This PDF file contains the front matter associated with SPIE Proceedings Volume 12735, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The goal of this paper is to assess the urban ecological environment quality index of the Chinese capital metropolitan area and analyze the evolution of land use patterns. In previous works of the authors, NTL and population remote sensing imagery were used to delineate the outline of the metropolitan area. In addition, this paper utilizes MODIS land cover data to reclassify land occupation types. And based on the NDVI and NDBI remote sensing images in winter and summer, a dynamic ecological Environment Quality Index (EQI) evaluation system suitable for different regions in different periods was established. We found that in 2001-2005, the ecological environment quality of each region was the best, then began to deteriorate, and reached the lowest valley around 2015, but the environment improved after that. At the same time, in these three major metropolitan areas, the ecological environment in the central part has been mainly managed and treated, thereby improving it, while the peripheral areas have lacked attention and deteriorated seriously. In addition, the degradation of agricultural land is a problem worthy of people's attention and an urgent need to take control measures.

Nighttime lights imagery is obtained using a low-light sensor on a satellite that captures quantitative measurements of light emissions at night. The value of this data is that artificial light emissions highlight human activity. It provides a unique perspective on the spread of our activities across the world. It has been used to estimate economic activity, as an alternative to traditional economic metrics such as Gross Domestic Product (GDP). Previous studies using nighttime lights data focused on the relationship between light intensity and GDP to determine if light intensity correlated strongly with GDP at national and subnational levels. In this study, annual composites of nighttime lights (for 2011, 2016, 2019 and 2021) and Landscan population estimates were used to create a model, using the Random Forest algorithm, that predicted Gross Value Added (GVA) at a one-kilometre spatial resolution for the City of Johannesburg – a metropolitan municipality located in Gauteng, South Africa that is the largest contributor to the country’s economy. The predicted model had a Spearman’s coefficient of 0.88 and an RMSD of 2468.54 Rands Million. The results in the predicted GVA showed a trend of increasing GVA from the period between 2011 and 2019, however, there was a noticeable decline in GVA and a contraction of the spatial spread of economic activity between 2019 and 2021. This coincides with the impact of Covid-19 and resulting lockdown measures, as well as the ongoing electricity interruptions, on the economy.

Urban Heat Island (UHI) driven effectively by urbanisation can directly affect environment and weather of city. UHI mitigation remains a hot topic for researchers around the world for sustainable urban planning. This study attempts on finding the relationship between spatial attributes of Land Use \ Land Cover (LULC) classes in city and their effect on Land Surface Temperature (LST) over the Built-up patches present inside the 694 districts of different States and Union Territories (UTs) of India. Pan India Decadal LULC by ORNL DAAC for year 2005 and Copernicus Global Land service LULC for year 2015 at 100 m resolution has been taken as classified data for the study. MODIS LST maps at 1km resolution were used to find temperature patterns across the districts of India. Class Level Landscape metrics (LSM) were calculated for Built-up and Shrub LU present inside every district and correlated with average LST over Built-up patches present in district. Results show that Average LST of built-up patches is affected by spatial attributes of Built-up patches with highest Variable Importance (VI) to Mean Perimeter-Area Ratio (para_mn) LSM in both years. Other common Important LSMs for LST of built-up patches are Core- Area index (cai_mn), Euclidian Mean Neighborhood Distance (enn_mn) and Aggregation index (ai). Region wise LSM for built-up patches in East and West regions of India show much significant relationship to LST. Shrub LU also shows a significant relationship with LST patterns of Built-up patches in the same district in terms of LSMs as spatial attributes.

The World Bank is interested in conducting least-cost electrification studies in developing countries with a view toward universal electricity access. Accurate and up-to-date knowledge of existing electrical transmission grid infrastructure is required for this purpose. To improve the quality of this data NEO has developed a novel smart-tracing algorithm to detect and trace electrical towers in Very High Resolution (VHR) satellite imagery. This smart-tracing approach uses existing open datasets alongside a deep learning model for object detection. The method is scalable and adaptable to arbitrary regions with satellite image coverage.

Enhancing road safety through improved skid resistance is a critical endeavor in preventing accidents and promoting secure driving conditions. This study delves into the investigation of skid resistance in asphalt pavements using digital holography, focusing on macro and microtexture attributes. By employing off-axis digital holography, the paper presents a novel method to accurately measure surface profiles and assess friction properties. The experimental setup leverages single-shot dual-wavelength holography, providing precise 3D topographic information. The study demonstrates how the obtained phase information aids in deriving surface profiles and subsequently determining friction coefficients. The approach overcomes limitations of conventional methods, offering micron-level accuracy for surface roughness measurements. Through experimental results, the versatility of adjusting measurement accuracy and range based on requirements is showcased. The paper concludes by highlighting the interplay between surface features and friction characteristics, paving the way for improved road safety assessment.

High resolution satellite imageries frequently contain shadows because of high-rise structures, especially in urban areas. One of the notable flaws in remotely sensed imaging that prevents information extraction is shadow. Building detection depends on the recognition of shadowy areas since they indicate a building's presence. Therefore, we can determine the structures' shapes and heights by utilizing the sun's illumination direction. But more crucially, detecting building shadows in high-resolution images can aid in better building detection. This work presents a shadow detection approach from a single RGB image to reduce the shadow effects on high-resolution images. We used LAB color space, which has one luminance (L) channel and two-color channels (A and B). To generate the buffer zone for building extraction, we transformed the RGB images to the CIELab color space to acquire the shadow masks. The proposed algorithm must be pre-processed to separate each shadow area into a separate data matrix to expand its functionality, evaluate the effectiveness of the method, and produce more accurate results when building shadows are close to one another. Additionally, by grouping the shadow zones into independent matrices, the method enables the detection of two or more distinct shadows of buildings with complex geometries and many parts in separate zones. Pre-processing helps us to prepare the images for further analysis. Using image pre-processing wisely, one can offer advantages and address issues that ultimately improve local and global feature detection. The effectiveness of building extraction and the outcomes of image analysis both dramatically improved results by image pre-processing.

While CO₂ is the main greenhouse gas responsible for climate change, it is a well-mixed gas in the atmosphere, meaning that its concentration is relatively uniform and does not vary much over short distances. This makes it difficult to monitor CO₂ levels in specific regions or to detect changes in CO₂ concentrations at small scales. On the other hand, NO₂ is emitted from combustion sources that also emit CO₂, and its concentration varies greatly depending on proximity, making it a useful tracer for identifying emissions sources. Therefore, NO₂ is widely assumed to be a robust proxy for combustion CO₂ and provides additional, valuable information for CO₂ monitoring such as plume detection. The combination of NO₂ and CO₂ observations is useful in determining the exact locations and intensities of anthropogenic CO₂ emissions. The idea of the present study is to design a very compact instrument for NO₂ plume detection that allows easy accommodation of both CO₂ and NO₂ sensors on a single platform. Conceding on the need to explore internal dynamics of plumes and focusing more on the spatial evolution, NO₂ detection can then rely on a relaxed set of requirements, which has a beneficial impact on instrument size, thus leading to a miniaturized instrument. In the present paper the driving requirements of such a miniaturized instrument will be introduced, and a compact design will be presented. Benefits and complexities of a compact design will be discussed.

Urban fugitive dust emission is an open pollution source that enters the atmosphere because of the dust on the ground being lifted by the wind or human activities. Dust pollution is a major contributor to atmospheric particulate matter, making it a focus for pollution control and environmental surveillance stakeholders. The identification and monitoring of dust sources hold profound practical implications. The use of remote sensing detection method facilitates extensive coverage, high accuracy, and non-invasive monitoring of urban fugitive dust emission sources. This approach enables timely alerts about potential air pollution threats, allowing swift interventions to alleviate adverse consequences. This paper mainly studies the semantic segmentation of fugitive dust sources from remote sensing images, employing advanced deep learning algorithms. In this paper, we selected Wuhai City in China as the experimental area and created Wuhai Dust Sources Dataset. This dataset, established through high-resolution satellite remote sensing data from Gaofen-1 satellite, contains 2,648 images, capturing 707 distinct dust sources. This work evaluates four different deep learning models utilising FCN and U-Net architectures as backbones in conjunction with a variety of feature extraction convolutional neural networks. The experimental results exhibit promising detection outcomes for all four models. Among these, the U-Net combined with VGG feature extraction network has the best performance, achieving an MIoU at 81% and a Mean Precision at 92%.

Currently, remote sensing applications are more diverse and numerous than when remote sensing was introduced as an environmental monitoring technology. New satellite options have appeared recently to monitor agricultural operations, environmental change, geological activity, and other Earth system processes in tandem with new data science approaches, including machine and deep learning cloud-based computing. Air emissions monitoring has recently emerged as an important application of remote sensing, particularly after introducing the Tropospheric Monitoring Instrument (TROPOMI) sensor aboard satellite Sentinel-5P. This high spatio-temporal resolution sensor was launched in 2017. The sensor collects daily aerosol, carbon monoxide (CO), formaldehyde, nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and methane (CH₄) concentrations providing global coverage. Due to the sensor’s characteristics, Sentinel-5P images constitute an alternative to urban air quality without needing other ground-based devices or methodologies. Moreover, with the development of cloud computing applications such as Google Earth Engine (GEE), which allows faster and more efficient access to remote sensing resources than traditional desktop environments, objective evaluations of environmental change can be done more effectively today than in the past. This study presents a novel methodology to build Sentinel-5p-based air quality control applications using GEE. We present an application that focuses on the Ecuadorian mainland. The application allows users to observe the CO, NO₂, and O₃ concentration at the province level as an interactive color map during user-determined periods. Thus, users can compare air pollution concentrations in particular areas of interest at different times. We validated the remote sensing-based air quality measurements using Quito’s Air Quality Monitoring Network (REEMAQ) data. Results showed stronger correlations between ground and remote remotely sensed measurements for NO₂ (R² =0.61 and RMSE= 2.669 for the training data; R²= 0.58 and RMSE=2.627 for validation data) than for any other pollutants. The product is available at the link https://cesarivanalvarezmendoza.users.earthengine.app/view/sentinel5p. Diverse municipalities can replicate the application in developing countries with insufficient air quality monitoring resources. In addition, intuitive tools, such as those developed in this study, could help promote air quality policies to improve urban citizens' living standards.

Light detection and ranging (lidar) have been valuable tools in remote sensing of aerosols near the ground. For the operation of a lidar system, the laser, as a transmitter, plays a vital role in the whole system. Laser Diodes (LD) and Diode-Pumped Solid State (DPSS) laser technology have evolved, making the lidar system more compact compared to Nd:YAG laser sources. However, the lidar system’s long time and continual operation need maintenance to keep the laser source output stable. Also, the laser source is vulnerable to static electricity and needs to stabilize electric power. In this work, a multiwavelength lidar system with a Light Emitting Diode (LED)-based light source is designed and developed to monitor aerosol distribution in the near-ground atmosphere during continuous observation. The LED light source does not require any heat dissipation system and can emit light for long periods with constant output. The LED lamp light sources with wavelengths of 365, 450, 525, and 630 nm (peak power of up to 2W) are used as lidar transmitters. This lidar system visualizes rapid activities of aerosols in the near-range measurement due to its repetition frequency of over 250 kHz. Analysis of the backscattering light intensity with four wavelengths from this LED lidar system produces real time extinction coefficient and size distribution in the near-ground atmosphere. This report discusses the design and practical test of the multi-wavelength LED Lidar.

Visualization of the wind field in the lower atmosphere and its dynamics are important for understanding the mixing and interaction between geology and atmosphere. The dispersion of that dust is a major problem not only for environmental protection, but also for human health, such as respiratory diseases, and air pollution caused by man-made dust in urban areas, so great demands for visualization and monitoring of wind fields and dust flow near the ground surface are raised. To observe atmospheric flows on small spatio-temporal scales near ground, we are developing a low coherence Doppler lidar. Low coherence Doppler lidars can capture the dynamics of the lower atmosphere because of the high spatial and temporal resolutions of 1 m and 5 ms, respectively. Dust flow measurements can be made, while the system is not sensitive enough to measure the atmospheric wind itself. That is current task. In this paper, we discuss the efficiency improvements of the lidar transmitting and receiving optical systems and the receiving system itself of fiber coupling by two orders of intensity with a fiber-type distributed feedback laser diode. The beam quality of this light source was higher than the previous bulk type one, and the interference light intensity was six times higher. The Doppler shift frequency measurement with a rotating target showed a larger signal-to-noise ratio of approximately 70 dB, 30dB higher than the previously reported system.

Coronavirus is known to cause severe acute respiratory syndrome (SARS). The effects of the infections were severe in the case of premedical conditions in the subjects. A case in this point; the prolonged exposure of air pollution and associated health risks. In this work we study the relation between mortality and the long-term exposure to air pollution in urban centers of Maharashtra, India. In addition to analyzing the general trend, we focused on the cities in western Maharashtra, which are more developed as compared to the rest of the Maharashtra. The main objective of the study was to establish the relation between the air pollution and COVID-19 morbidity. The secondary objective was to establish the air pollution as a parameter for susceptibility to COVID-19 like pandemics. We used Sentinel-5P data for extracting the pollution concentration of sulphur dioxide (SO₂), Nitrogen dioxide (NO₂), Aerosol index, carbon monoxide (CO), and ozone (O₃). The deaths in these cities were collected from the news reports. The relation between COVID-19 deaths and high-level of air pollution was amply evident from the analysis. The long-term exposure to pollution in the cities was found to be correlated with COVID-19 deaths. Furthermore, more industrialised cities showed stronger correlation. This may be attributed to the old part of cities where narrow roads confined by very closely space buildings on both the sides, heavy vehicular pollution, and poor ventilation often create a smoke chamber like situation. This needs to be investigated further using case specific data.

Thailand has experienced rapid urbanization and industrialization, resulting in increased air pollution. Urban air pollution is a major environmental issue contributing to respiratory diseases. The purpose of this study was to develop an air pollution platform, namely “Life Dee”, for near real-time monitoring of the fine particulate matter (PM2.5) in Chonburi province at microclimate conditions. This was achieved using a combination of remote sensing data, ground based stations, and microclimate modeling. The Weather Research and Forecasting with Chemistry (WRF-CHEM) model was used to simulate PM2.5 concentrations with 1 km × 1 km spatial resolution. A High Definition (HD) map was created using ArcGIS CityEngine, which utilizing for visualization of PM2.5 concentrations in urban areas. The user-friendly platform was developed to make the data accessible to the public via mobile applications. This platform can serve as a prototype model for other urban areas dealing with similar air pollution challenges. Users can utilize the platform to monitor air quality and receive information on appropriate action plans to protect their health. In addition, the platform provides valuable information to government agencies, allowing them to take proactive measures to mitigate air pollution and improve quality of life for citizens.

This study presents a methodology for conducting a retrospective and indirect health risk assessment in urban areas, due to the long-term exposure to particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3). Specifically, the risk of all-causes mortality is investigated. The methodology combines satellite-based settlement data, model-based air pollution data, land use information, demographic data, and regional-scale mobility patterns. The study examines the impact of population mobility on the population exposure and daily variations in pollutant levels on health risks. The results from the study show that neglecting the mobility patterns and the diurnal cycles of pollutants can lead to an underestimation of the health risk. The incorporation of satellite and model data makes this methodology scalable to perform a health risk assessment also in remote regions, where local sensors are limited. Additional studies are required to assess the uncertainty in the exposure when using these medium to- low resolution data.

The estimation of vegetation traits, which is essential to characterize the health of trees from remote sensing data, presents several challenges in urban environments, due to the topography of 3D buildings and associated shading, the spectral diversity of materials, or the variety of urban morphology. Moreover, the difficulty to estimate the vegetation traits increases with the decrease of spatial resolution, mixed pixels including information on trees and their environment. The objective of this study is to estimate the influence of tree-endogenous (chlorophyll, LAI...) and tree-exogenous (urban form, tree distance to buildings, street orientation, solar angles, material types...) factors on the reflectance of Sentinel-2 pixels (10/20 m resolution). For this, a sensitivity analysis was carried out with the DART 3D radiative transfer model. First, a design of experiments was built using 15 variables describing the trees and their environment. Four urban 3D scenes that were elaborated based on the Local Climate Zone (LCZ) typology. For each of these urban 3D scenes, 3000 simulations were generated. Then, Sobol indices were computed to estimate the influence of each factor on the Sentinel-2 reflectance, more specifically on the ten spectral bands and eight vegetation indices correlated to vegetation traits. These experiments were conducted on isolated and aligned trees. In addition, the influence of the geo-registration uncertainty of the Sentinel-2 products was assessed in comparing the results obtained using a single tree-centered pixel with those using pixels offset from the tree. Results showed that Sentinel-2 data at 10 m resolution, NDVI et ARVI indices are the most relevant for the estimation of vegetation traits both for isolated and aligned trees, especially in LCZ five and eight, and in using a single tree-centered pixel approach.

The Twin ANthropogenic Greenhouse Gas Observers (TANGO) mission will monitor and quantify greenhouse gas emissions at the level of individual facilities. A consortium consisting of ISISpace, TNO, SRON and KNMI are developing the TANGO mission for the ESA Scout program. ISISpace is the prime contractor and responsible for the spacecraft, SRON and KNMI are responsible for the atmospheric science, while TNO is developing the instruments. The TANGO space segment consists of two agile 16U CubeSat satellites flying closely in tandem, each equipped with an imaging spectrometer. TANGO Carbon measures the emission of CH₄ and CO₂ in the SWIR1 spectral band (1590-1675 nm at 0.45-nm spectral resolution), while TANGO Nitro measures the emission of NO₂ in the visible spectral range (405- 490 nm at 0.6-nm spectral resolution). Both instruments are reflective pushbroom spectrometers, made almost entirely from aluminum, and will cover a 30-km swath from a 500-km altitude with a spatial resolution of 300 m. The instruments share a similar architecture, using freeform mirrors to achieve high optical performance in a compact 8U envelope. In this paper, we will present the design and performance of the Carbon instrument, where a key engineering challenge is to achieve the desired spatial resolution and SNR from the limited instrument volume (8U). A tight integration of optical and mechanical design, coupled to detailed tolerance, alignment, straylight and STOP (structural thermal optical performance) analyses, allow us to reach that goal.

Methane gas is widespread in both industry and in domestic settings, but leaks into the environment pose a great risk both in terms of individual safety and climate. We previously presented a low cost methane imaging system that utilises backscattered illumination techniques to directly image methane gas in real time. This device was capable of imaging leaks with flow rates on the order of 0.05 L/min at a 3m range. The system illuminates a scene using tunable laser diodes centred at the methane absorption near 1653nm, with the backscattered light collected and imaged by a standard short-wave infrared focal plane array. Information obtained with the infrared images can be used in combination with a live visible feed to highlight exactly in the scene the source of the methane. We present a modified system for use on a UAV. Firstly the system, which utilises differential imaging, had to be altered in order to increase the image stabilisation to compensate for the increased amount of movement. Secondly, the impact of sunlight on the system was explored with imaging conducted both during the day and at night. We demonstrate the use of this system mounted to a UAV, imaging at an altitude of 3m and variety of horizontal range. It is demonstrated that performance is variable depending on surface-type, and sunlight level. It is envisaged that such a device would improve the ease of use for both routine facility or city pipeline inspections.

With the development of cities, it is an inevitable trend for cities to spread and expand to peripheral areas. Accurate measurement of urban boundaries and scope, so as to be able to obtain accurate results and laws of urban area expansion and the evolution characteristics of urban forms, is of great significance to the sustainable development of urban science. This paper attempts to use the method of yearly calibration, taking Beijing-Tianjin-Hebei China's capital economic circle as an example, to calibrate and sort out the multivariate and long-term night light remote sensing data of DMSP and VIIRS from 1992 to 2020, so that it can be applied to long-term evolution analysis and analysis and research. In addition, this paper also uses this data set to analyze the evolution pattern of urban expansion in the region and finds that cities in the Beijing-Tianjin-Hebei region mainly develop to the east and south, and the development is most obvious in the coastal areas. Urban sprawl in the region is accompanied by accelerated land area growth, low population growth, and low-density increases.

Various studies have been conducted to detect objects in urban areas by applying machine learning algorithms to UAV high-resolution images. However, most vehicle detection studies have limitations in that vehicle detection is performed as a bounding box instead of instance segmentation. Since instance segmentation requires labor-intensive labeling work of each object to train individual objects, research on how to perform unsupervised automatic instance segmentation is needed. Therefore, this study proposed unsupervised SVM classification of the vehicle bounding boxes in UAV images for instance segmentation. As a result of the extraction, it was confirmed that the vehicle could be detected with an accuracy of 89%. It was also confirmed that the vehicle could be detected even if the spectral characteristics within the vehicle were significantly different.

Digital Twins will unlock the potential of digital modelling of the Earth systems at a level that represents a real breakthrough in terms of accuracy, local detail, access-to-information speed and interactivity. The initial focus will be on the effects of climate change and extreme weather events, their socio-economic impact and possible adaptation and mitigation strategies. Surface and ground air temperatures are one of the variables that best distinguish and characterize the specific climate in urbanized spaces. Over the years, research has shown that urbanized spaces have experienced persistently higher temperatures, which is defined as the urban heat island effect (Urban Heat Island-UHI). The study covers examples from the six planning regions defined in the Law on Regional Development of the Republic of Bulgaria under Art. 11, which will support the Integrated Territorial Strategy for the Development of NUTS 2 planning district. These are territories whose selection is determined by the fact that they have extremely high economic and ecological importance for monitoring the normal course of natural processes, disasters and consequences of sudden changes. Different indicators and indices such as TCT (Tasseled cap transformation), NDVI (Normalized Difference Vegetation Index), NDGI (Normalized Differential Greenness Index), LST (The Land Surface Temperature) and etc., were used for the different groups of objects from the Sentinel and Landsat 9 (OLI / TIRS) satellites. The spectral reflectance characteristics of natural and anthropogenic objects have been used to classify them in optical or microwave images. Image classification methods based on the spectral characteristics of key spectral indicators and indices were previously used. Each of the metrics was further classified to provide detailed analysis for the period 2018 to 2023 and comparison with current drone footage and thermal camera with a measurement accuracy of +/- 2°C and a wavelength of 8 μm to 14 μm. The research used data from the Open Data Portal and the National Spatial Data Portal (Inspire) of Bulgaria.

Mediterranean regions are heavily exposed to wildfires that can result in devastating casualties and infrastructure damage. Greece has been particularly affected by wildfires during recent years and the accurate mapping of the fire-exposed areas is essential. This can enhance our process understanding on such natural hazards, also supporting practitioners and decision-makers. Here, we combined remote sensing observations from the Copernicus Sentinel-2A satellite with GIS techniques to delineate the spatial extent and built-up losses at three example locations over Greece that were substantially affected by the summer 2021 wildfires, namely the regions of Northern Evia, Eastern Attica, and Achaia. The overall burned areas, as quantified with the pre- and post-fire Normalized Burn Ratio (i.e., dNBR), ranged from about 3 km² to more than 500 km² , while the exposed built-up features (buildings, roads, etc.) vary between the study regions following site-specific characteristics (built-up density, urban/rural interface, topography, etc.). The combination of publicly available remote sensing Earth observations and GIS techniques allowed us to obtain quantitative insights on the urban features exposed to these wildfires and their variability between the examined locations.

Accurate segmentation of buildings in remote sensing images is crucial for various applications, like urban planning, disaster management, and environmental monitoring. Traditional methods often struggle to handle the complexity of building structures and appearances. In this work, we utilize a multi-level multiple attention-based approach in the DeepLabv3+ model for obtaining global context and local information through the dual attention mechanism and convolutional block attention module. Rather than deploying superficial convolution layers, EfficientNetB7 is used as an encoder. Dual attention comprising of position attention module and channel attention module are added to the output of atrous spatial pyramid pooling model. This is done to obtain the inter-relationship between spatial and channel dimensions. The position attention module obtains the interdependencies of similar features irrespective of their distances through a weighted sum of the features at all positions in the image. Whereas channel attention focuses on improvising correlated channel information by incorporating relevant features across all channel maps. Also, convolutional block attention module is incorporated for better representation of low-level features which is added to the top of the pre-trained residual network backbone. The result of the two attention modules provides better segmentation results. The proposed model was executed on a building dataset, namely Massachusetts Building Dataset. The experimental results demonstrate the improved performance of the proposed model by increasing the mIoU by 0.47% on the dataset, respectively as compared to current state-of-the-art models.