This PDF file contains the front matter associated with SPIE Proceedings Volume 11157 including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

The study of urban heat islands (UHI) is of great importance in the context of climate change (CC). The use of satellite images has helped considerably to understand UHI, especially in analyses of land surface temperature (LST). However, available sensors have a major limitation: their low spatial resolution (100 meters, Landsat; 1000 meters, MODIS) does not allow detailed analysis of UHI. Moreover, most remote sensors are limited to daytime data collection, while UHI mainly appear during the night. The images provided by satellites that study nocturnal thermal radiation, such as MODIS, have very low spatial resolution. There is abundant literature about the fusion of images from several satellites and sensors, especially information from MODIS and Landsat. However, most of these studies have concentrated on studying the combination of congruent images in the temporal plane, to extrapolate the results obtained to other temporal instances for which there is no detailed information. In general, few studies have focused on increasing the resolution of thermal images beyond the 100 meters/pixel of Landsat.

The objective of this paper is to combine information from various sensors (Modis, Landsat 8 and Sentinel 2) by constructing a set of OLS models of daytime and nighttime LST. These models provide a detailed view of daytime UHI (10 meters) and a robust evaluation of the range of cooling produced during the night. A modelling exercise at 1 meter/pixel of resolution has also been developed, using information from more detailed sensors installed on aircraft in the Barcelona Metropolitan Area.

Several studies have demonstrated that air quality and weather changes have influence in the prevalence of chronic respiratory diseases. Considering this context, the spatial risk modeling along the cities can help public health programs in finding solutions to reduce the frequency of respiratory diseases. With the aim to have a regional coverage and not only data in specific (point) locations, an effective alternative is the use of remote sensing data combined with field air quality data and meteorological data. During the last years, the use of remote sensing data allowed the construction of models to determine air quality data with satisfactory results. Some models using remote sensing based air quality data presented good levels of correlation (R² > 0.5), proving that it is possible to establish a relationship between remote sensing data and air quality data.

In order to establish a spatial health respiratory risk model for Quito, Ecuador, an empirical model was computed considering data between 2013 and 2017, using the median data values in each parish of the city. The variables are: i) 46 Landsat-8 satellite images with less than 10% of cloud cover and some indexes (normalized difference vegetation index NDVI, Soil-adjusted Vegetation Index SAVI, etc.); ii) air quality data (nitrogen dioxide - NO₂, Ozone - O₃, particulate matter less than 2.5μ - PM2.5 and sulfur dioxide - SO₂) obtained from local air quality network stations and; iii) the hospital discharge rates from chronic respiratory diseases (CRD). In order to establish a probability model to get a CRD, a logistic regression was used. The empirical model is expressed as the probability of occurrence during the studied time. All the procedures were implemented in R Studio. The methodology proposed in this work can be used by health and governmental entities to access the risk of getting a respiratory disease, considering an application of remote sensing in the environmental and health management programs.

This paper presents the results of an experiment aiming to measure the vibrational frequencies of the main structures of the medieval church of San Domenico (Matera, southern Italy) and relate them to the mechanical properties of geological stratigraphy and construction materials. Vibrational frequencies are measured by means of the ground-based radar inteferometry technique using a Ku-band radar. Time series of ground-based radar data are processed to measure displacements and vibration frequencies of the church structures. Data collected by a seismic triaxal station for the measurement of the H/V ratio are also processed to compare with radar-based frequencies measurements, providing information about the main vibration frequency ranges of the church structures and, more important, disentangle the site and structure vibration frequencies.

Due to now outdated construction technology, houses which have not been retrofitted since construction typically fail to meet modern energy performance levels. However, identifying at a city scale which houses could benefit the most from retrofit solutions is currently a labour intensive process. In this paper, a system that uses a vehicle mounted camera to capture pictures of residential buildings and then performs semantic segmentation to differentiate components of captured buildings is presented. An ensemble of U-Net semantic segmentation models are trained to identify walls, roofs, chimneys, windows and doors from building façade images and differentiate between window and door instances which are partially visible or obscured. Results show that the ensemble of U-Net models achieved high accuracy in identifying walls, roofs and chimneys, moderate accuracy in identifying windows and low accuracy in identifying doors and instances of windows and doors which were partially visible or obscured. When U-Net models were retrained to identify doors or windows, irrespective of partially visible and obscured instances, a significant rise in door and window identification accuracy was observed. It is believed that a larger training dataset would produce significantly improved results across all classes. The results presented here prove the operational feasibility in the first part of a process to combine this model with high-resolution thermography and GPS for automating building retrofitting evaluations.

In 2018 we introduced STERLING, a framework designed to encourage citizenship to develop recycling habits. This framework is composed by a low-cost, low-energy sensor installed in recycling containers to measure fill level and other physical parameters, together with a mobile app and an associated web-based server. 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 credits 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. During the first semester of 2019, we have launched a pilot demonstration of the STERLING solution at different schools of the University of Valladolid. This test included the deployment of ten STERLING recycling containers, making a public presentation to encourage users to improve their recycling habits, and running a rewarding mechanisms consisting on Amazon gift cards. In this paper we describe the experience in detail, showing the advantages and weaknesses of our solution, discussing some interesting lessons learned from this experience, and pointing to further research directions.

Pixel-based remote sensing (RS) is widely used for social science research in particular for geodemographic interpolation data studies i.e. population distribution, poverty mapping, etc. Basically, RS research needs adequate amount of ground-truth data either for developing, validating, or improving the accuracy of the RS model. The oldfashioned ground-truth collecting from the field is time-cost consuming notably if the area is large heterogeneity. Development of crowd-sourcing methods for geoinformation science (GIS) and RS is getting attention and become more popular, known as volunteered geographic information (VGI). VGI may offer time-cost efficiency technology for ground-truth collecting data. We use mobile phone-based survey123 for ArcGIS to collect the ground-truth data. These ground-truth data used for improving the geo-demographic estimation map. Firstly, the geo-demographic map was developed by the traditional dasymetric method. Then, Support Vector Machine algorithm is used for improving the accuracy of traditional dasymetric map by using several predictor variables from remote sensing dataset. DKI Jakarta Province was chosen as a case study. We compare the geo-demographic estimation model with the existing data from Central Statistics Bureau of Republic of Indonesia. Using the combination of crowdsourcing, VGI dataset and several remote sensing-based predictor variables, a new method of geodemographic estimation was produced with higher R² of 0.66 compared with the traditional dasymetric method with R² of 0.09.

Reliable and accurate land use/land cover (LULC) map is a crucial data source for the understanding of coupled human–environment systems, monitoring changes, timely low-cost planning, and management of natural resources. Improvements in sensor technologies and machine learning capabilities have shifted the attention of remote sensing community to data complementarity through fusion of multi-sensor data for accurate feature extraction and mapping. Amalgamation of optical and synthetic aperture radar (SAR) images has shown promising advantages in enhancing the accuracy of extracting LULC as such method allows exploitation of information in sensors. This study investigated the potential of using freely available multisource Sentinel images to extract LULC maps in semi-arid environments through multi-scale geographic object-based image analysis (GEOBIA). A multi-scale classification framework that integrates GEOBIA, correlation-based feature selection (CFS), and random forest (RF)-supervised classification was adopted to extract LULC from assimilation of Sentinel multi-sensor products. First, Sentinel-1 and -2 images were pre-processed. Second, optimum multi-scale segmentation levels were selected using F-score segmentation quality measures. Third, 70 features of various spectral indices and derivatives and geometrical features from optical data and multiple ratios and textural features from dual-polarization SAR images were computed, and a CFS based on wrapper approach was used to select the most significant features at multi-scale levels. Finally, a single and multi-scale RF classifier was used to extract LULC classes using the most relevant features extracted from Sentinel SAR and optical images. Results of multi-scale image segmentation optimization showed that scale parameter (SP) values of 40, 60, and 150 were optimal for extraction of LULC classes. Results of feature selection showed that 22, 24, and 27 features were selected at scale SP values of 40, 60, and 150, respectively. Half of the features were common among the three scales. Single RF classification yielded overall accuracy (OA) values of 92.10%, 93%, and 91% and kappa coefficients of 0.901, 0.912, and 0.89 at scale values of 150, 60, and 40, respectively. Multiscale RF classification from scale values of 150 and 60 produced better LULC classification with OA 96.06% and kappa coefficient of 0.95 compared with other scale SP values. The integrated approach demonstrated an effective and promising method for high-quality LULC extraction from coupling optical and SAR images. Overall, multi-sensor Sentinel images along with the adopted approach feature a remarkable potential for improving LULC extraction and can effectively be used to update geographic information system layers for various applications.

Urbanization is a spatiotemporal process that has significant role in economic, social, and environmental structures. Spatiotemporal analysis for urban growth is vital for city management planning. With highly recognized financial and social developing trends, Dubai City, UAE appears as one of most challenging cities in terms of research and preparation toward a smart city aspect. Integrated technologies of remote sensing and geographic information system (GIS) facilitate urban growth detection and its relation to population distribution. In this study Multi-temporal, medium-resolution Landsat images were used to detect and analyze the urbanization trend in Dubai over the last three decades(1986-2019). Moreover, the influence of urbanization on the aspects of smart city tendency was investigated. The study methodology consisted of three parts. First, classification algorithms along with change detection, segmentation, and extraction of urban areas were used to obtain land Use/land Cover (LULC) maps. Second, Shannon's entropy was used to investigate Dubai's growth toward compact or sprawl city based on two city centers and a major highway. Finally, CA-Markov, associated with the digital elevation model and road map of Dubai, was used to simulate the urban change for 2030, 2050, and 2100. With more than 90% overall accuracy, the statistical analysis for LULC percentages and Shannons entropy values indicated that Dubai experienced a considerable increase in urban fabric while maintaining a compact growth. CA-Markov model estimated 3% urban growth by 2030, which would result in potential loss of green areas and open spaces. This study could be used in improving planning and management methods to achieve sustainable urban growth.

A practical processing framework for EO-based detection of building damage in dense urban areas is proposed based on pre- and post-event shadow differencing. The basic data set used for the detection of damaged buildings includes LiDAR and multispectral images with high spatial resolution. The typical building damage types after a major earthquake, such as height-reduced, overturn collapse and inclination, have been considered in this study. Through a scenario case study based on simulations of both building damage and shadow, understandings of the relationship between shadow and building damage are improved for real-time response practices.

We describe in this paper how we mixed 3D information, i.e., a DSM (Digital Surface Model), for segmentation tasks in airport environments. The segmentation output classes were set to asphalt, concrete, and building classes because these are informative for distinguishing airport functionality. DSM is very informative for extracting buildings because airports are usually located on flat fields; however, high resolution DSMs are not provided for free. Therefore, we gathered adequate numbers of very-high-resolution satellite images and generated DSMs through stereo processing by ourselves. At the same time, we trained a modified U-NET for the initial segmentation. By leveraging the results of the segmentation, we identified ground pixels, i.e., asphalt or concrete, and calculated the ground height. Then, we applied an adaptive threshold algorithm to the DSMs by using the ground height and extracted building masks. Finally, we concatenated probability maps from the modified U-NET and building masks that represented the building class with a high precision in the flat airport fields. Consequently, we obtained better performance than the initial segmentation results, especially in the case of the building class. In experiments, we confirmed that the modified U-NET could detect asphalt and concrete with a high precision and that it was possible to identify ground pixels and extract building masks. The performance of our approach was improved by 20%, especially in detecting the building class. For future work, we will improve the quality of stereo processing and combine size specific detectors to achieve more accurate detection.

Satellite nocturnal images of the earth are a useful way to identify urbanisation. Nighttime lights have been used in a range of scientific contributions, including studies on building human development indices and on the identification of megalopolises and impacted landscapes. However, the study of the area and internal structure of urban systems by nighttime light imagery has had a fundamental limitation to date: the low spatial resolution of satellite sensors. Although the DMSP Operational Linescan System (OLS) has been gathering global low-light imaging data for over 40 years, its 2.7 km/pixel footprint has limited its use for in-depth studies of urban development. The 2011 launch by NASA and the NOAA of the Suomi National Polar Partnership (SNPP) satellite, with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on board, has led to a significant improvement. This instrument has better spatial resolution (742 m/pixel), on-board calibration, a greater radiometric range, and fewer saturation and blooming problems than DMSPOLS data. However, it still has considerable limitations for the in-depth study of the area and internal structure of urban systems.

The launch of Luojia 1-01 in June 2018 has increased expectations. LJ1-01 is a nano satellite weighing 20 kg that can obtain high-resolution nocturnal images (130 metres/pixel). The aim of this paper is to analyse, and compare with previous satellites, the new instrument’s capacity to delimit the urbanised area and its efficiency in identifying types of urban landscape (compact, dispersed and urban). The case study is Barcelona Metropolitan Region (3,200 km², 4.7 million inhabitants).

Urban green land plays a special significant role in our life. Many previous studies have already proven the feasibility of satellite imagery processing for urban green land detection, and many classification techniques were tested for this purpose. In this paper, two methods of Machine learning, such as Artificial Neural Networks (ANN) and Random Forest (RF) were tested on a series of very high spatial resolution satellite imagery to classify, highlight urban green lands and eventually study their change in three years. Firstly, we present Multilayer Perceptron (MLP), a simple feed-forward neural network which consists of two steps, SLIC superpixel segmentation and image classification through MLP trained model. Secondly, RF technique was applied and compared to the previous classification. Then the classification results were interpreted at the final stage. These methods are performed on satellite images Pléiades of the city of Brest (3 images acquired in 2016, 2017 and 2018), these multispectral images are already preprocessed and all pan-sharpened to 50 cm spatial resolution.

A near real-time airborne 3D scanning system has been successfully implemented at Fraunhofer IOSB. This remote sensing system consists of a small aircraft and a ground control station. The aircraft is equipped with the following components: a Digital Acquisition System (DAQ), Inertial Navigation and Global Positioning Systems (INS/GPS), an Airborne Laser Scanner (ALS), and four industrial cameras. Two of these cameras (RGB and near-infrared ones) are nadir oriented, while the other two RGB cameras have an oblique orientation. The acquired LiDAR point clouds, images, and corresponding metadata are sent from the aircraft to the ground control station for further post-processing procedures, such as radiometric correction, boresight correction, and point cloud generation from images.

In this paper, the procedure regarding point cloud generation of urban scenes, with images from the nadir RGB camera, is described in detail. To produce dense point clouds three main steps are necessary: generation of disparity maps, creation of depth maps, and calculation of world coordinates (X, Y, and Z).

To create disparity maps, two adjacent images (stereopair) were rectified. Afterwards, the PatchMatch Stereo (PMS) algorithm for 3D reconstruction was executed, since it is easy to implement and provides good results according to the Middlebury Computer Vision dataset. Some steps were parallelized to optimize execution speed. Since depth is inversely proportional to disparity, depth maps were calculated from disparity maps. The height of scene elements Z was obtained by subtracting their depth to the camera height.

To calculate the remaining world coordinates X and Y, the back-projection equation and the camera intrinsic and extrinsic parameters were used. To validate the PMS algorithm, its resulting point cloud was compared with a LiDAR point cloud and a PhotoScan point cloud. The root mean square errors of both comparisons showed similar values.

In the presented research, the change detection of the urban area in Poland was conducted. Due to the lack of availability of multi-temporal data from a similar sensor, the multi-sensor change detection was applied. For the research purpose data from low-resolution Landsat satellites, medium resolution Sentinel, along with high-resolution WorldView, Spot, and Pleiades satellites were used. In order to perform change detection, all data were preprocessed with radiometric calibration and geometric correction. The analysis of changes for the urban area was carried out for three dates. Because for each of the dates various sets of data were available, i.e. for each date, there were imagery data acquired with at least two different sensor-low and high-resolution data, and for different dates, the sets varied, in the first place data fusion for each date was conducted. For data fusion, pan-sharpening was applied. Thanks to this process, images with a similar spatial resolution for three dates were obtained. After such data preparation, the process of detecting changes was started. For change detection, both pixel and object-based change detection techniques were used, i.e. image differencing, image transformations, pixel-based and object-based classification, vegetation indices differencing, and image regression, were applied. As a result of the conducted analyses, many changes were noted in the analysed urbanised area, including changes in land use and changes in vegetation. In order to get complete information about the changes, basic detection techniques were integrated. The accuracy of the obtained results of change detection was affected by the accuracy of data integration for the different dates. In spite of several errors, the use of multi-sensors would enable the analysis of changes in a wider scope.

Land surface temperature (LST) is an important factor in global climate change, vegetation growth, and urban heat island (UHI). LST is one of the most important environmental variables measured by satellite remote sensing. Public domain data are available from the operational Landsat-8 Thermal Infrared Sensor (TIRS). The present study focuses on determining and mapping UHI for the metropolitan city of Turin in Piedmont Italy using Landsat 8 multitemporal collection dataset from 2013 to 2018. The main purpose of this research is to give an instrument for the present urban management and future urban planning in order to increase city resistance and resilience against climate change through mitigation and adaptation. Improving green areas using urban forestry can be a way to mitigate Summer heat waves and trying to regulate the high demand of energy for cooling buildings. LST has been estimated using the Radiative Transfer Equation (RTE) while the LSE (Land Surface Emissivity) according to the NDVI Thresholds Method. In the multitemporal collection the UHI has been detected after calculating zonal statistics. Surfaces with similar thermal behave have been mapped using an Unsupervised classification (K-means). Through the considered years, the analysis has revealed how UHI are very common and persistent in the metropolitan Turin area, where vegetation and water content are lower and where there are a high number of buildings in concrete and asphalt is widespread.

The accurate measurement of temperature changes is the key skill not only for predictions of various natural phenomena or to detect intrusion of the object. The temperature changes we nowadays measure in local climate zones or Urban Heat Islands. The environmental quality is an essential feature of life quality and to improve it serve many remote sensing-based urban planning indicators, which are the common part of present smart cities.

Continuously developing fibre optic sensors allow their benefits to be exploited in more and more applications. Defensive Perimeter Detection by Polarization Change of the Fiber Optic Signal offers an effective possibility to detect quickly and in time disturbing a predefined space. This detection system uses the polarizing properties of light and, in particular, the birefringence of optical fibres. The disclosed detection system focuses on temperature changes that may be caused by external or internal disruption of the site. The main detection equipment is the polarization maintaining optical fibre with the same excitation in both polarization axes. The transmission rate in both axes is in the ideal case the same, but due to birefringence, inhomogeneity, and imperfection of production and next causes the mutual delay of both signals causes the signal polarization state change or even the series of polarization states changes, which can be observed in laboratory conditions and described by known mathematical methods. However, these changes can be transformed by linear polarizer to the intensity changes. This conversion allows the changes significantly easier to evaluate.

This paper investigated the influences of urban growth on thermal environment in relationship with other biophysical variables for Bucharest and Timisoara cities in Romania. Satellite remote sensing provides a cost-effective and time-saving methodology for spatio-temporal analyses of land surface temperature (Ts) distribution and urban thermal environment monitoring. Time series satellite remote sensing data from Landsat TM/ETM+/OLI, MODIS Terra/Aqua and NOAA AVHRR were used to assess urban vegetation land cover– temperature interactions over period between 2000 and 2018 years. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. For this purpose, a set of meteorological and climatic data recorded in synoptic stations were used. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at city level. Based on these parameters, the urban growth, and urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results show that the city area ratio of impervious surface in both towns Bucharest and Timisoara 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, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively.

In this paper, we present the implementation of a differential-absorption measurement-technique for surface moisture detection into a laser scanner aiding modern tunnel inspections. The use of laser scanners for tunnel inspections can reduce costly tunnel closures and provide digital data, compliant with modern building information modeling (BIM). Unfortunately, available systems are typically limited to pure 3D mapping. The Fraunhofer Institute for Physical Measurement Techniques IPM is developing a novel multi-parameter laser scanning system. For the first time, this system allows the simultaneous measurement of 3D-geometry, remission and surface moisture. The scanner measures simultaneously with two collinear laser beams with distinct wavelengths. One is centered at the absorption band of water at 1450 nm wavelength, while the other, with 1320 nm wavelength, is used as an intensity reference. The intensity ratio gives a good estimate of the surface water content. Additionally, the power of both lasers is modulated with a high frequency. This enables simultaneous measurement of the distance by comparing the phase difference of the backscattered light with a local reference. With this approach, we are able to record up to two million points per second containing distance, intensity and moisture information. Besides the technical implementation, we present point clouds from multiple test objects and surfaces. The presented data nicely demonstrates the ability to differentiate between absolute intensity variations, e.g. caused by dirt, and actual water contamination.

Urban growth can profoundly change the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical factor for sustainable development strategies and it improves the urban residential environment and living quality. In recent years, the migration from rural area to urban is major driver for expansion of Ulaanbaatar and city was urbanized a quite rapidly in the past twenty years. The aggregation of high temperature occurs particularly in the more constructed area as much as evidenced in the city center, meanwhile the reduction and fragmentation of low temperature is even more apparent in suburban areas of city. Urban Heat Island (UHI) is the most significant issue towards urbanization and sustainable development of urban around in the world. This study analyzed the Land Surface Temperature (LST) differences on time series (1995 - 2016) of Ulaanbaatar, Mongolia and the relationships between LST and Normalized Difference Vegetation Index (NDVI) (green spaces in the city) using statistical analysis, such as box-plotting and regression. The analysis basically focused on the relationship between LST and change of green space area influenced by human activities. The analytical methodologies used in this study could help to calculate the urban thermal environmental functions under conditions of urban expansion and explore the local climate change of this city. In addition, the satellite images data based on Landsat time series between 1995 and 2016 were analyzed for change detection mapping. Landsat series images were gathered by thermal mapper (TM), enhanced thermal mapper plus (ETM+) and operational land imager (OLI). The results show that relationships between each impact become an important determinant of changes in the urban thermal environment. Furthermore, seasonal factor significantly impacts to the strength of this relationship and it is being to the highest contribution indices values in summer.

Remote sensing of the Earth allows to receive information of medium, a high spatial resolution from space vehicles and to conduct hyperspectral measurements. We propose a new approach of construction of 3D-models of the Earth's surface in urban environments using remote sensing technologies. The main idea is the use of the algebra of geometric objects (addition, subtraction, multiplication, a division of objects and grids, multiplication of an object by a factor, raising an object to power, factoring, etc.). These operations automate the process of multidimensional modeling. We have developed programs that automate the process of geometric programming of scenes, in particular, scenes on the surface of the Earth (for example, trees, buildings, roads, and their arrays). In the Matlab software environment, a script library has been developed in the field of processing geometric objects, including such categories as the generation of urban objects; algebraic operations; texturing, marking and painting; work with objects in dynamics; work with random objects; differentiation and integration of objects and others. Examples of programs for automating the construction of 3D models of residential arrays based on the simplest scenarios (using algebraic operations of multiplying an object by a coefficient, a coefficient by a coefficient, an object by an object, an object by a grid of points and a line of points) are given.

The authors are developing methods for the determination of the emissions from urban sources of key impurities basing on surface and high-detailed satellite measurements. For the applications in these researches we develop a simplified parameterized model of chemical transformations in the atmosphere. This work is devoted to estimation of the effective lifetimes and the decay rates of nitrogen oxides (NO_x) entering the atmosphere as a result of emissions of industrial enterprises basing on chemical-transport simulation. The estimation of effective decay rates, which allows to relatively simply parameterize chemical processes occurring in a plume, is necessary for further use in transport models based on systems of the diffusion-reaction-advection equations and describing the behavior of the plume. The effective decay rates are calculated as the inverse of the times over which the concentrations of the corresponding nitrogen oxides decrease by e times compared to their maximum values. The dependence of their concentrations on time is found by solving a system of kinetic equations describing the reactions occurring in the plume. For the numerical solution of the Cauchy problem, a finite-difference scheme is used that takes into account the structure of the kinetic equations and has the second order of the approximation error.