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Salas

Salas, Eric Ariel

Assistant Professor

ericariel.salas@usc.edu.ph

Degree

Ph.D. in Geospatial Science and Engineering, South Dakota State University

Research interest:

Water resources

List of publications:

  1. Salas, E.A.L.; Schrack, K.; Kumaran, S.S.; Bennett, R.. Hybrid spatiotemporal modeling of nutrient cycling in wetland ecosystems using advanced mapping techniques and machine learning approaches. Scientific Reports. 2026, https://doi.org/10.1038/s41598-026-40585-5.
  2. Salas, E.A.L.; Kumaran, S.S.; Bennett, R.; Partee, E.B.; Brownknight, J.; Schrack, K.; Willis, B.. Integration of Google Earth Engine, Sentinel-2 images, and machine learning for temporal mapping of total dissolved solids in river systems. Scientific Reports. 2025, https://doi.org/10.1038/s41598-025-12548-9.
  3. Salas, E.A.L.; Kumaran, S.S.. Perimeter-Area Soil Carbon Index (PASCI): modeling and estimating soil organic carbon using relevant explicatory waveband variables in machine learning environment. Geo Spatial Information Science. 2024, https://doi.org/10.1080/10095020.2023.2211612.
  4. Salas, E.A.L.; Kumaran, S.S.. Hyperspectral Bare Soil Index (HBSI): Mapping Soil Using an Ensemble of Spectral Indices in Machine Learning Environment. Land. 2023, https://doi.org/10.3390/land12071375.
  5. Thiam, S.; Salas, E.A.L.; Hounguè, N.R.; Almoradie, A.D.S.; Verleysdonk, S.; Adounkpe, J.G.; Komi, K.. Modelling Land Use and Land Cover in the Transboundary Mono River Catchment of Togo and Benin Using Markov Chain and Stakeholder’s Perspectives. Sustainability Switzerland. 2022, https://doi.org/10.3390/su14074160.
  6. Salas, E.A.L.; Kumaran, S.S.; Partee, E.B.; Willis, L.P.; Mitchell, K.. Potential of mapping dissolved oxygen in the Little Miami River using Sentinel-2 images and machine learning algorithms. Remote Sensing Applications Society and Environment. 2022, https://doi.org/10.1016/j.rsase.2022.100759.
  7. Salas, E.A.L.; Subburayalu, S.K.; Slater, B.; Dave, R.; Parekh, P.; Zhao, K.; Bhattacharya, B.. Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques. Heliyon. 2021, https://doi.org/10.1016/j.heliyon.2021.e07439.
  8. Salas, E.A.L.. Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling: a comprehensive review. Geo Spatial Information Science. 2021, https://doi.org/10.1080/10095020.2020.1761763.
  9. Salas, E.A.L.; Subburayalu, S.K.. Analysis of phosphorus and nitrogen concentrations in the Great Miami and Little Miami basins in Ohio, USA from 2015 to 2017. River Research and Applications. 2020, https://doi.org/10.1002/rra.3604.
  10. Salas, E.A.L.; Subburayalu, S.K.; Slater, B.; Zhao, K.; Bhattacharya, B.; Tripathy, R.; Das, A.; Nigam, R.; Dave, R.; Parekh, P.. Mapping crop types in fragmented arable landscapes using AVIRIS-NG imagery and limited field data. International Journal of Image and Data Fusion. 2020, https://doi.org/10.1080/19479832.2019.1706646.
  11. Rabara, R.C.; Sotto, R.C.; Salas, E.A.L.. Species distribution modeling and phenotypic diversity reveals collection gap in the Musa balbisiana germplasm conservation in Philippines. Asian Journal of Agriculture. 2020, https://doi.org/10.13057/asianjagric/g040203.
  12. Salas, E.A.L.; Subburayalu, S.K.. Correction: Modified shape index for object-based random forest image classification of agricultural systems using airborne hyperspectral datasets (PLoS One (2019) 14:3 (e0213356) DOI: 10.1371/journal.pone.0213356). Plos One. 2019, https://doi.org/10.1371/journal.pone.0222474.
  13. Salas, E.A.L.; Subburayalu, S.K.. Modified shape index for object-based random forest image classification of agricultural systems using airborne hyperspectral datasets. Plos One. 2019, https://doi.org/10.1371/journal.pone.0213356.
  14. Salas, E.A.L.; Subburayalu, S.K.. Implications of climate change on nutrient pollution: A look into the nitrogen and phosphorus loadings in the great miami and little miami watersheds in Ohio. Aims Environmental Science. 2019, https://doi.org/10.3934/environsci.2019.3.186.
  15. Salas, E.A.L.; Valdez, R.; Michel, S.; Boykin, K.G.. Habitat assessment of Marco Polo sheep (Ovis ammon polii) in Eastern Tajikistan: Modeling the effects of climate change. Ecology and Evolution. 2018, https://doi.org/10.1002/ece3.4103.
  16. Salas, E.A.L.; Valdez, R.; Michel, S.. Summer and winter habitat suitability of Marco Polo argali in southeastern Tajikistan: A modeling approach. Heliyon. 2017, https://doi.org/10.1016/j.heliyon.2017.e00445.
  17. Salas, E.A.L.; Seamster, V.A.; Harings, N.M.; Boykin, K.G.; Alvarez, G.; Dixon, K.W.. Projected future bioclimate-envelope suitability for reptile and amphibian species of concern in South Central USA. Herpetological Conservation and Biology. 2017, https://api.elsevier.com/content/abstract/scopus_id/85028753525.
  18. Alvarez, G.; Salas, E.A.L.; Harings, N.M.; Boykin, K.G.. Projections of future suitable bioclimatic conditions of parthenogenetic whiptails. Climate. 2017, https://doi.org/10.3390/cli5020034.
  19. Salas, E.A.L.; Seamster, V.A.; Boykin, K.G.; Harings, N.M.; Dixon, K.W.. Modeling the impacts of climate change on Species of Concern (birds) in South Central U.S. based on bioclimatic variables. Aims Environmental Science. 2017, https://doi.org/10.3934/environsci.2017.2.358.
  20. Salas, E.A.L.; Valdez, R.; Boykin, K.G.. Open-access geographic data for the argali habitat in the southeastern tajik pamirs. Data. 2016, https://doi.org/10.3390/data1010005.
  21. Salas, E.A.L.; Boykin, K.G.; Valdez, R.. Multispectral and texture feature application in image-object analysis of summer vegetation in eastern Tajikistan Pamirs. Remote Sensing. 2016, https://doi.org/10.3390/rs8010078.
  22. Salas, E.A.L.; Valdez, R.; Boykin, K.G.. Geographic layers as landscape drivers for the Marco Polo Argali habitat in the Southeastern Pamir Mountains of Tajikistan. ISPRS International Journal of Geo Information. 2015, https://doi.org/10.3390/ijgi4042094.
  23. Salas, E.A.L.; Henebry, G.M.. A new approach for the analysis of hyperspectral data: Theory and sensitivity analysis of the moment distance method. Remote Sensing. 2013, https://doi.org/10.3390/rs6010020.
  24. Salas, E.A.L.; Henebry, G.M.. Separability of maize and soybean in the spectral regions of chlorophyll and carotenoids using the Moment Distance Index. Israel Journal of Plant Sciences. 2012, https://doi.org/10.1560/IJPS.60.1-2.65.
  25. Salas, E.A.L.; Henebry, G.M.. Area between peaks feature in the derivative reflectance curve as a sensitive indicator of change in chlorophyll concentration. Giscience and Remote Sensing. 2009, https://doi.org/10.2747/1548-1603.46.3.315.

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