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Open AccessEditorial

Preface to the 1st International Conference on Advanced Remote Sensing—Shaping Sustainable Global Landscapes (ICARS 2025)

by Fabio Tosti, Andrea Benedetto and Luis Ángel Ruiz

Eng. Proc. 2025, 94(1), 27; https://doi.org/10.3390/engproc2025094027 - 29 Oct 2025

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The 1st International Conference on Advanced Remote Sensing—Shaping Sustainable Global Landscapes (ICARS 2025) was held in Barcelona, Spain, from 26–28 March 2025 [...] 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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Open AccessEditorial

Statement of Peer Review

by Fabio Tosti, Andrea Benedetto and Luis Ángel Ruiz

Eng. Proc. 2025, 94(1), 28; https://doi.org/10.3390/engproc2025094028 - 29 Oct 2025

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Abstract

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by [...] Read more.

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors [...] 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, 2671 KB

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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Abstract

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 KB

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 KB

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 KB

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 KB

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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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 KB

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 KB

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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11 pages, 3507 KB

Open AccessProceeding Paper

Resilient Cities and Urban Green Infrastructure—Nexus Between Remote Sensing and Sustainable Development

by Suman Kumari, Tesfaye Temtime Tessema, Laden Husamaldin, Sharad Kumar Gupta, Philip Cox, Dale Mortimer, Andrea Benedetto and Fabio Tosti

Eng. Proc. 2025, 94(1), 8; https://doi.org/10.3390/engproc2025094008 - 17 Jul 2025

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Abstract

Cities are the growth engines responsible for shaping the global economy, major contributors to climate change, and are significantly affected by it. However, the United Nations adopted the Sustainable Development Goals (SDGs) to make these cities and human settlements inclusive, safe, resilient, and [...] Read more.

Cities are the growth engines responsible for shaping the global economy, major contributors to climate change, and are significantly affected by it. However, the United Nations adopted the Sustainable Development Goals (SDGs) to make these cities and human settlements inclusive, safe, resilient, and sustainable. Yet, the rapid and unplanned urban expansion exacerbates various environmental challenges and reduces green cover in urban areas. To address these issues and meet the SDGs, stakeholders need to emphasise and optimise urban spaces. This study investigates the borough-level analysis of green spaces and human exposure to green spaces across London using satellite-derived datasets on vegetation and socio-economic factors to examine the variations in urban vegetation cover and urban population exposure to vegetation cover between 2017 and 2024. This study highlights the spatial disparity in green space coverage and exposure to green space between the inner and outer boroughs of London. The methodology used here suggests an average loss of approximately 11 and 9 percent in green space coverage and green space exposure to population, respectively, between 2017 and 2024 across London boroughs. 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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9 pages, 1701 KB

Open AccessProceeding Paper

Phenological Evaluation in Ravine Forests Through Remote Sensing and Topographic Analysis: Case of Los Nogales Nature Sanctuary, Metropolitan Region of Chile

by Jesica Garrido-Leiva, Leonardo Durán-Gárate, Dylan Craven and Waldo Pérez-Martínez

Eng. Proc. 2025, 94(1), 9; https://doi.org/10.3390/engproc2025094009 - 22 Jul 2025

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Abstract

Ravine forests are key to conserving biodiversity and maintaining ecosystem processes in fragmented landscapes. Here, we evaluated the phenology of plant species in the Los Nogales Nature Sanctuary (Lo Barnechea, Chile) using Sentinel-2 images (2019–2024) and the Alos Palsar DEM (12.5 m). We [...] Read more.

Ravine forests are key to conserving biodiversity and maintaining ecosystem processes in fragmented landscapes. Here, we evaluated the phenology of plant species in the Los Nogales Nature Sanctuary (Lo Barnechea, Chile) using Sentinel-2 images (2019–2024) and the Alos Palsar DEM (12.5 m). We calculated the Normalized Difference Vegetation Index (NDVI), the Topographic Position Index (TPI), and Diurnal Anisotropic Heat (DAH) to assess vegetation dynamics across different topographic and thermal gradients. Generalized Additive Models (GAM) revealed that tree species exhibited more stable, regular seasonal NDVI trajectories, while shrubs showed moderate fluctuations, and herbaceous species displayed high interannual variability, likely reflecting sensitivity to climatic events. Spatial analysis indicated that trees predominated on steep slopes and higher elevations, herbs were concentrated in low-lying, moisture-retaining areas, and shrubs were more common in areas with higher thermal load. These findings highlight the significant role of terrain and temperature in shaping plant phenology and distribution, underscoring the utility of remote sensing and topographic indices for monitoring ecological processes in complex mountainous environments. 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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8 pages, 2843 KB

Open AccessProceeding Paper

Coastal Erosion in Tsunami and Storm Surges-Exposed Areas in Licantén, Maule, Chile: Case Study Using Remote Sensing and In-Situ Data

by Joaquín Valenzuela-Jara, Idania Briceño de Urbaneja, Waldo Pérez-Martínez and Isidora Díaz-Quijada

Eng. Proc. 2025, 94(1), 10; https://doi.org/10.3390/engproc2025094010 - 24 Jul 2025

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Abstract

This study examines urban expansion, coastal erosion, and extreme wave events in Licantén, Maule Region, following the 2010 earthquake and tsunami. Using multi-source data—Landsat and Sentinel-2 imagery, ERA5 reanalysis, high-resolution Maxar images, UAV surveys, and the CoastSat algorithm—we detected significant urban growth in [...] Read more.

This study examines urban expansion, coastal erosion, and extreme wave events in Licantén, Maule Region, following the 2010 earthquake and tsunami. Using multi-source data—Landsat and Sentinel-2 imagery, ERA5 reanalysis, high-resolution Maxar images, UAV surveys, and the CoastSat algorithm—we detected significant urban growth in tsunami-prone areas: Iloca (36.88%), La Pesca (33.34%), and Pichibudi (20.78%). A 39-year shoreline reconstruction (1985–2024) revealed notable changes in erosion rates and shoreline dynamics using DSAS v6.0, influenced by tides, storm surges, and wave action modeled in R to quantify storm surge events over time. Results underscore the lack of urban planning in hazard-exposed areas and the urgent need for resilient coastal management under climate change. 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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8 pages, 4452 KB

Open AccessProceeding Paper

Synthetic Aperture Radar Imagery Modelling and Simulation for Investigating the Composite Scattering Between Targets and the Environment

by Raphaël Valeri, Fabrice Comblet, Ali Khenchaf, Jacques Petit-Frère and Philippe Pouliguen

Eng. Proc. 2025, 94(1), 11; https://doi.org/10.3390/engproc2025094011 - 25 Jul 2025

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Abstract

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s [...] Read more.

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s characteristics. Moreover, for a complex scene of interest with targets located on a rough soil, a composite scattering between the target and the surface occurs and creates distortions on the SAR image. These characteristics can make the SAR images difficult to analyse and process. To better understand the complex EM phenomena and their signature in the SAR image, we propose a methodology to generate raw SAR signals and SAR images for scenes of interest with a target located on a rough surface. With this prospect, the entire radar acquisition chain is considered: the sensor parameters, the atmospheric attenuation, the interactions between the incident EM field and the scene, and the SAR image formation. Simulation results are presented for a rough dielectric soil and a canonical target considered as a Perfect Electric Conductor (PEC). These results highlight the importance of the composite scattering signature between the target and the soil. Its power is 21 dB higher that that of the target for the target–soil configuration considered. Finally, these simulations allow for the retrieval of characteristics present in actual SAR images and show the potential of the presented model in investigating EM phenomena and their signatures in SAR images. 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, 9378 KB

Open AccessProceeding Paper

Robust U-Net Segmentation of Tree Crown Damages in Bavaria, Germany

by Javier Francisco Gonzalez and Adelheid Wallner

Eng. Proc. 2025, 94(1), 12; https://doi.org/10.3390/engproc2025094012 - 25 Jul 2025

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Abstract

The capability of U-Net methods and aerial orthoimagery to identify tree crown mortality in study areas in Bavaria, Germany was evaluated and aspects such as model transferability were investigated. We trained the models with imagery from May to September for the years 2019–2023. [...] Read more.

The capability of U-Net methods and aerial orthoimagery to identify tree crown mortality in study areas in Bavaria, Germany was evaluated and aspects such as model transferability were investigated. We trained the models with imagery from May to September for the years 2019–2023. One goal was to differentiate between damaged crowns of deciduous, coniferous, and pine trees. The results from a validation area containing an independent dataset showed the best average F1-scores of 68%, 52%, and 66% for deciduous, coniferous, and pine trees, respectively. This study highlights the potential of U-Net methods for detecting tree mortality in large areas. 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, 2626 KB

Open AccessProceeding Paper

SpaFLEX: Field Campaign for Calibration and Validation of FLEX-S3 Mission Products

by Pedro J. Gómez-Giráldez, David Aragonés, Marcos Jiménez, Mª Pilar Cendrero-Mateo, Shari Van Wittenberghe, Juan José Peón, Adrián Moncholí-Estornell and Ricardo Díaz-Delgado

Eng. Proc. 2025, 94(1), 13; https://doi.org/10.3390/engproc2025094013 - 31 Jul 2025

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Abstract

The FLEX-S3 mission by ESA will deliver key Level 2 products such as sun-induced chlorophyll fluorescence (SIF) and vegetation-reflected radiance. To validate these, the SpaFLEX project, funded by the Spanish Ministry of Science and Innovation, is developing a robust calibration and validation strategy [...] Read more.

The FLEX-S3 mission by ESA will deliver key Level 2 products such as sun-induced chlorophyll fluorescence (SIF) and vegetation-reflected radiance. To validate these, the SpaFLEX project, funded by the Spanish Ministry of Science and Innovation, is developing a robust calibration and validation strategy in Spain. This includes test site setup, instrument characterization, and sampling protocols. A field campaign was conducted in two Holm Oak forests in Teruel, analyzing Sentinel-2 spatial heterogeneity and collecting ground, UAV, and airborne data. The results support scaling procedures to match the 300 m pixel resolution of FLEX-S3, ensuring product accuracy and compliance with ESA standards. 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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8 pages, 5870 KB

Open AccessProceeding Paper

Classification of Urban Environments Using State-of-the-Art Machine Learning: A Path to Sustainability

by Tesfaye Tessema, Neda Azarmehr, Parisa Saadati, Dale Mortimer and Fabio Tosti

Eng. Proc. 2025, 94(1), 14; https://doi.org/10.3390/engproc2025094014 - 4 Aug 2025

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Abstract

Urban green infrastructure plays a vital role in the sustainable development of cities. As urban areas expand, green spaces are increasingly affected. The pressure from new developments leads to a reduction in vegetation and raises new public health risks. Addressing this challenge requires [...] Read more.

Urban green infrastructure plays a vital role in the sustainable development of cities. As urban areas expand, green spaces are increasingly affected. The pressure from new developments leads to a reduction in vegetation and raises new public health risks. Addressing this challenge requires effective planning, maintenance, and continuous monitoring. To enhance traditional approaches, remote sensing is becoming a vital tool for city-wide observations. Publicly available large-scale data, combined with machine learning models, can improve our understanding. We explore the potential of Sentinel-2 to classify and extract meaningful features from urban landscapes. Using advanced machine learning techniques, we aim to develop a robust and scalable framework for classifying urban environments. The proposed models will assist in monitoring changes in green spaces across diverse urban settings, enabling timely and informed decisions to foster sustainable urban growth. 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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11 pages, 1226 KB

Open AccessProceeding Paper

Assessment of Nature-Based Solutions’ Impact on Urban Air Quality Using Remote Sensing

by Paloma C. Toscan, Alcindo Neckel, Emanuelle Goellner, Marcos L. S. Oliveira and Eduardo N. B. Pereira

Eng. Proc. 2025, 94(1), 15; https://doi.org/10.3390/engproc2025094015 - 5 Aug 2025

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Abstract

Urban air pollution poses a significant challenge to public health and sustainable development, particularly in mid-sized cities with limited monitoring capabilities. This study investigates the impact of Nature-Based Solutions (NBS) on air quality and Land Surface Temperature (LST) in Guimarães, Portugal. The first [...] Read more.

Urban air pollution poses a significant challenge to public health and sustainable development, particularly in mid-sized cities with limited monitoring capabilities. This study investigates the impact of Nature-Based Solutions (NBS) on air quality and Land Surface Temperature (LST) in Guimarães, Portugal. The first phase involves mapping pollutants and assessing European guidelines, traditional monitoring methods, and emerging tools such as sensors and satellite data. The findings indicate gaps in spatial coverage, emphasizing the importance of integrating data from Sentinel-3, Sentinel-5P, local sensors, and drones. These insights establish a foundation for the next phase, which involves predictive modeling of NBS, LST, and pollutants using machine learning techniques to support data-driven policy-making. 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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11 pages, 2200 KB

Open AccessProceeding Paper

Analysis of Multi-Decadal Shoreline Changes at Topocalma Beach (O’Higgins Region, Chile) Using Satellite Imagery

by Waldo Pérez-Martínez, Idania Briceño de Urbaneja, Joaquín Valenzuela-Jara and Isidora Díaz-Quijada

Eng. Proc. 2025, 94(1), 16; https://doi.org/10.3390/engproc2025094016 - 6 Aug 2025

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Abstract

This study presents a 39-year spatiotemporal analysis of shoreline variability at Topocalma Beach (Chile) using satellite-derived data collected between 1985 and 2024. A total of 350 satellite images were processed with CoastSat and DSAS v6.0 to quantify erosional and accretional trends across distinct [...] Read more.

This study presents a 39-year spatiotemporal analysis of shoreline variability at Topocalma Beach (Chile) using satellite-derived data collected between 1985 and 2024. A total of 350 satellite images were processed with CoastSat and DSAS v6.0 to quantify erosional and accretional trends across distinct beach sectors. The results show persistent erosion in the proximal zone near the Topocalma wetland and localized accretion in the distal (southern) segment. These changes are closely associated with the 2010 Maule earthquake and tsunami, strong ENSO phases, and an increase in storm surge activity since 2015. The spatiotemporal beach width model reveals distinct phases of retreat and short-term post-seismic stabilization, followed by a shift to sustained erosion. Overall, this study underscores the limited natural recovery capacity of the beach and highlights the utility of satellite-based monitoring tools for coastal resilience planning in data-limited regions. 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, 5133 KB

Open AccessProceeding Paper

Fuel Species Classification and Biomass Estimation for Fire Behavior Modeling Based on UAV Photogrammetric Point Clouds

by Luis Ángel Ruiz, Juan Pedro Carbonell-Rivera, Pablo Crespo-Peremarch, Marina Simó-Martí and Jesús Torralba

Eng. Proc. 2025, 94(1), 17; https://doi.org/10.3390/engproc2025094017 - 12 Aug 2025

Cited by 1 | Viewed by 751

Abstract

In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behavior models, such as Fire Dynamics Simulator (FDS), can represent [...] Read more.

In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behavior models, such as Fire Dynamics Simulator (FDS), can represent heterogeneous fuels and simulate fire behavior processes with greater detail than conventional models. However, they require accurate information about species composition and 3D distribution of fuel mass and bulk density at the voxel level. Working in a Mediterranean ecosystem study area we developed a methodology based on the use of geometric and spectral features from UAS-based digital aerial photogrammetric point clouds for (i) species segmentation and classification using machine learning algorithms, (ii) generation of biomass prediction models at individual plant level, and (iii) creation of 3D fuel scenarios and modeling wildfire behavior. Field measurements were conducted on 22 circular plots with a radius of 5 m. Data from the field measurements, combined with species-specific allometric equations, were used for the evaluation of classification and prediction models. Fire behavior variables such as rate of spread, heat release rate, and mass loss rate were monitored and assessed as outputs from 20 different scenarios using FDS. The overall species classification accuracy was 80.3%, and the biomass regression R² values obtained by cross-validation were 0.77 for Pinus halepensis and 0.83 for Anthyllis cytisoides. These results are encouraging further improvement based on the integration of sensors onboard UAS, and the characterization of fuels for fire behavior modeling. These high-resolution fuel representations can be coupled with standard risk assessment tools, enabling fire managers to prioritize treatment areas and plan for resource deployment. 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, 1376 KB

Open AccessProceeding Paper

Mapping Soil Moisture Using Drones: Challenges and Opportunities

by Ricardo Díaz-Delgado, Pauline Buysse, Thibaut Peres, Thomas Houet, Yannick Hamon, Mikaël Faucheux and Ophelie Fovert

Eng. Proc. 2025, 94(1), 18; https://doi.org/10.3390/engproc2025094018 - 25 Aug 2025

Cited by 1 | Viewed by 2806

Abstract

Droughts are becoming more frequent, severe, and impactful across the globe. Agroecosystems, which are human-made ecosystems with high water demand that provide essential ecosystem services, are vulnerable to extreme droughts. Although water use efficiency in agriculture has increased in rec ent decades, drought [...] Read more.

Droughts are becoming more frequent, severe, and impactful across the globe. Agroecosystems, which are human-made ecosystems with high water demand that provide essential ecosystem services, are vulnerable to extreme droughts. Although water use efficiency in agriculture has increased in rec ent decades, drought management should be based on long-term, proactive strategies rather than crisis management. The AgrHyS network of sites in French Brittany collects high-resolution soil moisture data from agronomic stations and catchments to improve understanding of temporal soil moisture dynamics and enhance water use efficiency. Frequent mapping of soil moisture and plant water stress is crucial for assessing water stress risk in the context of global warming. Although satellite remote sensing provides reliable, periodic global data on surface soil moisture, it does so at a very coarse spatial resolution. The intrinsic spatial heterogeneity of surface soil moisture requires a higher spatial resolution in order to address upcoming challenges on a local scale. Drones are an excellent tool for upscaling point measurements to catchment level using different onboard cameras. In this study, we evaluated the potential of multispectral images, thermal images and LiDAR data captured in several concurrent drone flights for high-resolution mapping of soil moisture spatial variability, using in situ point measurements of soil water content and plant water stress in both agricultural areas and natural ecosystems. Statistical models were fitted to map soil water content in two areas: a natural marshland and a grassland-covered agricultural field. Our results demonstrate the statistical significance of topography, land surface temperature and red band reflectance in the natural area for retrieving soil water content. In contrast, the grasslands were best predicted by the transformed normalised difference vegetation index (TNDVI). 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, 4885 KB

Open AccessProceeding Paper

Enhancing Rainfall Measurement Using Remote Sensing Data in Sparse Rain Gauge Networks: A Case Study in White Nile State, Sudan

by Abdelbagi Y. F. Adam, Zoltán Gribovszki and Péter Kalicz

Eng. Proc. 2025, 94(1), 19; https://doi.org/10.3390/engproc2025094019 - 26 Aug 2025

Viewed by 2725

Abstract

Monitoring rainfall is essential to understanding hydrological processes, managing water resources, and mitigating drought and flood risks. Many regions, particularly in developing countries, have sparse rain gauge networks, which limit spatial coverage and result in inaccurate rainfall estimates. By combining remote sensing data [...] Read more.

Monitoring rainfall is essential to understanding hydrological processes, managing water resources, and mitigating drought and flood risks. Many regions, particularly in developing countries, have sparse rain gauge networks, which limit spatial coverage and result in inaccurate rainfall estimates. By combining remote sensing data with rain gauge measurements, rainfall estimates can be improved, and spatial coverage can be enhanced. Remote sensing techniques provide a valuable resource for supplementing and enhancing rainfall monitoring in such areas. This study leverages Global Precipitation Measurement (GPM) satellite data to enhance rainfall estimation in White Nile State, Sudan, where only two rain gauge stations are operational and the state’s total area is 39.600 km². GPM data, well-known for its high temporal and spatial resolution, offers a promising alternative to mitigate the limitations of sparse ground-based networks. The study integrates GPM satellite data with ground-based measurements through statistical and geostatistical techniques, as well as validation, to improve rainfall accuracy. The results show that, on average, GPM data and rain gauge measurements exhibit a strong correlation of 0.87, with an annual RMSE of 10.23 mm and an AME of 8.25 mm. These findings demonstrate that GPM data effectively complements traditional rain gauge observations by accurately capturing spatial rainfall distributions and extreme precipitation events. The findings underscore the potential of remote sensing to provide reliable rainfall information in data-scarce regions, contributing to better water resource management and disaster risk reduction strategies. 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, 2287 KB

Open AccessProceeding Paper

Urban Expansion Projections in Maricá/Rio De Janeiro—RJ: Modeling with Cellular Automata and Sentinel Images for 2030 and 2040

by Elizabeth Souza, Vandre Soares Viegas and Annely Teixeira

Eng. Proc. 2025, 94(1), 20; https://doi.org/10.3390/engproc2025094020 - 21 Aug 2025

Viewed by 1302

Abstract

Maricá, located on the eastern coast of Rio de Janeiro, experiences rapid urban growth driven by infrastructure and economic development. This study presents the first high-resolution projection of Maricá’s urban expansion (2030–2040), integrating oil industry impacts and protected area constraints. Using Sentinel-2 MSI [...] Read more.

Maricá, located on the eastern coast of Rio de Janeiro, experiences rapid urban growth driven by infrastructure and economic development. This study presents the first high-resolution projection of Maricá’s urban expansion (2030–2040), integrating oil industry impacts and protected area constraints. Using Sentinel-2 MSI data (10–20 m resolution) classified via Random Forest on Google Earth Engine (90% accuracy) and a Dinamica EGO Cellular Automata model (5 × 5 Moore neighborhood, calibrated on 2015–2020 transitions), results indicate 18.4% urban growth by 2030 (129 km²), expanding to 151 km² (+38.5% total) by 2040, with 72% replacing pastures. This supports sustainable urban management strategies. 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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9 pages, 11332 KB

Open AccessProceeding Paper

Investigating the Impact of Temperature Changes on Coastal Heritage Sites Using Remote Sensing

by Moein Motavallizadeh Naeini, Tesfaye Tessema, Anastasia Sofroniou, Andrea Benedetto and Fabio Tosti

Eng. Proc. 2025, 94(1), 21; https://doi.org/10.3390/engproc2025094021 - 1 Sep 2025

Viewed by 758

Abstract

Coastal heritage assets are crucial to a society’s history and must be preserved sustainably, despite their vulnerability to both natural and anthropogenic hazards. Their monitoring is challenging due to the interrelated nature of these attributes. While expert observations and on-site measurements are employed, [...] Read more.

Coastal heritage assets are crucial to a society’s history and must be preserved sustainably, despite their vulnerability to both natural and anthropogenic hazards. Their monitoring is challenging due to the interrelated nature of these attributes. While expert observations and on-site measurements are employed, they cover limited areas over time, whereas remote sensing can assess larger regions more regularly. This study examines the impacts of climate change on Old Town North, a conservation area within Southampton Harbour, UK, designated as “heritage at risk” by Historic England in 2024. Particular focus is given to temperature and moisture variations, which may accelerate material decay and heighten risks. Using a multidisciplinary approach, the study uses satellite data to extract land surface temperatures, monitor coastal changes, and identify vulnerable risk zones. Results show that the conservation area faces multiple pressures, including moisture deficiency, urban sprawl, and increased surface temperatures, that together could hasten the deterioration of heritage assets. 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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11 pages, 3218 KB

Open AccessProceeding Paper

Multitemporal Analysis of the Dynamics of High-Andean Wetlands in the Metropolitan Region of Chile Using Sentinel-2 Images and ERA5-Land Climate Data

by Fabián Llanos-Bustos, Leonardo Durán-Garate, Waldo Pérez-Martínez, Jesica Garrido-Leiva and Benjamín Castro-Cancino

Eng. Proc. 2025, 94(1), 22; https://doi.org/10.3390/engproc2025094022 - 1 Sep 2025

Viewed by 1059

Abstract

High-Andean wetlands are critical ecosystems for water regulation and carbon storage. This study analyzes the impact of climate variability on vegetation dynamics in eight wetlands located in the Estero Ortiga sub-basin, within the Los Nogales Nature Sanctuary (Metropolitan Region, Chile), between 2017 and [...] Read more.

High-Andean wetlands are critical ecosystems for water regulation and carbon storage. This study analyzes the impact of climate variability on vegetation dynamics in eight wetlands located in the Estero Ortiga sub-basin, within the Los Nogales Nature Sanctuary (Metropolitan Region, Chile), between 2017 and 2024. We used time series of NDVI and NDCI indices derived from Sentinel-2 imagery (January 2017 to September 2024), along with monthly temperature and precipitation data from ERA5-Land (January 2016 to September 2024). Trends were assessed through linear regression, and vegetation–climate relationships were analyzed using Pearson correlations with a one-year lag. Results show a progressive decline in vegetation cover (slope: −2.04 × 10⁻⁵ NDVI) and chlorophyll content (slope: −1.15 × 10⁻⁵ NDCI), with strong positive correlations between annual precipitation and vegetation indices in the subsequent summer (R = 0.83–0.88 for NDVI; R = 0.84–0.90 for NDCI). Annual NDVI reclassification highlighted a reduction in healthy vegetation cover from 2020 onward. This research provides novel evidence linking climate trends and vegetation health in high-Andean wetlands, reinforcing the utility of satellite-based indicators for conservation 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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10 pages, 1143 KB

Open AccessProceeding Paper

Remote Sensing and GIS Data Applied to Debris Flow and Debris Flood Susceptibility in the Northeastern Sector of the City of Santiago

by Benjamín Castro-Cancino, Waldo Pérez-Martínez, Paulina Vidal-Páez and Allison Jaña-Sepúlveda

Eng. Proc. 2025, 94(1), 23; https://doi.org/10.3390/engproc2025094023 - 3 Sep 2025

Viewed by 1861

Abstract

In the mountainous and foothill areas of Santiago, Chile, debris flows and debris floods have been recurrent over recent decades, triggered by short-duration, high-intensity summer rainfall events. These events have caused significant damage to infrastructure and have affected the population, including loss of [...] Read more.

In the mountainous and foothill areas of Santiago, Chile, debris flows and debris floods have been recurrent over recent decades, triggered by short-duration, high-intensity summer rainfall events. These events have caused significant damage to infrastructure and have affected the population, including loss of human lives. This study assesses the susceptibility to debris flow and debris flood generation in the Arrayán and Gualtatas stream basins, located in the Metropolitan Region, using satellite and cartographic data. A Susceptibility Index (SI) was determined through the analysis of 14 conditioning factors, grouped into three main categories: geology, geomorphology, and soil conditions. The weighting and ranking of each factor’s importance were carried out using the Analytic Hierarchy Process (AHP). The results, presented in a susceptibility map, indicate that 60.78% of the study area exhibits low to very low susceptibility, 24.64% moderate susceptibility, and 14.58% high to very high susceptibility, concentrated in stream headwaters, steep slopes, and areas with unconsolidated deposits. Recent debris flow events that have reached urban areas coincide with high-susceptibility zones, validating the methodology and cartographic products, which can support land-use planning and risk management 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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12 pages, 6091 KB

Open AccessProceeding Paper

Satellite-Based Assessment of Coastal Morphology Changes in Pichilemu Bay, Chile

by Isidora Díaz Quijada, Idania Briceño de Urbaneja, Waldo Pérez Martínez and Joaquín Valenzuela Jara

Eng. Proc. 2025, 94(1), 24; https://doi.org/10.3390/engproc2025094024 - 26 Sep 2025

Viewed by 1467

Abstract

Coastal erosion is a global issue exacerbated by extreme events, ENSO variability, storms, and anthropogenic pressures. In Chile, over 80% of beaches are affected by erosion, impacting more than one million people. This study analyzes the evolution of Pichilemu Bay between 1985 and [...] Read more.

Coastal erosion is a global issue exacerbated by extreme events, ENSO variability, storms, and anthropogenic pressures. In Chile, over 80% of beaches are affected by erosion, impacting more than one million people. This study analyzes the evolution of Pichilemu Bay between 1985 and 2024 using satellite imagery, spatio-temporal models, and drone-based surveys. A total of 554 shorelines were extracted, revealing and average shoreline retreat of −1.17 m/year, with maximum erosion of −1.76 m/year and maximum accretion of +0.9 m/year. Wave climate analysis (mean Hs 2.5 m, mean Tp 12.5 s) identified 10 major storm events exceeding 3 m, while sediment sampling showed significant negative correlations between grain size and erosion rates (r = −0.64, p < 0.05). The morphology before and after the 2010 earth-quake was assessed, evidencing up to 100 m of shoreline retreat in affected sectors. Remote sensing techniques proved highly effective for monitoring coastal dynamics, providing high-resolution insights that inform spatial planning, enhance regional erosion monitoring programs, and support adaptive management strategies in the face of climatic and tectonic challenges. 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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8 pages, 2675 KB

Open AccessProceeding Paper

Enhancing Tetracorder Mineral Classification with Random Forest Modeling

by Hideki Tsubomatsu and Hideyuki Tonooka

Eng. Proc. 2025, 94(1), 25; https://doi.org/10.3390/engproc2025094025 - 10 Oct 2025

Viewed by 772

Abstract

Hyperspectral (HS) remote sensing is a valuable tool for geological surveys and mineral classification. However, mineral maps derived from HS data can exhibit inconsistencies across different imaging times or sensors due to complex factors. In this study, we propose a novel method to [...] Read more.

Hyperspectral (HS) remote sensing is a valuable tool for geological surveys and mineral classification. However, mineral maps derived from HS data can exhibit inconsistencies across different imaging times or sensors due to complex factors. In this study, we propose a novel method to enhance the robustness and temporal consistency of mineral mapping. The method combines the spectral identification capabilities of the Tetracorder expert system, developed by United States Geological Survey (USGS), with a data-driven classification model, involving the application of Tetracorder to high-purity pixels identified through the pixel purity index (PPI) analysis to generate reliable training labels. These labels, along with hyperspectral bands transformed by the minimum noise fraction (MNF), are used to train a random forest classifier. The methodology was evaluated using multi-temporal images of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), acquired over Cuprite, Nevada, between 2011 and 2013. The results demonstrate that the proposed method achieves accuracy comparable to Tetracorder while improving map consistency and reducing inter-annual mapping errors by approximately 30%. 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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9 pages, 1899 KB

Open AccessProceeding Paper

Yield Prediction Model Based on Multitemporal Satellite Data and Open Public Data: Case Study for Bulgaria

by Paulina Raeva, Plamen Maldjanski and Dobromir Filipov

Eng. Proc. 2025, 94(1), 26; https://doi.org/10.3390/engproc2025094026 - 20 Oct 2025

Viewed by 2037

Abstract

The motivation behind this research is the fact that agricultural production in Bulgaria is a key contributor to the national economy. The current study presents a robust methodology for predicting crop yields using multitemporal Sentinel-2 satellite imagery, public agricultural statistics, and climate data [...] Read more.

The motivation behind this research is the fact that agricultural production in Bulgaria is a key contributor to the national economy. The current study presents a robust methodology for predicting crop yields using multitemporal Sentinel-2 satellite imagery, public agricultural statistics, and climate data in Bulgaria. Focusing on the municipalities of Medkovets, Yakimovo, and Knezha—regions with over 90% arable land—we conducted time-series analyses of the NDVI, EVI, and GCI for the 2023 and 2024 growing seasons. These indices were used to derive statistical features, which were then combined with ERA5-based climate variables and public yield records from the State Agricultural Fund. A Random Forest regression model was trained on 2023 and 2024 data and used to simulate predictions for 2025. The model achieved an R² of 0.78 and an RMSE of 1.24 t/ha, indicating good agreement between predicted and observed yields despite the relatively small dataset. The preliminary results reveal the importance of the EVI and NDVI as indicators of crop productivity and demonstrate variations in vegetation development between years. The findings highlight the potential of remote sensing and open data integration for regional yield forecasting while also identifying areas for future improvement, including dataset expansion and the use of ground-truth yields. 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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