بررسی کارایی شاخص های پوشش گیاهی در مطالعات خشکسالی با استفاده از تکنیک سنجش از دور (مطالعه موردی: استان کردستان)
آرشیو
چکیده
خشکسالی یکی از پدیده های طبیعی مخرب است که اثرات جدی بر پوشش گیاهی و فعالیت های کشاورزی می گذارد. هدف این پژوهش، بررسی تأثیر خشکسالی بر پوشش گیاهی استان کردستان در بازه زمانی ۲۰۰۰ تا ۲۰۲۲ با استفاده از شاخص های پوشش گیاهی به دست آمده از تکنیک های سنجش از دور است. برای این منظور، شاخص های NDVI، EVI و VCI که از تصاویر سنجنده MODIS استخراج شده اند، به کار گرفته شدند. تحلیل داده ها با استفاده از سامانه گوگل ارث انجین انجام شده و به بررسی تغییرات زمانی و مکانی پوشش گیاهی در طول دوره مطالعه پرداخته شد. به طور مشخص، تغییرات در طبقات پوشش گیاهی (متراکم، متوسط، ضعیف و بدون پوشش) و ارتباط آن با شدت خشکسالی بررسی گردید. مدل های استفاده شده در این پژوهش برای تحلیل روندها و همبستگی بین شاخص های پوشش گیاهی و شدت خشکسالی در سطح منطقه ای طراحی شده اند. نتایج نشان می دهد که طی دوره مورد مطالعه، نوسانات قابل توجهی در توزیع طبقات پوشش گیاهی مشاهده شده است. همچنین، همبستگی معناداری بین کاهش شدت خشکسالی و افزایش مساحت پوشش گیاهی متراکم شناسایی شد. این یافته ها اهمیت استفاده از شاخص های سنجش از دور در پایش و ارزیابی خشکسالی و تغییرات پوشش گیاهی را در مناطق حساس به خشکسالی مانند استان کردستان تأیید می کنند. پژوهش حاضر با ارائه بینش هایی در مورد رفتار پوشش گیاهی در مواجهه با خشکسالی، به بهبود مدیریت منابع طبیعی و کشاورزی در مناطق خشک و نیمه خشک کمک می کند.Assessing the Performance of Vegetation Indices in Drought Analysis Using Remote Sensing Techniques (Case Study: Kurdistan Province)
Drought is a natural phenomenon that can have detrimental effects on vegetation cover and agricultural activities. This study investigates the impact of drought on vegetation cover in Kurdistan Province, Iran, from 2000 to 2022, employing the remote sensing indices NDVI, EVI, and VCI. Vegetation cover data was extracted from MODIS satellite imagery and analyzed using the Google Earth Engine platform. The findings reveal significant changes in the distribution of vegetation cover classes (dense, moderate, weak, and no cover) over the study period. In 2020, the largest area was covered by the dense vegetation class (45.43%), while the smallest area was covered by weak vegetation cover (0.2%). In contrast, in 2004, the smallest area was covered by dense vegetation cover (3.25%), and the largest area was covered by weak vegetation cover (19.19%). In terms of drought, the study period experienced droughts of varying intensities. The years 2020 and 2012 had the least drought (with 95.43% and 67.75% of the area without drought, respectively), while the years 2004 and 2022 had the most drought (with 18.09% and 47.60% of the area without drought, respectively). A significant correlation was observed between dense vegetation cover and decreased drought severity. Years with the largest dense vegetation cover area (such as 2020) experienced the least drought, while years with an increase in weak vegetation cover area (such as 2004) witnessed an increase in drought severity. Extended Abstract Introduction Drought is a complex and slow-onset natural phenomenon that negatively affects human activities, ecosystems, and economic development, impacting various societies across the globe. It manifests in different forms, including meteorological, agricultural, hydrological, and socio-economic droughts, each reflecting a distinct aspect of water scarcity. Population growth, economic development, and land-use changes intensify the impacts of drought and make water resource management increasingly challenging. The wide geographical extent of drought and its influence on diverse regions highlight the necessity of accurate and continuous monitoring. Traditional drought monitoring methods, which mainly rely on precipitation data, face limitations such as inadequate spatial and temporal coverage and low accuracy. In this context, remote sensing technology has emerged as an innovative approach, enabling the collection of extensive and simultaneous data while overcoming many of the shortcomings of conventional methods. This technology serves as a valuable tool for managing natural resources, particularly water resources, under conditions of stress and scarcity. Methodology This study aimed to monitor drought and analyze vegetation cover changes in Kurdistan Province during 2000–2022 using NDVI, EVI, and VCI indices derived from MOD09GA data of the MODIS sensor. Calculations were performed in the Google Earth Engine environment, and the results were classified into four vegetation cover categories (dense, moderate, sparse, and bare). Vegetation distribution and drought severity maps were generated in ArcGIS Pro, and temporal trends were analyzed. Finally, the relationship between dense vegetation changes and drought severity was statistically evaluated, revealing significant correlations between these variables. Results and Discussion In this study, in order to examine vegetation changes and drought conditions in Kurdistan Province, May images from 2000 to 2022 were analyzed using NDVI, EVI, and VCI indices. Due to minimal changes in some years, only 2004, 2008, 2012, 2016, 2020, and 2022, which showed the most significant variations, were selected for detailed analysis. Results from NDVI and EVI indicated notable fluctuations in vegetation cover over the study period, with varying proportions of dense, moderate, sparse, and non-vegetated areas; for instance, in 2004 the dense vegetation class had relatively high coverage, whereas in 2022 it experienced a significant decline. Drought assessment using VCI revealed that the region experienced varying degrees of moderate to severe drought, with certain years, such as 2007, showing no severe drought, while in more recent years drought distribution increased. Correlation analysis between NDVI and EVI with VCI showed a positive and significant relationship between vegetation changes and drought intensity (0.515 and 0.460, respectively). These findings suggest that vegetation dynamics can serve as an effective indicator for monitoring and assessing drought conditions in Kurdistan Province. Conclusion The results of the vegetation indices indicated that, over the study period, significant changes occurred in the distribution of vegetation classes (dense, moderate, sparse, and non-vegetated). In 2020, the largest area was covered by dense vegetation (45.43%) and the smallest by sparse vegetation (0.2%). In contrast, in 2004, the smallest area was under dense vegetation (3.25%) and the largest under sparse vegetation (19.19%). From a drought perspective, varying intensities of drought occurred throughout the study period. The years 2020 and 2012 experienced the lowest drought levels (with 95.43% and 67.75% of the area without drought, respectively), whereas 2004 and 2022 had the highest drought levels (with 18.09% and 47.60% of the area without drought, respectively). A significant correlation was observed between dense vegetation and reduced drought intensity; years with the highest dense vegetation coverage (such as 2020) experienced the lowest drought, while years with increased sparse vegetation (such as 2004) corresponded with higher drought intensity.
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