Kamm, Matthew; Reed, J Michael (2018): Results from multiple independent observers classifying one aerial image of an American kestrel nest box site in Massachusetts, USA using ENVI [dataset]. Tufts University, PANGAEA, https://doi.org/10.1594/PANGAEA.884662,

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In: Kamm, M; Reed, JM (2018): Assessment of classification accuracy of habitat types in UAV aerial photos around American kestrel nest box sites in Massachusetts, USA [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.884669

Five naive observers (graduate students at Tufts University, MA, USA) each classified an aerial photo around an American Kestrel nest box site in Massachusetts, USA. Five different cover categories were used (Grass, Herbaceous, Woody, Human-modified, and Bare Ground) but each observer drew their own training samples. The results of a confusion matrix evaluating how many pixels of each type were correctly identified by the Supervised Classification scheme are included here. Each observer (five naive observers and the authors) was assigned a number, 1 through 6, and summary statistics of kappa coefficients are included.