This PDF file contains the front matter associated with SPIE Proceedings Volume 10793, including the Title Page, Copyright information, Table of Contents, Introduction, Author and Conference Committee lists

Chemical-transport models are a persuasive tool to predict and study air pollution on different temporal and spatial scales. However, due to the complexity of physics and chemistry of air pollutants’ interactions and lack of precise input data, these models have uncertainties. In particular, most of the emission data have a too coarse resolution and are not appropriate for application in urban scale air quality modelling. In this study, a downscaling approach is utilized for emission data in order to improve the air pollutants concentration simulation over Munich city using the POLYPHEMUS/DLR chemistry-transport model (CTM). Traffic emission from the Bavarian Emission Kataster (EKATBY) 2004 anthropogenic emissions dataset with 2 km resolution is downscaled to 100 m with regard to the highresolution OpenStreetMap roads paths and areal emission sources are relocated on the most populated and active sites which have been determined from VIIRS NOAA satellite-derived night light data. In addition, the EEA CORINE 2012 land use data is implemented with 100 m grid resolution to improve e.g. the biogenic emissions. Regarding aerosols, the SIze REsolved Aerosol Module (SIREAM) for aerosol dynamic and the Secondary Organic Aerosol Model (SORGAM) are applied. The CTM is driven by WRF 3.5 meteorological forecasts. In order to have reliable simulations, the one-way grid nesting method with four domains is employed, where the coarsest domain covers Europe and the finest covers Munich city area.

A method is presented to estimate photopollution (a.k.a night-lights pollution) in a macroscopic manner, i.e. that can be applied globally, using open-domain data. Photopollution has two components, direct illumination and skyglow i.e. the diffused scattering of light in the atmosphere. The proposed method is currently focusing on direct illumination only. The novelty is that viewshed analysis is deployed, taking into account the viewing distance as well as the amount of the light at each source. Moreover, monthly variation of photopollution is measured based on recently available suitable data.

The growth of vessel traffic has increased atmospheric pollution affecting not only the port areas, but also the surrounding cities. For this reason, initiatives have been taken to regulate these emissions at international level. As far as our country is concerned, an effort is also being made to have tools that allows the monitoring of emissions. In this work we present results of monitoring carried out using the DOAS technique in two ways. On one hand, high resolution spectra acquired from mobile platforms and zenith observations were analysed. From the available spectral region (315-460 nm), total columns of NO₂and SO₂ were obtained and their provenance identify. Afterwards, we calculate the emitted flows. On the other hand, sensitive cameras were used in the UV region with selective filters, in order to obtain SO₂ images and therefore, calculate the emission flows of specific vessels.

Urban Heat Island (UHI) effect is a multi-scaled phenomenon, i.e., it may occur within a city in different sizes, shapes, intensity and time. As a climatic event, UHI acquires characteristic variations at different temporal scales. In this study, the temporal and spatial variability of the Surface UHI (SUHI) over Delhi was examined for different temporal scales, based on remotely sensed data and thermal data collected during a time frame of 1-year, i.e., from June 2017 to June 2018. During the last few decades, Delhi has experienced unparalleled horizontal and vertical growth, leading to major modifications of land use/cover. Land Surface Temperature (LST) datasets of Delhi were generated from Landsat 8 OLI/TIRS imagery. LST data, thus, obtained was compared with rural reference data and analysed with respect to the thermal field data. Using these LST maps, average SUHI intensity over Delhi was calculated to be 3.86 ^oC for the whole year. The calculated SUHII showed high values in summer during the day than in winter. The relationship between LST and NDVI for each month was also assessed, which showed strong negative correlation with an average r value of -0.45. Furthermore, the Central Delhi Region and commercial areas displayed heat island conditions almost throughout the year. The results of this study suggest that the monthly variations of SUHI of Delhi are related with the climatic background, local meteorological conditions, and surface characteristics.

Besides new economical, managerial and social challenges associated with growing cities, the modifications caused in the energy budget of the urban surface intensifies the existing urban heat island (UHI). UHI can vary temporally and spatially according to meteorological conditions, landscape and urban typologies. Urban cover and form, as well as anthropogenic activities, pose an important effect on the city’s thermal behaviour that influence UHI and therefore the quality of life of the citizens. In this study, we focus on quantifying the air temperature spatiotemporal patterns across the urban and peri-urban area of Heraklion, Greece at a grid of 100 m x 100 m cells. We use point air temperature observations from the Wireless Sensors Network of Heraklion and interpolate spatially by means of sophisticated geostatistical modelling parameterized with satellite derived predictors. Regression kriging interpolation technique is implemented over the study area, using different predictors to minimize the uncertainty in air temperature estimation. We deal for multicollinearity between predictors and spatio-temporal correlations between measurements. A maximum magnitude of UHI ~ 4 oC has been observed between 04:00-05:00 (UTC+3). Cross-validations indicate a mean MAE ~0.86 oC in the estimated air temperature maps.

The present study investigates the spectral distribution of global, direct (E_bλ) and diffuse (E_bλ) irradiances over Delhi with the help of handheld Field Spectroradiometer in the year 2014 – 2015. Seasonal variation in the solar irradiances as well as the diffuse-to-direct ratio ( E_dλ/E_bλ]) was studied during three seasons (post-monsoon, winter and pre-monsoon) having different atmospheric conditions. Observations were carried out under clear sky days during day time when solar zenith angle is low in order to get maximum solar radiation. The ratio E_dλ/E_bλ is used as a function to measure the impact of aerosol load on incoming solar irradiance. NASA's Aqua satellite, MODIS (Moderate Resolution Imaging Spectroradiometer) AOD₅₅₀ data has been used to evaluate the relationship between aerosol load and ratio (E_dλ/E_bλ) on incoming solar radiation. A strong dependence of measured diffuse-to-direct irradiance ratio (E_dλ/E_bλ) on wavelength was observed. It was found to be decreasing exponentially with wavelength. Value of E_dλ/E_bλ was found to be maximum and minimum during Post-monsoon and Pre-monsoon seasons respectively. Also, the maximum values of E_dλ/E_bλ ratio were observed at the shorter wavelengths in all the studied seasons. The AOD₅₅₀ value was found to be maximum during postmonsoon followed by winter and Pre-monsoon seasons. Crop residue burning in addition to low wind speed was responsible for high aerosol load during the post-monsoon season where as inversion layer and calm wind conditions favored high aerosol load during winter season. A strong relation between the ratio E_dλ/E_bλ and AOD₅₅₀ is observed in all the studied seasons, indicating that high aerosol load was responsible for the attenuation of the incoming solar radiation in all the seasons

Landcover/landuse change and human activities will have impact in climate change. Environment change that dominated by human activity especially in landcover/landuse change caused in urban temperature change. The aims of this research are (1) to define the influenced of landcover/landuse change in variability of temperature change based on multiseries data analysis of thermal infrared bands (TIRS) of Landsat 8, (2) to determine the indicator of urban temperature change due to urban climate change, and derived urban heat island (UHI) more accurate based on land surface temperature (LST) change. Multiseries data of Landsat 8 that recorded in May, June, July, August, September, October 2013, and January, April, May, June, July, August, September, October 2014, are used in this research. Also, some algorithm to process and to extract some information due to the aims of the research, such as vegetation indices (VI), urban index (UI), split windows algorithm (SWA), fraction vegetation coverage (FVC), land surface temperature (LST), land surface emissivity (LSE). Direct measurement as real time data extraction, such as object temperature, vegetation coverage, type of landcover/landuse and percentage of coverage. Also statistic analysis to calculate and to know how relationship of each data that contribute on this research. The result of this research showed (1) TIRS of Landsat 8 be able to derived some important information due to defined of landcover/landuse change as an indicator of urban temperature change accurately. (2) TIRS of Landsat 8 derived UHI accurately and (3) Applying SWA algorithm on TIRS of Landsat 8, showed that the value of LST is higher (overestimate) than the real LST on the ground (both BMKG data and ground real time measurement).

This study attempts to examine the role of apparent thermal inertia in the variable behaviour of day-time and night-time Urban Heat Island formation over Delhi. LISS-III data of Resourcesat-I has been used for land-use/land-cover classification and MODIS data have been used for land surface temperature and surface albedo. The albedo map shows that albedo over Delhi is significantly less (~ 0.08 to 0.13) over major part of Delhi, while albedo in its surrounding regions ranges from 0.15 to 0.2. This suggests that a significantly greater fraction of the incoming solar radiation is utilized for heating up of land surface over Delhi as compared to its surrounding regions. The night-time land surface temperature maps reveal that temperatures over Delhi are significantly higher (8°C -10°C) than those over the surrounding regions showing the formation of nocturnal heat island. However, a cool island is observed in the month of May during the day-time. The day and night time temperature maps are then used to obtain the diurnal temperature range and together with albedo maps of the study region, are used to estimate the apparent thermal inertia over the region. The study reveals that the dense built-up urban area of Delhi has higher apparent thermal inertia than that of the surrounding rural areas during the study period.

Urbanisation contributed to the presence of urban heat island phenomenon, and aggravated urban heat island effect intensity with the improvement of urbanisation level. Overheat weather condition caused severe threat to human life and health, while green infrastructure including water bodies has been validated to be able to reduce urban land surface temperature in different extent. To examine the impact of green infrastructure on urban heat island effect in Harbin, with the aid of ENVI and geographic information system software, this paper retrieved seasonal Harbin land surface temperature from 2000 to 2015 using Landsat series and MODIS 8-day remote sensing data, and further computed surface urban heat island intensity(SUHII). Then, to build the quantitative relationship between green infrastructure fraction and urban heat island intensity applying regression analysis method. Finally, by means of ENVI-MET software, this article simulated urban heat island intensity change based on different green infrastructure scenarios. The results showed that, as far as administrative region of Harbin scale, surface urban heat island intensity both in summer and in winter reduced from 2000(6.55°C in summer, 4.15°C in winter) to 2015(2.6°C in summer, 0.47°C in winter), and SUHII in summer is higher than it in winter except 2005; Green infrastructure fraction is negative correlated with SUHII; Simulation result indicated that increase on green infrastructure would facilitate to mitigation of urban heat island effect. The result of this study would provide some help and advice for land use planning decision and urban construction in the future of Harbin.

Timeliness is a critical requirement for the provision of information during disasters such as floods in urban areas. Images from RGB remote sensors (such as those carried on satellites, aircraft, UAVs and on the International Space Station) are potentially cost-effective data sources for real-time applications. This paper describes work undertaken to evaluate two high resolution RGB image datasets for rapid response mapping and monitoring of urban visible flood water extent. Our overall goal is to develop a robust methodology for universally applicable extraction of the flooded area information. The methods and results are demonstrated as a case study of the characterization and delineation of visible flood extent and its changes that occurred during the June 2013 city of Calgary flood event. The input imagery included very high resolution aerial photography and imagery acquired with the International Space Station’s (ISS) SERVIR Environmental Research and Visualization System (ISERV). The methodology development involved analysis and comparison of the spectral responses of urban land surface types, shadows and turbid flood water. Based on an understanding of these spectral properties, a universally applicable method was developed and assessed to extract visible flood water from RGB imagery.

Waste disposal and recycling is becoming one of the main problems in Western countries. Improving both recycling culture among citizenship and waste collection and treatment logistics is critical to augment the percentage of waste being recycled. In this paper we present STERLING, an initiative that aims to help in both fields. STERLING is a framework composed by a low-cost, low-energy sensor installed in recycling containers to measure fill level and other physical parameters. The sensor is activated magnetically each time the container lid is opened by a user. Instead of directly sending this information to a cloud-based server, our sensor broadcasts a Bluetooth Low Energy (BLE) packet to the surrounding area. An App running in the mobile phone of the user performs two actions: To capture this information, re-sending it to the cloud-based server, and to assign credit to this particular user for having used the recycling container. In this way, users are rewarded for using the container, and the infrastructure benefits from cost-free communications from the container to the server. In this paper we will describe our idea in detail, showing how it can be used to develop a rewarding schema that encourages recycling.

Most of the large cities have an air quality network to measure air pollution including PM10. However, air quality monitoring network has a high cost and it is spatially limited. Quito, capital of Ecuador, is a city with an automatic air quality network (REMMAQ) composed by 9 stations. The REMMAQ works since 2002, measuring PM10 only in 4 regular stations located at different points along the city. This scarce quantity of PM10 measures led us to propose a new strategy to obtain PM10 data in all the city. Several studies have already considered the retrieving of PM10 from remote sensing data in cities with an air quality network. In order to find an optimal model to retrieve PM10 in Quito, this study compare the use of 3 different satellite sensors (Landsat-7 ETM+, Landsat-8 OLI and TERRA/MODIS) between 2013 to 2017. Additional to remote sensing data, we also use field data considering the REMMAQ. In each sensor, we used different variables and environmental indexes to model the best fit equation to quantify PM10 in all the city, finding the significant variables for each type of data and year. The variables considered were the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Stability Index (NSI), surface reflectance Blue Band (B1), surface reflectance Green Band (B2) and surface reflectance Red Band (B3). These variables were considered because most of them are used in different studies combined with meteorological data. All the procedures were implemented in R Studio. The empirical models using remote sensing data/derived products and air quality data can help in retrieving air pollutants in large cities. This work is a valuable contribution for the study of the spatialization of PM10 in order to find new alternatives in the use of remote sensing data to support government decisions.

Urban green is indispensable from an urban ecological and social point of view and fulfils important functions such as dust binding, temperature reduction, wind damping or groundwater recharge. Especially for bioclimatic modeling, knowledge of size, structure and green volume of the urban vegetation is essential. Manual mapping of vegetation structures is timeconsuming and cost-intensive and can only ever be carried out in locally limited study areas. Active and passive remote sensing technologies in combination with automated methods for information extraction offer the opportunity to record the green structure in urban areas differentiated according to vegetation types. The new globally and freely available data provided by the European Copernicus Program raises the question whether these data are suitable for mapping and quantifying the urban green structure, including an accuracy estimation. Previous studies on the usability of Sentinel-2 data for vegetation analysis were essentially limited to crop and tree species classification in open space. The approach presented here thus considers for the first time the application of this data in a purely urban environment. Here we present a modeling approach based on multiple regression models. A Sentinel-2A scene from July 4, 2015 covering the greater Dresden area served as the input data set. After atmospheric correction of the satellite image scene 10 spectral channels were available. A high-resolution vegetation cover model with a grid width of 50 cm was available as a reference data set for the entire study area (City of Dresden, Germany). This takes into account the vegetation classes deciduous trees, conifers, shrubs, low (grassy) vegetation and arable land. Thus the area share of these vegetation types could be determined aggregated for each pixel of the satellite image scene. In addition, vegetation indices (NDVI and others) were calculated using suitable channels. For the prediction of each vegetation class, estimation equations were drawn up and evaluated with regard to their quality. Especially for deciduous and coniferous trees, satisfactory model quality values could be obtained, so that the green component prediction in these cases represents a useful basis for the determination of the green structure at the building block level in urban areas.

Efficient and safe facility maintenance has been a serious social problem due to the decline in labor force, facility deterioration over the years, and the rise of large-scale natural disasters. For electric power companies, maintaining and inspecting power equipment spread in wide areas is an important management issues to deal with. Identifying the electric poles that require maintenance is one of the essential inspection tasks. To identify the electric poles in an image, several methods focusing on their unique features such as color and shape have been proposed. However, this feature-based approach suffers from noise caused by shooting conditions. Another approach using a laser scanning technique requires high computational cost for handling the obtained point cloud data. We explored methods to efficiently detect the electric poles in a large number of images taken by a vehicle-mounted camera run in an urban area and its suburbs. Here, we show that a single shot MultiBox detector (SSD), which has been successfully used for object detection in an image, can be effectively applied to the task. We trained SSD models using around 600 supervised image data and evaluated the performance with 100 test images. In the evaluation, we examined whether pole-like objects such as telephone poles, traffic light poles, or trunks of trees can be distinguished from the electric poles. We also evaluated the influence of the background and exteriors attached to the pole. We found that the electric poles can be detected with an average precision (AP) of 72.2%. Our results demonstrate operational feasibility of the autonomous electric pole inspection system that implements a deep network based object detector.

ATHENA is an on-going Horizon 2020 Twinning project aiming to promote remote sensing technologies for cultural heritage (CH) applications in Cyprus. ATHENA project brings together the Eratosthenes Research Center (ERC) of the Cyprus University of Technology (CUT) with two internationally leading institutions of Europe, namely the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR). The project’s scope is to position the ERC regionally and stimulate future cooperation through placements at partner institutions and enhance the research and academic profile of all participants. The scientific strengthening and networking achieved through the ATHENA project could be of great benefit not only for Cyprus but for the entire Eastern Mediterranean, bearing a plethora of archaeological sites and monuments urgently calling for monitoring and safeguarding.

The preservation of CH and landscape comprises a strategic priority not only to guarantee cultural treasures and evidence of the human past to future generations, but also to exploit them as a strategic and valuable economic asset. The objective of this paper is to present knowledge transfer examples achieved from the ATHENA project through intense training activities. These activities were also designed to enhance the scientific profile of the research staff and to accelerate the development of research capabilities of the ERC. At the same time the results from the training activities were also exploited to promote earth observation knowledge and best practices intended for CH. The activities included active and passive remote sensing data used for archaeological applications, Synthetic Aperture Radar (SAR) image analysis for change and deformation detection, monitoring of risk factors related to cultural heritage sites including archaeological looting etc.

Urban areas due to their dynamic nature often pose serious threats to environment causing overutilization of resources like ground water. The depleting ground water table often causes land subsidence leading to cracks in buildings. This subsidence can be easily mapped using PSInSAR (Persistent Interferometric Synthetic Aperture RADAR). In urban areas, there are many buildings per square kilometer which give permanent scatter and act as good corner reflectors at boundaries right angle to ground and walls. Rudrapur city, which is the headquarters, of Udham Singh Nagar district of Uttarakhand state in India, is also a major industrial hub and attracts skilled and unskilled labour force from the adjoining areas and this is leading to an unprecedented growth of urban sprawl. The city shows sprawling dense urban settlements and huge industrial setups on the outskirts, surrounded by agricultural fields and orchards. Main source of water supply is through bore wells and tube wells. Here, it was found that over the years, ground water has been harnessed for not only household supplies but also for agriculture and industrial purposes which has led to lowering of water table down from around 33.5 m to 45.7 m. This is leading to cracks developing in buildings particularly around the industrial area. The changes over a period of a year from 4th December 2014 till 2nd December 2015, were mapped using PSInSAR technique, with X-band TerraSAR-X datasets.

Multi-dwelling fires in informal settlements in South Africa are devastating for residents resulting in loss of life, homes and belongings. The aim of the IRIS-Fire project is to develop innovative methods of assessing and modelling fire risks with the goal of increasing informal settlement fire resilience. This paper outlines a new approach to mapping historic and ongoing fires in informal settlements using satellite imagery. A theoretical informal settlement fire curve for albedo is proposed based on the predicted reflectance of roof materials pre- and post-burn. Landsat OLI imagery in Google Earth Engine is used to map time series of albedo and the method is tested for a pilot site in Masiphumelele, South Africa. Results indicate that the detected albedo time series matches the theoretical informal settlement fire curve to some extent. Further research will investigate using (1) higher spatial resolution satellite data to reduce the impact of the mixed pixel effect, (2) higher temporal resolution satellite imagery such as Sentinel-2 to increase the revisit time and thus increase the number of high quality images, and (3) dark object subtraction to minimise scene noise, (4) use of statistical quality control charts to detect statistically significant change.

The eastern coastal region of China (ECR) has pioneered the nation in urbanization processes and economic development. The support vector machine (SVM) method was used to extract urban areas in ECR from DMSP-OLS night time lights and SPOT vegetation (VGT) images for the period of 1998-2010. Spatial-temporal urban areas changes were revealed using dynamic degree model, urbanization intensification index and the centriod shift model. The grey relational analysis was adopted to indentify the most influenced social-economic factors of urban expansion and the artificial neural network (ANN-BP) model was used to explore their relationship. Results showed that the average annual growth rate of urban sprawl in ECR was about 13.31 %, and the lowest value of 8.11% was found in 2007. Urban expansion of Beijing-Tianjin-Hebei metropolitan region (Jing-Jin-Ji) and the Pearl River Delta characterized from the center to surrounding areas, whereas the urban land development of the Yangtze River Delta showed a connecting pattern. During 2007-2010, the rate of urban expansion was 11237km2 per year, and the urban sprawl intensity reached the peak value of 2.565. The urban development center gradually shifted to the northeast in Beijing, while the urban land moved towards southwest in Yangtze River Delta and the Pearl River Delta. Urban expansion was closely related to the soci-economic factors including GDP, fiscal revenue, construction output, total fixed asset investment and annual per capita disposable income of urban residents derived from gray correlation model. The simulated urban area by BP neural network model had high correlation with actual values.

The world is undergoing the most significant wave of urban growth in history. It is expected that by 2030 the number of people living in the cities will increase to about 5 billion. The rapid urbanization has led to complex problems, including a reduction in vegetation cover, the formation of the urban heat island effect, environmental pollution, reduced open space, etc. This study intends to explore the spatial patterns of urbanization and its impact on the environment in and around Chandigarh- the first planned city of India. Chandigarh was originally planned for a population of 5 lakh, but the city has expanded rapidly over the last four decades and faces problems common to other growing cities in India, including the proliferation of slums and squatter settlements. The areas adjacent to the city boundary also face similar issues. The study presents the methods and results of an object-based classification and post-classification change detection on multi-temporal Landsat data (1978-2017). The processed data was used as an input for object-based classification using image segmentation algorithm of eCognition Developer software. The results show that maximum urbanization has taken place in the last decade in the southern and north-western directions outside the city as a result of the development of an international airport, new sectors and approach roads on the vegetated areas. As a result, maximum changes could be seen in the class vegetation as it has been rapidly changed to built-up areas. The results of this kind of study may hold immense value for planning the urban sprawl areas where up-to-date information is lacking because of the rapid pace of development.

Understanding spatio-temporal changes of urban environments is essential for local planning and environmental management. In this study, Landsat TM/OLI images covering Luoyang region were used as the main data source. The method of spectral indices was used to extract impervious surfaces in 1990 and 2016. The results show that the area of impervious surfaces in Luoyang showed a rapid increase trend, from 62.77 km² in 1990 (accounting for 7.79% of the total area of the study area) to 149.37 km² in 2016 (accounting for 18.55%). Luolong District has the largest impervious surface area change, followed by Yibin Development Zone and Jianxi District, while old cities of Laocheng District, Xigong District and Chanhe District have not changed much. The establishment of Luolong New District in 2000 gradually shifted Luoyang's urban center from the north bank of Luohe River to the south bank, forming a rapid growth of urban construction land in Luolong District from 2000 to 2010. Old cities mainly expanded northward to varying degrees, and its road network has a rapid growth in the surrounding area. Yibin Development Zone was a remote, small population village in 1990, and recently it has become a new urban expansion area. The main driving forces of changes in impervious surfaces in Luoyang include natural and geographical conditions such as topography, and tourist resources, and the policy as one dominant factor controlling the spatial pattern of impervious surface expansions in Luoyang City.

A spatio-temporal analysis of the aerosol concentrations in two size fractions (PM10 and PM2.5) in relation with air quality (AQI) and meteorological parameters was done through synergy of in-situ monitoring data as well as MODIS Terra/Aqua time-series satellite data for Bucharest metropolitan area during 2012 year. The C005 (version 5.1) Level 2 and Level 3 Terra MODIS AOD550 time-series satellite data for period 01/01/2012- 31/12/2012 have been used. ORIGIN 8.0 and ENVI 5.0 software were used. All these methods are important and complementary. It was found that Particle Materials PM2.5 and PM10 aerosols exhibit their highest concentrations mostly in the central part of the town, due to road traffic as well as in the industrialized periurban areas. In addition to the local and regional anthropogenic PM sources, both the levels and composition of air PM depend on meteorological parameters (temperature, humidity, precipitation, winds etc.), and season of the year. The results revealed a significant month-to-month variability in all AOD550 values, underlying the influence of varying aerosol load function of season. The AOD550 values (Level 3) lie in a wide range, as low as 0.2 up to 0.5. The influence of aerosol particles on climate, and how their properties are perturbed by anthropogenic activity, is one of the key uncertainties in climate change assessments. These results contribute to a better understanding of urban decision makers through considering the specific characteristics of different urban sectors for air quality improvement.

This study investigated the influences of urban growth 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 Landsat TM/ETM/OLI and time series MODIS Terra/Aqua and NOAA AVHRR sensors have been used to assess urban land cover– temperature interactions over 2000 - 2017 period. Time series Thermal InfraRed (TIR) satellite remote sensing data in synergy with meteorological data (air temperature- Ta, precipitations, wind, solar radiation, etc.) have been applied mainly for analyzing land surface temperature (LST) patterns and its relationship with surface landscape characteristics, assessing urban heat island (UHI), and relating urban land cover temperatures (LSTs). Based on these parameters, the urban size dynamics, urban heat island effect (UHI) and the relationships of LST to other biophysical and meteorological parameters (surface albedo, precipitations, wind intensity and direction, air temperature) have been analyzed. 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 Ta 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.

India is emerging as a major influence of socioeconomic change connecting diverse cultures, people with improved mobility across geographic boundaries. Rapid urbanizations owing to rural to urban migration have made the existing infrastructure and resources inadequate. A case study on Varanasi city has been conducted to demonstrate the urban growth pattern for the years 2000, 2013 and 2014. Satellite imagery of Cartosat-1 (2.5m) and LISS-IV (5m) is used to create the high-resolution multi-spectral data through image processing techniques. In the year 2000, the urban built-up covered an area of 42.75 sq km to 109.03 sq km and the cultivated land area has decreased by 16%. Every year cultivated land decreases for mounting urban sprawl with another 2% decrease in the year 2014 and a 9% increase in built-up. This paper demonstrates the utilization of open source GIS to generate results.

Unplanned urbanization has drastically altered the natural topography of the cities and the surrounding areas. Migration from rural areas to Cities has led the transformation of the nearby rural areas into the extended city. The increase in an urban environment as an outcome of socio-economic activities has resulted in the urban sprawl of major cities globally including India. Urban sprawl has raised concerns over unplanned land use but also day-to-day weather conditions of the city. Concentrated development in the major cities of Bangalore, Karnataka and Capital city of New Delhi has given rise to serious implications in form of an increase in local temperatures during the last decade. The study aims at representing the change with an increase in urbanization with the formation of UHI. Sophisticated techniques of remote sensing and GIS are used to perform the analysis. Land Surface Temperature (LST) extracted from the thermal band (band 10) of the Landsat-8 OLI data from the DN values is evaluated for detecting spatial-temporal variations in the formation of heat island of the two cities. The LST calculated for each city is analyzed with the NDBI and NDVI to study the relation of surface temperature and respective indices. It was found that the surface temperature follows a positive co-relation with NDBI and a negative co-relation with NDVI. The indices are used as a basis to delineate pervious and impervious areas in the two cities. Landsat and Sentinel 2a satellite imagery temporal data are used to study the area.

Remote sensing is one of the most dynamically developing fields of science, and due to its versatility, it can be applicable in many different areas of interest, i.e., biomedical science, forestry, water monitoring, agriculture, urban planning. At present, land cover classification, and precise classification of urban areas is extremely significant regarding environmental protection, particularly in relation to environmental protection and detection and identification of the roofing materials, i.e. roofs covered with asbestos, due to the mandatory removal of asbestos from the environment. Thus the use of advanced remote sensing techniques and various data can significantly accelerate and facilitate this process, depending on the data types and used algorithms. In this paper, the authors present the comparison of object classifications and object identification of chosen urban area- in the north part of Warsaw, Poland. As a basis for the analysis, data from different types of sensors were used, i.e., optical multispectral, SAR data, and LiDAR data. The results of this experiment can be useful when choosing data and methods for accurate and precise land cover classification, and particularly for rapid inventory of roofs’ coverages. The preliminary results shown in the paper demonstrate the potentiality of the joint processing of different remote sensing data.

Remote sensing of the Earth allows to receive information of medium, a high spatial resolution from space vehicles and to conduct hyperspectral measurements. In many cases, waste disposal sites (WDS) is not legal. So, it is very important to use the system for automatic detection such places using satellite images. In this paper, a model of the automated space monitoring system for the presence of waste disposal sites is developed. The proposed system includes the following blocks: a database of WDS; a subsystem for detecting unauthorized WDS; a subsystem for monitoring the design, operation and reclamation rules for existing WDS; a subsystem for estimating the parameters of the WDS and their impact on the environment; a subsystem of satellite monitoring. We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For system design, we use the apparatus of discrete orthogonal transformations. The impact of the WDS influence on agricultural crops is analyzed, based on the data of the Earth remote space sensing based on the orthogonal transform. The purpose of the work is the modeling of an automated space monitoring system for the presence of waste disposal facilities using regularization method in the problem of filtering of space images from the noise stored in the archives. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

This paper deals with the design and application issues of context-redefined computer languages for new information technologies. The discussion touches upon these languages implementation problems for intelligent learning agents (ILA), applied for solving the behavior prediction tasks for resource consumption in communal services. The article deals with the second problem in particular. The approach consists in the application of context-redefined language and its support system for problem solution. We focus on principal unpredicted changing of source function algorithms. Built-in context-redefined computer language is an essential tool for this kind of algorithm support. The main part of the intelligent learning agent is performance element. The performance element operates according to the current algorithm, which is described by means of built-in context-redefined language. The main idea of built-in language synthesis is to use main parts of the algorithm for ILA components with proper modification by means another algorithms and context connection. Due to this connection, the original algorithm can be changed directly or indirectly in the process of ILA functioning. We have to extract changing parts of component algorithms and organize proper interaction between every part and the context, which can be changed directly or indirectly. Required adaptive algorithm variation takes place on the base of obtained knowledge. At the same time, the algorithm must be implemented quickly, and the language must be simple and clear. The algorithm efficiency is based on flexibility and modifiability of the language. General constructions of the built-in context-redefined language have been demonstrated with proper comments.

It is nowadays a worldwide trend to implement the so-called digital power industry, which implies optimization of the energy resources dissemination and use, based on modern digital technologies and telecommunication systems. Control systems of this kind must take into account clearly inevident trends in changing energy consumption depending on the time of year, time of day, day of the week, etc. Since the end user is a specific person with his own individual preferences, the task of modeling his behavior in terms of energy consumption is an estimate of some realization of a random process. This process features a quasi-deterministic component and a pronounced random component. The goal of almost all digital power systems is to predict the trends in the quasi-deterministic component, the random component in this case is noise interference. The most appropriate solution when constructing a predictive system is to conduct a digital experiment on an array of data taken during the actual operation of a particular energy consumption accounting system.

The research was conducted on a real array of data from energy consumption by 61 households over a period of 730 days. Classical regression analysis methods were compared with neural network analysis (trained with teacher).