نقشه‌برداری رقومی پایداری خاکدانه‌ها و تأثیر عملیات حفاظت خاک روی آن‏ها در منطقۀ چاه‌ماری بهبهان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموختۀ کارشناسی ارشد، دانشکدۀ محیط زیست و منابع طبیعی، دانشگاه صنعتی خاتم الانبیاء (ص) بهبهان، بهبهان، ایران.

2 استادیار گروه مهندسی خاک، دانشکدۀ کشاورزی و منابع طبیعی دانشگاه لرستان، لرستان، ایران.

3 استادیار دانشکدۀ محیط زیست و منابع طبیعی، دانشگاه صنعتی خاتم الانبیاء (ص) بهبهان، بهبهان، ایران.

4 کارشناس ارشد ادارۀ کل منابع طبیعی و آبخیزداری استان خوزستان، اهواز، ایران.

چکیده

یکی از روش­های مناسب جهت بررسی تأثیر عملیات حفاظت خاک و مدل‏سازی فرسایش آبی خاک، بررسی پایداری خاکدانه­ها و تغییرات مکانی آن است. تحقیق حاضر با هدف مدل‏سازی پایداری خاکدانه­ها و تغییرات مکانی آن‏ها در یک منطقه تحت عملیات درخت­کاری و کنتورفارو و منطقه­ای مشابه و مجاور آن به عنوان منطقۀ شاهد در منطقۀ چاه­ماری بهبهان در استان خوزستان انجام شد. تعداد 150 نمونه خاک از عمق 0تا 5 سانتی­متری برداشته شد و میانگین وزنی قطر خاکدانه­ها (MWD) به روش الک خشک (MWDd) و الک تر (MWDw) تعیین گردید. برای تهیۀ نقشۀ توزیع مکانی MWD از تکنیک­های نقشه­برداری رقومی‏خاک (DSM) استفاده گردید. برای‏این منظور، متغیرهای محیطی دارای ارتباط با MWD از تصویر سنجندۀ لندست 8 و مدل رقومی ‏ارتفاع (DEM) استخراج و به منظور برقراری ارتباط بین ‏این متغیرها و MWD از مدل­های شبکه‏های عصبی مصنوعی (ANN) و درخت رگرسیون (RT) استفاده گردید. نتایج نشان داد اقدامات کنترلی انجام شده در منطقه روی MWDd تأثیر معنی­دار ولی روی MWDw تأثیر معنی­داری نداشت. آنالیز همبستگی نشان داد بین پارامترهای استخراج شده از DEM با MWDw همبستگی معنی­داری وجود نداشت ولی بین برخی پارامترهای استخراج شده از DEM با MWDd همبستگی معنی­داری وجود داشت. همچنین همبستگی MWDd و MWDw با اکثر پارامترهای تصویر ماهواره­ای معنی­دار بود. کارآیی مدل­های ANN و RT در تخمین MWDw نسبتاً بالا و تا حدودی مشابه ولی در تخمین MWDd کارآیی ANN بالاتر از RT بود. به طور کلی نتایج نشان داد روش­های نقشه­برداری رقومی ‏رویکردی مناسب برای تخمین و پهنه­بندی MWD می­باشند.

کلیدواژه‌ها


عنوان مقاله [English]

Digital mapping of soil aggregate stability and the effectiveness of soil erosion control practices in Behbahan region

نویسندگان [English]

  • Manizheh Razavi Hosain Abad 1
  • Alireza Amirian Chekan 2
  • Mohammad Faraji 3
  • Jamal Mosavian 4
1 Faculty of Natural Resources and Environment, Khatam Al-Anbia University of Technology, Behbahan
2 Assistant Professor, Department of Soil Science Engineering, Faculty of Agriculture and Natural Resources, Lorestan University
3 Assistant Professor, Behbahan Khatam Alanbia University of Technology
4 Master of Science of the General Department of Natural Resources and Watershed Management of Khuzestan Province
چکیده [English]

Soil aggregate stability and its spatial distribution can be considered as a good indicator for assessing the results of measures conducted for mitigation soil erosion. This study was conducted in two adjacent sites in Chahmari region, Kuzestan province. At one site afforestation and contour furrowing were conducted to control soil erosion and the adjacent site with no controlling measures was considered as control. A total of 150 soil samples were collected from the surface layer (0-5 cm) of two sites and mean weight diameter of aggregates (MWD) were measured using dry and wet sieving (MWDd and MWDw, respectively). Based on digital soil mapping (DSM) approach and to map MWD spatially, several environmental covariates were derived from a Landsat 8 image and a digital elevation model (DEM). Two machine learning algorithms including artificial neural networks (ANN) and regression trees (RT) were used to predict MWD with covariates as inputs. Results indicated a significant difference between MWDd in two sites, but no significant difference was found between MWDw. Correlation analysis revealed no correlation between MWDw and all terrain attributes derived from the DEM, but significant correlations were obtained between MWDd and some terrain attributes. Most covariates derived from Landsat images had significant correlation with both MWDw and MWDd. ANN and TR had relatively high and almost the same accuracy in predicting MWDw, but in predicting MWDd, ANN was superior to RT. In general, the findings showed good performance of DSM techniques in predicting and spatial mapping of MWD.

کلیدواژه‌ها [English]

  • Artificial Neural Networks
  • Digital Soil Mapping
  • Regression Tree
  • Spatial Modeling

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دوره 73، شماره 4
اسفند 1399
صفحه 771-785
  • تاریخ دریافت: 13 اردیبهشت 1399
  • تاریخ بازنگری: 24 آبان 1399
  • تاریخ پذیرش: 24 آبان 1399
  • تاریخ اولین انتشار: 01 اسفند 1399