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10 pages, 2671 KiB

Open AccessProceeding Paper

Enhancing Solar Radiation Storm Forecasting with Machine Learning and Physics Models at Korea Space Weather Center

by Ji-Hoon Ha, Jae-Hyung Lee, JaeHun Kim, Jong-Yeon Yun, Sang Cheol Han and Wonhyeong Yi

Eng. Proc. 2025, 94(1), 1; https://doi.org/10.3390/engproc2025094001 - 5 May 2025

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Solar radiation storms, caused by high-energy solar energetic particles (SEPs) released during solar flares or coronal mass ejections (CMEs), have a substantial impact on the Earth’s environment. These storms can disrupt satellite operations, interfere with high-frequency (HF) communications, and increase the radiation exposure [...] Read more.

Solar radiation storms, caused by high-energy solar energetic particles (SEPs) released during solar flares or coronal mass ejections (CMEs), have a substantial impact on the Earth’s environment. These storms can disrupt satellite operations, interfere with high-frequency (HF) communications, and increase the radiation exposure of high-altitude flights. To reduce these effects, the Korea Space Weather Center (KSWC) monitors and forecasts solar radiation storms using satellite data and predictive models. This paper introduces the space weather forecasting methods employed by the KSWC and the analysis approach for satellite data from GOES, SDO, the LASCO coronagraph, and STEREO. We introduce a predictive model for solar radiation storms, which is composed of two key components: (1) a machine learning model, which is trained using solar flare and CME data obtained from satellite observations, and (2) a physics-based model that incorporates the mechanisms of SEP generation through CMEs approaching the Earth. The machine learning model primarily forecasts the peak intensity of solar radiation storms based on real-time solar activity data, while the physics-informed model enhances the interpretability and understanding of the machine learning model’s predictions. The effectiveness and operability of this approach have been tested at the KSWC. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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7 pages, 3442 KiB

Open AccessProceeding Paper

Monitoring Ecosystem Dynamics Using Machine Learning: Random Forest-Based LULC Analysis in Dinder Biosphere Reserve, Sudan

by Ahmed M. M. Hasoba, Emad H. E. Yasin, Mohamed B. O. Osman and Kornel Czimber

Eng. Proc. 2025, 94(1), 2; https://doi.org/10.3390/engproc2025094002 - 16 Jun 2025

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Abstract

Dinder Biosphere Reserve (DBR), a UNESCO-recognized biodiversity hotspot in Sudan, faces escalating land-use pressure. We analyzed land cover changes from 2019 to 2024 using Sentinel-2 imagery processed in Google Earth Engine. A Random Forest classifier identified five land cover classes: water, built-up areas, [...] Read more.

Dinder Biosphere Reserve (DBR), a UNESCO-recognized biodiversity hotspot in Sudan, faces escalating land-use pressure. We analyzed land cover changes from 2019 to 2024 using Sentinel-2 imagery processed in Google Earth Engine. A Random Forest classifier identified five land cover classes: water, built-up areas, vegetation, bare land, and crops. The transition matrix revealed significant changes over this period. About 1501 km² of vegetation and 1648 km² of cropland were converted to bare land. Built-up areas lost 95 km² to bare land. Bare land remained largely unchanged (4749 km²), while water bodies were the most stable (13,473 km² unchanged). Only minor transitions involved water (27.6 km² to vegetation, 15.2 km² to bare land). Notably, 411 km² of cropland and 1773 km² of bare land transitioned to vegetation, indicating some regrowth. These land cover changes reflect a dynamic interplay between degradation and recovery processes; however, the results should be interpreted with caution due to potential classification inaccuracies, seasonal variation in imagery, and absence of field validation. Continued satellite monitoring is essential to guide adaptive land management and safeguard ecosystem function in DBR. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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6 pages, 1798 KiB

Open AccessProceeding Paper

Mineralogical Mapping of Pyroxene and Anorthosite in Dryden Crater Using M³ Hyperspectral Data

by Iskren Ivanov and Lachezar Filchev

Eng. Proc. 2025, 94(1), 3; https://doi.org/10.3390/engproc2025094003 - 19 Jun 2025

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Abstract

This study investigates the mineral composition of the lunar Dryden Crater using Moon Mineralogy Mapper (M³) data. A RGB false-color composite reveals distinct pyroxene, anorthosite, and possibly spinel distribution patterns. Orthopyroxenes, excavated from deep crustal layers, dominate steep slopes, while plagioclase-rich [...] Read more.

This study investigates the mineral composition of the lunar Dryden Crater using Moon Mineralogy Mapper (M³) data. A RGB false-color composite reveals distinct pyroxene, anorthosite, and possibly spinel distribution patterns. Orthopyroxenes, excavated from deep crustal layers, dominate steep slopes, while plagioclase-rich materials align with magma ocean models of lunar crustal formation. Minor clinopyroxenes indicate impact melt origins. While space weathering and shock metamorphism pose analytical challenges, integrating spectral data with geological context elucidates the crater’s complex history. The resulting mineral distribution map supports targeted exploration during upcoming lunar missions, resource prospecting and resource utilization initiatives within this geologically complex region. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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7 pages, 1570 KiB

Open AccessProceeding Paper

Evaluating the Influence of Missing Data from the Crop Vegetation Index Time Series on Copernicus HR-VPP Phenological Products

by Alexey Valero-Jorge, Mª. Auxiliadora Casterad and José-Tomás Alcalá

Eng. Proc. 2025, 94(1), 4; https://doi.org/10.3390/engproc2025094004 - 19 Jun 2025

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Abstract

Phenological parameters extracted from time series (TS) of spectral indices are essential to characterizing crops. However, the lack of data in the TS can affect their accuracy. The Copernicus Land Monitoring Service (CLMS) provides these parameters and their temporal quality. This paper evaluates [...] Read more.

Phenological parameters extracted from time series (TS) of spectral indices are essential to characterizing crops. However, the lack of data in the TS can affect their accuracy. The Copernicus Land Monitoring Service (CLMS) provides these parameters and their temporal quality. This paper evaluates the impact of missing vegetation index data on phenological parameters, namely, SOS, EOS, and MAX, for extensive arable crop between 2018 and 2023. The TSGenerator package was developed to download, process, and analyze the data. We used 252 images from the BIOPAR-VI module, 6 phenology parameters, and 2025 plots of barley and maize in Monegros and Zaidín, Spain. In barley, SOS and MAX showed 42.9% and 40.9% of missing data, while in maize, SOS and EOS showed 36.6% and 41.0%. The correlation between the Copernicus VPP quality parameter and the proposed one was r = 0.89 for barley and r = 0.74 for maize. This study advances the understanding of the effect of missing data on SOS, EOS, and MAX. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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6 pages, 1300 KiB

Open AccessProceeding Paper

Transition Metal Elemental Mapping of Fe, Ti, and Cr in Lunar Dryden Crater Using Moon Mineralogy Mapper Data

by Iskren Ivanov and Lachezar Filchev

Eng. Proc. 2025, 94(1), 5; https://doi.org/10.3390/engproc2025094005 - 9 Jul 2025

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Abstract

This study investigates the spatial distribution of transition metals—iron (Fe), titanium (Ti), and chromium (Cr)—within the Dryden crater on the Moon using hyperspectral data from the Moon Mineralogy Mapper (M³). By applying spectral parameters and false color composite techniques, geospatial maps [...] Read more.

This study investigates the spatial distribution of transition metals—iron (Fe), titanium (Ti), and chromium (Cr)—within the Dryden crater on the Moon using hyperspectral data from the Moon Mineralogy Mapper (M³). By applying spectral parameters and false color composite techniques, geospatial maps of chromite distribution and FeO, TiO₂ wt.% distribution were generated at a resolution of ~140 m. The findings reveal distinct elemental enrichments along geomorphologically active regions such as crater walls, terraces, and central peaks, highlighting impact-driven material differentiation, the influence of morphology, degradation, and space weathering. These results enhance our understanding of lunar crustal evolution and support future exploration and resource utilization efforts. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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10 pages, 2030 KiB

Open AccessProceeding Paper

Enhancing Urban Resource Management Through Urban and Peri-Urban Agriculture

by Asmaa Moussaoui, Hicham Bahi, Imane Sebari and Kenza Ait El Kadi

Eng. Proc. 2025, 94(1), 6; https://doi.org/10.3390/engproc2025094006 - 10 Jul 2025

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Abstract

Urbanization is one of the most important challenges contributing to the trend of replacing agricultural land with high-value land uses, such as housing, as well as industrial and commercial activities, as a result of significant population growth. To face these challenges and improve [...] Read more.

Urbanization is one of the most important challenges contributing to the trend of replacing agricultural land with high-value land uses, such as housing, as well as industrial and commercial activities, as a result of significant population growth. To face these challenges and improve urban sustainability, integrating an embedded concept of spatial planning, taking into account urban and peri-urban agriculture, will contribute to mitigating food security issues and the negative impact of climate change, while improving social and economic development. This project aims to analyze land use/cover changes in the Casablanca metropolitan area and its surrounding cities, which are undergoing rapid urban growth. To achieve this, time series of remote sensing data were analyzed in order to investigate the spatio-temporal changes in LU/LC and to evaluate the dynamics and spatial pattern of the city’s expansion over the past three decades, which has come at the expense of agricultural land. The study will also examine the relationship between urbanization and agricultural land use change over time. The results of this study show that Casablanca and its outskirts experience significant urban expansion and a decline in arable lands, with rates of 45% and 42%, respectively. The analysis of SDG indicator 11.3.1 has also shown that land consumption in the provinces of Mediouna, Mohammadia, and Nouaceur has exceeded population growth, due to rapid, uncontrolled urbanization at the expense of agricultural land, which highlights the need to develop a new conceptual framework for regenerating land systems based on the implementation of urban and peri-urban agriculture in vacant sites within urban and peri-urban areas. This will offer valuable insights for policymakers to investigate measures that can ensure sustainable land use planning strategies that effectively integrate agriculture into urban development. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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7 pages, 1068 KiB

Open AccessProceeding Paper

Modeling Wheat Height from Sentinel-1: A Cluster-Based Approach

by Andrea Soccolini, Francesco Saverio Santaga and Sara Antognelli

Eng. Proc. 2025, 94(1), 7; https://doi.org/10.3390/engproc2025094007 - 11 Jul 2025

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Abstract

Crop height is a key indicator of plant development and growth dynamics, offering valuable insights for temporal crop monitoring. However, modeling its variation across phenological stages remains challenging due to canopy structural changes. This study aimed to predict wheat height throughout the growth [...] Read more.

Crop height is a key indicator of plant development and growth dynamics, offering valuable insights for temporal crop monitoring. However, modeling its variation across phenological stages remains challenging due to canopy structural changes. This study aimed to predict wheat height throughout the growth cycle by integrating radar remote sensing data with a phenology-informed clustering approach. The research was conducted in three wheat fields in Umbria, Italy, from 30 January to 10 June 2024, using in-field height measurements, phenological observations, and Sentinel-1 acquisitions. Backscatter variables (VH, VV, and CR) were processed using two speckle filters (Lee 7 × 7 and Refined Lee), alongside additional radar-derived parameters (entropy, anisotropy, alpha, and RVI). Fuzzy C-means clustering enabled the classification of observations into two phenological groups, supporting the development of stage-specific linear regression models. Results demonstrated high accuracy during early growth stages (tillering to stem elongation), with R² values of 0.76 (RMSE = 6.88) for Lee 7 × 7 and 0.79 (RMSE = 6.35) for Refined Lee. In later stages (booting to maturity), model performance declined, with Lee 7 × 7 outperforming Refined Lee (R² = 0.51 vs. 0.33). These findings underscore the potential of phenology-based modeling approaches to enhance crop height estimation and improve radar-driven crop monitoring. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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