阶数设置,并在最佳阶数设置情况下,比较了预报不同时段的精度,进而对延迟量预报问题进行探讨,得出一些有益结论。可以通过该模型单独解算流动站站点的实时电离层延迟信息,这对多基站CORS的站间距离选择和单基站CORS基准站和流动站之间距离设计,尤其对提高单频接收机以及GIS产品用户的定位精度和差分模型的覆盖范围都具有实际参考意义。     

Possibilities of soil spectroscopy for the classification of contaminated areas in river floodplains

During the past decades, large amounts of diffuse contaminated soil material have been deposited in the floodplains of the river Rhine in the Netherlands. The dynamic character of this river causes a large spatial variability in the contamination level of its floodplain soils. Characterisation of the spatial variability exclusively based on soil sampling and analysis is often insufficient and expensive. Hyperspectral images can provide additional spatial information for a proper characterisation of the contamination situation of river floodplains. This paper describes the possible application of soil spectroscopy to estimate metal concentration levels in river floodplains. Soil reflectance spectra in the visible-near infrared region (VNIR) were measured in the laboratory for soil samples taken from two river floodplains along the river Waal, the main tributary of the river Rhine in the Netherlands. A multivariate calibration procedure using partial least squares (PLS) regression was applied to establish a relationship between reflectance spectra in the visible-near infrared (VNIR) region and spectrally active soil characteristics (organic matter and clay content) that are intercorrelated with concentration levels of Cd and Zn. Results of the analysis of two river floodplains are summarised and the influence of scale-level and sub soil material on the prediction capability is discussed.     

空间网络对时序InSAR相位解缠的影响——以Delaunay与Dijkstra网络为例EI北大核心CSCD

在时间序列InSAR相位解缠的过程中,相干目标需事先构建空间网络之后再进行模糊度解算。Delaunay三角网是当前时序相位解缠的主流构网方法,但其网络形态易包含高相位梯度的边缘,导致违背相位连续性假设。考虑到目前很少有关于空间网络对解缠影响的研究及相位解缠对InSAR技术测量精度的主导地位,本文在量化分析Delaunay网络对解缠影响的基础上,提出引入图论中的Dijkstra最短路径算法优化Delaunay网络中所有边的相位梯度,进而改善时序相位解缠的精度。本文采用模拟和真实数据对基于Delaunay网络和基于优化网络的相位解缠进行了对比验证。结果表明,本文提出的构网方法能够更好地满足相位连续性假设,减少约33%由解缠误差所导致的不闭合三角环数。较传统研究聚焦解缠方法和目标函数的改进而言,本文研究揭示了空间网络的改善对时间序列相位解缠的重要性。     

顾及有色噪声影响的甘肃境内CMONOC基准站坐标时间序列分析

针对甘肃省境内19个CMONOC基准站坐标时间序列结果,采用极大似然估计法(MLE)探讨了各基准站的最优噪声模型,确定了基准站的速度场,并分析了甘肃省地壳运动状况.研究结果表明:甘肃省境内CMONOC基准站各坐标分量噪声特性存在较大的差异,"白噪声+闪烁噪声(WN+FN)"为最优噪声模型,能够更好地描述基准站坐标时间序...