پیش‌بینی پراکنش مکانی مادۀ آلی خاک با استفاده از شاخص‌های توپوگرافی و تکنیک شبکۀ عصبی مصنوعی-کریجینگ

نوع مقاله : مقالات پژوهشی

نویسندگان

دانشگاه کردستان

چکیده

مادۀ آلی یکی از فاکتورهای مهم کیفی خاک است که تأثیر زیادی بر ویژگی‌های فیزیکی، شیمیایی و بیولوژیکی خاک دارد. هدف از این پژوهش برآورد تغییرات مکانی مادۀ آلی خاک و وضعیت آن با استفاده از تکنیک شبکۀ عصبی مصنوعی-کریجینگ در اراضی دشت قروه در استان کردستان بود. بدین منظور تعداد 150 نمونۀ خاک به روش سیستماتیک با فواصل 2×2 کیلومتر از عمق 0 تا 15 سانتی‌متری جمع‌آوری شد. مقدار مادۀ آلی خاک‌ها در آزمایشگاه اندازه‌گیری شد. با استفاده از شبکۀ عصبی مصنوعی رابطۀ بین مقدار مادۀ آلی و پارامترهای توپوگرافی (ارتفاع، درصد شیب، جهت شیب و شاخص خیسی توپوگرافی) بدست آمد. به کمک مدل بدست آمده نقشۀ اولیۀ مادۀ آلی خاک تهیه شد. سپس مقدار باقیمانده‌های مدل شبکۀ عصبی مصنوعی با روش کریجینگ معمولی درون‌یابی شد که پس از ادغام آن با نقشۀ اولیه نقشۀ نهایی مادۀ آلی خاک بدست آمد. نقشۀ وضعیت مادۀ آلی خاک از همپوشانی نقشۀ مادۀ آلی خاک با نقشۀ بافت خاک در چهار کلاس خیلی کم، کم، متوسط و زیاد بدست آمد. نتایج حاصل از شبکۀ عصبی مصنوعی نشان داد که متغیرهای ارتفاع و جهت شیب اثر معنی‌داری بر روی مقدار مادۀ آلی خاک داشتند (05/0>P). بر اساس نتایج حاصل از ارزیابی متقاطع روش شبکۀ عصبی مصنوعی-کریجینگ توانست 89 درصد از تغییرات مکانی مادۀ آلی خاک را توصیف کند. نقشۀ وضعیت مادۀ آلی خاک نشان داد که در حدود 79 درصد از خاک‌های منطقه از نظر مادۀ آلی در وضعیت خیلی کم و کم قرار دارند.

کلیدواژه‌ها


عنوان مقاله [English]

Prediction of Soil Organic Matter Spatial Distribution Using Topographic Indicators and Artificial Neural Network-Kriging Technique

نویسندگان [English]

  • Mohammad Ali Mahmoodi
  • Molood Mirzaie
  • Mohammad Taaher Hossaini
University of Kurdistan
چکیده [English]

Introduction: Soil organic matter (SOM) is an important soil quality factor that affects physical, chemical and biological properties of soil. Accurate estimation of SOM variability provides critical information especially in precision agriculture. Geostatistics and geographic information system (GIS) are powerful tools for characterizing and mapping the spatial distribution and variability of soil properties. Kriging is a basic geostatistical technique that provides the best linear unbiased estimation for a spatially dependent variable. This method will produce satisfying results if enough sample points are available. Unfortunately, laboratory measurements of the SOM are costly and time-consuming. Artificial neural network-kriging (ANNK) is another geostatistical method that extends kriging of a primary variable to the readily available auxiliary variables based on their relationship with the primary variable. This relationship is captured using an artificial neural network (ANN) model. The residuals of the model were then interpolated using kriging, and added to the prediction obtained from the ANN model. Terrain attributes, derived from digital elevation models (DEMs), are useful for estimating SOM at landscape scale. Topographic indicators including slope, aspect, elevation, and topographic wetness index may be the dominant factors affecting SOM variability in an area with same parent material and climate. Hence, these factors can be used as auxiliary variables for estimating spatial variability of SOM using ANNK. The objective of this study was to estimate SOM spatial variability using ANNK and topographic indices and assess its status in hilly areas of Ghorveh in Kurdistan province (Iran).
Materials and Methods: A total of 150 soil samples from a depth of 0-15 cm were systematically collected in a grid spaced 2 Km × 2 Km. The SOM content of soil samples was measured in the laboratory. Topographic indicators including slope, aspect, elevation, and topographic wetness index were derived from the DEM. ANN was used to predict SOM variability based on topographic index combinations. The feed-forward network consisted of an input layer, one hidden layer with sigmoid neurons, and an output layer with linear neurons. The network was trained with Levenberg-Marquardt backpropagation algorithm. According to the Kolomogrov’s theorem, the number of nodes in the hidden layer was 2n+1, in which n is the number of input neurons. The optimal subset of topographic index combinations correlating best with the SOM was selected as the best ANN model. This model was used to generate an initial SOM surface. The residuals of ANN model were interpolated using ordinary kriging (OK) and combined with the initial SOM surface to produce the final ANNK SOM surface. The SOM status map was derived from overlaying of soil texture and SOM maps in four different levels (very low, low, medium and high).
Results and Discussion: The results of ANN suggested that elevation was the most important variable determining the distribution of SOM across the landscape. Further, aspect was the other variable which had a significant influence on SOM distribution. The selected two inputs ANN model (elevation and aspect) can explain about 33% of total variance of SOM. The cross-validation results indicated that the OK and ANNK techniques can explain about 37 and 89% of total variance of SOM, respectively. The ANNK technique performed better than the OK and ANN techniques since it was able to capture most of the small variations of SOM. The resulting SOM status map indicated a low and very low SOM content in relation with soil texture in most regions surveyed (79%). Low SOM level can be attributed to the erosive processes under Mediterranean climate on hills coupled with intensive and/or inappropriate agricultural practices. Based on the results of this study, proper agronomical and environmental planning such as soil conservation strategy is highly required in this area to restore and increase the SOM content in agricultural soils, combat soil erosion and maintain soil ecological functions and productivity. The SOM replenishment can be achieved in the degraded areas (i.e., low SOM content) by adopting conservative practices such as conservation tillage or no-tillage (e.g., direct seeding), improving land use rotations with forage crops, returning crop residues to soil, growing green manure crops, and supplying the soil with proper exogenous organic matter (compost, manure, sewage sludge, etc.). Furthermore, the results highlighted the potential of ANNK in combination with GIS to provide improved distribution patterns of SOM.

کلیدواژه‌ها [English]

  • Artificial neural network-kriging
  • Soil organic matter
  • Spatial variability
  • Terrain attributes
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