A Reducing Saturation Method for DMSP/OLS Nighttime Light Image Combining Landsat Data

Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS)is one of the data sources,which can effectively reflect human activities of earth surfaces.During the past decade,DMSP/OLS had been extensively applied in urban extraction and extension study.In the recent year,the Vegetation Adjusted NTL Urban Index(VANUI)has been proposed and had proven to be a simple,convenient and high precision desaturation index to extract urban area.In VANUI method,negative values of imagery were directly eliminated to remove water body,which not only removed the bridge over the river and building but also extracted the aquaculture areas along the coast,thus,this method reduced the extraction accuracy.This paper proposed a new index\|RwNTLI,combining DMSP/OLS nighttime light data and the vegetation index (NDVI)and water index (MNDWI)which were constructed by Landsat data.In this study,Guangzhou was taken as experimental area.By comparing the VANUI index with the ability to identify ground objects as well as the ability to alleviate saturation regions,the result showed RwNTLI index could effectively solve the problem of VANUI as well as eliminate saturation effect of nighttime light imagery.Among them,the correlation between RwNTLI index and RCNTL is better than that of VANUI index and RCNTL.Therefore,RwNTLI index is a simple and effective index of luminous desaturation,which has more advantages than VANUI index in describing the characteristics of night lights of urban areas and will have higher application value in urban built\|up areas in the future.     

Spatial improvement of human population distribution based on multi-sensor remote-sensing data: an input for exposure assessment

A spatial mismatch of hazard data and exposure data (e.g. population) exists in risk analysis. This article provides an integrated approach for a rapid and accurate estimation of population distribution on a per-pixel basis, through the combined use of medium and coarse spatial resolution remote-sensing data, namely the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night-time imagery, enhanced vegetation index (EVI), and digital elevation model (DEM) data. The DMSP/OLS night-time light data have been widely used for the estimation of population distribution because of their free availability, global coverage, and high temporal resolution. However, given its low-radiometric resolution as well as the overglow effects, population distribution cannot be estimated accurately. In the present study, the DMSP/OLS data were combined with EVI and DEM data to develop an elevation-adjusted human settlement index (EAHSI) image. The model for population density estimation, developed based on the significant linear correlation between population and EAHSI, was implemented in Zhejiang Province in southeast China, and a spatialized population density map was generated at a resolution of 250 m?×?250 m. Compared with the results from raw human settlement index (59.69%) and single night-time lights (35.89%), the mean relative error of estimated population by EAHSI has been greatly reduced (17.74%), mainly due to the incorporation of elevation information. The accurate estimation of population density can be used as an input for exposure assessment in risk analysis on a regional scale and on a per-pixel basis.     

Static and dynamic SABR stochastic volatility models: Calibration and option pricing using GPUs

For the calibration of the parameters in static and dynamic SABR stochastic volatility models, we propose the application of the GPU technology to the Simulated Annealing global optimization algorithm and to the Monte Carlo simulation. This calibration has been performed for EURO STOXX 50 index and EUR/USD exchange rate with an asymptotic formula for volatility or Monte Carlo simulation. Moreover, in the dynamic model we propose an original more general expression for the functional parameters, specially well suited for the EUR/USD exchange rate case. Numerical results illustrate the expected behavior of both SABR models and the accuracy of the calibration. In terms of computational time, when the asymptotic formula for volatility is used the speedup with respect to CPU computation is around 200 with one GPU. Furthermore, GPU technology allows the use of Monte Carlo simulation for calibration purposes, the computational time with CPU being prohibitive.     

An improved method of night-time light saturation reduction based on EVI

Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) night-time light (NTL) data have been widely applied to studies on anthropogenic activities and their interactions with the environment. Due to limitations of the OLS sensor, DMSP NTL data suffer from a saturation problem in central urban areas, which further affects studies based on nocturnal lights. Recently, the vegetation-adjusted NTL urban index (VANUI) has been developed based on the inverse correlation of vegetation and urban surfaces. Despite its simple implementation and ability to effectively increase variations in NTL data, VANUI does not perform well in certain rapidly growing cities. In this study, we propose a new index, denoted enhanced vegetation index (EVI)-adjusted NTL index (EANTLI), that was developed by reforming the VANUI algorithm and utilizing the EVI. Comparisons with radiance-calibrated NTL (RCNTL) and the new Visible Infrared Imager Radiometer Suite (VIIRS) data for 15 cities worldwide show that EANTLI reduces saturation in urban cores and mitigates the blooming effect in suburban areas. EANTLI’s similarity to RCNTL and VIIRS is consistently higher than VANUI’s similarity to RCNTL and VIIRS in both spatial distribution and latitudinal transects. EANTLI also yields better results in the estimation of electric power consumption of 166 Chinese prefecture-level cities. In conclusion, EANTLI can effectively reduce NTL saturation in urban centres, thus presenting great potential for wide-range applications.     

Incorporating nocturnal UAV side-view images with VIIRS data for accurate population estimation: a test at the urban administrative district scale

ABSTRACT

In contrast to daytime remote sensing used for observing the Earth, night-time light remote sensing with satellites primarily assesses human activity using urban parameters such as building lights or lighted highways to help determine population density and other habitation characteristics. One limitation to conventional night-time remote sensing is that light emitted from high-rise buildings, for example, is not easily detected because of optical geometry as satellite sensors are generally pointed in only a downward direction. Furthermore, satellite sensors often receive weak optical signals because of streetlights reflected from the Earth’s surface. As a result, accurate information on night-time human activity cannot be gathered from existing satellite remote-sensing methods. To address this, a new method for night-time remote setting is presented. Specifically, an unmanned aerial vehicle (UAV) is used to capture panoramic images of night-time light and processed to reveal side-view light spot information from urban buildings. This dataset was used to predict population density alone, and with the Visible Infrared Imaging Radiometer Suite (VIIRS) data by simple multiple linear regression. The results confirm that nocturnal UAV side-view data or VIIRS data alone can be used to estimate population density, while the combination of the two significantly increases the accuracy of population density estimation compared against estimating population density using nocturnal UAV side-view data or VIIRS data alone. This outcome suggests that multi-angular night-time remote-sensing data sources increase the accuracy of urban population density estimation. One reason for this may be that the side-view night-time data and orthophoto data infer urban population density from different agent variables: building occupancy is a proxy of side-view night-time data, while density of illuminated road network is that of orthophoto data.     

A multiple dataset approach for 30-m resolution land cover mapping: a case study of continental Africa

Recent developments in global land-cover mapping have focused on spatial resolution improvement with more heterogeneous features to integrate spatial, spectral and temporal information. In this study, hundreds of features derived from four seasonal Landsat 8 OLI (Operational Land Imager) spectral bands, Moderate Resolution Imaging Spectroradiometer (MODIS) time series vegetation index (VI) data, night-time light (NTL), digital elevation models (DEM) and climatic variables were used for land cover mapping with a target 30-m resolution for the whole African continent. In total, 49,007 training samples (from 11,231 locations) and 23,803 validation samples (from 5,414 locations) interpreted from seasonal Landsat, MODIS Normalized Difference Vegetation Index (NDVI) time series and high-resolution images in Google Earth were used for classifier training (Random Forest) and map validation. Overall accuracy was 76% at 30-m spatial resolution, which is better than previous land cover mapping for the African continent. Besides, accuracies for cropland were improved dramatically by more than 10%. Our method also addressed many remaining issues for 30-m mapping (e.g. boundary effects and declines in resolution). This framework is promising for automatic and efficient global land cover mapping resulting in better visual effects and classification accuracy.     

Monte Carlo simulation program for ecosystems

A Monte Carlo computer simulation program is designed in order to describe the spatial and time evolution of a population of living individuals under preassigned environmental conditions of energy. The simulation is inspired by previous techniques developed in physics--in particular, in molecular dynamics and simulations of liquids--and it already provides some new insights regarding macroscopic deterministic models in ecology and concerning eventual control of artificial biomass production plants.     

Neural networks in stochastic mechanics

Summary  A state of art on the application of neural networks in Stochastic Mechanics is presented. The use of these Artificial Intelligence numerical devices is almost exclusively carried out in combination with Monte Carlo simulation for calculating the probability distributions of response variables, specific failure probabilities or statistical quantities. To that purpose the neural networks are trained with a few samples obtained by conventional Monte Carlo techniques and used henceforth to obtain the responses for the rest of samples. The advantage of this approach over standard Monte Carlo techniques lies in the fast computation of the output samples which is characteristic of neural networks in comparison to the lengthy calculation required by finite element solvers. The paper considers this combined method as applied to three categories of stochastic mechanics problems, namely those modelled with random variables, random fields and random processes. While the first class is suitable to the analysis of static problems under the effect of values of loads and resistances independent from time and space, the second is useful for describing the spatial variability of material properties and the third for dynamic loads producing random vibration. The applicability of some classical and special neural network types are discussed from the points of view of the type of input/output mapping, the accuracy and the numerical efficiency.     

Effect of band structure discretization on the performance of full-band Monte Carlo simulation

Full-band Monte Carlo simulation offers a very accurate simulation technique, but is often limited by its high demand on computation time. The advantage of a numerical representation of the band structure over an analytical approximation is the accurate representation of the energy bands in the high energy regime. This allows accurate treatment of hot carrier effects in semiconductors. In this work we outline an efficient full-band Monte Carlo (FBMC) simulator and investigate the accuracy of simulation results for different meshing approaches for the Brillouin zone.     

Probabilistic observation model correction using non-Gaussian belief fusion

This paper presents a framework for state estimation which tolerates uncertainty in observation model parameters by (1) incorporating this uncertainty in state observation, and (2) correcting model parameters to improve future state observations. The first objective is met by an uncertainty propagation approach, while the second is achieved by gradient-descent optimization. The novel framework allows state estimates to be represented by non-Gaussian probability distribution functions. By correcting observation model parameters, estimation performance is enhanced since the accuracy of observations is increased. Monte Carlo simulation experiments validate the efficacy of the proposed approach in comparison with conventional estimation techniques, showing that as model parameters converge to ground-truth over time, state estimation correspondingly improves when compared to a static model estimate. Because observation models cannot be known with perfect accuracy and existing approaches do not address parametric uncertainties in non-Gaussian estimation, this work has both novelty and usefulness in most state estimation contexts.     

A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole’s diameter (D), hole’s depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.     

The use of night-time lights satellite imagery as a measure of Australia's regional electricity consumption and population distribution

Satellite imagery of night-time lights provided by the US Air Force Defense Meteorological Satellite Program (DMSP), using the Operational Linescan System (OLS), has been used to estimate the spatial distribution of electricity consumption throughout Australia. For the period 1997 to 2002, there was very high correlation between state electricity consumption and night-time lights with an R ² value of 0.9346 at the state and territory spatial resolution. To increase the accuracy at which electricity consumption can be estimated at greater spatial resolution, an Overglow Removal Model (ORM) was developed to overcome the overglow effect caused by the dispersion of light into surrounding areas. The ORM makes use of the relationship between light source strength and the overglow/dispersion distance from the light source. As electricity consumption statistics at a greater spatial resolution than the state or territory level are not publically available in Australia, population statistics at the statistical local area (SLA) were used to demonstrate the increased accuracy of the ORM at returning the overglow light to its source, and, in turn, the accuracy of measuring electricity consumption. The ORM enabled an estimation of the electricity consumption of SLAs, greater than 10 km², with an R ² value of 0.8732, which is a 25.4% increase in accuracy over untreated data before applying the ORM. The increase in accuracy of the location of the origin of night-time lights can enable better georeferencing of satellite imagery of night-time lights and greater accuracy in locating population centres and centres of economic development, and assist with electricity infrastructure planning in regions of the world where statistics are not readily available. The result of the ORM is a map of Australian electricity consumption, and an estimation of the regional electricity consumption for all SLAs greater than 10 km² in size is included.     

Dimensional variation propagation analysis in straight-build mechanical assemblies using a probabilistic approach

Product quality in mechanical assemblies is determined by controlling the propagation of manufacturing variations as the structure is built. This paper focuses on straight-build assembly and uses a probabilistic approach to analyse the influence of component variation on the eccentricity of the build. Connective models are used to predict assembly variations arising from individual component variations, and a probabilistic approach is used to calculate the probability density function (pdf) for the eccentricity of the build. The probabilistic approach considers three different straight-build scenarios: (i) direct build; (ii) best build; and (iii) worst build, for two-dimensional “axi-symmetric” assemblies. The probabilistic approach is much more efficient than Monte Carlo simulation. The paper also uses numerical examples to investigate the accuracy of the probabilistic approach in comparison to Monte Carlo simulation.     

A Survey on Gradient‐Domain Rendering

Monte Carlo methods for physically‐based light transport simulation are broadly adopted in the feature film production, animation and visual effects industries. These methods, however, often result in noisy images and have slow convergence. As such, improving the convergence of Monte Carlo rendering remains an important open problem. Gradient‐domain light transport is a recent family of techniques that can accelerate Monte Carlo rendering by up to an order of magnitude, leveraging a gradient‐based estimation and a reformulation of the rendering problem as an image reconstruction. This state of the art report comprehensively frames the fundamentals of gradient‐domain rendering, as well as the pragmatic details behind practical gradient‐domain uniand bidirectional path tracing and photon density estimation algorithms. Moreover, we discuss the various image reconstruction schemes that are crucial to accurate and stable gradient‐domain rendering. Finally, we benchmark various gradient‐domain techniques against the state‐of‐the‐art in denoising methods before discussing open problems.     

Improved GDP spatialization approach by combining land-use data and night-time light data: a case study in China’s continental coastal area

Gross domestic product (GDP) reflects a nation or region’s economic growth as a whole, and is the sum of product in the primary, secondary, and tertiary sectors of the economy in the area. However, statistical GDP data is problematic in integrated application with geographical data. The GDP spatialization data, which shows the GDP in grid cells and often is obtained by operating a spatialization model, is more useful than its officially published statistical data recorded by administrative units in both spatial representation and application. Thus, there is a need to improve the GDP spatialization models, and to present these models in a way as clear and transparent as possible. In this article, by taking China’s continental coastal area as a case study area, we combined economic census data, land-use data, and night-time light data together, and developed a technique that we call the ‘dynamic regionalization’ method to improve the GDP spatialization products. We then created GDP spatialization models for three sectors of the economy (i.e. the primary, the secondary, and the tertiary sector) in 2000, 2005, and 2010, respectively. We find the following. (1) Because the ‘overglow’ effect of night-time light data has a bad influence on spatialization models, we used land-use data to distinguish the distribution plots of the tertiary sector on night-time light images. Compared with setting a threshold merely, land-use data can more effectively remove the ‘overglow’ effect. (2) Owing to the prominent spatial heterogeneity of GDP distribution in China’s continental coastal area, building one spatialization model for the whole area would probably produce the estimated products with poor accuracy, so the ‘dynamic regionalization’ method was adopted to dynamically divide the whole study area into several subregions, and build separate spatialization models for each subregion. The accuracy assessment showed that the new method improved the accuracy of GDP spatialization data, especially in the area with high spatial heterogeneity.     

Bayesian estimation and variable selection for single index models

We develop a fully Bayesian method to analyze the single index models, including variable selection, the index vector estimation and the link function fitting with free-knot splines. The proposed method is implemented by means of the reversible jump Markov chain Monte Carlo technique. We treat the marginal posterior of all the unknown quantities except the spline coefficients and error variance as the target distribution to reduce the dimension of the parameters and to obtain a rapid algorithm. We design a new random walk Metropolis sampler to sample from the conditional posterior distribution of the index vector. The proposed method is verified by simulation studies, and is applied to analyze two real data sets.     

RSSI辅助的蒙特卡罗盒定位算法

针对以蒙特卡罗为基础的几种无线传感器网络定位算法普遍存在定位精度和采样效率低的问题,文中提出了一种RSSI辅助的蒙特卡罗盒定位算法（RAMCB）。通过实验构造出符合实际环境的RSSI和距离区间的映射关系数据库;在定位阶段,未知节点利用来自一跳和两跳锚节点的RSSI值查询数据库,得到与一跳和两跳锚节点的距离区间,利用距离区间建立更为精确的采样箱,以提高采样效率;未知节点根据样本到一跳、两跳锚节点的估计距离和实际距离的差值来动态赋予样本的权值。仿真结果表明：RAMCB算法能有效提高定位精度和采样效率。     

Computer simulation of the skin reflectance spectra

The reflectance spectra of the human skin in visible and near-infrared (NIR) spectral region have been calculated using the Monte Carlo technique, and the specular and internal reflection on the medium surface is taken into account. Skin is represented as a complex inhomogeneous multi-layered highly scattering and absorbing medium. The model takes into account variations in spatial distribution of blood, index of blood oxygen saturation, volume fraction of water and chromophores content. The simulation of the skin tissues optical properties and skin reflectance spectra are discussed. Comparison of the results of simulation and in vivo experimental results are given.     

基于蒙特卡罗仿真的湖库水质预测及富营养化风险评估方法

已有的水质预测研究通常是单值预测,并以此为依据分析富营养化状态,具有一定的偶然性和不确定性。结合水质动力学模型,提出了一种基于蒙特卡罗仿真的湖库水质预测及富营养化风险评估方法。在已知水质动力学模型水质指标和模型参数的先验分布基础上,利用蒙特卡罗仿真预测水质指标的演化过程,获得未来时刻水质指标取值的概率分布,实现水质预测。进一步,构造综合营养状态指数,结合水质指标预测结果,计算综合营养状态指数的概率分布和处于不同营养程度的概率,实现富营养化风险评估。仿真结果表明,该方法能够有效实现水质预测和富营养化分析,且考虑更加全面、准确,克服了单值预测结果带来的偶然性。