presentation of suitable model to estimate vegetation fraction using satellite images in arid region (case study: sadough- yazd, iran)

نویسندگان

gholamreza zehtabian

hossein azarnivand

hasan ahmadi

saeideh kalantari

چکیده

one of the influential tools concerning the rangeland and vegetation sciences isthe technology of remote sensing and satellite data. satellite data have played essentialroles in preparing the needed information for studying the vegetation. vegetation has beenwidely recognized as one of the best indicators for determining the land conditions. usingvegetation indices is one of the techniques of remote sensing to study the vegetation. thepurpose of this research is to study the vegetation indices and also provide an appropriatemathematical model for estimating the vegetation fraction using the mentioned indicators.in this research, different vegetation indices were calculated by landsat tm (2006)satellite images in order to evaluate their capability to estimate the vegetation in the aridregions. at the outset, vegetation fraction was measured using the sample quadrates andthen the measured data were compared with the values of digital numbers for the sitepixels. multiple regression analysis was utilized for the actual values and parameters toselect the validation and optimization models. indices which have been calculated includemsr, dvi, trvi, sarvi, sr, rdvi, dvi, ndvi, savi, arvi and msavi. the resultsshowed that the model in which arvi was used had the highest accuracy (r2=0.86) andtherefore, it was chosen as the most suitable model for the estimation of vegetation fractionin the study area. one accomplishment of the above results is to suggest somemathematical equations with those indices to estimate the amount of vegetation. theseresults provide a strong foundation for the use of some vegetation indices for assessing thevegetation cover in central iran.

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عنوان ژورنال:
journal of rangeland science

ناشر: islamic azad university, boroujerd branch

ISSN 2008-9996

دوره 3

شماره 2 2013

میزبانی شده توسط پلتفرم ابری doprax.com

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