بررسی ارتباط بین وضعیت توپوگرافی و خشکسالی در غرب استان فارس با استفاده از تکنیک های سنجش از دور

نوع مقاله : مقاله کامل

نویسندگان

دانشیار ژئومورفولوژی بخش جغرافیا، دانشکده اقتصاد، مدیریت و علوم اجتماعی، دانشگاه شیراز

چکیده

شاخص پوشش گیاهی یکی از مهم‌ترین ابزارهای سنجش‌ازدور جهت بررسی خشک‌سالی در مقایسه‌های دوره‌ای می‌باشد. توپوگرافی سطح زمین (لند فرم‌ها) همواره ویژگی‌های پوشش گیاهی را تحت تأثیر قرار می‌دهند و تحت تأثیر آن کمربندهای درختی شکل می‌گیرد. این پژوهش سعی دارد که خشک‌سالی‌های غرب استان فارس را با استفاده از شاخص‌های پوشش گیاهی سنجش‌ازدور و در ارتباط با ویژگی‌های توپوگرافی منطقه برای سال‌های 2000، 2010 و2020 بررسی کند. برای این منظور بعد از تهیه نقشه‌های هر یک از شاخص‌های خشک‌سالی، با استفاده از روش زنجیره مارکوف و کومارکوف وضعیت خشک‌سالی در سال‌های آینده در منطقه موردمطالعه تعیین و با استفاده از روش TPI (شاخص موقعیت توپوگرافی)، وضعیت لند فرم‌های منطقه تعیین شد. در نهایت ارتباط بین وضعیت خشک‌سالی در منطقه موردمطالعه و لندفرم‌ها بررسی شد. نتایج نشان داد که در توزیع خشک‌سالی در همه شاخص‌ها در سال 2000 به ترتیب 96 و 78 درصد منطقه، در سال 2010 در حدود 81 و 97 درصد و در سال 2020 93 و 97 درصد منطقه در کلاس‌های خشک‌سالی متوسط  قرارگرفته است. نتایج حاصل از زنجیره مارکوف و کومارکوف برای پیش‌بینی مکانی شاخص‌های خشک‌سالی نشان داد که روند تغییرات به سمت مقادیر کمتر این شاخص‌ها و خشک‌سالی بیشتر می‌باشد. نتایج نشان داد که در سال 2040 در حدود 70 درصد  و 20 درصد از منطقه در کلاس‌های با خشک‌سالی زیاد قرار خواهند گرفت. وضعیت لند فرم‌های منطقه نشان داد که 10 نوع لندفرم در منطقه وجود دارد که شامل لند فرم‌های آبراهه‌ها، زهکش‌های شیب میانی و دره‌های کم‌عمق، زهکش‌های مناطق مرتفع، دره‌های u شکل، دشت، شیب‌های باز، شیب‌های بالایی، یال‌های موضعی، یال‌های شیب میانی، قله کوه و یال‌های مرتفع می‌باشد. ارتباط بین وضعیت خشک‌سالی و توپوگرافی در منطقه موردمطالعه نشان داد، در قسمت‌های جنوبی منطقه که ازنظر وضعیت توپوگرافی دارای شیب و ارتفاع کمتری بوده و کوهستان‌های کمتری مستقر هستند، دارای پتانسیل بیشتری برای خشک‌سالی بوده و ممکن است در آینده با چالش‌های بیشتری مواجه شوند، اما کوهستان‌های منطقه به دلیل دمای مناسب و دریافت بارش بیشتر، خطر خشک‌سالی برای آن‌ها کمتر است. 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigating the Relationship Between Topography and Drought in Southwestern Iran Using Remote Sensing

نویسندگان [English]

  • Saeed Negahban
  • marzieh mokarram
Department of Geography, shiraz university
چکیده [English]

Extended Abstract
Introduction
Human beings have faced one of the most important problems in recent years: the water crisis and the occurrence of drought. Due to this, it is important to study the drought situation when managing water resources. In any climate, drought is caused by a lack of rainfall. However, unfortunately, defining drought and how it relates to hydrological phenomena is very difficult. First of all, drought may not affect all components of the hydrological system simultaneously. The second point is that drought does not refer to an absolute lack of moisture, but to a relative one. As a result of climate change and reduced rainfall and increased evapotranspiration in recent years, drought has become a major problem in the world, in general in arid and semi-arid regions such as Iran. Therefore, drought monitoring and management are essential. Most traditional methods rely on observations from meteorological stations and emphasize droughts. However, researchers and experts have considered the use of remote sensing techniques and satellite images as a useful tool for spatial and temporal monitoring of agricultural drought. A variety of meteorological and remote sensing indicators influence the study of droughts. Standard Index-Evapotranspiration Index (SPEI) and RDI Drought Index  are two such methods.
Due to the importance of the topic of this study, the study examined drought status in the study area using remote sensing indicators (EVI, NDVI), drought prediction using Markov and Kumarkov chains, and the connection between them and drought conditions.
 
Materials and methods
EVI index
Hewitt and Liu introduced the EVI in 1994. As defined below, improved vegetation indices minimize atmospheric effects and differences in blue and red reflections.
                                                                                  (1)
NIR, RED, Blue are amount of reflections in the blue, red and infrared bands.   L is the aerosol penetration coefficient and soil separation parameter which was considered equal to 1 in this equation. The coefficients C1, C2, G were considered equal to 6, 7.5, 2.5, respectively.
NDVI index
Normalized difference vegetation index (NDVI) is an indirect measure of photosynthetic activity. The range of this index is -1 for the minimum and +1 for the maximum amount of photosynthetic activity. NDVI is defined as follows (Tucker et al., 2010):
                                                                                                                (2)
Pnear and Pred are the reflectance values of the near infrared and red wavelengths, respectively, for pixel i during the months j and year k, respectively.
 
Results and Discussion
According to the results, southern regions have lower values for all vegetation indices, which indicates a lack of vegetation in these regions and the existence of drought. There is no drought in the northern areas and parts of the east of the region. Also, the results show that the region often falls into the middle class of drought based on EVI and NDVI indices. The results indicate that in 2000, 96% and 78% of the region were in the middle classes of drought, and in 2020, 98% and 93% of the region were in the middle classes of drought.
The results for the EVI index showed that 47% class 1 to class 3, 20% class 2 to class 4, 12% class 4 to class 5, 29% class 5 to class 6, 27% class 6 to class 7, 21% class 7 to Class 8, 14% to Class 8 to Class 9, and about 8% to Class 9 to Class 10, indicating an increase in drought in the area. NDVI index 49% Class 1 to Class 7, 32% Class 4 to Class 5, 19, Class 5 to Class 6, 14, Class 6 to Class 7, 11% Class 7 to Class 8 and 27% Class 8 to Class 9 has done.
 
Conclusion
Then, in order to extract the landform map of the study area, the topographic position index (TPI) was used. The results showed that high areas such as ridges and hills, near zero codes indicate flat areas or areas with low slope changes and negative codes indicate low areas such as valleys and waterways. The results showed that the highest landform in the middle drainage and plain area (about 14) percent and the lowest area is related to narrow valleys (about 1 percent). The results showed that the NDVI and EVI index in low altitude areas is lower (dry condition) and the index in high altitude areas is the highest (wet condition). Thus, the topographic situation can be used to predict the drought situation.

کلیدواژه‌ها [English]

  • Drought
  • Remote Sensing
  • Landform
  • Markov Chain
  • The western half of Fars province
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