Proposing a methodology in preparation of olive orchards map by remote sensing and geographic information system

Abstract

Present study focuses on identification and mapping of olive in the part of Roodbar region, Guilan, Iran, based on IRS images using spectral reflectance's stochastic and supervised classification techniques. Two methods evaluated to images controlled classification in order to separate olive orchards spectrum reflex from the other surface covers. At the first method, upper and lower limit of digital number of olive orchards class pixels determined with the addition and subtraction of standard deviation from the mean in each band and initial classified map of olive orchards prepared in each band with them. The final map of olive orchards prepared from crossing three initial maps of olive orchards in there spectrum bands. At the second method, Olive orchards map was prepared by four models which include: 1. Classifcation using spectrum reflex statistics and slicing and 2. Classification with four methods Box Classifier, Maximum Likelihood, Minimum Distance and Minimum Mahalanobis Distance. Methods accuracy was evaluated from crossing the map of training points (pixel) with olive orchards map. The results indicated that in classification of less-condensed olive orchards, because of spectrum wave interference of olive green canopy cover and the soil zone between the canopy cover, the interference of digital number of low-condensed olive observed not only with the other vegetation cover but also with bare lands. There was this issue even for wave interference of low-condensed olive with urban and residential regions as some part of olive located in urban and residential regions and one pixel digital number can be an average of reradiating wave of olive canopy cover and urban and residential region. So, some true pixels of low-condensed olive had been classified as residential region or vice versa.