Estimating agricultural production in marginal and food insecure areas in Kenya using very high resolution remotely sensed imagery

Regularly monitoring the amount of food produced in food insecure, isolated, subsistence farming areas can be used to help identify households or communities who may be in need of additional food resources. Measuring seasonal food production in developing countries, particularly at a sub-national level, is complicated by lack of data. In this study we use high resolution remotely sensed data to calculate cultivated area in two different growing areas, during two different seasons in Kenya. The results of the research support the usefulness of this approach for agricultural monitoring in the developing world and suggest that monitoring cultivated area requires attention to the specific growing characteristics of an area.     

渤海风暴减水特征及其对深水航路影响的数值模拟

利用环渤海9个沿岸站近10a潮位资料分析渤海海域的风暴减水特征,结果表明:渤海年均出现50cm和100cm风暴减水分别超过30d和6d,每年的9月至翌年4月份风暴减水最为频繁;建立了一套精细化天文潮-风暴潮耦合模型用于渤海深水航路的潮位预报,各站天文潮模拟验证的平均均方根误差为18.5cm,由此计算得到航路代表点的潮汐特征值并作潮汐预报;后报模拟了近10a重大风暴减水过程,模拟与实测吻合较好,说明该耦合模型可为该航路的潮位预报提供有益参考。     

A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas

In recent years, it has been widely agreed that spatial features derived from textural, structural, and object-based methods are important information sources to complement spectral properties for accurate urban classification of high-resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high-dimensional feature space. The high-dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low-dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically, two categories of indices are proposed in the study: (1) Primitive indices (PI): High-resolution urban scenes are represented using a group of primitives (e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (VI): A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low-dimensional and semantic indices replacing the high-dimensional but low-level features (e.g., textures). The information indices are then learned via the multi-kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, QuickBird, WorldView-2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low-dimensional feature space, and, provide a practical strategy for processing large-scale high-resolution images.     

Spectral monitoring of moorland plant phenology to identify a temporal window for hyperspectral remote sensing of peatland

Recognising the importance of the timing of image acquisition on the spectral response in remote sensing of vegetated ecosystems is essential. This study used full wavelength, 350–2500 nm, field spectroscopy to establish a spectral library of phenological change for key moorland species, and to investigate suitable temporal windows for monitoring upland peatland systems. Spectral responses over two consecutive growing seasons were recorded at single species plots for key moorland species and species sown to restore eroding peat. This was related to phenological change using narrowband vegetation indices (Red Edge Position, Photochemical Reflectance Index, Plant Senescence Reflection Index and Cellulose Absorption Index); that capture green-up and senescence related changes in absorption features in the visible to near infrared and the shortwave infrared. The selection of indices was confirmed by identifying the regions of maximum variation in the captured reflectance across the full spectrum. The indices show change in the degree of variation between species occurring from April to September, measured for plant functional types. A discriminant function analysis between indices and plant functional types determines how well each index was able to differentiate between the plant functional groups for each month. It identifies April and July as the two months where the species are most separable. What is presented here is not one single recommendation for the optimal temporal window for operational monitoring, but a fuller understanding of how the spectral response changes with the phenological cycle, including recommendations for what indices are important throughout the year.     

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data

Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved.     

An Overview of Nowcasting Development, Applications, and Services in the Hong Kong Observatory

The Hong Kong Observatory （HKO） has been developing a suite of nowcasting systems to support op- erations of the forecasting center and to provide a variety of nowcasting services for the general public and specialized users. The core system is named the Short-range Warnings of Intense Rainstorm of Localized Systems （SWIRLS）, which is a radar-based nowcasting system mainly for the automatic tracking of the movement of radar echoes and the short-range Quantitative Precipitation Forecast （QPF）. The differential, integral （or variational）, and object-oriented tracking algorithms were developed and integrated into the nowcasting suite. In order to predict severe weather associated with intense thunderstorms, such as high gust, hail, and lightning, SWIRLS was enhanced to SWIRLS-II by introduction of a number of physical models, especially the icing physics as well as the thermodynamics of the atmosphere. SWIRLS-Ⅱ was further enhanced with non-hydrostatic, high resolution numerical models for extending the forecast range up to 6h ahead. Meanwhile, SWIRLS was also modified for providing nowcasting services for aviation community and specialized users. To take into account the rapid development of lightning events, ensemble nowcasting techniques such as time-lagged and weighted average ensemble approaches were also adopted in the nowcasting system. Apart from operational uses in Hong Kong, SWIRLS/SWIRLS-Ⅱ was also exported to other places to participate in several international events such as the WMO/WWRP Forecast Demon- stration Project （FDP） during the Beijing 2008 Olympics Games and the Shanghai Expo 2010. Meanwhile, SWIRLS has also been transferred to various regional meteorological organizations for establishing their nowcasting infrastructure. This paper summarizes the history and the technologies of SWIRLS/SWIRLS-Ⅱ and its variants and the associated nowcasting applications and services provided by the HKO since the mid 1990s.     

基于伪语义相似度模型的中文地址匹配方法

地址匹配中,由于传统相似度模型受字符重叠数影响大,在处理简写、缩写地址要素单元时,错误匹配问题突出;深度学习方法需要大量样本支撑,但庞大的数据量和多样的形式,导致生成样本的成本过高。为解决上述问题,本文首先应用基于条件随机场和双向长短时记忆神经网络的模型,对地址进行分词;然后通过建立一种伪语义相似度,对地址要素进行分级匹配。通过对公安业务中地址数据进行测试,在对缩写、简写等不规范地址描述方面,本文模型能较理想地完成任务,各参考指标均高于0.9。     

空天地一体化智慧农业服务与管理平台

空天地一体化智慧农业服务与管理平台融合北斗定位技术和高分遥感技术,通过实时接收北斗定位数据和定时接收卫星遥感数据,实现农作物的长势监测、病虫害和气象灾害监测、农业旱情监测和灌溉用水管理等应用,从而为农作物估产、病虫害预防、水肥药的变量喷施提供决策。     

谱模拟方法在高分辨率地震资料处理中的应用

为了克服地震子波的时变影响,避免复杂的时窗大小选取问题,使得谱模拟方法能更好地适应地震信号是平稳信号的假设条件,将改进S变换与谱模拟方法相结合,形成时频域谱模拟方法。在二维时频谱中进行谱模拟处理,该方法能够克服地震子波随地层深度变化的限制。理论模型及实际资料处理效果显示,时频域谱模拟方法能够有效提高地震分辨率,实现地震资料的高分辨处理。     

Prospect of Lithium Resources Supply and Demand

正1 Introduction The conflict between resources and the environment has been increasingly outstanding as the population,economy and society around the globe have developed rapidly since the 21st century.An important challenge that the human