Surface description for urban climate studies: A gis based methodology

In order to understand the nature of the urban climate, predict the effects of urbanization, or attempt to ameliorate some of the negative hydroclimatic effects of urbanization, it is necessary to have a good understanding of the role and significance of the urban surface. This paper presents a methodology which uses GIS to represent the characteristics and morphology of the urban surface, which can be used to describe a site objectively, model fluxes, or ensure spatial consistency between measured and modelled data, all of which can vary through time. The methodology is illustrated with respect to Chicago, Illinois. Surface data collected at three spatial scales were used to construct a georeferenced database which was linked to an objective, dynamic accessing system. Spatial variability of surface cover, derived hydroclimatic attributes, and modelled fluxes associated with changes in the urban environment are used to illustrate potential applications of the approach.     

A global inventory of coral reef stressors based on satellite observed nighttime lights

In this article, we present a satellite-based approach to gather information about the threat to coral reefs worldwide. Three chosen reef stressors – development, gas flaring and heavily lit fishing boat activity – are analysed using nighttime lights data derived from the Defense Meteorological Satellite Program (DMSP) produced at the National Oceanic & Atmospheric Administration, National Geophysical Data Center (NOAA/NGDC). Nighttime lights represent a direct threat to coral reef ecosystems and are an excellent proxy measure for associated human-caused stressors. A lights proximity index (LPI) is calculated, measuring the distance of coral reef sites to each of the stressors and incorporating the stressor's intensity. Colourized maps visualize the results on a global scale. Area rankings clarify the effects of artificial night lighting on coral reefs on a regional scale. The results should be very useful for reef managers and for state administrations to implement coral reef conservation projects and for the scientific world to conduct further research.     

Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index

In the present study, prediction of agricultural drought has been addressed through prediction of agricultural yield using a model based on NDVI-SPI. It has been observed that the meteorological drought index SPI with different timescale is correlated with NDVI at different lag. Also NDVI of current fortnight is correlated with NDVI of previous lags. Based on the correlation coefficients, the Multiple Regression Model was developed to predict NDVI. The NDVI of current fortnight was found highly correlated with SPI of previous fortnight in semi-arid and transitional zones. The correlation between NDVI and crop yield was observed highest in first fortnight of August. The RMSE of predicted yield in drought year was found to be about 17.07 kg/ha which was about 6.02 per cent of average yield. In normal year, it was 24 kg/Ha denoting about 2.1 per cent of average yield.     

North Carolina’s forest disturbance and timber production assessed using time series Landsat observations

Wood products provide a relatively long-term carbon storage mechanism. Due to lack of consistent datasets on these products, however, it is difficult to determine their carbon contents. The main purpose of this study was to quantify forest disturbance and timber product output (TPO) using time series Landsat observations for North Carolina. The results revealed that North Carolina had an average forest disturbance rate of 178,000 ha per year from 1985 to 2010. The derived disturbance products were found to be highly correlated with TPO survey data, explaining up to 87% of the total variance of county level industrial roundwood production. State level TPO estimates derived using the Landsat-based disturbance products tracked those derived from ground-based survey data closely. The TPO modeling approach developed in this study complements the ground-based TPO surveys conducted by the US Forest Service. It allows derivation of TPO estimates for the years that did not have TPO survey data, and may be applicable in other regions or countries where at least some ground-based survey data on timber production are available for model development and dense time series Landsat observations exist for developing annual forest disturbance products.     

Cartographically Wordy but not Necessarily Worthy

Abstract

A novel method called multidirectional visibility index (MVI) has been developed and verified. The MVI improves standard cartographic analytical shading with a number of enhancements to topographic detail and prominent structures, i.e. the portrayal of flat areas in lighter tones, the accentuation of morphologic edges, and the multiscale visualisation of morphologic terrain features. The procedure requires a digital elevation model (DEM) and involves the following steps: visibility mask computation; the respective multidirectional altering of the azimuth and elevation angle; the generation of continuous grid MVIs that indicate upper/lower views, quasi-slope, and relative relief; and an appropriate visualisation of the relevant MVI as a standalone technique or in combination with standard hill-shaded relief. The modelling parameters are robust and therefore highly adaptive to different landforms.     

智慧城市时空信息云平台评价指标体系研究

为明确评估在开展智慧城市时空信息云平台建设过程中城市相关智能化能力的保障水平及建设带来的实际成效,以及为开展智慧城市建设工作提供依据,本文依据《国家智慧城市评价指标体系》及其对部门评价指标建立的要求,研究并提出了智慧城市时空信息云平台建设评价指标体系的总体框架和评价要素,在试点城市进行了评分试验;并根据试验结果进行了指标的修订和完善,以引导和推动城市有目的、有计划、有步骤地开展智慧城市时空信息云平台建设。     

基于动态化简的多源数据相似性评估

随着空间数据获取手段和途径的丰富,多源数据的差异性对数据更新的影响一直备受关注。本文提出一种基于动态化简的多源空间数据相似性评估方法。首先,将多个来源数据分别与参考数据用传统方法进行匹配;其次,利用加入动态化简的匹配方法对多源数据重新匹配;最后,分析上述两次匹配结果的差异,从数据匹配的角度出发,通过整体一致性和数据详细程度评估对数据匹配质量进行对比,根据获取的评估结果实现更新数据的选择。通过实验和分析,验证了本文方法的科学性和可操作性。     

面向军事指挥人员空间认知规律的虚拟地理环境设计研究

虚拟地理环境提供了空间认知的新工具。虚拟地理环境的设计是一种创造性的思维过程,离不开人脑的参与。可以说,一个虚拟地理环境系统就是一个认知模型,不同的目的、不同的专题、不同的视角得到不同的模型。对于虚拟地理环境,人们更多的是关注技术,而忽略设计。把一般原理同具体特点结合起来,才能设计出既符合一般规则,又具有不同个性的虚拟地理环境系统。军事指挥人员是一个特殊的群体,本文分析了该群体的特点和地理空间认知过程,提出了面向中高级军事指挥人员的虚拟地理环境设计思路。     

Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.