Use Of Satellite Imagery Research Articles

Evaluating the Spatio-Temporal Distribution of Nitrogen Dioxide, Land Surface Temperature and NDVI in Nairobi City County

Abstract. Cities are becoming larger and it is estimated that by the year 2050, more than 6 billion people will be living in cities. As cities expand and grow, the quality of life and conditions will also transform. An integral part of environmental studies has been statistical analysis in modelling the spatial dynamics of land use changes. The research involved the use of satellite imagery to determine yearly averaged values of LST and NDVI from Landsat 8 OLI/TIR and monthly mean values of Nitrogen Dioxide (NO2) from Sentinel 5-Precursor (Sentinel-5P) across Nairobi City County. The datasets covered the period 2019, 2020, 2021, 2022 and 2023 and were analysed in Google Earth Engine. Results indicated that the yearly mean values in NO2 and LST in 2020 reduced by 2% and 12% respectively from 2019, while the mean NDVI value significantly increased by 28% in 2020 from 2019. NO2 has a negative correlation with LST in all years and a positive correlation with NDVI. Pearson correlation with population densities in constituencies in Nairobi in 2019 and 2023 indicate a negative correlation with NDVI and a positive correlation with NO2 and LST. Constituencies that have higher population densities tend to have lower vegetation densities and higher NO2 concentrations and temperature. Vegetation therefore plays a crucial role in air quality and that climatic factors such as precipitation and temperature influence the concentration of pollutants.

A framework for national-scale coastal storm hazards early warning

National weather forecasting agencies routinely issue a range of hazard warnings. But to our knowledge, along sandy coastlines where storm waves and storm surge can result in widespread but location-specific beach erosion and beachfront flooding, no national-scale early warning service for these hazards is presently operational. This paper outlines the scientific basis and implementation of a new framework for large area coastal storm hazards forecasting, currently being tested along the southwest (Indian Ocean) and southeast (Pacific Ocean) coasts of Australia. The system provides 7-day rolling predictions of localized beach erosion and/or coastal flooding linked to forecasted extreme weather events. Coastal setting influences the nature and occurrence of these hazards, with sandy beaches along wave-dominated coasts more prone to erosion and at surge-dominated coasts to flooding. An existing nearshore water-level forecasting system and a new inshore wave modeling capability are used to forecast beach erosion and coastal flooding at every 100 m along the shore. At the regional scale O(100–1 000 km of coastline), a threshold-based decision tree model categorises the predicted extent, location, and severity of erosion and flooding. At a more local scale O(100–1 000 m), physics-based modeling using XBeach focuses on vulnerable or high-value locations, providing specific storm hazard indicators tailored to local needs. This two-tier approach is feasible for national implementation due to the reduced computational effort, limiting intensive modeling to pre-identified critical locations. Delft-FEWS manages the data and modeling workflow, ensuring scalability and compatibility with existing forecast infrastructure. Initial evaluations of the system are promising, with a detailed 2-year evaluation in progress. Future enhancements could include the use of satellite imagery for real-time beach width and dune topography assimilation and exploring alternative modeling approaches to further improve forecast accuracy.

Pemanfaatan Citra Satelit Untuk Mengidentifikasi Perubahan Bentang Lahan

This study explores the use of satellite imagery in identifying and monitoring landscape change in the context of sustainable development and natural resource management. The research method used is literature study, by collecting, reviewing and analyzing various sources of information relevant to the research topic. The results of the analysis show that satellite imagery, especially from the Landsat satellite, plays an important role in mapping land use changes over a long period of time, which is useful for monitoring and analyzing land change. Various techniques and methods, such as manual digitization, use of GIS, and guided classification methods, are used to identify landscape changes. Factors such as sensor technology, image processing, ground truth data, and the complexity of the study environment influence the precision and accuracy of analysis results. The conclusion of this study is that the use of satellite imagery in managing and monitoring landscape change provides significant benefits in the context of sustainable development, but also requires a long-term and inclusive approach to ensure the use of this technology can take place effectively and sustainably.

Analyzing Urbanization using Satellite Image Processing

Abstract: Urbanization is directly proportional to the increase in population, which contributes to the increase in country’s GDP but affects the environment and ecosystem. This paper introduces an analysis of the satellite imagery of Mumbai for past 5 years (2019-2023). Mumbai has witnessed significant changes in the geographic region. In order to control and prevent the effects of urbanization, it is necessary to analyze the geographical changes and effects of the same. In this proposed analysis, we have analyzed urbanization with respect to changes in water, forest, urban land and barren land. Use of satellite imagery plays an important role in providing the opportunity for this analysis. Google Earth Engine (GEE) is used for the change detection for the selected area using the satellite imagery dataset provider COPERNICUS named Harmonized Sentinel-2 MSI as an input to this analysis. For this analysis we have used 3 classification methods i.e. supervised, unsurprised and NDVI

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

Feasibility of Using SWIR-Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from the optical spectral range as an alternative to the thermal range. It has been demonstrated that substituting surface temperature (Ts, ‘thermal approach’) with SWIR-transformed reflectance (STR, ‘optical approach’) to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020–2021 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by the optical approach were found to be comparable with those obtained by the thermal approach. The classification accuracy was assessed using water delivery records at the farm level. Although the overall accuracy was high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors.

MAXIMIZING EFFICIENT USE OF REMOTE SENSING IMAGERY FOR LAND AND SPATIAL PLANNING

Now all over the world there is a shift in the learning paradigm towards increasingly modern learning, the problems faced in Indonesia are very different from those in other countries. The need to accelerate spatial planning is increasingly needed day by day, but the lack of use of high resolution satellite imagery (CSRT) is very minimal because many still prefer to use aerial photography, even though satellite imagery has now developed rapidly and has an accuracy of 0.5 meters/centi, to obtain The data that I will use, I use the Mendeley software platform, and also search for several scientific journals from existing websites. The results that I got from the research study that I wrote as stated above is the importance of maximizing the use and utilization of images. This scientific study can conclude that the use of satellite imagery is no less important than aerial photography which requires more time in the field, the use of satellite imagery is a way to obtain information about the earth's surface from a certain distance. The concept is simple: if you observe a landscape object but it is still unclear, change perspective by standing up to see the object more clearly.

Temporal analysis of settlement areas and city footprints on construction and demolition waste quantification using Landsat satellite imagery

Municipal solid waste management has seen a surge in the use of satellite imagery in decision-making processes, yet its application to analyze quantitative variations in construction and demolition (C&D) waste remains under-investigated. This study employs satellite imagery and multivariate analysis to comprehensively assess and predict C&D waste generation in four diverse urban jurisdictions of Canada (Regina) and the USA (Seattle, Buffalo, and Philadelphia). Factors such as settlement area expansion, economic activities, and population growth significantly influence C&D waste rates. Stepwise multivariate regression models tailored to different city types, such as moderately populated (Group 1) and highly populated (Group 2), showcase acceptable predictive capabilities. For moderately populated cities, settlement area, average humidity, and GDP are identified as key predictors, while for highly populated cities, settlement area, unemployment rate, and building permit value prove effective indicators. These models, characterized by R² values from 0.70 to 0.94, provide tailored insights for distinct demographic conditions, aiding waste management planning. This research underscores the importance of satellite imagery and multivariate analysis in understanding C&D waste dynamics and empowers policymakers and waste management agencies with evidence-based strategies for effective waste management in urban centers.

Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data

Climate change has led to various adverse consequences, with natural disasters being one of the most striking outcomes. Natural disasters negatively impact life, causing significant disruptions to the ecosystem. Prompt identification of affected areas and initiation of the rehabilitation process are imperative to address the disturbances in the ecosystem. Satellite imagery is employed for the rapid and cost-effective detection of damages caused by natural disasters. In this conducted study, the outputs of climate change wildfire, forest change detection, and drought analysis, have been examined, all of which worsens the impacts on the ecosystem. The analysis of drought involved using MODIS data, while Sentinel-2A satellite images were utilized to identify wildfire areas and changes in forested regions caused by windthrow. The research focused on Ganja, Azerbaijan, as the area for drought analysis. The driest June between 2005 and 2018 was assessed using the Vegetation Condition Index (VCI) in conjunction with data from the National Centers for Environmental Information (NOAA). At the Düzce Tatlıdere Forest Management Directorate, the Normalized Difference Red Edge Index (NDRE) was utilized between the years 2018 and 2019 to detect the changes occurring in forested areas due to windthrow. The NDRE synthetic band was added to satellite images for the years 2018 and 2019, and a Random Forest (RF) algorithm was employed to classify the data. The classification results were evaluated using Total Accuracy and Kappa statistics. The study includes the detection of the Normalized Burn Ratio (NBR) applied to determine the extent of the wildfire that occurred in the Solquca village of the Qabala region in Azerbaijan in 2021. According to the analysis of the VCI and NOAA, June 2014 was identified as the driest month in Ganja. In the Tatlıdere region, the analysis indicated that 4.22 hectares experienced reforestation, while 24 hectares experienced deforestation. The NBR analysis has revealed that ~1007 hectares of land were burned in the Solquca village of Qabala. The analyses conducted provide information regarding the use of satellite imagery in relation to changes in forest areas due to drought, wildfire, and windthrow.

Assessment of Status and Distribution of Trees Inside Forest and Trees Outside Forest between 1990-2021 in Adamawa Central, Adamawa State, Nigeria

Forests significantly aid the stabilization of the global ecology by solving environmental concerns such as climate change and carbon sequestering. Therefore, the objective of this paper was to evaluate the status and distribution of Trees Inside Forest (TIF) and Trees Outside Forests (TOF) between 1990 and 2021in Adamawa Central, Adamawa State Nigeria, using standard methods of the Normalized Different Vegetation Index (NDVI), through the use of satellite imagery from 1990, 2000, 2013, and 2021 respectively. The findings showed that, the TOF decreased from 12.9 km2 in 1990 to just 3.6 km2 in 2021, the number of TIF decreased from 549.2 km2 in 1990 to 174.8 km2 in 2021. A shift in policy is necessary to mitigate the negative effects of changing land cover, boosting agricultural production, and restoring urban and forest trees by various means like afforestation and replanting.

A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance

Convolutional Neural Networks (CNNs) are leveraged for a wide range of satellite imagery information extraction tasks. However, for tasks which seek to estimate aggregated information across highly variable geographic extents, existing techniques are subject to critical limitations. We engage with a specific case study exploring this challenge: estimating census variables across 2358 Mexican municipalities, which range in scope from 2.21 km2 (˜74,000 30 m pixels) to 72,417.9 km2 (millions of pixels). Building on recent literature which has illustrated the capability of deep learning to extract socioeconomic information from satellite imagery, we specifically seek to establish baseline metrics of error that might be expected when estimating a range of census variables based on coarse-resolution (Landsat) satellite imagery alone. For each of 52 variables, we implement a multi-glimpse recurrent attention model, in which we parametrically determine subsets of each municipality to sample across iterative steps. Results of a five-fold validation indicate that nearly half of the tested variables (22) can be estimated with r2 values greater than 0.75. Results suggest considerable promise for the use of satellite imagery to estimate socioeconomic factors in both historic time periods for which surveys were not conducted, as well as contemporary inaccessible regions.

Use of satellite imagery to estimate distribution and abundance of Cumberland Sound beluga whales reveals frequent use of a glacial river estuary

Limiting disturbance in critical habitats is an important part of ensuring the well-being and sustainability of populations at risk, such as Cumberland Sound beluga whales (Delphinapterus leucas). Using non-disruptive Very High Resolution (VHR) satellite imagery, an emerging tool in cetacean monitoring, we aimed to estimate summer abundance and identify critical habitat for Cumberland Sound beluga whales. Specifically we looked in fiords that comprise their summer distribution, such as Clearwater Fiord where there is a large estuary, an important habitat to many beluga populations. Satellite images of the area were collected in 2020 and 2021, at 30 cm resolution, and in 2022 at 50 cm resolution. We evaluated beluga whale distribution using Kernel density, and identified critical habitats as areas consistently part of the beluga whale core distribution across years. Clearwater Fiord abundance estimates were corrected for whales that were too deep to be identified in the images. The estimates were significantly lower in 2021 (197 whales, 95%CI: 180-216) and 2022 (194 whales, 95%CI: 172-218) compared to 2020 (393 whales, 95%CI: 366-422). Other fiords were only imaged in 2021 and 2022, resulting in average corrected abundance estimates for all fiords of 462 (95% CI: 425-502) and 252 (95%CI: 226-280) beluga whales, respectively. Downsampling of 30 cm images to 50 cm resulted in up to 45% fewer whales detected. The only critical habitat identified within the summer distribution was in Clearwater Fiord, in or near the estuary freshwater plume and in a bay to the west of the plume. The identified critical habitats should be areas of consideration in the continued discussion on the protection and sustainability of the Cumberland Sound beluga whale population.

EURMARS: Use of Satellite Imagery as an Asset for Maritime Environment Rapid Tracking and Object Detection in Large Areas

EURMARS: Use of Satellite Imagery as an Asset for Maritime Environment Rapid Tracking and Object Detection in Large Areas

Klasifikasi Spesies Mangrove Menggunakan Drone di Nusa Lembongan, Bali

Mapping of mangroves using remote sensing technology has been widely carried out, especially in coastal areas of Indonesia. The most used remote sensing technology is satellite imagery. However, there are still some weaknesses in the use of satellite imagery for mapping mangrove, especially at the species level, including taking a long time, high costs, and low spatial resolution. One of the breakthroughs in remote sensing is drone. Drones have advantages, namely time flexibility, relatively low cost, and high spatial resolution. The purpose of this study was to determine the ability to use drones in mapping mangrove up to the species level and distribution of mangrove species in Nusa Lembongan. The method used is classification of GEOBIA (Geographic Object Based Image Analysis). Drone images were obtained with four flight missions in the northern and eastern areas of the Nusa Lembongan mangrove forest. Field data collection was carried out using a systematic random sampling method and high-resolution single band drones. The results showed that drone observations were able to identify two mangrove species in the northern research area and four mangrove species in the eastern Nusa Lembongan research area. In the northern area there are mangrove species Rhizophora apiculata and Bruguiera gymnorrhiza. In the eastern region, mangrove species Rhizophora apiculata, Avicennia alba, Sonneratia alba, and Avicennia lanata were found. The accuracy test in both research areas in the northern and eastern areas of Nusa Lembogan obtained an overall value with an average of 65% and a kappa coefficient of 0.56.

Tracking and detecting sargassum pathways across the tropical Atlantic

Pelagic sargassum (S fluitans and S natans) algal blooms and beach landings have become a regular occurrence in the Tropical Atlantic Basin since 2011; they have a variety of impacts on the marine ecosystem and blue economy. To reduce the impacts and enable effective management, forecasting and monitoring of the blooms are essential. Challenges associated with use of satellite imagery for sargassum detection in the Tropical Atlantic are spatial resolution and cloud cover, which is particularly dense in this region due to the inter-tropical convergence zone, tropical storms and hurricanes. Successful models of forecasting and prediction of pelagic sargassum are hindered by unreliable satellite data, uncertainty around windage and as well as growth and mortality. In the longer term, we aim to improve the forecast models of pelagic sargassum mat movements in open oceans by introducing evidence of the speed of travel, changing mat morphology, and size and health status of sargassum mats. To achieve this, we deployed eight trackers on floating sargassum mats in the Western Tropical Atlantic. In addition, we explore the coincidence of surface currents, wind stress and sea surface temperature as a parameter for growth on the tracker pathways. When used in conjunction with both remote sensing methods and climate data (wind, current and sea temperature), we find that GPS tracker data can facilitate more reliable monitoring of sargassum transport pathways, helps to overcome satellite-based challenges as well as model based uncertainties, and may improve the accuracy and general utility of sargassum early warning systems.

A discussion on the estimation of apparent colour of water with the Forel-Ule index for medium-width sections of rivers with high hydrodynamic activity

Apparent colour has been used as an essential optical variable for surface waters. Expressing the apparent colour of waters is usually performed using the Forel-Ule Index (FUI) colour comparator scale, defined by 21 classes. The use of multispectral satellite images to extract the apparent colour of water expands the applicability of the FUI, as satellite images are characterized by mapping wide areas with a fixed revisit period, enabling monitoring of the evolution of the apparent colour of water.The use of satellite imagery has been explored for open ocean waters, mainly to characterize the optical properties of water. Less explored is their application to river environments, especially to sections where variations in geometry related to the emergence of islets and sandbanks, caused by water flow variations or high hydrodynamic activity. That is the case with river sections that are around 100 m wide, a dimension that is compatible with the spatial resolution of available multispectral imagery, e.g. Sentinel-2, meaning that the river section features can be mapped as continuous swaths of pixels.In this paper, we discuss the importance of the clear definition of land/river boundary geometry for the FUI classification of river sections, in order to prevent its application to the out-of-water area, a problem that may arise when fluvial hydrodynamic activity is high. We propose a solution based on a segmentation that uses a water index obtainable by multispectral band composition of Sentinel-2 images. An advantage of the process is the use of a unique image for both the water index segmentation and the FUI classification. The case study is the lower/medium section of the Tagus River in Portugal, susceptible to high levels of spatial changes in its water/land boundary geometry. Results show that an adequate delimitation of the water/land boundary is critical to prevent the consideration of land area in the process and bias in the FUI interpretation.

Preliminary geological work, based on remote sensing analysis, using artificially enhanced satellite data

Abstract This study aims to identify areas that present similar spectral characteristics to collected rock samples through the use of satellite imagery and spectral analysis. The results indicate that pixels marked in the satellite images exhibit similarities to the spectral characteristics of the samples. Misclassified objects or areas may be included in the results due to mixed pixels and spatial resolution limitations. The similarities identified could result from the region’s mineral composition of building materials, bare land, or dry vegetation. The averaged spectral curve patterns of the samples show similarity overall, but they are not identical, as reflected by the tenth quantile of the similarity coefficient. This research provides a valuable support tool for preliminary geological assessment, and information relating to vast and challenging-to-access parts of prospective areas for further investigation.

Challenges and Advances of Inland Waters Monitoring: A Case Study Highlighting the Use of Satellite Imagery and Cloud Computing in Brazil

AbstractMonitoring of the quality of inland waters is important to maintain the aquatic ecosystems health and to ensure water quality standards established by environmental laws in each country. Our goal here is to present the possibilities and challenges of water quality mapping based on Sentinel 2 and Landsat 8 satellite images in geographically distinct Brazilian reservoirs as a study case. The satellite images were able to show chlorophyll a, turbidity, and Secchi data trends over 8 years of remote sensing monitoring. The time savings in the analysis of a large dataset and the availability to a wide range of stakeholders make remote sensing a good tool for monitoring inland waters. However, water quality parameters were highly influenced by cloud cover and the accuracy was low at the margin of tributary rivers. The integration of remote sensing and in situ data are needed to increase the effectiveness and accuracy of the remote sensing tools.

MATHEMATICAL, SOFTWARE AND HARDWARE SUPPORT OF THE CONCEPTUAL MODEL OF THE INFORMATION SYSTEM OF PRECISION AGRICULTURE

This study analyzes the current situation of application of precision farming technologies and solutions by agricultural enterprises of the Republic of Kazakhstan. The main players and used solutions have been identified. The statistics of application, as well as the potential of use is examined. Within the framework of the analysis of the applied solutions the advantages and disadvantages of competitors in the market were determined. It was defined that the applied systems provide the possibility of remote management, but EGISTIC is more focused on the management of all processes of the farm, including the warehouse, while John Deere is focused on the management and analytics of agricultural machinery. EGISTIC offers features for warehousing and inventory planning, something not found in the base version of John Deere Operations Center. John Deere focuses on data sharing which can be important for large farms or groups of farmers. EGISTIC makes extensive use of satellite imagery to analyze field conditions which can be a great asset for identifying problem areas and planning interventions. Depending on the specific needs and priorities of an agribusiness, one system may be preferable to another. If machinery management is the main focus, John Deere might be the best choice. If in-depth analysis of field conditions and inventory control is important, EGISTIC may be more appropriate. By analyzing, the directions for research are highlighted. A conceptual model of information system for precision farming is developed. Hardware for realization of the conceptual model is possible on the basis of universal programmable logic controller of modular architecture being developed. Within the limits of the given research the conceptual model of the universal programmable logic controller of modular architecture and the structural model of the software of the universal programmable logic controller of modular architecture have been developed. The interaction with the conceptual adaptive model of information and communication system is also considered. This paper analyzes the key principles and functions of both the universal programmable logic controller and the information and communication system, as well as their possible integration within a single concept.

Satellite Remote Sensing for Wildlife Research in the Polar Regions

Abstract Wildlife research in the polar regions has historically been limited by the logistical constraints of site access, but recent developments in the use of satellite imagery for animal detection has unlocked new possibilities for pan-Arctic and pan-Antarctic monitoring of animal populations. A range of different sensor systems have been used for wildlife research, but most have focused on optical sensors that collect data in the visible spectrum and can be directly interpreted similar to a photograph. These include medium-resolution sensors like Landsat (30 m) and Sentinel-2 (10 m) and very high-resolution sensors such as Maxar's Worldview-2 (51 cm) and Worldview-3 (31 cm). These long-established satellite systems have been joined more recently by constellations of smaller satellites (so-called “Small Sats”) that offer imagery of comparable spatial and spectral resolution to those operated by Maxar. This rapidly expanding portfolio of earth observation satellites offers the potential for a radical transformation of wildlife research in polar regions, but the sheer volume of data being collected now eclipses our capacity for manual imagery interpretation. To meet this challenge, researchers are now harnessing advances in computer vision that, coupled with improvements in computing capacity, promise to deliver a new era in our ability to monitor polar wildlife.