Special Issues

Application of Remote Sensing and GIS in Environmental Monitoring and Management

Submission Deadline: 31 December 2025 View: 2158 Submit to Special Issue

Guest Editors

Assist. Prof. Jerome Gabutero Gacu

Email: jeromegabuterogacu@rsu.edu.ph

Affiliation: Department of Civil Engineering, Romblon State University, Romblon, 5505, Philippines

Homepage:

Research Interests: GIS, Remote Sensing, environmental monitoring, land suitability assessment, urban and rural planning, disaster risk mapping, vulnerability assessment, climate resilience, spatial decision support, environmental indices


Dr. Cris Edward F. Monjardin

Email: cefmonjardin@mapua.edu.ph

Affiliation: School of Civil, Environmental and Geological Engineering, Mapua University, 1002, Intramuros Manila, Philippines

Homepage:

Research Interests: environmental engineering, water resources management, flood risk assessment, GIS, hazard analysis, heavy metal contamination, machine learning in environmental monitoring


Dr. Kevin Lawrence De Jesus

Email: kmdejesus@feutech.edu.ph

Affiliation: Department of Civil Engineering, FEU Institute of Technology, Manila, 1015, Philippines

Homepage:

Research Interests: GIS, Remote Sensing, environmental engineering, groundwater contamination, heavy metal pollution, environmental risk assessment, machine learning, spatial analysis, geospatial modeling, sustainable resource management.


Dr. Sheikh Kamran Abid

Email: shkamranabid@gmail.com

Affiliation: Faculty of Technology Management and Business (FPTP), Universiti Tun Hussein Onn Malaysia, Johor, 86400, Malaysia

Homepage:

Research Interests: GIS, disaster management, flood vulnerability, disaster risk reduction, climate & disaster resilience


Summary

The accelerating pace of environmental changedriven by urbanization, climate variability, and resource pressureshas placed geospatial technologies at the forefront of modern environmental science. Remote Sensing (RS) and Geographic Information Systems (GIS) are not only essential for observing and understanding these dynamics but also for designing adaptive and data-driven solutions.


This Special Issue of Revue Internationale de Gomatique (RIG) invites high-quality, peer-reviewed contributions that showcase novel and impactful applications of RS and GIS in environmental monitoring and sustainable management. We seek interdisciplinary research that integrates spatial technologies with machine learning, artificial intelligence, cloud computing, citizen science, and policy tools to tackle environmental challenges at various scales.


We encourage submissions that bridge theory and practice, demonstrate methodological innovations, or present significant case studies from both developed and developing contexts. This Special Issue aims to serve as a platform for advancing geospatial science in support of climate resilience, ecosystem health, disaster risk reduction, and sustainable development.Specific themes:
· Advanced Remote Sensing and GIS Applications for Monitoring Environmental Degradation and Ecosystem Change
· Climate Impact Assessment and Resource Management through GIS-based Modeling
· AI and Machine Learning Integration with RS/GIS for Predictive Environmental Analysis
· Geospatial Tools in Environmental Policy, Planning, and Decision-Making
· Real-Time and Cloud-Based Geospatial Platforms for Environmental Monitoring
· Participatory GIS and Crowdsourced Data in Community-Based Environmental Monitoring
· Development of Novel Indices and Indicators from Multi-source Geospatial Data


Keywords

Remote Sensing, GIS, Environmental Monitoring, Sustainable Management, Geospatial Innovation, Machine Learning, Climate Resilience, Ecosystem Assessment, Spatial Decision Support, Environmental Policy

Published Papers


  • Open Access

    ARTICLE

    Predicting Soil Carbon Pools in Central Iran Using Random Forest: Drivers and Uncertainty Analysis

    Shohreh Moradpour, Shuai Zhao, Mojgan Entezari, Shamsollah Ayoubi, Seyed Roohollah Mousavi
    Revue Internationale de Géomatique, Vol.34, pp. 809-829, 2025, DOI:10.32604/rig.2025.069538
    (This article belongs to the Special Issue: Application of Remote Sensing and GIS in Environmental Monitoring and Management)
    Abstract Accurate spatial prediction of soil organic carbon (SOC) and soil inorganic carbon (SIC) is vital for land management decisions. This study targets SOC/SIC mapping challenges at the watershed scale in central Iran by addressing environmental heterogeneity through a random forest (RF) model combined with bootstrapping to assess prediction uncertainty. Thirty-eight environmental variables—categorized into climatic, soil physicochemical, topographic, geomorphic, and remote sensing (RS)-based factors—were considered. Variable importance analysis (via) and partial dependence plots (PDP) identified land use, RS indices, and topography as key predictors of SOC. For SIC, soil reflectance (Bands 5 and 7, ETM+), topography, More >

    Graphic Abstract

    Predicting Soil Carbon Pools in Central Iran Using Random Forest: Drivers and Uncertainty Analysis

  • Open Access

    ARTICLE

    Machine Learning-Assisted Denoising of Raman Spectral Remote Sensing Data for Improved Land Use Mapping

    Fawad Salam Khan, Noman Hasany, Sheikh Kamran Abid, Muhammad Khurram, Jerome Gacu, Cris Edward Monjardin, Kevin Lawrence de Jesus
    Revue Internationale de Géomatique, Vol.34, pp. 415-432, 2025, DOI:10.32604/rig.2025.067026
    (This article belongs to the Special Issue: Application of Remote Sensing and GIS in Environmental Monitoring and Management)
    Abstract Noise present in remote sensing data creates obstacles to proper land use and land cover (LULC) classification methods. The paper evaluates machine learning (ML) denoising methods that adapt Raman spectroscopy’s spectral techniques to optimise remote sensing spectra for land-use/land-cover (LULC) mapping. A basic Raman spectroscopy model demonstrates that Savitzky-Golay (SG) filtering, Wavelet denoising, and basic 1D Convolutional Autoencoder have different effects on synthetic spectral features relevant to LULC classification. Savitzky-Golay filtering yielded the most efficient results, increasing classification accuracy from 0.71 (noisy) to 1.00 (denoised), resulting in perfect classification with zero errors and enhancing the More >

Share Link