Standard References

This document links the standard references we use in papers across multiple projects. If you have a paper that’s relevant to a specific project, but not the majority of projects across the lab, add it to that project page instead.

Data

  • FIA:
    • Gray, Andrew N, Thomas J Brandeis, John D Shaw, William H McWilliams, and Patrick Miles. 2012. “Forest Inventory and Analysis Database of the United States of America (FIA).” Biodiversity and Ecology 4: 225–31. https://doi.org/10.7809/b-e.00079.
  • LCMAP:
    • Brown, Jesslyn F., Heather J. Tollerud, Christopher P. Barber, Qiang Zhou, John L. Dwyer, James E. Vogelmann, Thomas R. Loveland, Curtis E. Woodcock, Stephen V. Stehman, Zhe Zhu, Bruce W. Pengra, Kelcy Smith, Josephine A. Horton, George Xian, Roger F. Auch, Terry L. Sohl, Kristi L. Sayler, Alisa L. Gallant, Daniel Zelenak, Ryan R. Reker, Jennifer Rover. 2020. “Lessons Learned Implementing an Operational Continuous United States National Land Change Monitoring Capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) Approach.” Remote Sensing of Environment 238: 111356. https://doi.org/10.1016/j.rse.2019.111356.

Stats

Machine Learning Methods

  • Random forests:
    • Breiman, Leo. 2001. Machine Learning 45 (1): 5–32. https://doi.org/10.1023/a:1010933404324. (PDF)
  • Gradient boosting machines:
    • Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29 (5). (PDF) https://doi.org/10.1214/aos/1013203451.
    • Friedman, Jerome H. 2002. “Stochastic Gradient Boosting.” Computational Statistics & Data Analysis 38 (4): 367–78. (PDF) https://doi.org/10.1016/s0167-9473(01)00065-2.
    • lightGBM:
      • Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. “LightGBM: A Highly Efficient Gradient Boosting Decision Tree.” In Advances in Neural Information Processing Systems, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett. Vol. 30. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf. (PDF)
  • Support vector machines:
    • Cortes, Corinna, and Vladimir Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20 (3): 273–97. https://doi.org/10.1007/bf00994018. (PDF)
  • Neural networks:
    • LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Learning.” Nature 521: 436–44. https://doi.org/10.1038/nature14539. (PDF)
  • Model averaging:
    • Dormann, Carsten F., Justin M. Calabrese, Gurutzeta Guillera-Arroita, Eleni Matechou, Volker Bahn, Kamil Bartoń, Colin M. Beale, et al. 2018. “Model Averaging in Ecology: A Review of Bayesian, Information-Theoretic, and Tactical Approaches for Predictive Inference.” Ecological Monographs 88 (4): 485–504. https://doi.org/10.1002/ecm.1309. (PDF)
    • Wintle, B. A., M. A. Mccarthy, C. T. Volinsky, and R. P. Kavanagh. 2003. “The Use of Bayesian Model Averaging to Better Represent Uncertainty in Ecological Models.” Conservation Biology 17 (6): 1579–90. https://doi.org/10.1111/j.1523-1739.2003.00614.x. (PDF)
    • Wolpert, David H. 1992. “Stacked Generalization.” Neural Networks 5 (2): 241–59. https://doi.org/10.1016/S0893-6080(05)80023-1. (PDF)

Metrics

  • Agreement Coefficient:
    • Ji, Lei, and Kevin Gallo. 2006. “An Agreement Coefficient for Image Comparison.” Photogrammetric Engineering & Remote Sensing 72 (7): 823–33. https://doi.org/10.14358/pers.72.7.823.
  • Willmott’s Dr:
    • Willmott, Cort J, Scott M Robeson, and Kenji Matsuura. 2011. “A Refined Index of Model Performance.” International Journal of Climatology 32 (13): 2088–94. https://doi.org/10.1002/joc.2419.

Model Assessment

  • Riemann-style multi-scale assessment
    • Riemann, Rachel, Barry Tyler Wilson, Andrew Lister, and Sarah Parks. 2010. “An Effective Assessment Protocol for Continuous Geospatial Datasets of Forest Characteristics Using USFS Forest Inventory and Analysis (FIA) Data.” Remote Sensing of Environment 114 (10): 2337–52. https://doi.org/10.1016/j.rse.2010.05.010.

Software

You should always cite all software used in the process of generating any paper. Not only is this a good way to say “thanks” to the people making your tools, but it also helps ensure your paper is reproducible. Including the package (and version!) used to produce a result means that people can re-create that result even when defaults change or when the new hotness replaces the tool you used as the default choice.

In R, you can get the citation for any package by running citation("packageName"). You can get the citation for R itself by running citation() without any arguments.

  • future:
    • Bengtsson, Henrik. 2021. “A Unifying Framework for Parallel and Distributed Processing in R Using Futures.” The R Journal 13 (2): 208–27. https://doi.org/10.32614/RJ-2021-048.
  • GDAL:
    • GDAL/OGR contributors. 2021. GDAL/OGR Geospatial Data Abstraction Software Library. Open Source Geospatial Foundation. https://gdal.org.
  • targets:
    • Landau, William Michael. 2021. “The Targets r Package: A Dynamic Make-Like Function-Oriented Pipeline Toolkit for Reproducibility and High-Performance Computing.” Journal of Open Source Software 6 (57): 2959. https://doi.org/10.21105/joss.02959.