Volume 9, Issue 4 (winter 2006)
jwss 2006, 9(4): 29-44 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
A. Sarreshtehdari. Detection of the Sedimentation Process in the Systems of Flood Spreading using LANDSAT Satellite Images Data and TM & ETM+ Sensores. jwss 2006; 9 (4) :29-44
URL: http://jstnar.iut.ac.ir/article-1-500-en.html
URL: http://jstnar.iut.ac.ir/article-1-500-en.html
Abstract: (22896 Views)
Of the applications of remote sensing and satellite images in natural resources is distinguishing and detection of changes in land surface. The image classification using Maximum Likelihood (MLC) is one the prevalent method which is used in a study of the application of TM and ETM+ satellite images to detect sediment deposition on an implemented floodwater spreading scheme. In order to implement the research, field sampling and checking were done using transect networking method by selection of 30 sample points in floodwater spreading area as well as another 30 control points in the study area. The results of the study are shown that detection of sediment deposition using MLC method by application of LANDSAT TM and ETM+ can lead to increase the precision of change detection up to 82 percent. Furthermore, the results also show that the trend and changes due to sediment deposition on water spreading area can be precisely detected. Considering the present and potential applicability of the applied method in distinguishing changes due to sediment deposition on land surface which is absorbed on 450 hectares of water spreading area in this research study, it can be pointed out that the use of this method in larger area could be tend to increase the precision of change detection and to decrease the required time.
Keywords: Sediment detection, Flood spreading, Remote sensing, LANDSAT images, Maximum likelihood Classification method.
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
