The maximum likelihood method valuation in detecting land use change using NDVI index ( Case study: Hamadan- Bahar Watershed)

Timely and accurate change detection of Earth’s surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. In order to provide the land use map of Hamadan- Bahar Watershed, digital data obtained from TM (1985), ETM(2000) and  OLI (2013) sensor of digital data of the Landsat 5 , 7 and 8 were used. To classify images, were used Maximum likelihood method, using samples of  ground truth. According to the acceptance Kappa coefficient classification by Landis and Koch kappa coefficient, acceptance Kappa coefficient in 1985 (93.11 %), 2000 (90.01%) and 2013 (85.06 %) was excellent. Comparing  NDVI maps with those  of maximum likelihood classification, It also was found that the produced NDVI maps match with Irrigation farming category, indicating the accuracy of maximum likelihood method in classifying images. Result showed that between the years 1992 to 2013 settlements and Irrigation farming have increased 139.93% and 12.29% respectively, while Dry farming and Rangeland have decrease 0.33% and 17.12% respectively.In addation, the results of the conversion of non-residential to residential Maps showed that between the years 1992 to 2013, 2000 to 2013 and 1992 to 2013 among agricultural and pastures, agricultural lands with an area 44. 67, 73. 35 and 94. 84 square kilometers the, respectively, will be allocated the greatest area converted from non-residential to residentia. Also, in order to assess land use changes due to its status in the past, land use map for 2030 was predicted using cellular automata model, Then land use changes trend was plotted during 1990 to 2030. The results indicate a growing trend in settlements and Irrigation farming in the future period. In the period Dry farming and Rangeland will decrease.