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Abstract

Summary

Climate neutrality is now a highly debated issue. One of the research directions within this topic is to reveal the relationship between Land Use Land Cover (LULC) structure and carbon balance of the territory. A number of spatial modelling tools have been developed for this purpose, among which InVEST model seems to provide the most accurate assessments for urbogeosystems. The main problem that arises when using the InVEST model is the insufficiency of data available to researchers. This study aims to show the solutions of this problem using remote sensing data. The results obtained indicate that remote sensing data can provide enough information for LULC identification. Taking in account the spatial resolution of open-source remote sensing data, Sentinel-2 mission seems to be the best data source. Aboveground biomass can be estimated using LiDAR Global Ecosystem Dynamics Survey (GEDI) satellite data. However, remote sensing data alone is insufficient to provide accurate information on carbon stored in different pools. Thus, field research and/or literature sources are still needed.

EAGE Publications BV © 2025 The Authors © European Association of Geoscientists and Engineers
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2025-10-06
2026-01-14

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References

  1. Acar, R. U. and Zengin, E. (2023). Performance Assessment of Landsat 8 And Sentinel-2 Satellite Images for The Production of Time Series Land UseLand Cover (LULC) Maps. Journal of Scientific Reports-A, 53, 1–15. https://doi.org/10.59313/jsr-a.1213548
     [Google Scholar]
  2. AngaritaH. et al. (2025). Natural Capital Project InVEST 3.16.1. Stanford University, University of Minnesota, Chinese Academy of Sciences, The Nature Conservancy, World Wildlife Fund, Stockholm Resilience Centre and the Royal Swedish Academy of Sciences. https://doi.org/10.60793/natcap-invest-3.16.1
     [Google Scholar]
  3. Copernicus Data Space Ecosystem (2025). Copernicus Sentinel Data URL: https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2
     [Google Scholar]
  4. European Commission, Copernicus Land Monitoring Service, European Commission's Joint Research Centre (2015) Global Dynamic Land Cover. DOI: https://doi.org/10.2909/c6377c6e-76cc-4d03-8330-628a03693042
     [Google Scholar]
  5. LiuYang, YaboYuan, JinhuaSun, LeiZhang, TingWang, HuiZhao, XiangfengLiu, YazhenLiang (2025). Estimation of forest aboveground carbon storage based on GEDI and GF-6 in Jiyuan City, China. Journal of Applied Remote Sensing, 19(2), 024508. https://doi.org/10.1117/1.JRS.19.024508
     [Google Scholar]
  6. PascualA., GodinhoS., Guerra-HernándezJ., 2023. Integrated LiDAR-supported valuation of biomass and litter in forest ecosystems. A showcase in Spain. Science of The Total Environment, 897, 165364. https://doi.org/10.1016/j.scitotenv.2023.165364
     [Google Scholar]
  7. Sharma, R., Pradhan, L., Kumari, M., Bhattacharya, P., Mishra, V. N., & Kumar, D. (2024). Spatio-Temporal Assessment of Urban Carbon Storage and Its Dynamics Using InVEST Model. Land, 13(9), 1387. https://doi.org/10.3390/land13091387
     [Google Scholar]
  8. University of Maryland, NASA Goddard Space Flight Center (2025). Global Ecosystem Dynamics Investigation (GEDI) URL: https://gedi.umd.edu/
     [Google Scholar]
  9. USGS Earth Resources Observation and Science (EROS) Center (2025). Landsat-8 images. URL: https://earthexplorer.usgs.gov/
     [Google Scholar]
  10. van Wesemael, B., Chabrillat, S.; Sanz Dias, A.; Berger, M.; Szantoi, Z. (2023). Remote Sensing for Soil Organic Carbon Mapping and Monitoring. Remote Sensing, 15, 3464. https://doi.org/10.3390/rs15143464
     [Google Scholar]
  11. VeerkampC.J., LoretiM., BenavidezR., JacksonB, Schipper, A.M. (2023). Comparing three spatial modeling tools for assessing urban ecosystem services, Ecosystem Services, 59, 101500, ISSN 2212-0416, https://doi.org/10.1016/j.ecoser.2022.101500
     [Google Scholar]
  12. YeonsuLee, BokyungSon, JunghoIm, ZhenZhen, Lindi J.Quackenbush (2024) Two-step carbon storage estimation in urban human settlements using airborne LiDAR and Sentinel-2 data based on machine learning. Urban Forestry & Urban Greening, 94, 128239. https://doi.org/10.1016/j.ufug.2024.128239
     [Google Scholar]
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Remote Sensing Data for Climate Neutrality of Urbogeosystems Assessment using InVest Model
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202552054
/content/papers/10.3997/2214-4609.202552054
/content/papers/10.3997/2214-4609.202552054
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