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محمداسماعیل, زهرا, نویدی, میرناصر, خوارزمی, رسول. (1404). بررسی روند تغییرات کاربری و پوشش زمین در کلان‌شهر تهران (1370-1400 خورشیدی). سامانه مدیریت نشریات علمی, 13(2), 175-190. doi: 10.22092/lmj.2025.370601.393
زهرا محمداسماعیل; میرناصر نویدی; رسول خوارزمی. "بررسی روند تغییرات کاربری و پوشش زمین در کلان‌شهر تهران (1370-1400 خورشیدی)". سامانه مدیریت نشریات علمی, 13, 2, 1404, 175-190. doi: 10.22092/lmj.2025.370601.393
محمداسماعیل, زهرا, نویدی, میرناصر, خوارزمی, رسول. (1404). 'بررسی روند تغییرات کاربری و پوشش زمین در کلان‌شهر تهران (1370-1400 خورشیدی)', سامانه مدیریت نشریات علمی, 13(2), pp. 175-190. doi: 10.22092/lmj.2025.370601.393
محمداسماعیل, زهرا, نویدی, میرناصر, خوارزمی, رسول. بررسی روند تغییرات کاربری و پوشش زمین در کلان‌شهر تهران (1370-1400 خورشیدی). سامانه مدیریت نشریات علمی, 1404; 13(2): 175-190. doi: 10.22092/lmj.2025.370601.393

بررسی روند تغییرات کاربری و پوشش زمین در کلان‌شهر تهران (1370-1400 خورشیدی)

مدیریت اراضی
مقاله 4، دوره 13، شماره 2، دی 1404، صفحه 175-190    اصل مقاله (1.38 M)
نوع مقاله: پژوهشی
شناسه دیجیتال (DOI): 10.22092/lmj.2025.370601.393
نویسندگان
زهرا محمداسماعیل* 1؛ میرناصر نویدی2؛ رسول خوارزمی3
1محقق، موسسه تحقیقات خاک و آب کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران.
2دانشیار پژوهش، موسسه تحقیقات خاک و آب کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران.
3استادیار پژوهش، موسسه تحقیقات خاک و آب کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران.
چکیده
تغییر کاربری و پوشش زمین در کلان‌شهر تهران، به‌عنوان یکی از پرجمعیت‌ترین شهرهای خاورمیانه، تأثیرات عمیقی بر عوامل مؤثر پایداری زیست‌محیطی و منابع طبیعی داشته است. این مطالعه با استفاده از داده‌های سنجش-از‌دور، شامل تصاویر ماهواره‌ای لندست (سنجنده‌های TM, ETM+, OLI) و Sentinel-2 از سال‌های 1370 تا 1400 (1991 تا 2021)، به بررسی پویایی‌ شهری تهران پرداخته است. تصاویر از پلتفرم‌های گوگل ارث انجین و درگاه سازمان زمین‌شناسی آمریکا استخراج و تصحیحات هندسی، رادیومتریک و اتمسفری با الگوریتم FLAASH انجام شد. برای شناسایی الگوهای تغییرات کاربری و پوشش زمین، شاخص‌های نرمال شده پوشش-گیاهی و شاخص نرمال شده رشد شهری محاسبه و طبقه‌بندی غیرنظارت‌شده با الگوریتم میانگین کا (K-means) برای استخراج پنج کلاس اصلی (اراضی انسان‌ساخت، اراضی سبز غیردرختی، باغ‌ها، اراضی بایر و منابع آبی) اعمال شد. به‌منظور ارزیابی تغییر کاربری، روش‌های طبقه‌بندی نظارت‌شده شامل ماشین بردار پشتیبان، حداقل فاصله از میانگین و حداکثر احتمال مورد استفاده قرار گرفت. بر اساس نتایج، ماشین بردار پشتیبان با دقت کلی 91 درصد و ضریب کاپا 0.89 بهترین عملکرد را داشته است. تحلیل تغییرات با روش مقایسه پس از طبقه‌بندی نشان داد که مساحت مناطق ساخته‌شده 39.7 درصد (از 410.6 به 573.51 کیلومترمربع) افزایش‌یافته، درحالی‌که اراضی کشاورزی و باغی به ترتیب 69.3 و 9 درصد کاهش‌یافته‌اند. این گسترش، عمدتاً در غرب و جنوب تهران، با تبدیل اراضی کشاورزی مرغوب به کاربری‌های شهری و صنعتی همراه بوده و خطراتی مانند افزایش جزیره گرمایی شهری، کاهش امنیت غذایی و تخریب منابع آب‌وخاک را به دنبال داشته است. این یافته‌ها بر ضرورت سیاست‌های پایدار شهری، از جمله حفاظت از اراضی کشاورزی، توسعه عمودی و مدیریت منابع آب تأکید دارند تا تهران به سمت پایداری زیست‌محیطی حرکت کند و الگویی برای کلان‌شهرهای در حال توسعه ارائه دهد
کلیدواژه‌ها
الگوریتم ماشین بردار پشتیبان؛ تحلیل کاربری و پوشش زمین؛ اعتبارسنجی متقاطع؛ ضریب کاپا
عنوان مقاله [English]
Analysis of the Trends in Land Use and Land Cover Changes in Tehran (1991–2021)
نویسندگان [English]
Zahra MohammadEsmaeil1؛ MirNasser Navidi2؛ Rasoul Kharazmi3
1Researcher, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
2Associate prof., at Soil and Water research institute, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran.
3Assistant prof., at Soil and Water research institute, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran.
چکیده [English]
Land use and land cover (LULC) changes have always had profound impacts on environmental sustainability and natural resources, especially in a city like Tehran as one of the most densely populated metropolises in the Middle East. The urban dynamics in Tehran was investigated in this study using remote sensing data, including Landsat (TM, ETM+, and OLI) and Sentinel-2 images from 1991 to 2021 collected from Google Earth Engine and USGS platforms before being pre-processed using geometric, radiometric, and atmospheric corrections via the FLAASH algorithm. To identify the LULC patterns, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were calculated, followed by unsupervised classification using the K-means algorithm to delineate five primary land use classes: built-up areas, agricultural lands, orchards, barren lands, and water bodies. Subsequently, supervised classification methods, including Support Vector Machine (SVM), Minimum Distance (MD), and Maximum Likelihood Classifier (MLC), were evaluated to reveal the superior performance (an overall accuracy of 91% and a Kappa coefficient of 0.89) of SVM. Change detection using the post-classification comparison method revealed a 39.7% increase in built-up areas (from 410.36 to 573.51 km²) but decreases of 69.3% and 9% in agricultural and orchard lands, respectively. The expansion in built-up areas, primarily towards western and southern stretches of Tehran, has led to the conversion of fertile agricultural lands into urban and industrial uses, posing such risks as intensified urban heat islands (projected to increase by 3.43 °C by 2050), diminished food security, and water and soil resource degradation. These findings underscore the urgent need for sustainable urban policies, including protection of the remaining agricultural lands, promotion of vertical urban development, and integrated water resource management, to steer Tehran towards environmental sustainability and provide a model for other developing metropolises.
کلیدواژه‌ها [English]
LULC, Multitemporal Satellite Imagery, Support Vector Machine, Cross-Validation, Kappa Coefficient
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آمار
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