Volume 3, Issue 1 (9-2016)                   jfer 2016, 3(1): 19-32 | Back to browse issues page

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mirzaei zadeh V, niknejad M, haydari M. (2016). Monitoring and predicting changes in vegetation density using remote sensing (Case study: Venet watershed, Ilam province). jfer. 3(1), 19-32.
URL: http://yujs.yu.ac.ir/jzfr/article-1-70-en.html
university of Ilam , m_heydari23@yahoo.com
Abstract:   (11059 Views)

The importance of vegetation as a dynamic factor affecting the biological conditions requires that a detailed qualitative and quantitative information about its changes be prepared in short intervals. In this study, in order to monitor and predict vegetation density in Venet watershed in Ilam province the Normalized difference vegetation index (NDVI) and Landsat images from 1988 and 2007 was used and vegetation density maps in three classes without canopy cover, thinned and dense canopy covers were prepared. Comparing the extent of vegetation density classes indicated that the extent of bare soil areas have increased as 1158/837 hectares while the extent of thinned and dense canopy covers 360/8277 and 797/9544 hectares respectively has been reduced. Assessing changes in vegetation density showed that from the classes with thinned and dense canopy covers as 1233/4828 and 210/4539 ha respectively have become no vegetation cover class. Also as 246 /2742 and 38 /8255 hectares of the bare soil areas have been converted to thinned and dense canopy cover classes respectively. Using Markov models and automated cells to predict changes in vegetation density showed that the highest and lowest probability of transition to no vegetation cover lands has been seen respectively in thinned (0.5059) and dense canopy cover (0.1023) lands. Finally, assuming a continuation of current trends, watershed vegetation density map of the target for 2020 was forecast. This map shows in the near future the land without vegetation cover will devote itself about 87 percent of this region.

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Type of Study: Research | Subject: Special
Received: 2015/07/4 | Accepted: 2016/01/22

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