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تهیه نقشه رقومی کیفیت خاک با استفاده از تصاویر ماهوارهای و الگوریتمهای یادگیری ماشین (مطالعه موردی: لوشان، استان گیلان) | ||
| تحقیقات آب و خاک ایران | ||
| دوره 57، شماره 3، خرداد 1405، صفحه 611-630 اصل مقاله (1.32 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2026.410504.670095 | ||
| نویسندگان | ||
| سمیرا همتی1؛ کامران مروج2؛ احمد گلچین1؛ میرناصر نویدی* 3؛ محمدصادق عسکری4 | ||
| 1گروه خاکشناسی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران. | ||
| 2گروه علوم خاک دانشکده کشاورزی، دانشگاه زنجان | ||
| 3مؤسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران | ||
| 4گروه خاکشناسی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران | ||
| چکیده | ||
| کیفیت خاک یکی از شاخصهای بنیادین در ارزیابی پایداری اکوسیستمها و بهرهوری اراضی به شمار میرود و تحت تأثیر مجموعهای از عوامل طبیعی و انسانی قرار دارد. هدف این پژوهش، تحلیل تغییرات مکانی شاخص کیفیت خاک در مقیاس منطقهای با استفاده از الگوریتم یادگیری ماشین جنگل تصادفی (RF) و رگرسیون خطی چندمتغیره (MLR) با استفاده از متغیرهای محیطی در اراضی منطقه لوشان در استان گیلان بود. بدین منظور، تعداد 76 نمونه خاک از عمق 0 تا 30 سانتیمتر برداشت شد و ویژگیهای فیزیکی، شیمیایی و زیستی خاک با استفاده از روشهای معمول اندازهگیری گردید. علاوه بر این، شاخصهای سنجشازدور شامل شاخص گیاهی تفاوت نرمال شده (NDVI)، شاخص تفاوت نرمال شده آب (NDWI)، شاخص اختلاف رطوبت نرمال شده (NDMI)، شاخص تفاوت نرمالشده مناطق ساختهشده (NDBI)، شاخص خاک برهنه (BSI) و دمای سطح زمین (LST) بهعنوان متغیرهای کمکی محیطی استخراج شدند. شاخص کیفیت خاک (SQI) با استفاده از دو رویکرد مجموعه کامل دادهها (TDS) و مجموعه حداقل دادهها (MDS) و بهکمک توابع نمرهدهی فازی محاسبه گردید. نتایج نشان داد مدل جنگل تصادفی با دقت بالاتری (R2= 0.75) نسبت به رگرسیون خطی چندمتغیره (R2= 0.53) ، قادر به پیشبینی تغییرات مکانی SQI بوده و ضرایب تعیین بالاتری را ارائه میدهد. همچنین شاخصهای طیفی، بهویژه NDVI، NDWI و BSI بیشترین نقش را در تبیین تغییرات کیفیت خاک ایفا کردند. این مطالعه نشان میدهد رویکرد پیشنهادی نقشهبرداری دیجیتال کیفیت خاک میتواند بهعنوان ابزاری کارآمد در مدیریت پایدار اراضی، حفاظت خاک و پشتیبانی از تصمیمگیریهای کشاورزی دقیق مورد استفاده قرار گیرد. | ||
| کلیدواژهها | ||
| ارزیابی خاک؛ نقشهبرداری دیجیتال خاک؛ سنجشازدور؛ جنگل تصادفی؛ مدلسازی مکانی | ||
| عنوان مقاله [English] | ||
| Digital Mapping of Soil Quality Using Satellite Imagery and Machine Learning Algorithms (A Case Study of Lushan, Guilan Province, Iran) | ||
| نویسندگان [English] | ||
| Samira Hemmati1؛ Kamran Moravej2؛ Ahmad Golchin1؛ Mir Naser Navidi3؛ Mohammad Sadegh Askari4 | ||
| 1. Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
| 2Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
| 3Soil and Water Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran, | ||
| 4Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
| چکیده [English] | ||
| Soil quality is a fundamental indicator for assessing ecosystem sustainability and land productivity, and it is influenced by a combination of natural and anthropogenic factors. This study aimed to analyze the spatial variability of the soil quality index (SQI) at the regional scale using the random forest (RF) machine learning algorithm and multiple linear regression (MLR) based on environmental variables in the lands of the Loshan region in Guilan Province. For this purpose, 76 soil samples were collected from the 0–30 cm soil layer, and soil physical, chemical, and biological properties were measured using standard laboratory methods. In addition, remote sensing-based indices, including normalised difference vegetaion index (NDVI), normalised difference water index (NDWI), normalised difference moisture index (NDMI), normalized difference built-up index (NDBI), the bare soil index (BSI), and land surface temperature (LST), were derived as environmental auxiliary variables. The SQI was calculated using both the total data set (TDS) and minimum data set (MDS) approaches, combined with fuzzy scoring functions. The results showed that the random forest model predicted the spatial variability of SQI with higher accuracy (R² = 0.75) than multiple linear regression (R² = 0.53). Moreover, spectral indices particularly NDVI, NDWI and BSI played the most important roles in explaining the spatial variation of soil quality. This study demonstrates that the proposed digital soil quality mapping framework can serve as an effective tool for sustainable land management, soil conservation, and supporting decision-making in precision agriculture. | ||
| کلیدواژهها [English] | ||
| Digital soil mapping, Random forest, Remote sensing, Soil assessment, Spatial modeling | ||
| مراجع | ||
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