Requirements for labelling forest polygons in an object-based image analysis classification.
The ability to spatially quantify changes in the landscape and create land-cover maps is one of the most powerful uses of remote sensing. Recent advances in object-based image analysis (OBIA) have also improved classification techniques for developing land-cover maps. However, when using an OBIA technique, collecting ground data to label reference units may not be straightforward, since these segments generally contain a variable number of pixels as well as a variety of pixel values, which may reflect variation in land-cover composition. Accurate classification of reference units can be particularly difficult in forested land-cover types, since these classes can be quite variable on the ground. This study evaluates how many prism sample locations are needed to attain an acceptable level of accuracy within forested reference units in southeastern New Hampshire (NH). Typical forest inventory guidelines suggest at least 10 prism samples per stand, depending on the stand area and stand type. However, because OBIA segments group pixels based on the variance of the pixels, fewer prism samples may be necessary in a segment to properly estimate the stand composition. A bootstrapping statistical technique was used to find the necessary number of prism samples to limit the variance associated with estimating the species composition of a segment. Allowing for the lowest acceptable variance, a maximum of only six prism samples was necessary to label forested reference units. All polygons needed at least two prism samples for classification. †Present address: School of Forestry and Environmental Studies, Yale University, New Haven, CT, USAPresent address: Math and Science Division, Babson College, Babson Park, MA, USA.
International Journal of Remote Sensing
Taylor & Francis
Digital Object Identifier (DOI)
acLean, M.G., Campbell, M.J., Maynard, D.S., Ducey, M.J., Congalton, R.G. Requirements for labelling forest polygons in an object-based image analysis classification. (2013) International Journal of Remote Sensing, 34 (7), pp. 2531-2547. DOI: 10.1080/01431161.2012.747017
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