Wyniki wyszukiwania - BazTech

1

V3DI Ensemble Modelfor High-Accuracy Aerial Scene Classification

Shastry K Aditya, Itagi Reshma

Machine Graphics and Vision

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2025

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Vol. 34, No. 1

25--52

EN

Aerial images are valuable for observing land, allowing detailed examination of Earth’s surface features. As remote sensing (RS) imagery becomes more abundant, there is a growing need to fully utilize these images for smarter Earth observation. Understanding large and complex RS images is crucial. Satellite image scenery categorization, which involves labeling images based on their content, has diverse applications. Deep Learning (DL), using neural networks’ powerful attribute learning capabilities, has made significant strides in categorizing satellite imagery scenes. However, recent advances in DL for scenery categorization of RS images are lacking. In our study, we employed three transfer learning(TL) models - VGG16, Densenet201 (D-201), and InceptionV3 (IV3) - for classifying aerial images.VGG16 achieved 94% accuracy, while D-201 and IV3 reached 97% accuracy. Combining these models into an ensemble (V3DI ensemble model) improved accuracy to an impressive 99%. This ensemble model combines individual models’ classification decisions using majority voting. We demonstrate the efficiency of this approach by showing how ensemble classification accuracy surpasses that of training individual models. Additionally, we preprocess the dataset with a Gabor filter for edge enhancement and denoising to enhance the model’s overall performance.

2

Advances in remote sensing for monitoring soil conditions in forest ecosystems: techniques, challenges, and applications

Kubiak Katarzyna, Spiralski Marcin, Pompeu Joao, Levavasseur Vincent, Wawer Rafał

Transactions on Aerospace Research

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2024

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Nr 4 (277)

1--13

EN

Advances in remote sensing technologies have revolutionized the monitoring of soil conditions in forest ecosystems, providing valuable insights into soil moisture, nutrient content, and degradation without requiring physical access to remote areas. This article explores the application of key techniques, including satellite-based L-band radiometry, UAV-enabled LiDAR, and visible-NIR spectroscopy, in assessing forest soil properties. Challenges such as canopy interference, spatial resolution limitations, and data validation are discussed, alongside innovative solutions like machine learning and high-resolution digital elevation models. Case studies highlight the effectiveness of remote sensing in addressing environmental and forestry challenges, such as tracking the effects of climate change, logging, and erosion. By integrating advanced imaging technologies with ground-based observations, remote sensing supports sustainable forest management, conservation practices, and ecological research. Future developments in sensor technology, data integration, and machine learning hold promise for even greater precision and scalability in forest soil monitoring.

3

Analysis of the Effectiveness of Selected Machine Learning Algorithms in the Classification of Satellite Image Content Depending on the Size of the Training Sample

Kupidura Przemysław, Niemyski Stanisław

Teledetekcja Środowiska

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2024

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T. 64

24--38

EN

The article presents an analysis of the accuracy of 3 popular machine learning (ML) methods: Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), and Random Forest (RF) depending on the size of the training sample. The analysis involved performing the classification of the content of a Landsat 8 satellite image (divided into 6 basic land cover classes) in 10 different variants of the number of training samples (from 2664 to 34711 pixels), estimating individual results, and a comparative analysis of the obtained results. For each classification variant, an error matrix was developed and on their basis, accuracy metrics were calculated: f1-score, precision and recall (for individual classes) as well as overall accuracy and kappa index of agreement (generally for the entire classification). The analysis showed a stimulating effect of the size of the training sample on the accuracy of the obtained classification results in all analyzed cases, with the most sensitive to this factor being MLC, showing the best effectiveness with the largest training sample and the smallest - with the smallest, and the least SVM, characterized by the highest accuracy with the smallest training sample, comparing to other algorithms.

PL

Artykuł przedstawia analizę dokładności 3 popularnych metod uczenia maszynowego: Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) oraz Random Forest (RF) w zależności od liczebności próbki treningowej. Analiza polegała na wykonaniu klasyfikacji treści zdjęcia satelitarnego Landsat 8 (w podziale na 6 podstawowych klas pokrycia terenu) w 10 różnych wariantach liczebności próbek uczących (od 2664 do 34711 pikseli), oszacowaniu poszczególnych wyników oraz analizie porównawczej uzyskanych wyników. Dla każdego wariantu klasyfikacji opracowano macierz błędów, a na ich podstawie obliczono metryki dokładności: F1-score, precision and recall (dla pojedynczych klas) oraz ogólną dokładność i wskaźnik zgodności Kappa (ogólnie dla całej klasyfikacji). Analiza wykazała stymulujący wpływ rozmiaru próbki uczącej na dokładność uzyskiwanych wyników klasyfikacji we wszystkich analizowanych przypadkach, przy czym najbardziej wrażliwym na ten czynnik był MLC, wykazujący się najlepszą skutecznością przy największej próbce treningowej i najmniejszą - przy najmniejszej, a najmniej SVM, cechujący się największą dokładnością przy najmniejszej próbce treningowej, w porównaniu do pozostałych algorytmów.

4

Assessing the impact of land cover and land use change on urban infrastructure resilience in Abuja, Nigeria: a case study from 2017 to 2022

Adewumi Rowland, Agbasi Okechukwu Ebuka

Structure and Environment

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2024

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Vol. 16, no. 1

6--17

EN

The remarkable feature of rapid urbanisation, which has fundamentally altered the distribution of land cover and land use (LULC), is what sets the contemporary era apart. The impact of these modifications on the resilience of Abuja’s metropolitan infrastructure from 2017 to 2022 is examined in this study. Our study examined the dynamic changes in LULC using information from remote sensing, geospatial analysis software, and land cover categorization techniques. The findings indicate major changes in Abuja’s topography, including a decrease in the number of water bodies, a decrease in the number of trees, the expansion of urban areas, changes in agricultural land use, and fluctuations in the amount of grazing land. The previously mentioned changes have significant consequences for urban infrastructure resilience, affecting various sectors such as water supply, transportation, housing, utilities, and food distribution systems. The infrastructure supporting water supply and sanitation may be severely stretched as the number of water bodies decreases, affecting the quantity and quality of accessible water supplies. As metropolitan areas expand, greater strain is placed on transportation infrastructure, exacerbating traffic congestion and complicating road maintenance difficulties. Changes in agricultural land use can have an impact on food production and distribution, hence affecting food security. Deforestation can cause ecological deterioration, affecting a variety of aspects such as temperature regulation, biological diversity, and atmospheric purity. Adaptive approaches, green infrastructure, and adopting sustainable urban design can all strengthen the resilience of urban infrastructure, according to this study. The significance of renewable energy adoption, community participation, green building laws, the establishment of public-private partnerships, integrated water resource management, and data-driven decision-making is emphasised. Improving legal frameworks that prioritise resilience and sustainability is critical. It is critical to have a complete grasp of the complicated links between changes in LULC, and the resilience of urban infrastructure in order to enable educated urban design and decision-making processes. Policymakers and urban planners may address and minimise the negative consequences of climate change while improving the overall quality of life in cities by using sustainable development practises. The findings of this study have the potential to dramatically improve Abuja’s people’s well-being and sustainability, especially given the variety of urban difficulties they encounter.

PL

Współczesną erę wyróżnia niezwykle szybka urbanizacja, która zasadniczo zmieniła rozkład pokrycia terenu i użytkowania gruntów (LULC). W niniejszym badaniu przeanalizowano wpływ tych zmian na odporność infrastruktury metropolitalnej Abudży w latach 2017-2022. Dynamiczne zmiany LULC zbadano przy użyciu informacji z teledetekcji, oprogramowania do analizy geoprzestrzennej oraz technik kategoryzacji pokrycia terenu. Wyniki wskazują na poważne zmiany w topografii Abudży, w tym spadek liczby zbiorników wodnych, spadek liczby drzew, ekspansję obszarów miejskich, zmiany w użytkowaniu gruntów rolnych i wahania w ilości pastwisk. Zmiany te mają znaczące konsekwencje dla odporności infrastruktury miejskiej, wpływając na różne sektory, takie jak zaopatrzenie w wodę, transport, mieszkalnictwo, usługi komunalne i systemy dystrybucji żywności. Infrastruktura wspierająca zaopatrzenie w wodę i urządzenia sanitarne może być poważnie obciążona, ponieważ malejąca liczba zbiorników wodnych odbija się na ilości i jakości dostępnych zasobów wody. Wraz z rozwojem obszarów metropolitalnych rośnie obciążenie infrastruktury transportowej, co zwiększa natężenie ruchu i komplikuje utrzymanie dróg. Zmiany w użytkowaniu gruntów rolnych wpływają na produkcję i dystrybucję żywności, a tym samym na bezpieczeństwo żywnościowe. Wylesianie może powodować pogorszenie stanu środowiska, wpływając na regulację temperatury, różnorodność biologiczną i czystość atmosfery. Według naszych badań podejście adaptacyjne, zielona infrastruktura i przyjęcie zrównoważonego projektowania urbanistycznego mogą wzmocnić odporność infrastruktury miejskiej. Podkreśla się znaczenie energii odnawialnej, udziału społeczności, przepisów dotyczących zielonego budownictwa, ustanowienia partnerstw publiczno-prywatnych, zintegrowanego zarządzania zasobami wodnymi i podejmowania decyzji w oparciu o dane. Kluczowe znaczenie ma poprawa ram prawnych, które powinny priorytetowo traktować kwestie odporności miejskiej oraz zrównoważonego rozwoju. Świadome projektowanie urbanistyczne i procesy decyzyjne możliwe są jedynie przy zrozumieniu skomplikowanych powiązań między zmianami w LULC a odpornością infrastruktury miejskiej. Zastosowanie praktyk zrównoważonego rozwoju umożliwi decydentom i urbanistom zminimalizowanie negatywnych konsekwencji zmian klimatycznych oraz podniesienie ogólnej jakości życia w miastach. Wyniki tego badania mogą potencjalnie znacznie poprawić dobrobyt i zrównoważony rozwój mieszkańców Abudży, zwłaszcza biorąc pod uwagę różnorodność napotykanych przez nich trudności miejskich.

5

Sat4BIM4D - the concept of using satellite remote sensing to monitor construction progress in conjunction with BIM

Glinka Szymon

Reports on Geodesy and Geoinformatics

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2024

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Vol. 118

142--154

EN

Monitoring the progress of construction work and adhering to the schedule is crucial for the timely completion of projects. Integrating data from various sensors (e.g., cameras, laser scanners) mounted on diverse platforms (rovers, drones, satellites) with BIM 4D (Building Information Modelling) enables effective construction control solutions. By leveraging 3D models enriched with temporal information, project management can be significantly enhanced. This paper focuses on a comprehensive review of current literature and state-of-the-art practices to design a framework for integrating satellite remote sensing data with BIM 4D, termed the Sat4BIM4D method. Proposals for this method are developed alongside algorithms for processing satellite-derived data to monitor construction progress, particularly for infrastructure projects. The study emphasizes the compatibility and synergy between satellite data and BIM 4D, providing a structured direction for future research. Advantages, limitations, and potential challenges of the proposed approach are also critically analyzed to pave the way for further development in this domain.

6

Geoprocessing of archival aerial photos and their scientific applications: A review

Kostrzewa Adam

Reports on Geodesy and Geoinformatics

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2024

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Vol. 118

1--16

EN

Poland as well as other countries keep extensive collections of 20th and 21st-century aerial photos, which are underexploited compared to such other archival materials as satellite imagery. Meanwhile, they offer significant research potential in various areas, including urban development, land use changes, and long-term environmental monitoring. Archival photographs are detailed, often obtained every five to ten years, and feature high resolution, from 20 cm to 1 m. Their overlap can facilitate creating precise digital models that illustrate topography and land cover, which are essential variables in many scientific contexts. However, rapidly transforming these photographs into geographically accurate measurements of the Earth’s surface poses challenges. This article explores the obstacles in automating the processing of historical photographs and presents the main scientific research directions associated with these images. Recent advancements in enhancing workflows, including the development of modern digital photogrammetry tools, algorithms, and machine learning techniques are also discussed. These developments are crucial for unlocking the full potential of aerial photographs, making them easier accessible and valuable for a broader range of scientific fields. These underutilized photographs are increasingly recognized as vital in various research domains due to technological advancements. Integrating new methods with these historical images offers unprecedented opportunities for scientific discovery and historical understanding, bridging the past with the future through innovative research techniques.

7

Comparative analysis of the performance of selected machine learning algorithms depending on the size of the training sample

Kupidura Przemysław, Kępa Agnieszka, Krawczyk Piotr

Reports on Geodesy and Geoinformatics

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2024

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Vol. 118

53--69

EN

The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. Several variants of each algorithm were tested, adopting different parameters typical for each of them. Each variant was classified multiple (20) times, using training samples of different sizes: from 100 pixels to 200,000 pixels. The tests were conducted independently on 3 Sentinel-2 satellite images, identifying 5 basic land cover classes: built-up areas, soil, forest, water, and low vegetation. Typical metrics were used for the accuracy assessment: Cohen's kappa coefficient, overall accuracy (for whole images), as well as F-1 score, precision, and recall (for individual classes). The results obtained for different images were consistent and clearly indicated an increase in classification accuracy with the increase in the size of the training sample. They also showed that among the tested algorithms, the XGB algorithm is the most sensitive to the size of the training sample, while the least sensitive is SVM, which achieved relatively good results even when using training samples of the smallest sizes. At the same time, it was pointed out that while in the case of RF and XGB algorithms the differences between the tested variants were slight, the effectiveness of SVM was very much dependent on the gamma parameter -- with too high values of this parameter, the model showed a tendency to overfit, which did not allow for satisfactory results.

8

Teledetekcja wymaga wsparcia naziemnymi systemami obserwacyjnymi: przypadek polsko-tureckiego projektu - Silva Nympha

Bęcek Kazimierz

Przegląd Geodezyjny

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2024

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R. 96, nr 10

15--17

PL

W pracy przedstawiono podstawowe informacje o projekcie Silva Nympha pt. „Zrównoważone użytkowanie i inteligentne zarządzanie lasami”, który zostały wyselekcjonowany do finansowania w konkursie Narodowego Centrum Badań i Rozwoju (NCBR) w ramach bilateralnej współpracy pomiędzy Polską i Turcją w zakresie badań naukowych. Omówiono organizację projektu, podkreślono konieczność łączenia danych teledetekcyjnych z pomiarami i obserwacjami naziemnymi dla uzyskania poprawnych i użytecznych opracowań. Przedstawiono model łączenia danych w postaci hiperkostki, która tworzona jest dla każdego piksela obiektu badawczego wzdłuż osi czasu. Zawartość hiperkostek, reprezentujących obszar badawczy, będzie przedmiotem studiów metodami sztucznej inteligencji. W pracy opisano składowe systemu obserwacyjnego zainstalowanego na potrzeby projektu na polu badawczym.

EN

The paper presents basic information about the Silva Nympha project entitled "Sustainable use and intelligent management of forests", which was selected for funding in the competition of the National Center for Research and Development (NCBR) within the framework of bilateral cooperation between Poland and Turkey in the field of scientific research. The organization of the project was discussed, and the need to combine remote sensing data with measurements and ground observations to obtain correct and valid results was emphasized. A model for integrating remote sensing and in situ data was proposed as a hypercube, created for each pixel of the research object along the time axis. The content of hypercubes, representing the research area, will be the subject of studies using artificial intelligence methods. The paper describes the components of the observation system installed to meet the project's needs in the research field.

9

Wykorzystanie teledetekcji i uczenia maszynowego do oceny stanu terenów zielonych

Radkowski Adam, Kluwak Konrad, Rusiecki Andrzej, Ciskowski Piotr, Jeleń Łukasz, Wiącek Paweł

Przegląd Budowlany

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2024

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R. 95, nr 5

187--191

PL

Prezentowane badania skupiają się na wykorzystaniu technik teledetekcyjnych w analizie stanu terenów zielonych. W pierwszej części badań główny nacisk kładziony jest na ocenę wizualną, wskaźniki wegetacyjne oraz chemiczną analizę próbek roślin. Za pomocą kamery multispektralnej i RGB pozyskuje się wielokanałowe obrazy, służące do wnioskowania na temat zdrowia roślin, ich kondycji oraz zawartości różnych składników chemicznych. Do tego celu użyto głębokich sieci neuronowych i zaawansowanych metod uczenia maszynowego. Praca prezentuje pionierskie narzędzie do monitorowania i zarządzania zielonymi przestrzeniami w duchu zrównoważonego rozwoju.

EN

The presented research focuses on the use of remote sensing techniques in the analysis of the condition of green areas. In the first part of the research, the main emphasis is placed on visual assessment, vegetation indicators and chemical analysis of plant samples. Using a multispectral and RGB camera, multi-channel images are obtained, used to draw conclusions about the health of plants, their condition and the content of various chemical components. For this purpose, deep neural networks and advanced machine learning methods were used. The work presents a pioneering tool for monitoring and managing green spaces in the spirit of sustainable development.

10

Sub-Micrometer Particles Remote Detection in Enceladus’ Plume Based on Cassini’s UV Spectrograph Data

Kotlarz Jan, Kubiak Katarzyna, Zalewska Natalia

Pomiary Automatyka Robotyka

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2024

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R. 28, nr 1

107--111

EN

Enceladus is the Saturnian satellite is known to have water vapor erupting from its south pole region called „Tiger Stripes”. Data collected by Cassini Ultraviolet Imaging Spectrograph during Enceladus transiting Saturn allow us to estimate water plume absorption from 1115.35-1912.50 Å and compare it to the Mie solutions of Maxwell equations for particles with a diameter in the range from 10 nm up to 2 μm. The best fit performed using Gradient Descent method indicates a presence of sub-micrometer particles of diameters: 120-180 nm and 240-320 nm consistent with Thermofilum sp., Thermoproteus sp., and Pyrobaculum sp. cell sizes present in hydrothermal vents on Earth.

PL

Enceladus, księżyc Saturna, jest charakterystyczny ze względu na erupcje pary wodnej z regionu jego bieguna południowego, tzw. „Tiger Stripes”. Dane zebrane przez instrument sondy Cassini: Ultraviolet Imaging Spectrograph podczas tranzytu Enceladusa przed tarczą Saturna pozwalają oszacować absorpcję światła przez pióropusze wodne w zakresie 1115,35-1912,50 Å i porównać ją z rozwiązaniami Mie równań Maxwella dla cząsteczek o średnicach w zakresie od 10 nm do 2 μm. Najlepsze dopasowanie wykonane metodą Gradient Descent wskazuje na obecność cząstek sub-mikrometrowych o średnicach: 120-180 nm i 240-320 nm zgodnych z rozmiarami komórek Thermofilum sp., Thermoproteus sp. i Pyrobaculum sp. obecnych w kominach hydrotermalnych na Ziemi.

11

Variability of chlorophyll a concentration in surface waters of the open Baltic Sea

Stramska Małgorzata, Jakacki Jaromir

Oceanologia

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2024

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No. 66 (2)

365--380

EN

In situ, satellite and reanalysis data from numerical models were used to study the characteristic features of Chl variability in the Baltic Sea. The analysis is focused on the years 2003–2020 when regular observations of ocean color with the MODIS AQUA are available. In the Baltic Sea, there is a pronounced annual cycle in physical conditions in the water column, driven by seasonal cycles in atmospheric forcing. The seasonal cycle of Chl concentration does not conform to the picture known from classical models, with low phytoplankton concentration when nutrients are low. In contrast, in the Baltic Sea, the concentration of Chl is high even during the summer months when nutrients are depleted. This can be explained by a continuous supply of nutrients by runoff from land, as well as by a significant contribution to primary production by phytoplankton able to survive in environment poor in dissolved nutrients. There is also a considerable interannual variability in Chl. There are many possible cause/effect interactions involved, but the data series are still too short to make clear which of them are the most important. The most striking event was a spring bloom in 2008.

12

Typology characterisation and monitoring of arid soils in an agroecosystem environment : Case of Ziban oasis, Algeria

Belghemmaz Salah, Fenni Mohamed, Chomontowski Chrystian, Louadj Yacine, Afrasinei Mihaela G., Degui Nouara

Journal of Water and Land Development

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2024

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no. 61

213--226

EN

This study addresses date palm growth and Saharan agriculture’s substantial environmental changes in Ziban agroecosystems (ZAE). Arid climate and vulnerable soils make oasis environments fragile. Most soils are sandy and rich in saline accumulations. This study characterised ZAE dry soils, determined its typology using the World Reference Base for Soil Resources (WRB) classification and US soil taxonomy (ST), and assessed their degradation using remote sensing (RS). Fieldwork identified representative oasis based on gypsum, calcareous crusts, and salinity. Ten soil profiles were selected using two topo-sequences, and 27 samples were obtained at 0-30, 30-60, and 60-120 cm. Analyses were carried out on organic matter (OM), pH, electrical conductivity (diluted extract 1:5), CaCO 3 , gypsum, and soil texture. Oasis soils are dominated by gypsum and are all affected by salinity. The rates of OM and CaCO 3 are low to moderate. The land use and degraded areas were identified using RS data, field research, and soil analytical results. Soil classification revealed variability in soil diversity. The Typic and Gypsic Haplosalids’ ST soil group (SG) and the WRB Reference Soil Group (RSG) of Gypsic Solonchaks (Hypersalic) and Yermic Gypsic Solonchaks are equivalent. The Typic Haplogypsids and Typic Petrogypsids (ST) correspond to the Gypsisols (WRB). The Typic Torripsamments (ST) are correlated with the Arenosls (WRB). Differentiating degraded areas according to their degree of degradation and specific soil features is made possible by characterising the soils and identifying their typology. Farmers must use the right management strategies for each situation to sustain the oasis agroecosystem.

13

Impact Assessment of Urban Development Patterns on Land Surface Temperature and Urban Heat Islands Using Remote Sensing Techniques–A Case Study of Prishtina, Kosovo

Krasniqi Vlerë, Rapuca Almedina

Journal of Ecological Engineering

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2024

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Vol. 25, nr 10

91--100

EN

The swift expansion of urban areas worldwide has triggered significant environmental shifts, notably impacting land surface temperature (LST) and fostering the development of urban heat islands (UHIs). This study examines the impact of urban development patterns on LST and UHIs, focusing on Prishtina, Kosovo. As urbanization accelerates, the global population migrates to cities, increasing energy consumption, greenhouse gas emissions, and altering land cover. The purpose of this research is to utilize remote sensing techniques, including land use/ land cover (LU/LC) classification, satellite data analysis in conjunction with official LST data, measured from local institutions to identify whether urbanization has an impact on LST in Prishtina and therefore UHIs. The study, conducted over the years 2000 to 2018, reveals a substantial increase in urban settlements by two-fold in Prishtina municipality, accompanied by a reduction in vegetation cover. Utilizing LU/LC maps, a detailed analysis illustrates the correlation between LST and urban parameters: a positive association between LST and built-up areas, signifying their contribution to heightened urban temperatures, and a negative correlation between green spaces and LST, indicating a cooling effect. Statistical analyses through multiple-line regression and correlation demonstrate the significant impact of urbanization on LST, emphasizing the necessity of incorporating additional urban parameters for more comprehensive precision in future assessments. The findings underscore the critical role of urban planning interventions in mitigating UHI effects, preserving green spaces, and managing urban growth for sustainable and climate-resilient cities. This research provides valuable insights into the relationship between urban development, land surface temperature, and UHIs, offering a basis for informed urban planning and environmental management strategies to address rising temperatures in rapidly urbanizing areas like Prishtina.

14

Monitoring Coastal Erosion and Deposition in Sam Son City, Vietnam – a Contribution from Remote Sensing Data

Pham Lan Thi, Le Dung KLim, Tong Son Si, Le Ha Thi Thu

Inżynieria Mineralna

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2024

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Vol. 2, no. 1

79--87

EN

Studying the trends in shoreline erosion and accretion is essential for a wide range of investigations conducted by coastal scientists, and coastal managers. Shoreline erosion and accretion occur as a result of both natural and human influences. Some areas along shoreline in Sam Son are eroded and deposed by natural coastal processes and human actions, such as storm, wave, tourism activities. Purpose of this work is to study the erosion and deposition in Sam Son over 33 years (1989–2022). Coastlines were extracted using multi-temporal Landsat images, and the shoreline change rate was determined using Digital Shoreline Analysis Systems (DSAS). The results of this paper inlustrated that the shoreline change in Sam Son undergoes significant and varied fluctuations across different areas. At the Hoi estuary, erosion rates vary from -2.22 m/year to -40.32 m/year. The construction of FLC Sam Son is one of the factors contributing to sedimentation loss in the northern part of Sam Son City, which is situated adjacent to the East Sea and next to the Ma River. Furthermore, the accretion rate has strongly increased, reaching 9.7 m/year in the Do River estuary. The phenomenon of sediment deposition serves as the basic for constructing hotels to cater to tourism in Sam Son.

PL

Badanie trendów erozji i akrecji linii brzegowej jest istotne dla szerokiego zakresu badań prowadzonych przez naukowców nadmorskich i menedżerów wybrzeża. Erozja i akrecja linii brzegowej występują zarówno w wyniku procesów naturalnych, jak i wpływu człowieka. Pewne obszary wzdłuż linii brzegowej w Sam Son ulegają erozji i akrecji w wyniku naturalnych procesów przybrzeżnych i działań ludz-kich, takich jak burze, fale, działalność turystyczna... Celem tej pracy jest zbadanie erozji i akrecji w Sam Son na przestrzeni 33 lat (1989–2022). Linie brzegowe zostały wyodrębnione z wykorzystaniem wieloczasowych obrazów satelitarnych Landsat, a wskaźnik zmian linii brzegowej został określony przy użyciu systemów cyfrowej analizy linii brzegowej (DSAS). Wyniki tej pracy ilustrują, że zmiany linii brze-gowej w Sam Son podlegają znacznym i zróżnicowanym fluktuacjom w różnych obszarach. W ujściu rzeki Hoi wskaźniki erozji wahają się od -2,22 m/rok do -40,32 m/rok. Budowa FLC Sam Son jest jednym z czynników przyczyniających się do utraty osadów w północnej części miasta Sam Son, która sąsiaduje z Morzem Wschodnim i rzeką Ma. Ponadto, wskaźnik akrecji znacząco wzrósł, osiągając 9,7 m/rok w ujściu rzeki Do. Zjawisko osadzania się osadów służy jako podstawa do budowy hoteli obsługujących turystykę w Sam Son

15

Identification of salt-affected soils in the coastal area Of Krishna District, Andhra Pradesh, using remote sensing data and machine learning techniques

Anuradha Govada, Chivukula Venkata Sai Sankara Vineeth, Kothagundla Naga Ganesh

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2024

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T. 14, nr 2

83--88

EN

In agricultural soil analysis, the challenge of soil salinization in regions like Krishna District, Andhra Pradesh, profoundly impacts soil health, crop yield, and land usability, affecting approximately 77,598 hectares of land. To address this issue, three machine learning algorithms are compared for classifying salinity levels in the coastal area of Krishna district, Machilipatnam. This study utilizes Landsat-8 images from 2014 to 2021, correcting for cloud cover and creating a true-color composite. The study area is defined and visualized. Twelve indices, derived from Landsat imagery, are incorporated into the analysis. These indices, including spectral bands and mathematical expressions, are added as image bands. The median of these indices is calculated, and sample points representing both non-saline and saline areas are used for supervised machine learning. The data is divided into two sets: training and validation. The study evaluates Random Forest, Classification and Regression Trees, and Support Vector Machines for classifying soil salinity levels using these indices. The RF algorithm produced an accuracy of 92.1%, CART produced 91.3%, and SVM produced 86%. Results are displayed on the map, representing predicted salinity levels with distinct colors. Performance metrics are evaluated, and they assess algorithm performance. The research involved gives insights into the classification of soil salinity using machine learning, which could represent an efficient solution to the problem of soil salinization in Machilipatnam.

PL

W rolniczej analizie gleby, wyzwanie zasolenia gleby w regionach takich jak dystrykt Krishna, Andhra Pradesh, głęboko wpływa na zdrowie gleby, plony i użyteczność gruntów, wpływając na około 77 598 hektarów ziemi. Aby rozwiązać tę kwestię, porównano trzy algorytmy uczenia maszynowego do klasyfikacji poziomów zasolenia w obszarze przybrzeżnym dystryktu Krishna, Machilipatnam. W badaniu wykorzystano obrazy Landsat-8 z lat 2014-2021, korygując je pod kątem zachmurzenia i tworząc kompozycję w prawdziwych kolorach. Obszar badań został zdefiniowany i zwizualizowany. Do analizy włączono dwanaście wskaźników pochodzących ze zdjęć Landsat. Wskaźniki te, w tym pasma widmowe i wyrażenia matematyczne, są dodawane jako pasma obrazu. Mediana tych wskaźników jest obliczana, a przykładowe punkty reprezentujące zarówno obszary niezasolone, jak i zasolone są wykorzystywane do nadzorowanego uczenia maszynowego. Dane są podzielone na dwa zestawy: treningowy i walidacyjny. W badaniu oceniono Random Forest, Classification and Regression Trees i Support Vector Machines pod kątem klasyfikacji poziomów zasolenia gleby przy użyciu tych wskaźników. Algorytm RF uzyskał dokładność 92,1%, CART 91,3%, a SVM 86%. Wyniki są wyświetlane na mapie, przedstawiając przewidywane poziomy zasolenia za pomocą różnych kolorów. Oceniane są wskaźniki wydajności i wydajność algorytmów. Przeprowadzone badania dają wgląd w klasyfikację zasolenia gleby przy użyciu uczenia maszynowego, co może stanowić skuteczne rozwiązanie problemu zasolenia gleby w Machilipatnam.

16

Geomatics-enabled Interdisciplinary Approach Based on Geospatial Data Processing for Hydrogeological Risk-analysis

Di Stefano Francesco, Chiappini Stefano, Sanità Marsia, Pierdicca Roberto, Malinverni Eva Savina

Geomatics and Environmental Engineering

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2024

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Vol. 18, no. 3

63--80

EN

Hydrogeological risks that are associated with rivers have emerged as a significant concern worldwide, impacting both natural ecosystems and human settlements. This contribution presents an interdisciplinary project that leverages many technologies for data-acquisition and modeling to comprehensively analyze and manage risks in riverine environments. The project integrates geomatics, geological, and hydrological techniques to provide a holistic understanding of river dynamics and their associated hazards. As a central component of this project, geomatics plays a pivotal role in instrumental field surveying through the deployment of photogrammetry and LiDAR instruments. Remote-sensing data from satellite imagery further enriches the project’s temporal analysis capabilities. By analyzing this data over time, researchers can monitor changes in river patterns, land use, and climate-related variables; this helps identify trends and potential triggers for hydrological events. To manage and integrate the vast amount of geospatial information that is generated, a geodatabase within a geographic information system (GIS) has been established. It enables efficient data storage, retrieval, and analysis, fostering collaboration among multidisciplinary researcher teams. This system offers tools for risk-assessment, modeling, and scenario planning; these allow for proactive measures for mitigating hydrological risks.

17

Delineation of Groundwater Storage and Recharge Potential Zones Using Multi-Influencing Factors (MIF) Method: Application in Synclinal Coastal Basin of Essaouira (Western High Atlas of Morocco)

Agli Saloua, Ahmed Algouti, Abdellah Algouti, Abdelouahed Farah, Moujane Said, Salma Kabili, Maryam Errami

Geomatics and Environmental Engineering

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2024

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Vol. 18, no. 4

117--145

EN

Unpredictable rainfall caused by climate change and pollution directly impacts groundwater demand, making the exploitation of groundwater reserves necessary. To achieve this, a study in the synclinal basin of Essaouira (Western High Atlas) used GIS, remote sensing, and the Multi-Influencing Factors (MIF) method, to identify areas ideal for the installation of productive wells. An overlay analysis created a groundwater potential zone (GWPZ) map, showing 30% of the basin with high potential, 51% with moderate potential, and 19% with low to very low potential. The groundwater potential zone map was validated using geophysical surveys, piezometric data, and well water levels, showing a 69.3% prediction accuracy with the ROC curve.

18

Spatiotemporal Mapping of Agricultural and Meteorological Drought in Wasit Province Based on GIS and Remote Sensing Data

Said Lubna W., Aziz Nadia A., Hussein Luay Y.

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 6

115--122

EN

One of the most common natural hazards that can endanger life and property is drought, which can happen under a variety of weather and environmental circumstances. This study aims to monitor the agricultural and metrological drought in the Wasit Province using remote sensing data. Landsat 8 images were used to create the agriculture drought maps based on the NDVI for the years 2013, and 2023. Additionally, SPI-12 was used for the same years to evaluate the meteorological drought. The findings demonstrated that while SPI readings in 2023 were lower, the SPI-12 in 2013 indicated near-normal drought types. Two types of drought have been identified: moderate and slight. The result shows that, for the year 2013 the percentage of moderate drought is 31%, slight drought 64%, and no drought 3.9% from the total area. While, in 2023 the percentage of moderate drought is 47%, slight drought 49%, and no drought 3.2% from the total area. It is noticed that in 2023, the moderate drought class increased by about 16%. Government planners may find this results valuable when developing and managing drought consequences.

19

Risk Screening Environmental Indicators Model Change Based on SpectralTransformation Around New Yogyakarta International Airport

Sidiq Wahid Akhsin Budi Nur, Fariz Trida Ridho, Saputro Purnomo Adi, Sholeh Muh

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 8

143--160

EN

The development of New Yogyakarta International Airport (NYIA) in Temon sub-district is aimed at improving the progress of the surrounding region, where the construction has an impact on the increase in built-up land of 572.38 hectare (2013–2017) and 268.67 hectare (2017–2023) which is potentially a decrease in the environmental quality index. The purpose of the research was to analyze changes in the environmental quality index Risk Screening Environmental Indicators (RSEI) of 2013, 2017 and 2024 around NYIA. The research designs used quantitative approaches with scoring approaches, while research methods used spectral transformation and Principal Component Analysis transformation. The research has limited the use of Landsat 8 image data as a primary data source with a spatial resolution of 30 meters, where the image has not yet been able to deliver the results of the research with a high degree of exhaustion. The originality of the research is the identification of changes in the environmental quality index that are correlated with changes in built-up land and vegetation coverage. The results of the study showed a decrease in the RSEI values, where high-level RSEIs decreased by about 295.17 hectare (2013–2017) and 1720.91 hectare (2017–2024), in addition there was an increase in the area of low-level RSEI by about 122.33 hectare (2013–2017) and 1898.79 hectare (2017–2024). The decline in RSEI in the area study has been correlated with increased built-up land and decreased vegetation area, with built-up land increasing by 572.38 hectare (2013–2017) and 269.97 hectare (2017–2024), besides decreasing vegetation areas by 137.82 hectare (2013–2017), and 97.34 hectare (2017–2024). The study concluded that there was a decrease in the environmental quality index, where increased built-up land and decreased vegetation area were influential factors. This research opens up further research opportunities to predict the environmental quality index with the cellular automata model.

20

Monitoring Agricultural Drought in Savanna Ecosystems Using the Vegetation Health Index – Implications of Climate Change

Susila Ketut Dharma, Trigunasih Ni Made, Saifulloh Moh

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 9

54--67

EN

This study aims to monitor the implications of climate change on savanna ecosystem drought using time series data from the Landsat 8 sensor, spanning from 2013 to 2022. We employed a remote sensing computational approach with the semi-automatic classification plugin (SCP) in the open-source QGIS software. Specifically, we utilized channels from the operational land imager (OLI), including Band 4 Red (0.636–0.673 µm) and Band 5 Near-Infrared (0.851–0.879 µm), as well as Thermal Infrared Sensor (TIRS) channels Band 10 TIRS-1 (10.60–11.19 µm) and Band 11 TIRS-2 (11.50–12.51 µm). These channels were used to calculate the vegetation health index (VHI) using the raster calculator, followed by data reclassification with specific thresholds to compare drought-affected areas. Our findings reveal a significant impact of climate change on savanna ecosystem drought over the decade, with the most extreme conditions observed in 2015 and 2019, where drought coverage reached 42.74% and 26.58%, respectively. Other years exhibited relatively low drought dynamics, affecting less than 3% of the area. This period aligns with the el niño-southern oscillation (ENSO) phenomenon, particularly the transition from El Niño to La Niña, known to cause global weather variations, and significantly influenced by the positive phase of the Indian Ocean dipole (IOD). The novelty of this research lies in two main aspects: firstly, the use of Landsat satellite sensors for this specific region has not been extensively studied before; secondly, the discovered impacts of drought in relation to global climate change phenomena are particularly noteworthy. A limitation of this study is the relatively short investigation period of just one decade, which does not fully capture the long-term impacts of climate change. Future research is recommended to utilize imagery with higher temporal resolution over extended periods to better represent extreme climate events and derive drought patterns over durations exceeding one decade.