高分1号冬小麦解译面积核算方法研究

[目的]以高分1号(GF-1)融合2m卫星遥感影像为基础数据源,结合土地利用现状数据、高分多源遥感影像和地面样方等数据,对冬小麦分类提取中存在的面积误差问题进行研究和分析。[方法]文章以河南省永城市为研究区,在冬小麦提取结果聚类处理基础上,基于线性地物缓冲区数据,采用GIS空间运算实现线性地物面积扣除,接着分析了样方数据和土地利用现状数据再扣除零星地物面积比例上的差异,并采用样方零星地物平均扣除系数对全市各乡镇耕地与非耕地中冬小麦提取面积进行了相关统计和误差分析。[结果]永城市冬小麦最终解译面积11. 29万hm~2,其中线性地物和零星地物扣除面积分别为6 613. 08hm~2和3 875. 22hm~2,占研究区冬小麦解译面积的5. 86%和3. 32%,与统计上报数据相比,其处理前后误差由14. 12%降低至4. 41%,有效地提高冬小麦提取面积精度。[结论]误差来源分析与修正对冬小麦解译面积核算精度具有重要影响,该研究为县级区域尺度下冬小麦面积提取核算提供了思路和借鉴。     

Some Potential Linkages for Input-Output Analysis with Flow-of-Funds

Three major, interrelated accounting statements, at the frontiers of quantitative economic analysis, are three interrelated systems, namely: (1) National income and product accounts (NIPA), (2) The input-output tableaux, (IO), and (3) flow-of- funds accounts (FF). The third-mentioned system is somewhat less available and used in only limited areas of macroeconomic analysis. This paper is mainly concerned with use of FF accounting systems. This system shows where financial resources originate, and where they go in support of real capital formation. In this respect, interest rates and other market-based financial rates are of key importance. While much macroeconomic analysis is based on the rates that fit the yield curve, showing the interest rate structure over various maturities of debt associated with a given degree of risk. In contrast, the FF accounts throw light on the whole spectrum of interest rates, across maturities and debt qualities. For example, in analysis of the real estate market and funding of capital formation there, it is important to have a full understanding of the course of mortgage rates of different maturities and qualities. In short, it is necessary to develop a full appreciation of supply and demand forces in the mortgage market, which often is not obviously related to movements of the operative rate for monetary policy, such as very short-term inter bank rates or call money rates. This paper attempts to provide material from the flow-of-funds accounts that would make it possible to analyze the movement of relevant mortgage rates or whatever other rates are needed to understand the financing of capital formation in real estate.     

光学遥感观测技术在IDI保险行业的应用

IDI保险是工程质量类保险的一种,在国内主要应用于住宅项目。IDI保险需要在工程建设的全过程中进行质量管控和风险评估。常规水准检测、GPS等技术难以满足IDI保险对风险建筑进行大范围、快速、长期监测的需要。光学遥感观测技术是一种可以实现大面积地物监测的遥感观测技术,该技术可以为IDI保险的监测工作提供数据信息支持。论文整理了近几年关于光学遥感观测技术应用于建筑高度监测的文献,对分类法、边缘检测法、阈值法进行介绍和总结,说明光学遥感观测技术在IDI保险行业中有较高的应用价值和广阔的应用前景。     

Spread spectrum of knowledge network based on information integration environment

The concept and the characteristic of spread spectrum technology of knowledge network based on information integration environment are given at first. And the basic principle of the wireless network of knowledge and information is recommended. Then a project alternatives using the spread spectrum technology and wireless network is given to build a across-the abroad knowledge network system.     

Forecasting accuracy evaluation of tourist arrivals

This paper evaluates the use of several parametric and nonparametric forecasting techniques for predicting tourism demand in selected European countries. We find that no single model can provide the best forecasts for any of the countries in the short-, medium- and long-run. The results, which are tested for statistical significance, enable forecasters to choose the most suitable model (from those evaluated here) based on the country and horizon for forecasting tourism demand. Should a single model be of interest, then, across all selected countries and horizons the Recurrent Singular Spectrum Analysis model is found to be the most efficient based on lowest overall forecasting error. Neural Networks and ARFIMA are found to be the worst performing models.     

城市三维形态与热环境的相关关系研究——以大连市金普新区为例

由城市各种实体空间结构所构成的三维形态是城市形态的重要组成部分。而城市的空间结构与城市空间环境的热场分布也有着密不可分的关系。本文运用地理信息系统与遥感技术,获取三维形态指标与地表温度,对各因子进行回归分析,建立城市空间形态热场分布研究的回归模型。分析了金普新区空间实体的占空度、错落度、建筑密度等空间形态对城市亮度温度的影响,发现占空度与城市亮温成正相关并且相关性较高,绿化率与亮温成负相关,而建筑密度与错落度对城市热场的影响较小。     

Implications of land use transitions on soil nitrogen in dynamic landscapes in Tanzania

Land use transitions are rated among the leading sources of greenhouse gas emissions in the tropics. They significantly challenge the functioning of ecosystems and affect multi-temporal stability of greenhouse gases such as N₂O and soil properties. Studies on dynamics in nitrogen balances are essential in understanding greenhouse gas emissions such as N₂O and to manage their impacts on productivity. In this study, multi-temporal Landsat images (1975, 1995 and 2012) were classified to determine land use transitions and potential drivers. The classified images were categorized into degraded and non-degraded lands and eighty sampling plots generated within the entire study area. Soil samples were then collected at 0–15, 15–30 and 30–60 cm depths on each plot and soil nitrogen determined. A regression analysis was developed to determine the influence of forest and grassland degradation on soil nitrogen. Results indicated a significant change in major land use and land cover types. Specifically, there was a decrease in areas covered by forests, woodland and grassland, however, area covered by less dense forest increased. Results also indicated variability in mean nitrogen content between degraded and non-degraded areas and depths. Furthermore, levels of degradation influence nitrogen content up to a soil depth of 30 cm. The present study is relevant in the detailed assessment of the extent of damage and threats posed to biodiversity hotspots in sub-Saharan Africa. These results are transferable to other parts of the world characterized by dynamic ecological transformation.     

A model of West African millet prices in rural markets

In this article we specify a model of millet prices in the three West African countries of Burkina Faso, Mali, and Niger. Using data obtained from USAID’s Famine Early Warning Systems Network (FEWS NET) we present a unique regional millet price forecasting model that takes advantage of the panel nature of our data and accounts for the distance of rural markets from capital cities. Another novel aspect of our analysis is our use of the Normalized Difference Vegetation Index (NDVI) to detect and control for variation in conditions for productivity. We find that including NDVI information significantly improves price forecasts.     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Quantifying and understanding land cover changes by large and small oil palm expansion regimes in the Peruvian Amazon

With rapid increases in global food demand and production, oil palm expansion constitutes a major emerging challenge for forest conservation in Amazonia and other tropical forest regions. This threat is evident in the Peruvian Amazon, where local and national incentives for oil palm cultivation along with growing large-scale investments translate into accelerated oil palm expansion. Environmental sustainability of oil palm cultivation in the Peruvian Amazon is contingent on policy incentives for expansion onto already-cleared lands instead of biodiverse, high carbon primary rainforests. Previous research indicates that while industrial plantations use less land area than local smallholders, companies have a higher tendency to expand into primary rainforests. However, the motivations behind these differing expansion scenarios remain unclear. In this study we combine data from optical and radar satellite sensors with training information, field discussions, and review of public documents to examine the policy incentives and spatial patterns associated with oil palm expansion by smallholders and industries in one of Peru’s most rapidly changing Amazonian landscapes: the Ucayali region of the city of Pucallpa. Based on our satellite-based land cover change analysis, we found that between 2010 and 2016, smallholders utilized 21,070 ha more land area for oil palm than industries but industrial expansion occurred predominantly in old growth forests (70%) in contrast to degraded lands for smallholders (56%). Our analysis of national policies related to oil palm expansion reveal policy loopholes associated with Peru’s “best land use” classification system that allow for standing forests to undergo large-scale agricultural development with little government oversight. We conclude that both sectors will need careful, real-time monitoring and government engagement to reduce old-growth forest loss and develop successful strategies for mitigating future environmental impacts of oil palm expansion.