نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه علوم و مهندسی خاک، دانشکدة کشاورزی، دانشگاه لرستان، خرم‌آباد

2 دانشیار گروه علوم و مهندسی خاک، دانشکدة کشاورزی، دانشگاه لرستان، خرم‌آباد

3 دانشیار گروه علوم و مهندسی خاک، دانشگاه تربیت مدرس

10.52547/gisj.2021.220031.1001

چکیده

کربن آلی نقشی حیاتی در پایداری زیست‌محیطی, شاخص کیفیت و سلامت خاک دارد؛ بنابراین شناسایی توزیع مکانی ترسیب کربن از الزامات برنامه‌ریزی محیطی و مدیریت خاک است. پژوهش حاضر به‌منظور بررسی میزان ترسیب کربن در کاربری‏های کشت و صنعت نیشکر، کشاورزی سنتی و بایر انجام شد. در هر کاربری، شصت نمونه خاک برداشت و کربن آلی، شوری، آهک، واکنش خاک و سدیم محلول خاک اندازه‌گیری شد. با استفاده از داده‏های طیفی سنجندة OLI و TIRS ماهوارة لندست 8، مقادیر باندها و شاخص‏های خاکی و پوشش گیاهی شامل NDVI، SAVI، TSAVI، OSAVI، MSAVI، SOCI، WDVI، PVI، RVI و BI در نقاط نمونه‌برداری به‌دست آمد و رابطة بین آنها و مقدار مادة آلی خاک محاسبه شد. نتایج نشان می‌دهد، بیشترین همبستگی را با مقدار مادة آلی خاک به خود اختصاص داده‌اند: در کاربری کشت و صنعت، شاخص SOCI با 30/50% و باند 3 با 82/53%؛ در کشاورزی سنتی، شاخص PVI با همبستگی 35/60% و باند 7 با 63/60%؛ در اراضی بایر، شاخص RVI با همبستگی 27/34% و باند 2 با 67/36%. نتایج تحلیل آماری به‌روش برازش حداقل مربعات جزئی نشان داد میانگین نتایج واسنجی و اعتبارسنجی به‌ترتیب 48/43 و 08/39% است. نتایج برآورد مادة آلی خاک به‌روش کریجینگ و مدل درخت M5 نشان می‏دهد که همبستگی مقادیر مادة آلی اندازه‏گیری و پیش‌بینی‌شده به‌ترتیب 20/66 و 00/82% بود. طبق این نتایج، بین مقادیر مادة آلی خاک و شاخص‏ها و باندهای ماهوارة لندست 8 همبستگی معنی‌داری وجود دارد و می‌توان مقادیر مادة آلی خاک منطقة مورد مطالعه و سایر مناطق دارای شرایط مشابه را با احتمال مورد قبولی تخمین زد.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation of Soil Organic Carbon Storage in Different Land Uses Using Partial Least Squares, Geostatistics, M5 Tree Model and Landsat 8 Statistical Methods

نویسندگان [English]

  • Alireza Zahirnia 1
  • Hamid Reza Matinfar 2
  • Hossianali Bahrami 3

1 Ph.D. Student of Soil Science, Agriculture Faculty, Lorestan University

2 Associate Prof. of Soil Science, Agriculture Faculty, Lorestan University

3 Associate Prof. of Soil Science, Agriculture Faculty, Tarbiat ModaRres University

چکیده [English]

Organic carbon plays a activate role in environmental sustainability, soil quality and health index, so identifying the spatial distribution of carbon sequestration is a requirement of environmental planning and soil management. The purpose of this study is to investigate the amount of carbon sequestration in sugarcane and traditional uses of sugarcane, traditional agriculture and barren. In each land use, 60 soil samples were taken and organic carbon, salinity, lime, soil reaction and solution sodium were measured. Using Landsat 8 satellite OLI and TIRS spectral data, the variable ​​of soil and vegetation indices including: NDVI, SAVI, TSAVI, OSAVI, MSAVI, SOCI, WDVI, PVI, RVI and BI in the sample points was obtained and the relationship between them and the amount of soil organic matter was calculated. The results show that in agro-industrial use, SOCI index with 50.30% and band 3 with 53.82% have the highest correlation, in traditional agriculture, PVI index with a correlation of 60.35% and band 7 with 60.63% and in Barren lands ,RVI index with a correlation of 34.27% and band 2 with 36.67% have the highest correlation with the amount of soil organic matter. The results of statistical analysis by partial least squares fitting method showed that the average of calibration and validation results are 43.48 and 39.08%, respectively. The results of estimating soil organic matter by kriging method and M5 tree model show that the correlation between measured and predicted organic matter was 66.20% and 82.00%, respectively. The results show that there is a significant correlation between soil organic matter and Landsat 8 satellite indices and bands, and it is possible to estimate the soil organic matter levels of the study area and other areas with similar conditions with acceptable probability.

کلیدواژه‌ها [English]

  • Soil organic matter
  • Southwest Khuzestan
  • PLSR statistical method
  • Kriging method
  • M5 tree model
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