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1

Assessing climate change impacts on vegetation and water dynamics in semi-arid watersheds using remote sensing techniques

Charrouf Nour, Zighmi Karim, Amel Maou, Benmarce Kaddour, Hadji Riheb, Hamed Younes, Gentilucci Mateo, Farid Zahri

Studia Quaternaria

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2025

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Vol. 42, Nr 1

61--73

EN

This study aims to assess the impact of climate change on vegetation and water resources in the Safsaf watershed of northeastern Algeria. We analyzed Landsat satellite imagery from 2012 to 2022 to compute the Normalized Difference Vegetation Index (NDVI) and employed MODIS and CHIRPS data through Google Earth Engine (GEE) to explore the relationships between NDVI, Land Surface Temperature (LST), and precipitation. Our analysis revealed a significant decline in water surfaces and vegetation cover, which correlates with increased human activities and rapid urbanization due to population growth. Pearson’s correlation analysis indicated a moderate negative relationship between NDVI and LST, alongside a weak negative correlation between the Normalized Difference Water Index (NDWI) and precipitation. These findings highlight the urgent need for integrated environmental management strategies to mitigate climate change impacts and prevent further degradation of the Safsaf watershed, emphasizing the importance of incorporating ecological and hydrological considerations into future planning efforts.

2

Recommendations for planning UAV flight missions for geodata collection

Hutsul Taras, Lysko Bogdan, Zhezhera Ivan, Tkach Vladyslav, Tsvyk Tetyana

Reports on Geodesy and Geoinformatics

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2025

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Vol. 119

62--70

EN

A number of different types of information are generally associated with places. It is estimated that about 75-90 % of information may contain an official link to a specific area, expressed as, for example, coordinates, or addresses, and therefore has a spatial character, making data collection a responsible and important stage, which reasonably affects the quality of its results. Information and its sources are treated with particular care and rigor in the scientific field: in most cases, the data must be relevant, reliable, technically simple, and collected quickly at reasonable costs. The analysis of geographic information makes it possible to obtain qualitatively new information and reveal previously unknown patterns. Modern data collection methods are divided into three distinct groups: terrestrial, cartographic, and remote. Remote or aerospace methods are considered to be those that allow information to be collected. It refers to objects on the Earth's surface, phenomena, or processes from space or the atmosphere, recorded by detecting electromagnetic radiation on the ground across various spectral ranges. The involvement of various platforms (providers) of surveillance equipment makes it possible to divide them into: space, aerial photography, and images from Unmanned Aerial Vehicles (UAVs). As a technology justified on security grounds, UAVs show great promise in many areas of application. Effective planning of drone missions allows for the collection of larger sets of data with a higher level of detail and in a shorter period of time. The continuity of information collection for a given territory allows for the most accurate and reliable three-dimensional modelling, spatial analysis and geostatistics of the local situation.

3

Hoofed animal detection in UAV thermal images using Balanced Random Forest and CNN features

Włodarczyk Dorota, Jóźków Grzegorz

Reports on Geodesy and Geoinformatics

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2025

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Vol. 120

1--13

PL

Wildlife monitoring is vital to conservation efforts and the prevention of animal-related negative impacts on human activities and ecosystems. The use of Unmanned Aerial Vehicles (UAVs) enables data collection with no harm to wildlife and in difficult field conditions. This study proposes a method of detecting hoofed animals in UAV-acquired thermal images, addressing the challenges of low-resolution thermal imaging and the presence of other heated objects hindering simple temperature analysis and image segmentation. The proposed method uses machine learning algorithms and is designed to work with a limited size of training dataset. The method consists of an initial segmentation step that detects potential animals based on thermal and geometrical signatures, followed by classification using a Balanced Random Forest (BRF) algorithm. One of the key aspects of the proposed method is the use of geometric and thermal features along with multi-scale Convolutional Neural Network (CNN) extracted feature representations in BRF. The benefit of the BRF is its speed, little requirement regarding the amount of training data, and its capacity to work with an imbalanced number of objects in different classes. The dataset was collected during two UAV flights over a fenced enclosure with wild hoofed animals. The proposed approach showed high efficiency, achieving an overall accuracy of 90%. These results confirm the feasibility of UAV-based animal detection based solely on thermal images collected during the day and showing many other heated objects. The method provides a solution for wildlife monitoring, with potential adaptability to different species and further applications.

4

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.

5

The suitability of advanced geospatial technologies in monitoring mine surface displacement

Long Nguyen Quoc, Dang Minh T., Lipecki Tomasz

Journal of Sustainable Mining

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2025

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Vol. 24, iss. 4

506--519

EN

This study conducts a thorough review of the current scientific literature on the application of geospatial methods in the assessment of mining-induced displacement. The scope of research included technologies for determining deformation, subsidence, and landslide in mining areas. Global Navigation Satellite Systems, Unmanned Aerial Vehicles, Terrestrial Laser Scanners, Remote Sensing, and fusion methods are approaches used to solve the research objectives. Additionally, the paper also mentions some advantages, disadvantages, and scope of application of these methods. The investigation revealed that the displacement detection method most commonly used at the moment is satellite radar interferometry.

6

Spatiotemporal variation of vegetation cover in mining areas of YSR Kadapa district, Andhra Pradesh using remote sensing and GIS

Yarlanki Vinod Kumar, Muppala Prudhvi R., Yasam Prem K., Gollapudi Balaji, Moddu Sumanth, Besta Appanna

Journal of Sustainable Mining

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2025

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Vol. 24, iss. 1

102--116

EN

The spatiotemporal variation of vegetation cover in the mining areas of YSR Kadapa district, Andhra Pradesh, using Remote Sensing and GIS techniques. By focusing on the years 2014, 2018, and 2023, the analysis provides insights into changes observed in water bodies, bare soil, sparse vegetation, moderate vegetation, and dense vegetation. The results reveal dynamic trends in land cover categories, highlighting the environmental impact of mining activities in the region. A significant decline in water bodies is observed, with the area reducing from 3.48 km2 in 2014 to 1.91km2 in 2023. This decrease raises concerns about the potential degradation of aquatic ecosystems, reflecting the ecological consequences of mining operations. The fluctuating pattern in bare soil areas, increasing from 37.64 km2 in 2014 to 40.37 km2 in 2018 and subsequently decreasing to 34.65 km2 in 2023, indicates the complex nature of land use changes and reclamation efforts in the mining regions. The study highlights a significant decrease in sparse vegetation from 6.88 km2 in 2014 to 4.43 km2 in 2018, followed by a substantial increase to 13.49 km2 in 2023. This suggests the resilience of vegetation in certain areas or potential reforestation initiatives. A consistent decline in moderate vegetation is observed, with the area decreasing from 5.72 km2 in 2014 to 4.25 km2 in 2023, indicating the lasting impacts of mining on plant health and ecosystem stability. Fluctuations in dense vegetation areas are noted, with a decrease from 2.31 km2 in 2014 to 1.72 km2 in 2023. This decline may signify habitat disruption and environmental stress resulting from mining operations. The consequences of these spatiotemporal changes in vegetation cover extend beyond the immediate landscape, impacting the ecosystem and environment. The reduction in water bodies and vegetation, coupled with an increase in bare soil, suggests potential biodiversity loss, soil erosion, and altered hydrological patterns. These changes pose significant challenges, affecting local fauna and flora and contributing to broader ecological imbalances. The study emphasizes the importance of employing sustainable mining practices to mitigate these adverse effects, ensuring the long-term environmental health and resilience of the region. Sustainable practices could include measures to protect and restore water bodies, prevent soil erosion, and promote reforestation and habitat conservation. By adopting such practices, the mining industry can help preserve biodiversity, maintain ecosystem services, and support the overall environmental sustainability of the YSR Kadapa district.

7

Multi-faceted analysis of land use impact on rangeland health: Insights from normalized difference vegetation index assessment in stream, road, and mining areas

Muminov Mahmud A, Nosirov Muhtor G., Mukimov Tolibjon, Normuradov Davlat S., Khodjibabayev Khusan, Bohodirkhodja Ismailkhujaev, Sirojiddin Urokov, Kholiyev Askar, Eshquvvatov Bekzod B., Mamadoliev Ikromjon I.

Journal of Ecological Engineering

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2025

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Vol. 26, nr 1

196--203

EN

This study provides an in-depth analysis of specific land use areas within a semi-arid rangeland region by utilizing the normalized difference vegetation index (NDVI). The stream areas, local roads, main roads, and rock mining areas were subjected to NDVI analysis, revealing distinct vegetation health patterns. The stream areas, encompassing a 10-meter buffer, exhibited NDVI values ranging from 0.0098 to 0.447, covering 0.3% of the total study area. NDVI values for local roads (5 m buffer) ranged from 0.07 to 0.438, while main roads (10 m buffer) showed values between 0.017 and 0.172. In the rock mining areas, NDVI values varied at 10-meter and 20-meter buffer distances, with a polygon region indicating values from 0.012 to 0.276. The findings underscore the impact of specific land use practices on rangeland health and advocate for integrating NDVI techniques in monitoring and decision-making processes. The study also emphasizes the importance of selective management strategies to preserve healthy rangeland areas and mitigate the negative effects of degradation drivers, such as population density, grazing intensity, deforestation, unmanaged mining, and unplanned road networks. These insights contribute to of developing sustainable land use practices and ecological resilience in semi-arid rangeland ecosystems.

8

Machine learning algorithms and geographic information system techniques to predict land suitability maps for wheat cultivation in the Central Anatolia Region

Aljanabi Firas K., Dedeoğlu Mert

Journal of Ecological Engineering

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2025

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Vol. 26, nr 1

373--387

EN

Maintaining food security through increased agricultural production is a major concern for decision-makers, especially in areas with arid and semi-arid climatic conditions and limited natural resources. Land suitability prediction for cultivating strategic crops, including wheat, has emerged as a crucial subject for academics, decision-makers, and economists to ensure the sustainability of natural resources. This paper aims to use three soil morphological parameters, three soil physical parameters, four soil chemical parameters, and a long-term remote sensing index as input factors to produce land suitability maps for wheat cultivation based on five machine learning algorithms (MLAs): ANN, KNN, RF, SVM, and XgbTree, in the Gozlu agricultural enterprise, which is located in a semi-arid region of the Central Anatolian Plateau. To achieve this target, an inventory of 238 appropriateness points for cultivated wheat has been executed over five years, from 2019 to 2023. The outcomes revealed that the soil texture and soli available water capacity parameters were the most influential in land suitability prediction. The best performance among the MLAs was achieved by the XgbTree algorithm, which had an accuracy of 0.98 and a kappa coefficient of 0.81. Additionally, the area under the curve (AUC) was 0.90 according the receiver operating characteristics (ROC) curve approach. The results of the study demonstrated an excellent ability of the MLAs to predict land suitability for wheat cultivation in semi-arid climate conditions. This approach can play a significant role in ensuring food security and serves as an important tool for decision-makers in sustainable development. However, we propose that the approach should be examined in comparable climatic conditions with diverse crops to ensure it is a viable solution with widely cases.

9

Engineering carbon dynamics and vegetation indices in oil palm plantations: An integrated assessment of carbon stocks, normalized difference vegetation index, and net ecosystem exchange in Riau Province

Siallagan Eko Jaya, Siregar Yusni Ikhwan, Nofrizal Nofrizal, Ismail Ujang Paman

Journal of Ecological Engineering

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2025

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Vol. 26, nr 2

342--349

EN

This research examines carbon dynamics and vegetation indices in oil palm plantations across Riau Province through an integrated analysis of carbon stocks, normalized difference vegetation index (NDVI), and Net Ecosystem Exchange (NEE). Observations were conducted in six districts (Kampar, Siak, Pelalawan, Rokan Hulu, Indragiri Hulu, and Indragiri Hilir) from August 2022 to May 2024, using a nested sampling design focusing on productive oil palmsaged 8–16 years. Results showed significant variations in carbon stocks among districts, with Rokan Hulu and Indragiri Hilir consistently demonstrating the highest carbon storage capacity (41.43–43.46 tC·ha-1). NDVI analysis revealed increasing values from 2022 to 2024, with Siak District reaching the highest value (0.81) in 2024. Meanwhile, NEE in all districts showed negative values (-1.64 to -1.82 gC/m2/day), indicating that oil palm plantations serve as net carbon sinks. This research provides a comprehensive understanding of carbon dynamics in oil palm plantation systems and their contribution to climate change mitigation, while highlighting the importance of sustainable management practices in optimizing carbon sequestration.

10

Lithological mapping using Landsat data 9 in the Daknong province, Vietnam

Huynh Tien Dat, Pham Thuy Thanh, Hoang Thi Thanh Thuy, Pham Khanh Huyen

Inżynieria Mineralna

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2025

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Vol. 2, no. 1

art. no. 44

EN

The study utilizes Landsat 9 data (OLI-2 and TIRS-2 sensors) to create a geological map of Dak Nong province, Vietnam. By applying spectral analysis methods (PCA, band ratios), image classification techniques (SAM, CEM), and integrating field surveys, the research identified four main geological units: Quaternary sediments, Jurassic sedimentary rocks, basalt, and Cretaceous granite. The results demonstrate that Landsat 9 achieves high accuracy in geological mapping, particularly for basalt (with an error of only 0.68%). However, its ability to distinguish sub-units and areas with complex surface coverage remains limited, requiring support from field surveys to improve reliability

11

Remote sensing and GIS based crop monitoring: a case study of Tavra village in Vadodara

Zalavadiya Umangkumar B., Sabhadiya Himanshi R., Kapupara Parth J.

GIS Odyssey Journal

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2025

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Vol. 5, no. 1

167--175

EN

Food security is increasingly challenged by environmental changes, natural resource degradation, and population growth. Crop yields have already stagnated in many regions and are further affected by rising temperatures. The growing global population imposes a direct demand on agriculture to produce food, fiber, and fodder, necessitating the consumption of vast amounts of water. To maximize agricultural productivity and ensure sustainable crop yields, continuous crop monitoring is essential. Remote sensing has emerged as a powerful technology for vegetation monitoring, enabling spectral analysis of high-resolution satellite imagery to assess crop health and development. This study utilizes remote sensing techniques in conjunction with Geographic Information Systems (GIS) to monitor crop conditions. The Green Chromatic Coordinate (GCC) and Normalized Difference Vegetation Index (NDVI) were estimated using Landsat-9 satellite imagery. The analysis was conducted using QGIS for Tavra Village Farm, near Parul University, Waghodia, Vadodara, Gujarat, India. The observed GCC values ranged from 0.9352 to 0.3297, while NDVI values varied between 0.3300 and 0.0398 over the temporal period. The trend analysis of GCC and NDVI indicated an initial increase from November (early crop growth stage) to January (mid-growth stage), followed by a decline by February (crop maturity stage). These findings demonstrate the effectiveness of remote sensing and GIS in monitoring crop growth patterns, offering valuable insights for precision agriculture and resource management.

12

Identification of potential groundwater zones using the analytical hierarchical process technique: Case study of the region of Constantine – Northeastern Algeria

Rebouh Nouh, Tout Faicel, Dinar Haythem, Benzid Yacine, Oudni Abdelkader, Khiari Abdelkader, Özgür Nevzat

Geomatics, Landmanagement and Landscape

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2025

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no. 1

147--171

EN

This study presents an integrated approach for identifying groundwater potential zones in the Constantine region, northeastern Algeria, by combining the Analytical Hierarchical Process (AHP) with Geographic Information Systems (GIS). The methodology incorporates a multi-criteria analysis based on seven critical parameters: geomorphology, slope, drainage density, fault density, land use, lithology, and soil types. Each parameter was weighted using the AHP technique to quantify its relative influence on groundwater accumulation. Subsequently, areas were classified into zones of varying groundwater potential, ranging from ‘very poor’ to ‘excellent’. Field verification was conducted to validate the model’s results, demonstrating its effectiveness. Specifically, 80% of the 22 drilled wells in ‘good’ potential zones were found to exhibit reliable performance and sustainability. In contrast, wells located in areas classified as ‘poor’ potential zones were non-performing. These findings highlight the practical reliability of the AHP-GIS methodology in delineating groundwater-rich areas and its potential application in strategic water resource management. Moreover, the results reinforce the utility of this approach in addressing water scarcity challenges prevalent in arid and semi-arid regions. By accurately mapping groundwater concentration zones, this method offers a valuable tool for resource planners. The study also emphasizes its broader implications, including drought risk mitigation, particularly in regions where sustainable water management is critical for economic and environmental resilience.

PL

W niniejszym badaniu przedstawiono zintegrowane podejście do identyfikacji stref potencjału wód gruntowych w regionie Konstantyny, w północno-wschodniej Algierii, poprzez połączenie Analytical Hierarchical Process (AHP) z Geographic Information Systems (GIS). Metodologia obejmuje wielokryterialną analizę opartą na siedmiu krytycznych parametrach: geomorfologii, nachyleniu, gęstości drenażu, gęstości uskoków, użytkowaniu gruntów, litologii i typach gleby. Każdy parametr został ważony przy użyciu techniki AHP w celu określenia jego względnego wpływu na akumulację wód gruntowych. Następnie obszary zostały sklasyfikowane do stref o różnym potencjale wód gruntowych, od „bardzo słabego” do „doskonałego”. Przeprowadzono weryfikację terenową w celu walidacji wyników modelu, wykazując jego skuteczność. W szczególności stwierdzono, że 80% z 22 odwiertów w strefach „dobrego” potencjału wykazało niezawodną wydajność i zrównoważoność. Natomiast odwierty zlokalizowane na obszarach sklasyfikowanych jako strefy „słabego” potencjału nie wykazywały wydajności. Wyniki te podkreślają praktyczną niezawodność metodologii AHP-GIS w wyznaczaniu obszarów bogatych w wody gruntowe i jej potencjalne zastosowanie w strategicznym zarządzaniu zasobami wodnymi. Ponadto wyniki wzmacniają użyteczność tego podejścia w rozwiązywaniu problemów niedoboru wody, powszechnych w regionach suchych i półsuchych. Dzięki dokładnemu mapowaniu stref koncentracji wód gruntowych ta metoda oferuje cenne narzędzie dla planistów zasobów. Badanie podkreśla również jej szersze implikacje, w tym łagodzenie ryzyka suszy, szczególnie w regionach, w których zrównoważone zarządzanie wodą ma kluczowe znaczenie dla odporności gospodarczej i środowiskowej.

13

Climate-induced dynamics of ice cover in southeastern Greenland (2000–2024) revealed by satellite remote sensing

Głowienka Ewa, Mizera Mikołaj

Geomatics, Landmanagement and Landscape

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2025

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no. 3

71--81

EN

This study investigates multidecadal changes in the extent of glacierized surfaces in Kommune Kujalleq, southeastern Greenland, between 2000 and 2024. Multispectral satellite imagery from Landsat 7 and Landsat 8, combined with climate data from MODIS and the Global Precipitation Measurement (GPM) mission, was used to quantify changes in summer ice cover and to evaluate their relationship with atmospheric drivers. The Normalized Difference Snow Index (NDSI ≥ 0.4) was applied to classify ice-covered pixels during the melt season (July–September), and climate variables were derived for both summer and winter seasons. The results reveal an overall net decline in ice-covered area of approximately 4% (about 1,600 km²) over the 24-year period, with substantial interannual variability. Years such as 2015 and 2020 exhibited temporary increases in ice extent, coinciding with anomalously high snowfall and below-average summer temperatures, whereas significant losses occurred during warm and dry periods, notably in 2010 and 2024. Despite these fluctuations, the general trend remains one of retreat, driven primarily by sustained Arctic warming. The study highlights the effectiveness of remote sensing and cloud-based geospatial platforms for long-term cryospheric monitoring and contributes to a better understanding of regional glacier sensitivity to climatic variability in the context of global sea-level rise.

PL

W niniejszym badaniu analizowano zmiany zasięgu powierzchni zlodowaconych w Kommune Kujalleq, w południowo-wschodniej Grenlandii, w okresie wielu dekad, w latach 2000–2024. Wielospektralne zdjęcia satelitarne z satelitów Landsat 7 i Landsat 8, w połączeniu z danymi klimatycznymi z MODIS i misji Global Precipitation Measurement (GPM), wykorzystano do ilościowego określenia zmian letniej pokrywy lodowej i oceny ich związku z czynnikami atmosferycznymi. Do klasyfikacji pikseli pokrytych lodem w sezonie topnienia (lipiec–wrzesień) zastosowano znormalizowany wskaźnik różnicy śniegu (NDSI ≥ 0,4), a zmienne klimatyczne wyznaczono zarówno dla sezonu letniego, jak i zimowego. Wyniki wskazują na ogólny spadek netto powierzchni pokrytej lodem o około 4% (około 1600 km²) w ciągu 24 lat, ze znaczną zmiennością międzyroczną. Lata takie jak 2015 i 2020 charakteryzowały się tymczasowym wzrostem zasięgu lodu, zbiegającym się z anomalnie wysokimi opadami śniegu i temperaturami lata poniżej średniej, natomiast znaczne straty wystąpiły w okresach ciepłych i suchych, zwłaszcza w 2010 i 2024 roku. Pomimo tych wahań, ogólna tendencja nadal zmierza w kierunku cofania się lodu, napędzanego głównie przez utrzymujące się ocieplenie Arktyki. Badanie podkreśla skuteczność teledetekcji i chmurowych platform geoprzestrzennych do długoterminowego monitorowania kriosfery i przyczynia się do lepszego zrozumienia regionalnej wrażliwości lodowców na zmienność klimatu w kontekście globalnego wzrostu poziomu morza.

14

River Area Segmentation Using Sentinel-1 SAR Imagery with Deep-Learning Approach

Dewi Ni Putu Karisma, Suputra Putu Hendra, Paramartha A.A. Gede Yudhi, Dewi Luh Joni Erawati, Varnakovida Pariwate, Aryanto Kadek Yota Ernanda

Geomatics and Environmental Engineering

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2025

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Vol. 19, no. 4

39--63

EN

River segmentation is important in delivering essential information for environmental analytics such as water management, flood/disaster management, observations of climate change, or human activities. Advances in remote-sensing technology have provided more complex features that limit the traditional approaches’ effectiveness. This work uses deep-learning-based models to enhance river extractions from satellite imagery. With Resnet-50 as the backbone network, CNN U-Net and DeepLabv3+ were utilized to perform the river segmentation of the Sentinel-1 C-Band synthetic aperture radar (SAR) imagery. The SAR data was selected due to its capability to capture surface details regardless of weather conditions, with VV+VH band polarizations being employed to improve water surface reflectivity. A total of 1080 images were utilized to train and test the models. The models’ performance was measured using the Dice coefficient. The CNN U-Net architecture achieved an accuracy of 0.94, while DeepLabv3+ attained an accuracy of 0.92. Although DeepLabv3+ showed more stability during the training and performed better on wider rivers, CNN U-Net excelled at identifying narrow rivers. In conclusion, a river-segmentation model was conducted using Sentinel-1 C-Band SAR data, with CNN U-Net outperforming DeepLabv3+; this enabled detailed river mapping for irrigationand flood-monitoring applications – particularly in cloud-prone tropical regions.

15

Real-Time Building-Damage-Extraction Technology from Ground-Based Video Footage Using Normalized Difference Red/Green Redness Index

Shiraishi Haruhiro, Usuda Yuichiro

Geomatics and Environmental Engineering

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2025

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Vol. 19, no. 1

143--159

EN

When an earthquake occurs, promptly identifying the presence or absence of damage is crucial. This study developed a real-time building-damageextraction technique using ground-based imagery and evaluated its effectiveness. The technique applies the redness index (RI) (which was previously used in remote-sensing corrections for vegetation in arid regions) to identify “building damage” in those cases where buildings are partially or completely destroyed by earthquakes or tsunamis. To capture near-field and distant perspectives in the images, each image was divided into four quadrants (upper-left, upper-right, lower-left, and lowerright). The lower-left and lower-right quadrants were analyzed to assess the conditions on either side of a road in the near field using image recognition. Since the images contain latitudinal and longitudinal information, mapping the damage along the road can be automated by recording the route. Finally, a comparative analysis with other indices was conducted in order to evaluate RI’s superiority in damage mapping. The EMS-98 damage scale was used for damage assessment, classifying D5 (RI ≥ 0.08) as “building-collapse damage” and D0–D4 as “no building-collapse damage.” The average damage values for D5-classified buildings were significantly higher than others, thus demonstrating that RI provides practical and reliable results. Additionally, the study discussed comparisons with other indices and real-time evaluation methods. The authors sincerely hope this research contributes to life-saving efforts and deliveries of relief supplies in the aftermaths of earthquakes, ultimately saving many lives.

16

An empirical analysis of changes in the Błędów Desert using machine learning methods

Czernik Anna, Borowiec Natalia, Marmol Urszula

Geology, Geophysics and Environment

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2025

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Vol. 51, no. 1

71--88

EN

The aim of the study was to determine changes in the land cover of the Błędów Desert, which is a habitat for rare flora and fauna species protected under the Natura 2000 program. Invasive plants, which pose a threat to protected species, are present in this area. Additionally, human activities can have negative impacts on the desert ecosystem. Therefore, the land manager is obligated to carry out actions aimed at maintaining the appropriate size and character of the desert. The analysis was conducted using satellite imagery from the Sentinel-2 mission, which provides images with high temporal and spatial resolution. The study covered the years 2015–2022 and took into account seasonal variability due to the presence of green vegetation. Change detection methods based on data integration, including photointerpretation and machine learning classification, were used for land cover analysis. Five representative land cover classes were defined, enabling a quantitative presentation of changes in the Błędów Desert and a qualitative assessment of the classification performed. The results of the study indicate variability in land cover depending on the season, with an increasing number of protected plant species, including grasslands. Simultaneously, a slight increase in the desert area was noted, manifesting as an increase in sand in forested areas. The results obtained demonstrate the effective implementation of the Natura 2000 program objectives.

17

Mapping forest fire risk: A comprehensive approach using analytical hierarchy process, geographic information system, and remote sensing integration

Salman Reem, Karouni Ali, Hamadeh Nizar, Rachid Elias

Ecological Engineering & Environmental Technology

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2025

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Vol. 26, iss. 2

66--85

EN

Wildfire is one of the natural hazards that is escalating globally. While it can cause extensive harm worldwide, significant economic losses, infrastructural damage, and severe social disruption worldwide, the Mediterranean region is vulnerable because of its distinct climate and vegetation patterns. This study uses geospatial technologies and the multi-criteria decision-making method with Analytical Hierarchy Process to assess and map wildfire risk, using different factors like anthropogenic, meteorological and topographic data. The resulting fire risk map categorizes the area into five zones: very high, high, moderate, low, and very low risk. Findings indicate that 34.89% of the area is at moderate risk, 33.45% at high risk, and 7.62% at very high risk. The model’s final susceptibility map was found to be consistent with the historical fire events that occurred in the area of study, demonstrating the efficacy of the approach utilized to identify and map fire risk zones. This model will enhance disaster response capabilities and preparedness through coordination with stakeholders and development of sustainable forest management contingency plans for more resilient communities.

18

Implementation of Object Detection Algorithms in the Inventory of Technical Infrastructure, Case Study of Utility Poles Detection

Czyrzniak Mateusz, Rapiński Jacek

Civil and Environmental Engineering Reports

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2025

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Vol. 35, no. 1

177--189

EN

Roads, bridges, railways, poles, and power lines are one of main elements of technical infrastructure. Their proper condition is essential for ensuring people, goods, and electricity transportation. Inventory is a crucial process for maintaining the health of technical infrastructure. Conducting inventory with traditional surveying techniques, such as Global Navigation Satellite Systems (GNSS) or trigonometry, is both time and money consuming. In recent years, modern remote sensing techniques like LiDAR (Light Detection and Ranging) and aerial and ground based imaging have been employed for inventory purposes. These methods enable data collection over large areas, reducing both the time and cost of measurement processes. Data from these devices is often processed in conjunction with artificial intelligence algorithms that assist in processing large data sets. This paper aims to introduce deep learning (DL) algorithms for inventory purposes using object detection with utility poles as a case study. The research was conducted using empirical data. A key element in training a DL model is the optimal selection of hyperparameters. During the study, ten models were trained, and their performance was compared based on selected metrics. For this study, a dataset of 1,736 original utility pole images was used. To augment the data, new images were created through rotation and mirroring. The best model achieved the following results: Precision = 98.82%, Recall = 97.29%, F1-score = 98.05%, mAP50 = 97.92%, mAP75 = 89.93%. The performance of the model gives a solid base for further implementation of DL object detection techniques for inventory of technical infrastructure.

19

Machine learning methods of remote sensing data processing for mapping salt pan crust dynamics in Sebkha de Ndrhamcha, Mauritania

Lemenkova Polina

Artificial Satellites : Journal of Planetary Geodesy

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2025

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Vol. 60, No. 2

37--69

EN

The advances in Machine Learning (ML) and computer technologies enabled to process satellite images using programming. Environmental applications that handle Remote Sensing (RS) data for spatial analysis use such an approach, for example, Python’s library scikit-learn using algorithms on pattern identification, predictions or image classification. This paper presents an ML method of satellite image processing using Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). The aim is to classify multispectral Landsat images using ML for identification of changes in salt pans of West Mauritania, Africa over the period 2014-2023. We define 10 classes of land cover categories and perform analysis of geological, lithological and landscape setting, and then introduce the principles, algorithms and processing of the ML methods of GRASS GIS. The following classification models were employed to implement image classification with training: Random Forest (RF), Decision Tree, Gradient Boosting and Support Vector Machine (SVM). The results were compared with clustering performed by k-means and maximum likelihood discriminant analysis. The cartographic visualisation and validation was implemented through accuracy analysis. Results for the best performing SVM model with seven-band input produced an overall accuracy of 76%, for the RF model - 73%, compared to 69% for Decision Tree Classifier - 69% and for Gradient Boosting Classifier - 67%. The SVM model embedded in GRASS GIS generates robust land cover maps with good accuracy from multispectral satellite images. The paper demonstrated an ML-based automated approach to satellite image processing, which links Artificial Intelligence (AI) with cartographic tasks.

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Integrated environmental impact assessment of the Sasa tailing dam failure using remote sensing techniques

Adjiski Vancho, Zubíček Václav

Archives of Mining Sciences

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2025

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Vol. 70, no. 3

525--556

EN

Failures of tailings dams represent a critical environmental hazard, releasing mining by-products that cause long-term damage to nearby ecosystems. This research presents a detailed analysis using remote sensing techniques of the 2003 Sasa tailing dam disaster in North Macedonia. By utilising Landsat 5 imagery and Google Earth Engine (GEE), multiple spectral indices — including the Normalised Difference Vegetation Index (NDVI), Normalised Difference Moisture Index (NDMI), Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), and a turbidity proxy — were integrated to examine the immediate and spatial impacts on vegetation health, soil moisture, water presence, and sediment levels. The findings indicated significant ecological fluctuations along the river path, including vegetation stress, changes in soil moisture, water pooling, and turbidity. These effects displayed spatial gradients, diminishing further from the contamination pathway and forming distinct zones of influence. Certain intermediate areas showed anomalous disturbances, where sediment and hydrological changes impeded vegetation recovery. Pixel-level, buffer-based, and zone-based analyses - combined with Z-scores and correlation studies — revealed a complex post-disaster landscape. Weak correlation with topographic features suggested that localised conditions, rather than large-scale gradients, governed short-term ecological recovery. The study provides a framework for integrated, multi-index and multiscale environmental impact assessment, contributing to improved remediation strategies, disaster response planning, and sustainable management of post-mining landscapes.