references

references

Briottet, X. et al (2022). BIODIVERSITY – A new space mission to monitor Earth ecosystems at fine scale. Revue Française de Photogrammétrie et de Télédétection, 224(1), 33–58. https://doi.org/10.52638/rfpt.2022.568

Cabezas, M., Kentsch, S., Tomhave, L., Gross, J., Caceres, M. L. L., & Diez, Y. (2020). Detection of invasive species in Wetlands: practical dl with heavily imbalanced data. Remote Sensing, 12(20), 1-17. Article 3431. https://doi.org/10.3390/rs12203431

Add the following:  (Canon, 2015)

Chimner, R. A., Lemly, J. M., & Cooper, D. J. (2010). Mountain Fen Distribution, Types and Restoration Priorities, San Juan Mountains, Colorado, USA. Wetlands, 30(4), 763–771. https://doi.org/10.1007/s13157-010-0039-5

Davenport, M.A. et al (2010). Building Local Community Commitment to Wetlands Restoration: A Case Study of the Cache River Wetlands in Southern Illinois, USA Environmental Management (2010) 45:711–722 DOI 10.1007/s00267-010-9446-x

Feldman, A. (2024). Emerging Methods to Validate Remotely Sensed Vegetation Water Content. AGU: Advancing Earth and Space Sciences. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024GL110505

Frazier, A. & Singh, K., eds. (2022) Fundamentals of Capturing and Processing Drone Imagery and Data (Boca Raton: CRC Press).

Lake, T.A., Runquist, R.D.B., & David A. Moeller, D.A. (2022). Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery, Remote Sensing in Ecology and Conservation 2022;8 (6):875–889 (citation URL)

Sowder, B. (2024).  Rocky Mountain Fens: Vegetation Mapping. Long-term monitoring plan for Prospect Basin to assess fen vegetation changes as an indicator of ecosystem integrity. Unpublished Master’s Thesis submitted to the faculty of Western Colorado University.

Stedman, S. (2003). An Introduction and User’s Guide to Wetland Restoration, Creation, and Enhancement. An Interagency study sponsored by the National Oceanic and Atmospheric Administration, Environmental Protection Agency, Army Corps of Engineers, Fish and Wildlife Service, and Natural Resources Conservation Service. https://www.csu.edu/cerc/documents/AnIntroductionandUsersGuidetoWetlandsRestoration.pdf

Udall, B., & Overpeck, J. (2017). The twenty-first century Colorado River hot drought and implications for the future. Water Resources Research, 53(3), 2404–2418. https://doi.org/10.1002/2016WR019638

U.S. Fish and Wildlife Service (2025). National Wetlands Inventory website. U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C. 

Zhu, W., Feng, Z., Dai, S., Zhang, P., & Wei, X. (2022). Using UAV Multispectral Remote Sensing with Appropriate Spatial Resolution and Machine Learning to Monitor Wheat Scab. Agriculture, 12(11), Article 11. https://doi.org/10.3390/agriculture12111785

Zuhao Ou et al (2022) An Automatic Marking Method Based on Object Detection Algorithm in Aerial Images, Journal of Physics.: Conf. Ser. 2218 012041 https://iopscience.iop.org/article/10.1088/1742-6596/2218/1/012041

Journals

Remote Sensing Environment https://www.sciencedirect.com/journal/remote-sensing-of-environment

Wetlands

Official Scholarly Journal of the Society of Wetland Scientists

https://link.springer.com/journal/13157

Video

Remote Sensing and Machine Learning for Environmental Monitoring (2025) 

https://www.youtube.com/watch?v=gqhr2OiAsDM

This video is from the "AI for Good Global Summit."  AI for Good is considered to be the leading action-oriented United Nations platform promoting AI to advance health, climate, gender, inclusive prosperity, sustainable infrastructure, and other global development priorities. AI for Good is organized by the International Telecommunication Union (ITU) – the UN specialized agency for information and communication technology – in partnership with 40 UN sister agencies and co-convened with the government of Switzerland. 

Wu, Q., & Osco, L. (2023). samgeo: A Python package for segmenting geospatial data with the Segment Anything Model (SAM). Journal of Open Source Software, 8(89), 5663. https://doi.org/10.21105/joss.05663.  https://www.youtube.com/watch?v=p6WjtykBG2M