پايش تغييرات پوشش اراضي با استفاده از سنجش از دور و سامانه اطلاعات جغرافيايي (مطالعه موردي: حوضه آبخيز خوجين خلخال)

Monitoring of Land Cover Changes Using Remote Sensing Technique and GIS (Case study: Khojin watershed, Khalkhal)

زﻣﯿﻨﻪ و ﻫﺪف: ﻣﻄﺎﻟﻌﻪ ﻣﯿﺰان ﺗﻐﯿﯿﺮات و ﺗﺨﺮﯾﺐ ﻣﻨﺎﺑﻊ در ﺳﺎلﻫﺎي ﮔﺬﺷﺘﻪ ﻣﯽﺗﻮاﻧﺪ در ﺑﺮﻧﺎﻣﻪرﯾﺰي و اﺳﺘﻔﺎدة ﺑﻬﯿﻨﻪ از ﻣﻨﺎﺑﻊ و ﮐﻨﺘﺮل و ﻣﻬﺎر ﺗﻐﯿﯿﺮات ﻏﯿﺮ اﺻﻮﻟﯽ در آﯾﻨﺪه ﮔﺎم ﻣﻬﻤﯽ ﺑﺎﺷﺪ و از آﻧﺠﺎ ﮐﻪ اﯾﻦ ﺗﻐﯿﯿﺮات در ﺳﻄﻮح وﺳﯿﻊ و ﮔﺴﺘﺮده اﺗﻔﺎق ﻣﯽ اﻓﺘﺪ، ﺗﮑﻨﻮﻟﻮژي ﺳﻨﺠﺶ از دور ﻣﯽ ﺗﻮاﻧﺪ اﺑﺰاري ﺿﺮوري و ارزش ﻣﻨﺪ ﺟﻬﺖ ﭘﺎﯾﺶ ﺗﻐﯿﯿﺮات ﺑﺎﺷﺪ. ﻫﺪف از ﺗﺤﻘﯿﻖ ﺣﺎﺿﺮ، ﭘﺎﯾﺶ ﺗﻐﯿﯿﺮات ﭘﻮﺷﺶ اراﺿﯽ در ﺣﻮﺿﻪ آﺑﺨﯿﺰ ﺧﻮﺟﯿﻦ ﺧﻠﺨﺎل در ﺟﻨﻮب اﺳﺘﺎن اردﺑﯿﻞ ﺑﺎ ﺗﮑﻨﯿﮏ ﺳﻨﺠﺶ از دور ﻣﯽﺑﺎﺷﺪ. روش ﺑﺮرﺳﯽ: اﺑﺘﺪا ﺗﺼﺎوﯾﺮ ﻣﺎﻫﻮاره ﻟﻨﺪﺳﺖ ﻣﺮﺑﻮط ﺑﻪ ﺗﯿﺮ ﻣﺎه ﺳﺎلﻫﺎي 1365، 1380 و 1395 ﺗﻬﯿﻪ و ﭘﺲ از ﺗﺼﺤﯿﺢ ﻫﻨﺪﺳﯽ و ارﺗﻔﺎﻋﯽ، ﻃﺒﻘﻪ ﺑﻨﺪي ﺗﺼﺎوﯾﺮ ﺑﻪ روش ﻧﻈﺎرت ﺷﺪه ﯾﺎ ﺧﻮارزﻣﯿﮏ ﺣﺪاﮐﺜﺮ اﺣﺘﻤﺎل اﻧﺠﺎم ﭘﺬﯾﺮﻓﺖ. ﺑﻪ ﻣﻨﻈﻮر اﻓﺰاﯾﺶ دﻗﺖ ﻃﺒﻘﻪﺑﻨﺪي ﺗﺼﺎوﯾﺮ ﻧﯿﺰ از ﺷﺎﺧﺺ NDVI، ﻻﯾﻪ ﻣﺪل رﻗﻮﻣﯽ ارﺗﻔﺎﻋﯽ و ﻻﯾﻪ ﺷﯿﺐ اﺳﺘﻔﺎده ﺷﺪ و دﻗﺖ ﻃﺒﻘﻪﺑﻨﺪي ﻧﯿﺰ ﺑﺎ ﺷﺎﺧﺺ ﮐﺎﭘﺎ و ﺻﺤﺖ ﮐﻠﯽ ﺑﺮرﺳﯽ ﮔﺮدﯾﺪ. -1395) و در دوره آﺧﺮ ﯾﻌﻨﯽ20/73 (1380-1395 درﺻﺪ، در دوره دوم )22/36 (1365-1380ﯾﺎﻓﺘﻪ ﻫﺎ: ﺑﺮ اﺳﺎس ﻧﺘﺎﯾﺞ در دوره اول ) 28/08 (1365 درﺻﺪ از ﻣﻨﻄﻘﻪ دﭼﺎر ﺗﻐﯿﯿﺮ ﮐﺎرﺑﺮي ﺷﺪه اﺳﺖ ﮐﻪ در ﻫﺮ ﺳﻪ دوره، ﺗﺒﺪﯾﻞ ﮐﺎرﺑﺮي ﻣﺮﺗﻊ ﺑﻪ زراﻋﺖ دﯾﻢ ﺑﯿﺶ ﺗﺮﯾﻦ ﺗﻐﯿﯿﺮات را دارد. ﺑﺤﺚ و ﻧﺘﯿﺠﻪ ﮔﯿﺮي: ﻧﺘﺎﯾﺞ ﺗﺤﻘﯿﻖ ﺣﺎﺿﺮ ﻧﺸﺎن دﻫﻨﺪه اﯾﻦ اﻣﺮ اﺳﺖ ﮐﻪ ﻋﻠﯽرﻏﻢ ﺑﺎﻻ ﺑﻮدن ﻗﺪرت ﺗﺼﺎوﯾﺮ ﻟﻨﺪﺳﺖ در ﺗﻬﯿﻪ ﻧﻘﺸﻪ ﮐﺎرﺑﺮي اراﺿﯽ، ﻣﯽﺗﻮان ﺑﺎ اﺳﺘﻔﺎده از ﺷﺎﺧﺺﻫﺎ و ﻻﯾﻪﻫﺎي ﺟﺎﻧﺒﯽ ﻧﻈﯿﺮ NDVI، ارﺗﻔﺎع و درﺻﺪ ﺷﯿﺐ، ﻗﺪرت ﺗﻔﮑﯿﮏ ﮐﺎرﺑﺮيﻫﺎ از ﯾﮑﺪﯾﮕﺮ را ﺑﻬﺒﻮد ﺑﺨﺸﯿﺪ.

Background and Objective: Study of resources changes and destruction in previous can be useful in the planning and optimal use of resources to control inappropriate changes. Because of land use changes occur on large-scale, remote sensing techniques is a useful and valuable tool for monitoring the changes. The aim of this research is to monitoring of land cover changes in Khojin watershed located in the south of Ardabil province using remote sensing technique. Method: First TM, ETM+ and OLI images for the year 1986, 2001 and 2016 were collected respectively and after geometric and elevation corrections, images were classified using maximum likelihood classification method. To increase classification accuracy, NDVI, elevation and slope percent layers were used. Kappa and overall accuracy indexes were used to calculate classified images accuracy. Findings: Based on results, in the first period (1986-2001) 22.36%, in the second period (2001-2016) 20.73% and in the third period (1986-2016) 28.08% of area has changed and in all 3 periods, changing from rangeland to dry farming reached the most change. Discoution and Conclusion: Results showed despite high capability of Landsat images in mapping land use, using additional layers like NDVI, elevation and slope percent can improve classification accuracy of classified images