Feasibility of satellite imagery for poplar plantation mapping (Case study: Sowme`eh Sara)

تحقیقات جنگل و صنوبر ایران. 2014;22(3):392-401 DOI 10.22092/ijfpr.2014.12415


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Journal Title: تحقیقات جنگل و صنوبر ایران

ISSN: 1735-0883 (Print); 2383-1146 (Online)

Publisher: Research Institute of Forests and Rangelands of Iran

LCC Subject Category: Agriculture: Forestry

Country of publisher: Iran, Islamic Republic of

Language of fulltext: Persian

Full-text formats available: PDF, XML



Ali Asghar Darvishsefat (Prof., Faculty of Natural Resources, University of Tehran, Karaj, I.R. Iran.)
Fatemeh Ghaffari Dafchahi (M. Sc. Forestry, Faculty of Natural Resources, University of Tehran, Karaj, I.R. Iran.)
Amir Eslam Bonyad (Associate Prof., Faculty of Natural Resources, University of Guilan, Sowme`eh Sara, I.R. Iran.)


Double blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 28 weeks


Abstract | Full Text

To investigate the capability of satellite imagery to map poplar plantations, IRS-P6-LISSIII and IRS-P6-LISSIV scenes from 2007 as well as Landsat TM scenes from 2009 and 2011 were analyzed over Sowme`eh Sara region in Guilan province. The IRS-P6-LISSIII and IRS-P6-LISSIV scenes were geometrically corrected with root mean square error (RMSE) of ca. 11m and 60cm, respectively. Informative auxiliary bands ratios were calculated. In addition, the scenes were spatially enhanced by resolution merge of panchromatic and multispectral bands. Principal component analysis (PCA) and inverse PCA were also conducted for the spatially-fused data. Ground reference data was samples via field survey and included two classes of “maximum 5-year medium density canopy” and “dense canopy >5-years of age” poplar plantations. The ssatellite images were classified by crisp (supervised) and soft (fuzzy) classifiers using three different strategies of 1) classification into three classes including poplar plantation with “maximum 5-year medium density canopy”, “dense canopy >5-years of age” and others, 2) classification into eight classes including the two above-mentioned classes, rice, alder, conifer, sea, urban area and others, in which all non-poplar classes were aggregated into one class after the classification, and 3) independent classification of three sub-regions on the images. Results of accuracy assessment for the first and second strategy showed very low overall accuracy and kappa coefficient, whereas the third strategy showed a higher performance. This way, the highest rates of overall accuracy and kappa coefficient of the TM scene 2009 in the first sub-region were reported to be %80 and 0.58, respectively.