کاربرد شاخص موران در تشخیص داده‌های پرت مکانی و ارزیابی اثر آن‌ها بر برآورد توزیع مکانی ماده آلی خاک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشگاه ارومیه

2 دانشجوی کارشناسی ارشد دانشگاه ارومیه

چکیده

ماده آلی خاک به‌عنوان شاخصی کلیدی از درجه تخریب خاک‌ها و قابلیت ترسیب کربن در آن‌ها بوده و تعیین الگوی پراکنش مکانی آن در یک منطقه از اقدامات اساسی برای تدوین استراتژی‌های مؤثر مدیریت خاک و اکوسیستم محسوب می‌شود. در این خصوص، روش‌های زمین‌آماری مانند کریجینگ به‌طورگسترده‌ای به‌منظور تعیین الگوی پراکنش مکانی کربن آلی و سایر ویژگی‌های خاک به‌کار برده شده است. ویژگی‌های آماری داده‌های اولیه تأثیر بسیار زیادی بر کیفیت نقشه‌های تولیدشده داشته و از این‌رو تشخیص و حذف داده‌های پرت کلی و مکانی به‌عنوان یک گام اساسی اولیه در تهیه نقشه‌های دقیق‌تر از ماده آلی محسوب می‌شود که در پژوهش‌های پیشین کمتر مورد توجه قرار گرفته است. بنابراین، این پژوهش باهدف ارزیابی اثر داده‌های پرت مکانی بر چگونگی توزیع ماده آلی خاک در حوزه آبخیز روضه‌چای واقع در شهرستان ارومیه، استان آذربایجان غربی، صورت گرفت. برای این منظور 89 نمونه خاک سطحی (10-0 سانتی‌متری) بر اساس روش نمونه‌برداری تصادفی نظارت‌شده از حوزه تهیه شد. داده‌های پرت کلی مربوط به ماده آلی با استفاده از نمودار جعبه‌ای و پس از نرمال کردن توزیع مقادیر حذف شد. به‌منظور حذف داده‌های پرت مکانی از شاخص‌های موران کلی و محلی استفاده شد. کورلوگرام شاخص موران کلی در فاصله900 متری حداکثر همبستگی مکانی را نشان داد که این فاصله به‌عنوان مبنای تعیین نقاط پرت مکانی با استفاده از شاخص موران محلی در نظر گرفته شد. نقشه خوشه‌بندی به‌دست‌ آمده از شاخص موران محلی، چهار داده پرت مکانی را در منطقه نشان داد که با حذف آن‌ها مقادیر ضرایب کارایی نقشه­کریجینگ شامل MAE و RMSE به ترتیب از 97/0 و 31/1 به 85/0 و 12/1 کاهش یافته و در نتیجه دقت نقشه در مقایسه با شرایط عدم حذف این داده‌ها 5/13 درصد افزایش یافت. نتایج کلی، مؤید کارایی استفاده از شاخص موران برای تشخیص داده‌های پرت مکانی و اثر مثبت حذف این داده‌ها در افزایش دقت نقشه کریجینگ ماده آلی در منطقه بود.

کلیدواژه‌ها


عنوان مقاله [English]

Identification of Spatial Outliers by Moran’s Index and Evaluation of Their Effects on the Spatial Distribution of Soil Organic Matter

نویسندگان [English]

  • F. Asadzadeh 1
  • M. Rahmati 2
  • H. Asgarzadeh 1
چکیده [English]

Soil organic matter (SOM) is a key index in evaluation of the soil degradation and soil carbon sequestration. Therefore, determination of the SOM spatial patterns is essential for developing the suitable strategies of soil and ecosystem management. Geostatistical methods such as kriging have been widely employed to investigate the spatial pattern of different soil properties like SOM. The quality of the spatial maps is significantly influenced by the statistical properties of the raw data. Thus, identification and elimination of spatial outlier data, which has rarely been considered in previous works, is an important step in preparation of accurate and suitable maps of soil organic matter. The aim of the present study was to evaluate the effect of spatial outliers on the spatial pattern of soil organic carbon at the Rozeh-Chay watershed, Urmia, west Azarbayjan province. A total of 89 surface soil samples (0-10 cm) were collected based on the stratified random sampling scheme. After the normalization of the raw SOM data, global outliers were eliminated by box plot method. Spatial outliers were identified by the global and local Moran’s I indices. Spatial correlogram of the global Moran’s I showed the highest spatial autocorrelation at distance of 900 m, which was used as a distance band for preparation of spatial clusters map by local Moran’s I index. Cluster map of the local Moran’s I index showed four spatial outliers which were eliminated to assess their effects on the accuracy of kriging map of SOM. With the elimination of the spatial outliers, the MAE and RMSE of the SOM map were decreased from 0.97 and 1.31 to 0.85 and 1.12, respectively. Therefore, the accuracy of the kriging map increased by 13.5 percent. Generally, it can be concluded that the combination of Moran’s I index and kriging method improves the efficiency of organic matter map in the study area.

کلیدواژه‌ها [English]

  • Global outlier
  • Kriging
  • spatial structure
  • Urmia
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