shahrebanoo rahmani; Ataollah Ebrahimi; alireza davoudian
Abstract
In this study, three methods were evaluated for vegetation mapping. For remote sensing method, in addition to IRS data of LISSIII, Ddigital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) were used for classification of 14 classes of land covers mostly vegetation types using a ...
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In this study, three methods were evaluated for vegetation mapping. For remote sensing method, in addition to IRS data of LISSIII, Ddigital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) were used for classification of 14 classes of land covers mostly vegetation types using a maximum likelihood algorithm. After comparing of produced vegetation maps, overall accuracy and Kappa index were 82% and 79.43% respectively when only the IRS were used. Whereas, the overall accuracy and Kappa index were increased to 93% and 90.63% respectively, when ancillary data of DEM and NDVI were added. Slope, slope direction, elevation above sea level, annual precipitation, temperature, and sun radiation were selected as the main physiographic after a broad literature review. Then the relationship between of these six factors with vegetation types was evaluated. so a multivariate logistic regression was used to draw vegetation map of the study area based on the sixth independent variables. The result showed a predicted vegetation map of 47.08% accuracy.Finally, in the morphological method, relationship between three maps of lithology, undulating form of geomorphology and faces with vegetation/land cover were determined using a neural network synthetic approach and predict vegetation map was drawn as the output. The accuracy of resulted map was 39.1%. Comparison of accuracy of vegetation mapping by three methods of RS, physiographic and geomorphological methods revealed that RS method of vegetation/land cover mapping is significantly promising due to a meaningfully higher accuracy even without using ancillary data such as DEM and NDVI in this method.
Shahrebanoo Rahmani; Ataollah Ebrahimi; Alireza Davoudian
Abstract
The analysis of the relationship between spatial distribution of environmental factors andvegetation types is crucial for understanding mountainous ecosystems. In this research aGIS based approach was used to produce a vegetation map for Sabzkouh protected area inthe Chaharmahal-Va-Bakhtiari province. ...
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The analysis of the relationship between spatial distribution of environmental factors andvegetation types is crucial for understanding mountainous ecosystems. In this research aGIS based approach was used to produce a vegetation map for Sabzkouh protected area inthe Chaharmahal-Va-Bakhtiari province. To identify environmental parameters affecting thevegetation cover, 6 primary and secondary environmental parameters including hypsometric,slope steepness, slope direction, annual precipitation, temperature and sun radiation maps werederived from the study area DEM. To investigate the relationship between these factors andthe spatial distribution of vegetation cover, quantitative analyses using statistical techniqueslike Principal Components Analysis(PCA) were undertaken. Then, the spatial distributionof vegetation types was predicted using a multi-logistic regression. Results showed thattopographic variables derived from the DEM were very useful for indicating habitats ofrange and forest types. Although lack of information on the anthropogenic effects led to someuncertainties in the interpretation of spatial pattern of vegetation types, the topographic andclimatic variables, derived from the DEM, were considerably effective in modelling the spatialdistribution of vegetation types.
