Modeling the effects of green alternative on heat island mitigation of a meso level town,West Bengal,India

Urban heat island (UHI) is emerged as a ubiquitous phenomenon that affects the outdoor thermal comfort. Hence urban microclimatic studies using numeric simulation software to find out adaption strategies are attracting the great attention of researchers and policymaker. Number of scientists carried out their research on this topic using different tools and technique whereas ENVI-met model is the most widely used simulation tools. The present study intended to examine the implication of green infrastructure of the town on enhanced microclimatic condition and to evaluate the best suitable mitigation strategy modelling cool city with ENVI-met software (V4). Five greening condition such as existing UHI condition (C1), 100% greening of roof (C2), 100% greening of roof and walls (C3), 50% greening of roof and walls (C4) and: Plantation at suitable area with 50% greening of roof and walls (C5) have been taken into consideration for the microclimate simulation for all three selected sites. Among these five conditions, C3 for the open mid-rise and compact low-rise, C5 for the open low-rise are identified as one of the most suitable strategies which can reduce the air temperature of peak hours by 2.6 °C, 1.33 °C and 1.87 °C respectively. These models are validated by simple linear regression between simulated and existing air temperature in case of existing UHI condition (C1) and in all the cases coefficient of determination value is high such as for open mid-rise, compact low-rise and open low-rise, it is 0.92, 0.92 and 0.75 respectively. Therefore it can be concluded that the application of those strategies can improve the urban thermal environment as well as the outdoor thermal comfort of English bazaar Town and its surroundings.     

Substitution of satellite-based land surface temperature defective data using GSP method

Land surface temperature (LST) as an important environmental variable provides valuable information for earth environmental system modelling. Currently, LST is obtained through satellite thermal sensors at various spatial and temporal resolutions. Although spatially continuous satellite-based LST measurements are intended to overcome the shortcomings of sparse ground-based LST measurements, LST images often contain anomalous values due to the existence of clouds or sensor malfunctioning. The problem becomes more serious where the users deal with high spatial resolution characterized by low temporal resolution. This study examines the capability of a newly developed graph signal processing (GSP) method using two-dimensional single-date thermal data. For this purpose, four Landsat/TIRS datasets are analyzed. The data of five elliptical regions on thermal images are eliminated and then reconstructed through the GSP method and using the LST values of the enclosing rectangles containing the ellipsoids. The results indicate that the temperature variation determined by the GSP method generally conforms to the original image LST values. According to a correlation test conducted on the original image LST and those obtained through the GSP method, the values vary from 58% to 95%, which is an above-the-average rate (RMSE from 0.69 to 2.27). The statistical analysis of the original image LST in both the elliptical regions and the enclosing rectangles containing the ellipsoids indicates that an increase in the variance of LST data causes an increased error in the calculation of temperature by the GSP method, and vice versa. The results of the analysis of variance (ANOVA) and Duncan test indicated that an increase in the number of the non-zero spectral bins would result in increased RMSE values for all the dates and the regions. Moreover, the model errors were significant at the 0.05 level across all the image date and five elliptical study regions. Based on the results, the use of this method is recommended for the reconstruction of LST missing values, where dissimilarity of atmospheric conditions limits the use of other methods that depend on the time series data of various dates and a great deal of data calculation.     

红外目标与环境的视景重建技术研究

研究了图像环境生成过程中红外目标特征信息提取所面临的重要技术问题,提出了自适应背景感知与对消处理、抑制背景的自动门限选取和形状分析与目标特征识别的层次化数据处理流程.基于数学形态学理论实现了图像背景感知的应用研究算法.实验测评表明,该算法对复杂变化的图像环境具有良好的滤波性能和稳健的适应能力     

基于CDMA容量的星载多波束天线赋形分析

建立LEO-CDMA通信系统容量分析模型,给出系统容量的计算方法,从系统容量的角度出发对比分析运用遗传算法赋形得到的波束增益模型和基于二阶锥形模型的波束增益模型的系统容量。结果表明,基于二阶锥形增益模型的系统容量约为基于赋形增益模型系统容量的两倍。分析结果为多波束接收天线的系统设计及赋形方案设计提供了理论依据。     

Subsidence rate monitoring of Aghajari oil field based on Differential SAR Interferometry

Land subsidence, due to natural or anthropogenic processes, causes significant costs in both economic and structural aspects. That part of subsidence observed most is the result of human activities, which relates to underground exploitation. Since the gradual surface deformation is a consequence of hydrocarbon reservoirs extraction, the process of displacement monitoring is amongst the petroleum industry priorities. Nowadays, Differential SAR Interferometry, in which satellite images are utilized for elevation change detection and analysis – in a millimetre scale, has proved to be a more real-time and cost-effective technology in contrast to the traditional surveying method. In this study, surface displacements in Aghajari oil field, i.e. one of the most industrious Iranian hydrocarbon sites, are being examined using radar observations. As in a number of interferograms, the production wells inspection reveals that surface deformation signals develop likely due to extraction in a period of several months. In other words, different subsidence or uplift rates and deformation styles occur locally depending on the geological conditions and excavation rates in place.     

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

分布式创新中技术知识整合框架研究

基于演化、资源和知识的视角,提供了分布式创新中技术开发背景下技术知识整合的分析框架。从三个视角研究分布式创新要素,阐述了知识、知识转移和执行之间的结构关系,为企业整合知识资源,提高分布式创新能力提供借鉴和指导。     

Assessing ecohydrological factors variations and their relationships at different spatio-temporal scales in semiarid area,northwestern China

As a typical semiarid farming-pastoral ecotone sensitive to the environmennt, the Plain of West Liaohe Basin (WLBP) is currently experiencing drastic environmental changes. To identify how environmental change affect vegetation in the WLBP, we analysed spatiotemporal variation characteristics of Ecological environment factors based on monthly and annual air temperature (T), precipitation (P) and Normalized Difference Vegetation Index (NDVI) from 1982 to 2015. And the correlations between them were investigated by correlation analysis (Simple correlation, partial correlation and complex correlation) at temporal and spatial scale. The results showed that: (1) the vegetation growth of the WLBP showed ameliorated trend, with a change rate of 0.004/yr.; (2) P was more sensitive to NDVI than T; (3) and the influence of hydrothermal changes on vegetation growth was more significant than that of the change of single climate factors at time scales; (4) the effects of anthropogenic factors on vegetation change were 75.07% (1982–1993) and 98.08% (1994–2015), respectively. At the temp-special scales, P&T and land use type change (LUCC) were the main climatic and anthropogenic factors that affect vegetation changes, respectively.     

Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang,Indonesia, using multi-sensor satellite data

Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68?km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters?=?53%, CVI 7 parameters?=?93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7?km of the total 48.7?km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.     

Experiment for mapping land cover and it’s change in southeastern Sri Lanka utilizing 250 m resolution MODIS imageries

Utilizing freely available MODIS NDVI and Natural color imageries of 250 m spatial resolution produced by NASA, an experiment was made to map land-cover and its change with an emphasis on vegetation cover in southeastern Sri Lanka, which plays a vital role for control of green house gas. For the change detection purpose, 1987 land cover map made by present authors from Landsat MSS image and extensive ground truth survey data was used as the base map. The result of the experiment shows that MODIS data are useful to make a land cover map of 250 m spatial resolution for tropical areas with high cloud coverage like Sri Lanka. It was found that the forest cover decrease amounted as large as 21% in 19 years time span in southeastern Sri Lanka, the prominent forest region of the country. On the other hand homestead/vegetation and mixed vegetation/scrub dominant categories increased by 13.7% and 7.1%, respectively. These changes are considered due to a large clearance of forest areas for agriculture and building houses to accommodate increasing inhabitants.