Journal of Crop Production and Processing

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In order to evaluate the capability of ETM+ remotely- sensed data to provide "Forest- shrub land- Rangeland" cover type map in areas near the timberline of northern forests of Iran, the data was analyzed in a portion of nearly 790 ha located in Neka- Zalemroud region. First, ortho-rectification process was implemented to correct the geometric errors of the image, which yielded 0/68 and 0/69 pixels of RMS error toward X and Y axis, respectively. The original multi-spectral bands were fused to the panchromatic band using PANSHARP Statistical module. The ground truth map was prepared using 1 ha field plots in a systematic- random sampling grid. Vegetative form of trees, shrubs and rangelands was recorded as a criterion to allocate the plots. A set of channels including original bands, NDVI and IR/R indices, and first components of PCA was used for classification procedure. Automatic band selection command was used to select the appropriate channel set.. Classification was carried out using ML classifier on both original and fused data sets. It showed 67% of overall accuracy and 0/43 of Kappa coefficient in original data set. Due to the results present presented above, it's concluded that ETM+ data has an intermediate capability to fulfill the spectral variations of 3 form- based classes, in the studied area. Furthermore, applying complementary methods to minimize the background spectral effect is proposed for future studies.

Keywords: ETM+ sensor, Forest- Shrub land- Range classes, ML classifier, Accuracy assessment, Neka- Zalemroud

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