چکیده:
Soil Salinity has been a large problem in arid and semi arid regions. Preparation of such maps is useful for Natural
resource managers. Old methods of preparing such maps require a lot of time and cost. Multi-spectral remotely
sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. This
investigation is conducted to provide saline soil maps with sensor LISS-III of IRS-P6 satellite data, in Najmabad of
Savojbolagh. Satellite images belonging to 25 June 2006. For enhancement of images, salt Indices, Digital Elevation
Model (DEM), False Color Composite imageries (FCC) and Principal Component Analysis (PCA), were used.
Supervised classification method includes Box classifier, Minimum Distance, Minimum Mahalanobis Distance and
Maximum Likelihood classifier, DEM, PCA1, PCA4 and Saline Indices (SI) were used. After classification, the class
map salinity S0, S1, S2, S3 S4, were prepared. The results shows highest overall accuracy and kappa coefficient for
the maximum Likelihood classifier estimate, respectively 99% and 97% and the lowest overall accuracy and kappa
coefficient for PCA1 estimate, respectively 1% and 0% were obtained. Using Digital Elevation Model (DEM) also
due to the difference in height position to the separation of saline lands is usefully. Most spectral interference related
to non-saline soils and low saline soil. From among indices INT2 and PVI greatest ability to segregate is salty soils
(especially classes S0 and S1).
خلاصه ماشینی:
Therefore, the use of digital elevation models (DEM) for land application of soils has a large separation This study identified salinity-affected soils using LISS-III image sensor area is dry Najmabadi Savojbolagh.
/ DESERT 17 (2013) 277-289 (View the image of this page) The study area landuse classes from satellite imageries extracted with the help of accessories data and field studies and classes of salinity were obtained.
At this stage to classification, of algorithms using methods is box classifier, minimum distance, minimum Mahalanobis distance, and maximum likelihood and other classification method is combining satellite imagery with a map list as salinity indices; Principal Component Analysis (PCA1 & PCA4) and Digital Elevation Model (DEM).
(View the image of this page)2- Intensity within the visible spectral range 3- Intensity within the VIS_NIR spectral range 4- Saline Index one 5- Perpendicular Vegetation Index 6- Vegetation Normalized infrared Ratio 7- Triangular Vegetation Index The satellite imageries supervised classification was performed and each class algorithm to salinity map produced and crossed with the ground truth map and error matrix was prepared.
Other classification method is, combining satellite imagery with a map list as salinity indices; Principal Component Analysis (PCA1 & PCA4) and Digital Elevation Model (DEM).
Study of vegetation cover and saline soil maps with remote sensing and geography information system (case study: Salt river of Karaj).
provide saline and alkaline soil maps with remote sensing and geography information system (case study: Chamestan region).