Land Cover Classification and Feature Extraction from National Agriculture Imagery Program (NAIP) Orthoimagery: A Review

This review describes the National Agriculture Imagery Program (NAIP), explores strengths and weaknesses of the data, and summarizes how the data are used in land cover and feature extraction tasks in order to provide some recommendations for future research and best practices for working with NAIP data. NAIP orthoimagery is an often-overlooked source for remote sensing classification and feature extraction applications in the contiguous United States (CONUS). NAIP data are free, or nearly free; are in the public domain; are available for all of the CONUS; comprise a multitemporal data set that spans more than a decade; and are collected at a high spatial resolution with generally very low cloud coverage. However, there are challenges associated with the use of these data. The low spectral resolution limits spectral differentiation, while the high spatial resolution results in very large data sets for study areas even as small as a single county. Differences in acquisition dates and time of day result in varying illumination conditions and potentially even varying phenological state. As a consequence, image digital number (DN) values can vary between adjacent tiles, and shadow size and direction can be inconsistent between different tiles and different acquisitions. Therefore, taking full advantage of this valuable data source requires the analyst to be cognizant of such concerns and take measures to deal with such inconsistency and minimize its impact on the classification results; future research addressing these concerns would further enhance the value of NAIP data.