This PDF file contains the front matter associated with SPIE Proceedings Volume 10431 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Retrieval of sensible heat flux requires the input of, among others, temperature gradient and wind speed. The lower level of temperature measurement is commonly replaced by remotely-sensed radiative surface temperature. Studies that utilize simultaneous satellite and in-situ retrievals of sensible heat flux are scarce. In this paper, we present preliminary results of the analyses of the impact of urban morphology and land cover on the sensible heat flux. It was calculated by two approaches, the first was based on satellite observations of radiative surface temperature and the second utilized in-situ measurements of road kinetic temperature. Except for the surface temperature, for both approaches we used the same input parameters. Road kinetic temperature and wind speed were measured by automatic road weather network. Nine stations were located across the city of Warsaw outside the city centre in low-rise urban structure. A time series of MODIS thermal data for the period 2005-2014 was utilized.

The purpose of the article is evaluating spatial patterns of social vulnerability to heat in Greater Atlanta in 2015. The social vulnerability to heat is an index of socioeconomic status, household composition, land surface temperature and normalized differential vegetation index (NDVI). Land surface temperature and NDVI were derived from the red, NIR and thermal infrared (TIR) of a Landsat OLI/TIRS images collected on September 14, 2015. The research focus is on the variation of heat vulnerability in Greater Atlanta. The study found that heat vulnerability is highly clustered spatially, resulting in “hot spots” and “cool spots”. The results show significant health disparities. The hotspots of social vulnerability to heat occurred in neighborhoods with lower socioeconomic status as measured by low education, low income and more poverty, greater proportion of elderly people and young children. The findings of this study are important for identifying clusters of heat vulnerability and the relationships with social factors. These significant results provide a basis for heat intervention services.

Cities can be characterized with the roughest aerodynamic boundaries, which results in the enhanced turbulent motion and increased drag effect. This leads to reduced wind speeds and directly increases negative effects of living within urban areas. Urban Heat Island, decreased air quality or densely built-up residential/industrial areas occur in many cities, both in temperate and tropical regions, and are included in these negative effects. This case study investigates Warsaw, the capital of Poland, representing a dense, urban environment, located in the temperate zone. It suffers from immense air pollution levels, as well as Urban Heat Island, and the local government is seeking ways to resolve these issues. Among many mitigation techniques, air restoration and exchange system were suggested as appropriate measures, as they address many of the aforementioned issues. The essential elements of such system are ventilation corridors. This paper describes mapping these corridors utilizing the morphometric methods of urban roughness aided by remote sensing data. We focus especially on the terrain topology and texture of single elements, including high vegetation canopy layer. This study considers DSM and different porosity of obstacles, deriving a new outlook at the morphometric methods as a way to improve them. The mapped areas of low roughness characteristics might be appointed as ventilation corridors and play a crucial role in air restoration and exchange system. They may also be included in further planning processes by the local government as preservation areas.

Present study shows the seasonal variation of the Aerosol Optical Depth (AOD) and aerosols characteristics in an urban and rural environment over Delhi-NCR. Aerosol sampling was carried out using a Mini-Volume sampler at an urban and rural location in Delhi-NCR. A relatively higher PM_2.5 (particulate matter of size < 2.5 μm) concentrations were observed at the urban sampling site than the rural one in the summer as well as winter season. PM_2.5 samples were further analyzed by Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX) in order to understand the morphology and elemental composition of the PM_2.5 aerosols. Summer SEM results showed the dominance of fluffy agglomerate (soot) in urban area whereas the rural area was relatively clean. The winter season SEM results showed the presence of aggregates of smaller particles at urban site whereas flaky, round and irregular shaped particles were observed at the rural site. EDX analysis showed the presence of elements such as C, Cu, Zn, Ga and Fe (representative elements) in varying concentrations at both the urban and rural sampling locations. NASA’s Aqua satellite MODIS sensor AOD data for summer and winter seasons have been used to study the spatial distributions of aerosols over the study region. AOD was found to be relatively higher in urban area as compared to the rural area in both the summer and winter seasons indicating the contribution of high amount of anthropogenic aerosols in the urban atmosphere.

Extraction of collapsed buildings from a pair of Lidar data taken before and after the 2016 Kumamoto, Japan, earthquake was conducted. Lidar surveys were carried out for the affected areas along the causative faults by Asia Air Survey Co., Ltd. The density of the collected Lidar data was 1.5 - 2 points/m² for the first flight on April 15, 2016 and 3 - 4 points/m² for the second flight on April 23, 2016. The spatial correlation coefficient of the two Lidar data was calculated using a 101 x 101 pixels window (50 m x 50 m), and the horizontal shift of the April-23 digital surface model (DSM) with the maximum correlation coefficient was considered as the crustal movement by the April-16 main-shock. The horizontal component of the calculated coseismic displacement was applied to the post-event DSM to cancel it, and then the vertical displacement between the two DSMs was calculated. The both horizontal and vertical coseismic displacements were removed to extract collapsed buildings. Then building-footprints were employed to assess the changes of the DSMs within them. The average of difference between the pre- and post-event DSMs within a building footprint was selected as a parameter to evaluate whether a building is collapsed or not. The extracted height difference was compared with the spatial coherence value calculated from pre- and post-event ALOS-2 PALSAR-2 data and the result of field damage surveys. Based on this comparison, the collapsed buildings could be extracted well by setting a proper threshold value for the average height difference.

The present work is devoted to analyze the potentiality of satellite-based techniques for structural monitoring of bridges. Specifically, the well-known case study of the cable stayed bridge across the river Garigliano and Ausente stream is presented. The available “in situ” data have been compared and integrated with satellite-based measurements (ERS, ENVISAT satellites) for the common monitoring period (1993-2004); thus, the temporal observation window has been extended until 2010 (ENVISAT satellite). DInSAR represents a consolidated tool for deformation monitoring and its application on man-made structures and infrastructures can make easier the detection of potential problems with a consequent improvement of risks management.

The separation between the countryside and the city, from rural and urban areas, has been one of the central themes of the literature on urban and territorial studies. The seminal work of Kingsley Davis [10] in the 1950s introduced a wide and fruitful debate which, however, has not yet concluded in a rigorous definition that allows for comparative studies at the national and subnational levels of a scientific nature. In particular, the United Nations (UN) definition of urban and rural population is overly linked to political and administrative factors that make it difficult to use data adequately to understand the human settlement structure of different countries. The present paper seeks to define a more rigorous methodology for the identification of rural and urban areas. For this purpose it uses the night lights supplied by the SNPP satellite, and more specifically by the VIIRS sensor for the determination of the urbanization gradient, and by means of the same construct a more realistic indicator than the statistics provided by the UN. The arrival of electrification to nearly every corner of the planet is certainly the first and most meaningful indicator of artificialization of land. In this sense, this paper proposes a new methodology designed to identify highly impacted (urbanized) landscapes worldwide based on the analysis of satellite imagery of night-time lights. The application of this methodology on a global scale identifies the land highly impacted by light, the urbanization process, and allows an index to be drawn up of Land Impacted by Light per capita (LILpc) as an indicator of the level of urbanization. The methodology used in this paper can be summarized in the following steps: a) a logistic regression between US Urban Areas (UA), as a dependent variable, and night-time light intensity, as an explanatory variable, allows us to establish a nightlight intensity level for the determination of Areas Highly Impacted by Light (AHIL); b) the delimitation of the centers and peripheries is made by setting a threshold of night-time light intensity that allows the inclusion of most of the centers and sub-centers; c) once identified urbanized areas, or AHIL, it is necessary to delimit the rural areas, or Areas Little Impacted by Light (ALIL), which are characterized by low intensity night light; d) finally, rurban landscapes are those with nightlight intensities between ALIL and AHIL. The developed methodology allows comparing the degree of urbanization of the different countries and regions, surpassing the dual approach that has traditionally been used. This paper enables us to identify the different typologies of urbanized areas (villages, cities and metropolitan areas), as well as “rural”, “rurban”, “periurban” and “central” landscapes. The study identifies 186,134 illuminated contours (urbanized areas). 404 of these contours have more than 1,000,000 inhabitants and can be considered real “metropolitan areas”; on the other hand there are 161,821 contours with less than 5,000 inhabitants, which we identified as “villages”. Finally, the paper shows that 40.26% live in rural areas, 15.53% in rurban spaces, 26.04% in suburban areas and only 18.16% in central areas.

Night-lights obtained by the DMSP/OLS sensor offer a unique opportunity to measure urban expansion in the past two decades. We apply a method to project the existing night-lights time series in the future in order to forecast urban expansion. The rapidly expanding Moscow city in the Russian Federation is selected as the case study. Night-lights are projected up to the year 2025 by exponential smoothing. It is demonstrated by the results that the method can be used to obtain both spatially explicit, i.e. actual maps, as well as synoptic forecasts, e.g. by means of forecast variables such as the Sum of Lights (SoL). These forecasts are accompanied by estimation of confidence intervals, providing upper and lower bounds for future values. The method presented can be applied globally.

Mapping large heterogeneous urban areas using object-based image analysis (OBIA) remains challenging, especially with respect to the segmentation process. This could be explained both by the complex arrangement of heterogeneous land-cover classes and by the high diversity of urban patterns which can be encountered throughout the scene. In this context, using a single segmentation parameter to obtain satisfying segmentation results for the whole scene can be impossible. Nonetheless, it is possible to subdivide the whole city into smaller local zones, rather homogeneous according to their urban pattern. These zones can then be used to optimize the segmentation parameter locally, instead of using the whole image or a single representative spatial subset. This paper assesses the contribution of a local approach for the optimization of segmentation parameter compared to a global approach. Ouagadougou, located in sub-Saharan Africa, is used as case studies. First, the whole scene is segmented using a single globally optimized segmentation parameter. Second, the city is subdivided into 283 local zones, homogeneous in terms of building size and building density. Each local zone is then segmented using a locally optimized segmentation parameter. Unsupervised segmentation parameter optimization (USPO), relying on an optimization function which tends to maximize both intra-object homogeneity and inter-object heterogeneity, is used to select the segmentation parameter automatically for both approaches. Finally, a land-use/land-cover classification is performed using the Random Forest (RF) classifier. The results reveal that the local approach outperforms the global one, especially by limiting confusions between buildings and their bare-soil neighbors.

This study evaluates the impact of three Feature Selection (FS) algorithms in an Object Based Image Analysis (OBIA) framework for Very-High-Resolution (VHR) Land Use-Land Cover (LULC) classification. The three selected FS algorithms, Correlation Based Selection (CFS), Mean Decrease in Accuracy (MDA) and Random Forest (RF) based Recursive Feature Elimination (RFE), were tested on Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest (RF) classifiers. The results demonstrate that the accuracy of SVM and KNN classifiers are the most sensitive to FS. The RF appeared to be more robust to high dimensionality, although a significant increase in accuracy was found by using the RFE method. In terms of classification accuracy, SVM performed the best using FS, followed by RF and KNN. Finally, only a small number of features is needed to achieve the highest performance using each classifier. This study emphasizes the benefits of rigorous FS for maximizing performance, as well as for minimizing model complexity and interpretation.

Building change information extracted from remote sensing imageries is important for various applications such as urban management and marketing planning. The goal of this work is to develop a methodology for automatically capturing building changes from remote sensing imageries. Recent studies have addressed this goal by exploiting 3-D information as a proxy for building height. In contrast, because in practice it is expensive or impossible to prepare 3-D information, we do not rely on 3-D data but focus on using only RGB aerial imageries. Instead, we employ deep convolutional neural networks (CNNs) to extract effective features, and improve change detection accuracy in RGB remote sensing imageries. We consider two aspects of building change detection, building detection and subsequent change detection. Our proposed methodology was tested on several areas, which has some differences such as dominant building characteristics and varying brightness values. On all over the tested areas, the proposed method provides good results for changed objects, with recall values over 75 % with a strict overlap requirement of over 50% in intersection-over-union (IoU). When the IoU threshold was relaxed to over 10%, resulting recall values were over 81%. We conclude that use of CNNs enables accurate detection of building changes without employing 3-D information.

The term Underused Urban Spaces (UUS) refers to spaces within urban areas that have become unused, or that are being used to a lesser degree than they could or should be such as former industrial zones, abandoned facilities or buildings and Expo or Olympic Games cities. The Islamic pilgrimage sites known as Hajj sites (HS) are considered form of the UUS concept as they are used lesser degree than they should be. However, the emergence of such spaces has therefore encouraged researchers, urban planner, social and local authorities to discuses about the appropriate decision regarding their future towards conversion or alternatively using those spaces in order to achieve positive social, economic and environmental benefits, according to Pagano and Bowman (2000), UUS can be a powerful tool for governments and investors to use during the urban growth (UG) of their cities. Since, remote sensing and GIS technologies are used recently to study and analyze the UG of cities; the main objective of this paper is to demonstrate the efficiency of those technologies in addressing the future decisions regarding the underused status of Hajj sites in relation to UG of the city of Mecca. Tow classified land cover maps of Mecca for two years (1998 and 2013), in addition to entropy index and multiple regression analyses were utilized in order to quantify the relationship between HS and Mecca UG. The results showed that the urban growth of Mecca has increased by approximately 56%, and almost 32% of that increased were around HS in on hand, and on the other hand the entropy and the regression analysis showed that there is 51% probability that the future growth to be also around HS. These findings will better addressing the future decisions regarding the underused status of HS, simultaneously revel that the use of RS and GIS was highly effective to be adopted within similar cases of UUS.

Urban growth can profoundly alter the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical to develop strategies for sustainable development and to improve the urban residential environment and living quality. Ulaanbaatar city was urbanized very rapidly caused by herders and farmers, many of them migrating from rural places, have played a big role in this urban expansion (sprawl). Today, 1.3 million residents for about 40% of total population are living in the Ulaanbaatar region. Those human activities influenced stronger to green environments. Therefore, the aim of this study is determined to change detection of land use/land cover (LULC) and estimating their areas for the trend of future by remote sensing and statistical methods. The implications of analysis were provided by change detection methods of LULC, remote sensing spectral indices including normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI). In addition, it can relate to urban heat island (UHI) provided by Land surface temperature (LST) with local climate issues. Statistical methods for image processing used to define relations between those spectral indices and change detection images and regression analysis for time series trend in future. Remote sensing data are used by Landsat (TM/ETM+/OLI) satellite images over the period between 1990 and 2016 by 5 years. The advantages of this study are very useful remote sensing approaches with statistical analysis and important to detecting changes of LULC. The experimental results show that the LULC changes can image on the present and after few years and determined relations between impacts of environmental conditions.

Large amounts of Earth observation (EO) data have been collected to date, to increase even more rapidly with the upcoming Sentinel data. All this data contains unprecedented information, yet it is hard to retrieve, especially for nonremote sensing specialists. As we live in an urban era, with more than 50% of the world population living in cities, urban studies can especially benefit from the EO data. Information is needed for sustainable development of cities, for the understanding of urban growth patterns or for studying the threats of natural hazards or climate change. Bridging this gap between the technology-driven EO sector and the information needs of environmental science, planning, and policy is the driver behind the TEP-Urban project. Modern information technology functionalities and services are tested and implemented in the Urban Thematic Exploitation Platform (U-TEP). The platform enables interested users to easily exploit and generate thematic information on the status and development of the environment based on EO data and technologies. The beta version of the web platform contains value added basic earth observation data, global thematic data sets, and tools to derive user specific indicators and metrics. The code is open source and the architecture of the platform allows adding of new data sets and tools. These functionalities and concepts support the four basic use scenarios of the U-TEP platform: explore existing thematic content; task individual on-demand analyses; develop, deploy and offer your own content or application; and, learn more about innovative data sets and methods.

Despite the efforts of the authorities, that promote the use of alternative transportation systems, the traffic still increases in European cities, leading not only to traffic jams but also to pollution episodes. Delivery vehicles are part of both problems, because of their intensive use, the advent of e-commerce, the limited number and sizes of loading and unloading areas in many ancient European cities, and the difficulties associated to keep track of the correct use of these spaces. In this work we propose an holistic solution to the management of delivery vehicles in urban environments. Our solution, called RYDER, is based on the use of BLE (Bluetooth Low Energy) devices that should be provided by the local authority to delivery vehicles, as part of their authorization to use the loading and unloading areas. With the help of low-cost, low-power antennas with Bluetooth and 4G capabilities installed next to each loading/unloading area, the authorities are able to know in real time (a) the use of these areas by delivery vehicles, (b) the paths of the vehicles while they travel across the city, (c) the time spent in each area by each one of them, and (d) with the help of a mobile/tablet App, the local Police can check in seconds the permissions of each vehicle using these public spaces. Moreover, the use of a GIS-based platform allows the Traffic Department to track online each particular vehicle, based on the loading/unloading spaces being used, and to infer the most representative paths they follow, an information that may guide the decision about where these spaces are really necessary and whether each particular vehicle follows their associated usage rules. The deployment of RYDER low-cost antennas can also serve for other purposes, such as to track the routes followed by public loan bicycles, or by other fleets of public vehicles. With the help of low-cost sensors, antennas can also return an estimation of pollution values, such as levels of ozone, particulate matter, carbon monoxide, sulfur dioxide, and nitrous oxide, among others. This information may in turn drive the installation of certified pollution detectors.

The use of multiple sets of images with high level of overlapping to extract 3D point clouds has increased progressively in recent years. There are two main fundamental factors in the origin of this progress. In first, the image matching algorithms has been optimised and the software available that supports the progress of these techniques has been constantly developed. In second, because of the emergent paradigm of smart cities which has been promoting the virtualization of urban spaces and their elements. The creation of 3D models for urban elements is extremely relevant for urbanists to constitute digital archives of urban elements and being especially useful for enrich maps and databases or reconstruct and analyse objects/areas through time, building and recreating scenarios and implementing intuitive methods of interaction. These characteristics assist, for example, higher public participation creating a completely collaborative solution system, envisioning processes, simulations and results. This paper is organized in two main topics. The first deals with technical data modelling obtained by terrestrial photographs: planning criteria for obtaining photographs, approving or rejecting photos based on their quality, editing photos, creating masks, aligning photos, generating tie points, extracting point clouds, generating meshes, building textures and exporting results. The application of these procedures results in 3D models for the visualization of urban elements of the city of Barcelona. The second concerns the use of Augmented Reality through mobile platforms allowing to understand the city origins and the relation with the actual city morphology, (en)visioning solutions, processes and simulations, making possible for the agents in several domains, to fundament their decisions (and understand them) achieving a faster and wider consensus.

In order to retrieve a highly accurate view of their environment, autonomous cars are often equipped with LiDAR sensors. These sensors deliver a three dimensional point cloud in their own co-ordinate frame, where the origin is the sensor itself. However, the common co-ordinate system required by HAD (Highly Autonomous Driving) software systems has its origin at the center of the vehicle’s rear axle. Thus, a transformation of the acquired point clouds to car co-ordinates is necessary, and thereby the determination of the exact mounting position of the LiDAR system in car coordinates is required. Unfortunately, directly measuring this position is a time-consuming and error-prone task. Therefore, different approaches have been suggested for its estimation which mostly require an exhaustive test-setup and are again time-consuming to prepare. When preparing a high number of LiDAR mounted test vehicles for data acquisition, most approaches fall short due to time or money constraints. In this paper we propose an approach for mounting position estimation which features an easy execution and setup, thus making it feasible for on-field calibration.

Hyperspectral HyMap image with synchronous in-situ spectral data were used to survey the environmental condition in Shenzhen of South China. HyMap image was measured with 3.5m spatial resolution and 15nm spectral resolution from 0.44μm-2.5μm and corrected with Modtran5 model and synchronous solar illuminance and atmospheric visibility to the ground. The spectra of rocks, soils, water and vegetation were obtained by ASD spectrometer in reflectance. Both the fresh granite and eroded sandy soil was found with absorption at 2200nm±in-situ spectra, but the weathered granite and sandy soil have another absorption at 880nm~940 nm. Polluted water with high ammonia nitrogen and phosphorous and BOD5 get the strongest reflectance at 550 ~570nm, while polluted water of high COD_cr and heavy metal ions content get the peak reflectance at 450~490nm. The in-situ spectra was resampled in wavelength range and spectral resolution to that of Hymap sensor for image classification with SAM algorithm, the unpaved granite among cement the paved mine pits , the newly excavated land surface and the eroded soil was mapped out with the accuracy over 95%. We also discriminate the artificial forest from the natural with the spectral endmember extracted from the image.

A problem of state estimation with a new constraints named incomplete nonlinear constraint is considered. The targets are often move in the curve road, if the width of road is neglected, the road can be considered as the constraint, and the position of sensors, e.g., radar, is known in advance, this info can be used to enhance the performance of the tracking filter. The problem of how to incorporate the priori knowledge is considered. In this paper, a second-order sate constraint is considered. A fitting algorithm of ellipse is adopted to incorporate the priori knowledge by estimating the radius of the trajectory. The fitting problem is transformed to the nonlinear estimation problem. The estimated ellipse function is used to approximate the nonlinear constraint. Then, the typical nonlinear constraint methods proposed in recent works can be used to constrain the target state. Monte-Carlo simulation results are presented to illustrate the effectiveness proposed method in state estimation with incomplete constraint.

This study investigated the influences of city land cover changes and extreme climate events on land surface temperature in relationship with several biophysical variables in Bucharest metropolitan area of Romania through satellite and in-situ monitoring data. Remote sensing data from IKONOS, Landsat TM/ETM+ and time series MODIS Terra/Aqua and NOAA AVHRR sensors have been used to assess urban land cover– temperature interactions over 2000 - 2016 period. Time series Thermal InfraRed (TIR) satellite remote sensing data in synergy with meteorological data (air temperatureAT, precipitations, wind, solar radiation, etc.) were applied mainly for analyzing land surface temperature (LST) pattern and its relationship with surface landscape characteristics, assessing urban heat island (UHI), and relating urban land cover temperatures (LST). The land surface temperature, a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) at city level. Results show that in the metropolitan area ratio of impervious surface in Bucharest increased significantly during investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, LST and AT possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at metropolitan scale respectively. The NDVI was significantly correlated with precipitation. The spatial average air temperatures in urban test areas rise with the expansion of the urban size.

Urban green areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index time series provide a useful way to monitor urban vegetation phenological variations. This study quantitatively describes Normalized Difference Vegetation Index NDVI) /Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) temporal changes for Bucharest metropolitan region land cover in Romania from the perspective of vegetation phenology and its relation with climate changes and extreme climate events. The time series from 2000 to 2016 of the NOAA AVHRR and MODIS Terra/Aqua satellite data were analyzed to extract anomalies. Time series of climatic variables were also analyzed through anomaly detection techniques and the Fourier Transform. Correlations between NDVI/EVI time series and climatic variables were computed. Temperature, rainfall and radiation were significantly correlated with almost all land-cover classes for the harmonic analysis amplitude term. However, vegetation phenology was not correlated with climatic variables for the harmonic analysis phase term suggesting a delay between climatic variations and vegetation response. Training and validation were based on a reference dataset collected from IKONOS high resolution remote sensing data. The mean detection accuracy for period 2000- 2016 was assessed to be of 87%, with a reasonable balance between change commission errors (19.3%), change omission errors (24.7%), and Kappa coefficient of 0.73. This paper demonstrates the potential of moderate - and high resolution, multispectral imagery to map and monitor the evolution of the physical urban green land cover under climate and anthropogenic pressure.

An novel low-temperature method was used to enhance the corrosion resistance of copper or gold-coated optical fibers. A characterization of the elaborated materials and reports on selected studies such as cyclic temperature tests together with tensile tests is presented. Gold-coated optical fibers are proposed as a component of optical fiber sensors working in oxidizing atmospheres under temperatures exceeding ~900 °C.

We developed a Raman lidar with ultraspectral resolution for automatic airborne monitoring of pipeline leaks and for oil and gas exploration. Test flights indicate that a sensitivity of 6 ppm for methane and 2 ppm for hydrogen sulfide has been reached for leakage detection.

According to the current great interest concerning Large-Scale Metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance, are assuming a more and more important role among system requirements. The project is planned to conduct experimental studies aimed at identifying the impact of the application of the basic laws of microlasers as radiators on the linear-angular characteristics of existing measurement systems. The project is planned to conduct experimental studies aimed at identifying the impact of the application of the basic laws of microlasers as radiators on the linear-angular characteristics of existing measurement systems. The system consists of a distributed network-based layout, whose modularity allows to fit differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load.

Land use change is at the nexus of human territory expansion and urbanization. Human intrusion disturbs the natural heat energy balance of the area, although a new equilibrium of energy flux is attained but with greater diurnal range and adversely affecting the geo/physical variables. Modification in the trend of these variables causes a phenomenon known as Urban Heat Island (UHI) i.e. a dome of heat is formed around the city which has 7-10 °C high temperature than the nearby rural area at night. The study focuses on Surface UHI conventionally studied using thermal band of the remotely sensed satellite images. Land Surface Temperature (LST) is determined for the year 2015 using Landsat 8 for Delhi National Capital Region (NCR). This region was chosen because it is the biggest urban agglomeration in India, many satellite cities are coming in periphery and it has temperate climate. Quantification of UHI is predictably done using UHI intensity that is the difference between representative Urban and rural temperature. Recently the definition of urban and rural has been questioned because of various kinds of configurations of urban spaces across the globe. Delhi NCR urban configurations vary spatially- thus one UHI intensity does not give a deep understanding of the micro-climate. Advancement was made recently to standardize UHI intensity by dividing city into Local Climate Zones (LCZ), comes with 17 broad categories. LCZ map of Delhi NCR has been acquired from World Urban Database. The seasonality in LST across LCZ has been determined along with identifying warmest and coolest LCZ.