I wonder if there is some useful research or literature about getting ground truth for supervised classification. I am just thinking about ground truth in scope of citizen science, effectivness and representativness and I didn't find much thorough articles out there! Do you have some recommendation where to start look? THX!
Cheers
Lukas
You may look to our articles for a variety of fast but systematic ground truthing of remotely sensed cover in forested landscapes (keywords: Bolivia, Rwanda gorilla, Rwanda forest; Majella fire, South Africa alien shrub; Majella Fagus; Apennines Pinus mugo). These are all driven by forest/park administrations and/or researchers.
Large citizen science projects on tree, bird and grass species are presented and discussed in our SEA Namibia article.
In our sample, RS and citizin scientist surveys seem unconnected activities. However, I know of a citizin science botanical survey projected over aerial photography online.
Dear Lukas
I send you some references that may be useful to start:
Richards, J.A. 1999. Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p. 240.
Jensen, J.R. (1986): Introductory Digital Image Processing. New Jersey, Prentice-Hall, 379 p.
Calculating Sample Size for Stratified Random Sample. U.S. Department of Agriculture. 2000. Forest Service Handbook, FSH 2409.12 – Timber Cruising Handbook, Washington, DC, 237 p.
Selkowitz, D. J., & Stehman, S. V. (2011). Thematic accuracy of the National
Land Cover Database (NLCD) 2001 land cover for Alaska. Remote Sensing of Environment, 115(6), 1401–1407. doi:10.1016/j.rse.2011.01.020
Di Gregorio, A. 2005. Land cover classification system: Classification concepts and user manual: LCCS. Vol. 8, Food & Agriculture Org.
Daniel Gómez, Javier Montero. 2011. Determining the accuracy in image supervised classification problems.
Gong, P.,Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., et al. (2013). Finer resolution observation and monitoring of global land cover: First mapping results with landsat tm and etm + data. International Journal of Remote Sensing, 34, 2607–2654.
REX PEACOCK. 2014. ACCURACY ASSESSMENT OF SUPERVISED AND UNSUPERVISED CLASSIFICATION USING LANDSAT IMAGERY OF LITTLE ROCK, ARKANSAS.Thesis.
Sarah Lynn Lewis-Gonzales. 2015. Accuracy of Supervised Classification of Cropland in sub–Saharan Africa. Thesis.
Zhao, Y.; Feng, D., Yua, L., Wang, X., Chen, Y., Hernández, H.J., Galleguillos, M., Estades, C., Biging, G., Radke, J. & Gong, P. 2016. Detailed dynamic land cover mapping of Chile: accuracy improvement by integrating multi-seasonal land cover data. Remote Sensing of Environment 183, 170–185.
Ramakalavathi Marapareddy, James V. Aanstoos and Nicolas H. Younan.2017.Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery. Electronics 6, 83; doi:10.3390/electronics6040083.
Best regards
LM
You can see this link, may be useful for you
http://www.nwmissouri.edu/library/theses/2014/PeacockRexA.pdf
https://pdfs.semanticscholar.org/6961/3390ca76bf103791ef251e1568deb5fe91dd.pdf
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