Monitoring Land-Use Change in Nakuru (Kenya) Using Multi-Sensor Satellite Data

Recently land-use change has been the main concern for worldwide environment change and is being used by city and regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergone rapid urban growth in last decade. This paper focused on urban growth using multi-sensor satellite imageries and explored the potential benefits of combining data from optical sensors (Landsat, Worldview-2) with Radar sensor data from Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping. Landsat has sufficient spectral bands allowing for better delineation of urban green and impervious surface, Worldview-2 has a higher spatial resolution and facilitates urban growth mapping while PALSAR has higher temporal resolution compared to other operational sensors and has the capability of penetrating clouds irrespective of weather conditions and time of day, a condition prevalent in Nakuru, because it lies in a tropical area. Several classical and modern classifiers namely maximum likelihood (ML) and support vector machine (SVM) were applied for image classification and their performance assessed. The land-use data of the years 1986, 2000 and 2010 were compiled and analyzed using post classification comparison (PCC). The value of combining multi-temporal Landsat imagery and PALSAR was explored and achieved in this research. Our research illustrated that SVM algorithm yielded better results compared to ML. The integration of Landsat and ALOS PALSAR gave good results compared to when ALOS PAL- SAR was classified alone. 19.70 km2 of land changed to urban land-use from non-urban land-use between the years 2000 to 2010 indicating rapid urban growth has taken place. Land-use information is useful for the comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social Eq- uity, economic efficiency and environmental sustainability.

Predicting Nitrogen Status of Rice Using Multispectral Data at Canopy Scale

Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.

Research on Track Fastener Service Status Detection Based on Improved Yolov4 Model

As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.

Assessing the benthic quality status of the Bohai Bay(China)with proposed modifications of M-AMBI

Multivariate AZTI＇s Marine Biotic Index （M-AMBI） was designed to indicate the ecological status of European coastal areas. Based upon samples collected from 2009 to 2012 in the Bohai Bay, we have tested the response of variations of M-AMBI, using biomass （M-BAMBI） in the calculations, with different transformations of the raw data. The results showed that the ecological quality of most areas in the study indicated by M-AMBI was from moderate to bad status with the worse status in the coastal areas, especially around the estuaries, harbors and ouffalls, and better status in the offshore areas except the area close to oil platforms or disposal sites. Despite large variations in nature of the input data, all variations of M-AMBI gave similar spatial and temporal distribution patterns of the ecological status within the bay, and showed high correlation between them. The agreement of new ecological status obtained from all M-AMBI variations, which were calculated according to linear regression, was almost perfect. The benthic quality, assessed using different input data, could be related to human pressures in the bay, such as water discharges, land reclamation, dredged sediment and drilling cuts disposal sites. It seems that M-BAMBI were more effective than M-NABMI （M-AMBI calculated using abundance data） in indicating human pressures of the Bay. Finally, indices calculated with more severe transformations, such as presence/absence data, could not indicate the higher density of human pressures in the coastal areas of the north part of our study area, but those calculated using mild transformation （i.e., square root） did.

Foundation Treatment in Urban Underground Engineering Using Big Data Analysis for Smart City Applications

A core element of the sustainable approach to global living quality improvement can now become the intensive and organized usage of underground space.There is a growing interest in underground building and growth worldwide.The reduced consumption of electricity,effective preservation of green land,sustainable wastewater and sewage treatment,efficient reverse degradation of the urban environment,and reliable critical infrastructure management can improve the quality of life.At the same time,technological innovations such as artificial intelligence(AI),cloud computing(CC),the internet of things(IoT),and big data analytics(BDA)play a significant role in improved quality of life.Hence,this study aims to integrate the technological innovations in urban underground engineering to ensure a high quality of life.Thus,this study uses big data analytics to carry out the status quo of foundation treatment and proposes a conceptual framework named the BDA with IoT on urban underground engineering(BI0T-UUE).This framework connects hidden features with various high-level sensing sources and practical predictive model characterization to lower building costs,productive infrastructure management,preparedness for disasters,and modern community smart services.The IoT integration gives an optimum opportunity to work towards the functionality of‘‘digital doubles’’of secret infrastructure,both economical and scalable,with the increasing sophistication and tooling of the underworld.The simulation analysis ensures the highest efficiency and cost-effectiveness of the underground engineering with a value of 96.54%and 97.46%.

Passenger Flow Status Evaluation in Subway Station Based on Probabilistic Neural Network

This paper select the escalator with large flow in the station as the object, analysing the correlation of the AFC data of the in and out gates and the passenger flow parameters by passenger flow density and the passing time acquired and calculated in the waiting area of the prediction escalator to select the gates related to the predicted the escalator. NARX neural network is used to predict the model of the passenger flow parameters of the escalator waiting area based on the related gates＇ AFC data, then a probabilistic neural network model was established by using the AFC data and predicted passenger flow parameters as input and the passenger flow status in the escalator waiting area of subway station as output.The result shows the predicting model can predict the passenger flow status of the escalator waiting area better by the AFC data in the subway station. Research result can provide decision basis for the operation management of the subway station.

Numerical Simulations of Impacts of Urbanization on Heavy Rainfall in Beijing Using Different Land-Use Data

A summer strong convective precipitation event on 10 July 2004 over Beijing is numerically simulated in this paper, and the impact of urban heat island （UHI） on summer convective rain is investigated. The analysis reveals that a mesoscaie convective cloud cluster system leads to this heavy rainfall event, suggesting the supply of moisture by the large scale circulation before the initiation of precipitation, a generally weaker UHI of 2-3℃ existed in the urban area. Much like a sea breeze, the anomalously warm urban air created relatively low pressure, inducing the inflow of cooler rural air towards the urban center, which is favorable to the ascending motion and the formation of convective precipitation over the urban area. In addition, the numerical simulation of the strong convective precipitation event suggests that the simulated result of precipitation using the 2002 LANDSAT-7 land-use data with 30-m resolution is much better than that using the 1992-1993 USGS land-use data with 1-km resolution, whether in the magnitude of rainfall or in the location of precipitation. The simulation confirms to some extent that the UHI has a significant role in causing extreme rainfall event.

Impacts of Land-Use Data on the Simulation of Surface Air Temperature in Northwest China

This study examines the impacts of land-use data on the simulation of surface air temperature in Northwest China by the Weather Research and Forecasting(WRF) model. International Geosphere–Biosphere Program(IGBP) landuse data with 500-m spatial resolution are generated from Moderate Resolution Imaging Spectroradiometer(MODIS)satellite products. These data are used to replace the default U.S. Geological Survey(USGS) land-use data in the WRF model. Based on the data recorded by national basic meteorological observing stations in Northwest China, results are compared and evaluated. It is found that replacing the default USGS land-use data in the WRF model with the IGBP data improves the ability of the model to simulate surface air temperature in Northwest China in July and December 2015. Errors in the simulated daytime surface air temperature are reduced, while the results vary between seasons. There is some variation in the degree and range of impacts of land-use data on surface air temperature among seasons. Using the IGBP data, the simulated daytime surface air temperature in July 2015 improves at a relatively small number of stations, but to a relatively large degree; whereas the simulation of daytime surface air temperature in December 2015 improves at almost all stations, but only to a relatively small degree(within 1°C). Mitigation of daytime surface air temperature overestimation in July 2015 is influenced mainly by the change in ground heat flux. The modification of underestimated temperature comes mainly from the improvement of simulated net radiation in December 2015.

Spatial patterns of urban green space and its actual utilization status in China based on big data analysis

Urban green space(UGS)is essential for sustainable urbanization and human well-being.The utilization status of UGS is closely related to the provision of ecosystem services for urban residents.Limitations on data availability,however,have led to the absence of a comprehensive approach for evaluating the actual utilization status of UGS at a large scale.Furthermore,differences in actual UGS utilization between intra-urban and peri-urban areas have not received enough attention.This study used big data analysis by combining point of interest(POI)and land use and cover change(LUCC)to quantify the spatial patterns of UGS utilization,and to evaluate the actual utilization status of UGS in 366 cities on the Chinese mainland.We also explored the differences in the actual utilization of UGS in intra-urban and peri-urban areas.The results showed that 94.01%of UGS resources in China had not been utilized.There was a clear pattern of spatial mismatch between the stock and the actual utilization of UGS,especially in the north-western region indicated by the Hu Huanyong Line.The actual utilization rate of UGS was closely related to the regional develop-ment level.There was a certain mismatch between the actual utilization and stock of intraurban green space(IUGS).The hot spots of the actual utilization rate of IUGS were in Yunnan,Guizhou,and Sichuan Provinces in southwestern China.The differ-ences in UGS actual utilization rates between IUGS and peri-urban green space(PUGS)were small in eastern China,but large in south-western and northwestern China.The actual utilization rate of IUGS in most Chinese cities was significantly larger than that of PUGS,indicating that PUGS were not well utilized.Our results provide scientific support for urban and regional planners in targeting specific areas for UGS design and development,and in optimizing future UGS planning in China.

Efficient estimation for additive hazards regression with bivariate current status data

This paper discusses efficient estimation for the additive hazards regression model when only bivariate current status data are available. Current status data occur in many fields including demographical studies and tumorigenicity experiments （Keiding, 1991; Sun, 2006） and several approaches have been proposed for the additive hazards model with univariate current status data （Linet M., 1998; Martinussen and Scheike, 2002）. For bivariate data, in addition to facing the same problems as those with univariate data, one needs to deal with the association or correlation between two related failure time variables of interest. For this, we employ the copula model and an efficient estimation procedure is developed for inference. Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations. An illustrative example is provided.

Regression Analysis of Misclassified Current Status Data with Informative Observation Times

Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity.For the situation,another issue that may occur is that the observation time may be correlated with the interested failure time,which is often referred to as informative censoring or observation times.It is well-known that in the presence of informative censoring,the analysis that ignores it could yield biased or even misleading results.In this paper,the authors consider such data and propose a frailty-based inference procedure.In particular,an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established.The numerical results show that the proposed method works well in practice and an application to a set of real data is provided.

Hydro-hypsometric analysis of tropical river basins,Southwest Coast of India using geospatial technology

The key aspect in planning and management of water resources is to analyze the runoff potential and erosion status of the river basin.For the detailed investigation of hydrological response freely available Cartosat-1(IRS-P5) data was used for the preparation of digital elevation model(DEM).The runoff potential and type of erosive process of 22 river basins originating in the global biodiversity hotspot of Western Ghats,was inferred through hypsometric analysis.Several parameters like Hypsometric integral(HI),maximum concavity(Eh),coordinates of slope inflection point(I) given by a* and h* and normalized height of hypsometric curve(h) were extracted from the hypsometric curves and used for understanding the hydrological responses.From the hypsometric curves,the landform evolution processes were inferred.Contribution of diffusive and fluvial processes in slope degradation of the river basins was understood.Basins with lesser area(<100 km^2) were found to have a positive correlation between hypsometric integral and basin area,whereas for large basins no such correlation exists.Based on the study,river basins can be prioritized for the appropriate conservation measures.

Efficient Estimation for Semiparametric Varying-Coefficient Partially Linear Regression Models with Current Status Data

This article considers a semiparametric varying-coefficient partially linear regression model with current status data. The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are conducted to examine the small-sample properties of the proposed estimates and a real dataset is used to illustrate our approach.

A New Approach for Regression Analysis of Multivariate Current Status Data with Informative Censoring

Regression analysis of interval-censored failure time data has recently attracted a great deal of attention partly due to their increasing occurrences in many fields.In this paper,we discuss a type of such data,multivariate current status data,where in addition to the complex interval data structure,one also faces dependent or informative censoring.For inference,a sieve maximum likelihood estimation procedure is developed and the proposed estimators of regression parameters are shown to be asymptotically consistent and efficient.For the implementation of the method,an EM algorithm is provided,and the results from an extensive simulation study demonstrate the validity and good performance of the proposed inference procedure.For an illustration,the proposed approach is applied to a tumorigenicity experiment.

SIEVE LEAST SQUARES ESTIMATOR FOR PARTIAL LINEAR MODELS WITH CURRENT STATUS DATA

Current status data often arise in survival analysis and reliability studies, when a continuous response is reduced to an indicator of whether the response is greater or less than an observed random threshold value. This article considers a partial linear model with current status data. A sieve least squares estimator is proposed to estimate both the regression parameters and the nonparametric function. This paper shows, under some mild condition, that the estimators are strong consistent. Moreover, the parameter estimators are normally distributed, while the nonparametric component achieves the optimal convergence rate. Simulation studies are carried out to investigate the performance of the proposed estimates. For illustration purposes, the method is applied to a real dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of cataract treatment.

A TWO-STAGE ESTIMATION ALGORITHM FOR A TYPE OF CURRENT STATUS DATA

Success-failure life tests are widely used in reliability engineering research to evaluate the storage life of products, where the observed data are the current status data, usually summarized as the form of ＂binomial life data＂. For this type of data, this paper proposes a two-stage algorithm to estimate some commonly used lifetime distributions. This Mgorithm is automatic, intuitively appealing and simple to implement. Simulation studies show that compared with some existing methods, the proposed algorithm is more stable and efficient, especially in small sample situations, and it can also be extended to deM with some complicated lifetime distributions.

An Analysis of Ambient Air Quality Conditions Over Delhi, India From 2004 to 2009

We analyzed 1-hour, 8-hour and 24-hour averaged criteria pollutants (NO2, SO2, CO, PM22.5, and PM10) during 2004 - 2009 at three observational sites i.e. Income Tax Office (ITO), Sirifort and Delhi College of Engineering (DCE) in Delhi, India. The analysis reveals increased pollutant concentrations at the urban ITO site as compared to the other two sites, suggesting the need to better locate hot spots in designing the monitoring network. There is also significant year to year variation in the design value trends of criteria pollutants at these three sites, which may be attributed to meteorological variations and local-level emission fluctuations. Correlations among criteria pollutants vary annually and spatially from site to site, indicating the heterogeneous nature of air mix. The annual ratios of CO/NOx are considerably higher than SO2/NOx confirming that vehicular source emissions are the primary contributors to air pollution in Delhi. The seasonal analysis of criteria pollutants reveals relatively higher concentrations in winter because of limited pollutant dispersion and lower concentrations during the monsoon period (rainy season). The diurnal averages of criteria pollutants reveal that vehicular emissions strongly influence temporal variations of these pollutants. Weekdays and weekend diurnal averages do not show noticeable differences.

An Improved Model for Evaluating Ecosystem Service Values Using Land Use/Cover and Vegetation Parameters

It is crucial to maintain the balance of economic development and ecosystem protection. The value of ecosystem services is an indicator to help people understand the importance of ecosystem protection. Traditional models estimate ecosystem service values only according to land use/cover data while ignoring vegetation status differences in the same land use/cover. This study uses the normalized difference vegetation index(NDVI), the leaf area index(LAI),and net primary productivity(NPP) as vegetation status data to describe the differences in the same land use/cover type. The principal component analysis(PCA) approach is used to reduce the correlations among the three types of vegetation status data. Then, the calculated vegetation status index after PCA is input into the estimation model. The case study in China shows that the improved model has two major advantages. First, it can clearly distinguish the differences in ecosystem service values even for the same land use/cover type. Second, it can clearly describe the transitional zones between different land use/cover types through continuous changes in ecosystem service values. This improved model can provide a more detailed description of the distribution characteristics of ecosystem service values in China and help policymakers balance economic development and ecosystem protection.

Urban Traffic Information Service Application Grid

Traffic information processing is very complicated because of dynamic, cooperative and distributed features. This paper describes the prototype system version 2.0 of Urban Traffic Information Service Application Grid (UTISAG), which is based on the previous version. In this version, a new architecture and more enhanced services are introduced, The. remarkable characteristic of the new system is providing dynamic information services for travelers by grid technology. Therefore, the key research includes integrating large multi-source traffic data, forecasting route status, simulating regional traffic flow parallelly, and implementing optimum dynamic travel scheme based on massive GPS data.

Multivariate normal spatial scan statistic for detecting the most severe cluster of a disease

This paper focuses on the problem of detecting the geographical cluster with the most severe status in multiple groups of population given limited medical resources.Populations are grouped based on characteristics such as age,gender,and race.In the early stages of a disease,an outbreak may only present in specific population groups.Therefore,to efficiently detect the outbreak,we are particularly interested in monitoring and evaluating such groups.We define the objective of detection as the most severe cluster(MSC).Taking into account the interactions between population groups,a multivariate normal scan statistic is proposed to simultaneously determine the location and size of a significant MSC,as well as the specific population groups in which the MSC is located.The proposed method is applied to an example of lung cancer in New York State,where the MSC with the highest mortality rate at the aggregate level is detected.Further,the detection capacity of this method is evaluated using a simulation study based on the lung cancer example.