بررﺳﯽ روﻧﺪ تغییرات ﮐﺎرﺑﺮی اراﺿﯽ ﺷﻬﺮ ﻋﺴﻠﻮﯾﻪ ﺑﯿﻦ ﺳﺎﻟﻬﺎی 1989 ﺗﺎ 2019 میلادی و ﭘﯿﺶﺑﯿﻨﯽ تغییرات ﺑﺎ اﺳﺘﻔﺎده از ﺳﻨﺠﺶ از دور و ﻣﺪﻟﻬﺎی ca-markov و lcm

ﺗﻐﻴﻴﺮ ﻛﺎرﺑﺮی اراﺿﻲ ﺑﻪ ﻋﻨﻮان ﻋﺎﻣﻠﻲ ﭘﺎﻳﻪ در ﺗﻐﻴﻴﺮات زﻳﺴﺖﻣﺤﻴﻄﻲ ﻋﻤﻞ ﻛﺮده و ﺑﻪ ﻳﻚ ﺧﻄﺮ ﺟﻬﺎﻧﻲ ﺗﺒﺪﻳﻞ ﺷﺪه اﺳﺖ. ﺑﺎزﺑﻴﻨﻲ اﻳﻦ ﺗﻐﻴﻴﺮات از ﻃﺮﻳﻖ ﺗﺼﺎوﻳﺮ ﻣﺎﻫﻮارهای و ﭘﻴﺶﺑﻴﻨﻲ و ارزﻳﺎﺑﻲ ﭘﺘﺎﻧﺴﻴﻞ آﻧﻬﺎ از ﻃﺮﻳﻖ ﻣﺪﻟﺴﺎزی ﻣﻲﺗﻮاﻧﺪ ﺑﻪ ﺑﺮﻧﺎﻣﻪرﻳﺰان ﻣﺤﻴﻂ زﻳﺴﺖ و ﻣﺪﻳﺮان ﻣﻨﺎﺑﻊ ﻃﺒﻴﻌﻲ ﺑﺮای ﺗﺼﻤﻴﻤﺎت آﮔﺎﻫﺎﻧﻪﺗﺮ ﻛﻤﻚ ﻛﻨﺪ. ﻫﺪف اﻳﻦ ﺗﺤﻘﻴﻖ ﺑﺎزﺑﻴﻨﻲ، ﻣﺪﻟﺴﺎزی و ﭘﻴﺶﺑﻴﻨﻲ ﺗﻐﻴﻴﺮات ﻛﺎرﺑﺮی اراﺿﻲ در دوره 30 ﺳﺎﻟﻪ (1989تا 2019) ﺗﻮﺳﻂ ﻣﺪل زﻧﺠﻴﺮة ﻣﺎرﻛﻮف- lcm در ﻣﻨﻄﻘﻪ عسلویه می باشد اﺳﺖ. ﺑﺪﻳﻦ ﻣﻨﻈﻮر ﻧﻘﺸﻪﻫﺎی ﻛﺎرﺑﺮی اراﺿﻲ ﺑﺎ اﺳﺘﻔﺎده از ﺗﺼﺎوﻳﺮ ﺳﻨﺠﻨﺪه +etm و tm و oliﻣﺎﻫﻮاره ﻟﻨﺪﺳﺖ در ﺳﻪ دوره زﻣﺎﻧﻲ ﻣﺮﺑﻮط ﺑﻪ ﺳﺎلﻫﺎی (2019-2009-1989) ﺗﻬﻴﻪ ﮔﺮدﻳﺪ. ﺳﭙﺲ ﺻﺤﺖﺳﻨﺠﻲ ﻧﻘﺸﻪﻫﺎ و آﺷﻜﺎرﺳﺎزی ﺗﻐﻴﻴﺮات اﻧﺠﺎم ﺷﺪ. با استفاده از طبقه بندی شبکه عصبی و همچنین به کارگیری مدل پیش بینی تغییرات کاربری (land change modeler) lcmمارکوف و رویکرد مدلساز تغییر کاربری اراضی انجام شده است. ﻧﺘﺎﻳﺞ آﺷﻜﺎرﺳﺎزی ﺗﻐﻴﻴﺮات دوره اول ﺑﺎ ﺿﺮﻳﺐ ﻛﺎﭘﺎی 97 % و دوره دوم 2019-2009 ﺑﺎ ﺿﺮﻳﺐ ﻛﺎﭘﺎی 94% ﻧﺸﺎن ﻣﻲدﻫﺪ ﻛﻪ ﺑﻴﺶﺗﺮﻳﻦتغییرات ﻣﺴﺎﺣﺖ در ﻧﺎﺣﻴﻪ آب و ﺑﻴﺶﺗﺮﻳﻦ ﻛﺎﻫﺶ ﻣﺴﺎﺣﺖ در ﻧﺎﺣﻴﻪ پوشش گیاهی رخ داده اﺳﺖ. ﺑﻪ ﻣﻨﻈﻮر ﻛﺎﻟﻴﺒﺮه ﻛﺮدن ﻣﺪل زﻧﺠﻴﺮة ﻣﺎرﻛﻮف، ﻧﻘﺸﻪ ﻛﺎرﺑﺮی ﺳﺎل 2013 ﭘﻴﺶﺑﻴﻨﻲ ﺷﺪ و ﻣﺎﺗﺮﻳﺲ ﺧﻄﺎی ﺑﻴﻦ ﻧﻘﺸﻪ ﺣﺎﺻﻞ از ﻣﺪلﺳﺎزی و ﻧﻘﺸﻪ ﻛﺎرﺑﺮی ﻣﺮﺟﻊ ﺳﺎل 2019، ﺿﺮﻳﺐ ﻛﺎﭘﺎی 93% ﺑﺪﺳﺖ داد ﻧﺘﺎﻳﺞ ﻣﺪل ﺳﺎزی ﻧﻴﺮوی اﻧﺘﻘﺎل ﺑﺎ اﺳﺘﻔﺎده از ﺷﺒﻜﻪ ﻋﺼﺒﻲ ﻣﺼﻨﻮﻋﻲ در ﺑﻴﺸﺘﺮ زﻳﺮ ﻣﺪلﻫﺎ ﺻﺤﺖ ﺑﺎﻻﻳﻲ را 60 ﺗﺎ 93 درﺻﺪ ﻧﺸﺎن داد.

on the current changes of votes between 1989 and 2019 changes and predicting the changes using remote sensing and ca-markov and lcm models

land use change has acted as a pivotal factor in environmental change and has become a global threat. reviewing these changes through satellite imagery and predicting and evaluating their potential through modeling can help environmental planners and natural resource managers to make informed decisions. the purpose of this study was to review, model, and predict land use changes in the 30-year period of 1993-2013 by the markow-lcm chain model in kangan and assaluyeh. for this purpose, land use maps were prepared using etm +, tm and oli satellite imagery in three periods of 1993, 2003, and 2013. then verifying the maps and detecting the changes. using the classification of the neural network and applying the land change modeler (lcm markov model) and the land use change modeling approach have been implemented. the results of detection of changes in the first period with a kappa coefficient of 97% and the second period of 1993-2003 with a kappa coefficient of 94% indicate that the largest changes in the area in the water area and the largest decrease in the area in the vegetation area occurred. in order to calibrate the markov chain model, the 2013 map was predicted and the error mapping matrix of the 2013 map reference model and mapping utilization yielded a copper coefficient of 93%. the results of modeling the transfer force using the artificial neural network in most of the sub-models the high accuracy was 60-93%.