Open Access
Issue
E3S Web Conf.
Volume 563, 2024
International Conference on Environmental Science, Technology and Engineering (ICESTE 2024)
Article Number 03009
Number of page(s) 5
Section Green Environment
DOI https://doi.org/10.1051/e3sconf/202456303009
Published online 30 August 2024
  1. Kamran, M., Yamamoto, K., Evolution and use of remote sensing in ecological vulnerability assessment: A review, Ecological Indicators 148, 110099 (2023) [CrossRef] [Google Scholar]
  2. Avtar, R., Komolafe, A.A., Kouser, A., Singh, D., Yunus, A.P., Dou, J., Kurniawan, T.A., Assessing sustainable development prospects through remote sensing: A review, Remote sensing applications: Society and environment 20, 100402 (2020) [CrossRef] [Google Scholar]
  3. Cuca, B., Zaina, F., Tapete, D., Monitoring of Damages to Cultural Heritage across Europe Using Remote Sensing and Earth Observation: Assessment of Scientific and Grey Literature, Remote Sensing 15(15), 3748 (2023) [CrossRef] [Google Scholar]
  4. Geiß, C., Taubenböck, H., Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap, Natural hazards 68, 7–48 (2013) [CrossRef] [Google Scholar]
  5. de Araujo Barbosa, C.C., Atkinson, P.M., Dearing, J.A., Remote sensing of ecosystem services: A systematic review, Ecological Indicators 52, 430–443 (2015) [CrossRef] [Google Scholar]
  6. Szpakowski, D.M., Jensen, J.L., A review of the applications of remote sensing in fire ecology, Remote sensing 11(22), 2638 (2019) [CrossRef] [Google Scholar]
  7. Hoque, M.A.A., Phinn, S., Roelfsema, C., A systematic review of tropical cyclone disaster management research using remote sensing and spatial analysis Ocean Coastal Management 146, 109–120 (2017) [CrossRef] [Google Scholar]
  8. Blaschke, T., Object based image analysis for remote sensing, ISPRS journal of photogrammetry and remote sensing 65(1), 2–16 (2010) [CrossRef] [Google Scholar]
  9. Mashala, M.J., Dube, T., Mudereri, B.T., Ayisi, K.K., Ramudzuli, M.R., A systematic review on advancements in remote sensing for assessing and monitoring land use and land cover changes impacts on surface water resources in semi-arid tropical environments, Remote Sensing 15(16), 3926 (2023) [CrossRef] [Google Scholar]
  10. Ang, M.L.E., Owen, J.R., Gibbins, C.N., Lèbre, É., Kemp, D., Saputra, M.R.U., Lechner, A.M., Systematic Review of GIS and Remote Sensing Applications for Assessing the Socioeconomic Impacts of Mining, The Journal of Environment Development 32(3), 243–273 (2023) [CrossRef] [Google Scholar]
  11. Yengoh, G.T., Dent, D., Olsson, L., Tengberg, A.E., Tucker IIICJ, Use of the Normalized Difference Vegetation Index (NDVI) to assess Land degradation at multiple scales: current status future trends and practical considerations, Springer (2015) [Google Scholar]
  12. Rather, T.A., Kumar, S., Khan, J.A., Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm, Ecological Processes 9(1), 1–17 (2020) [CrossRef] [Google Scholar]
  13. Wang, S.W., Munkhnasan, L., Lee, W.K., Land use and land cover change detection and prediction in Bhutan's high altitude city of Thimphu using cellular automata and Markov chain, Environmental Challenges 2, 100017 (2021) [CrossRef] [Google Scholar]
  14. Li, L., Vrieling, A., Skidmore, A., Wang, T., Muñoz, A.R., Turak, E., Evaluation of MODIS spectral indices for monitoring hydrological dynamics of a small seasonally-flooded wetland in southern Spain, Wetlands 35, 851–864 (2015) [CrossRef] [Google Scholar]
  15. Lee, J., Im, J., Kim, K., Quackenbush, L.J., Machine learning approaches for estimating forest stand height using plot-based observations and airborne LiDAR data, Forests 9(5), 268 (2018) [CrossRef] [Google Scholar]
  16. Araujo, J.C., Seoane, J.C.S., Lima, G.V., da Silva, E.G., França, L.G., de Souza Santos, E.E., Pereira, P.H.C., High-resolution optical remote sensing geomorphological mapping of coral reef: Supporting conservation and management of marine protected áreas, Journal of Sea Research 196, 102453 (2023) [CrossRef] [Google Scholar]
  17. Filippi, A.M., Güneralp, İ., Castillo, C.R., Ma, A., Paulus, G., Anders, K.H., Comparison of image endmember-and object-based classification of very-high-spatial-resolution unmanned aircraft system (UAS) narrow-band images for mapping riparian forests and other land covers, Land 11(2), 246 (2022) [CrossRef] [Google Scholar]
  18. Halder, B., Bandyopadhyay, J., Khatun, R., Google Earth Engine and Sentinel 1/2 data-based forest degradation monitoring of Sundarban Biosphere Reserve, Sustainable Horizons 9, 100088 (2024) [CrossRef] [Google Scholar]
  19. Van Nguyen, O., Kawamura, K., Trong, D.P., Gong, Z., Suwandana, E., Temporal change and its spatial variety on land surface temperature and land use changes in the Red River Delta Vietnam using MODIS time-series imagery, Environmental monitoring and assessment 187, 1–11 (2015) [CrossRef] [PubMed] [Google Scholar]
  20. Li, M., Wang, J., Li, K., Ochir, A., Togtokh, C., Xu, C., Spatial-Temporal Pattern Analysis of Grassland Yield in Mongolian Plateau Based on Artificial Neural Network, Remote Sensing 15(16), 3968 (2023) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.