| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,005 |
| تعداد مقالات | 33,631 |
| تعداد مشاهده مقاله | 44,479,670 |
| تعداد دریافت فایل اصل مقاله | 30,128,977 |
روش‌های نوین در پژوهش‌های خاک‌شناسی آبخیز‌های زوجی با روی‌کرد به‌روزرسانی شرح خدمات در آبخیز‌های زوجی دهگین هرمزگان | ||
| پژوهش های آبخیزداری | ||
| مقاله 6، دوره 35، شماره 3 - شماره پیاپی 136، مهر 1401، صفحه 60-80 اصل مقاله (2.84 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2021.356597.1441 | ||
| نویسندگان | ||
| صدیقه ملکی1؛ خدیجه خرمن دار2؛ محسن حسینعلی زاده3؛ عباس گلی جیرنده4؛ آیدینگ کرنژادی4؛ حمید رضا پورقاسمی* 5 | ||
| 1پژوهشگر پسادکترا، گروه علوم خاک، دانشگاه فردوسی مشهد، مشهد، ایران | ||
| 2دانشجوی دکترا، گروه مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 3دانشیار گروه مدیریت مناطق بیابانی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| 4کارشناس، شرکت مهندسین مشاور نوآوران علوم مکانی، تهران، ایران | ||
| 5استاد بخش مهندسی منابع طبیعی و محیط زیست دانشکده ی کشاورزی دانشگاه شیراز، شیراز، ایران | ||
| چکیده | ||
| یکی از مهمترین چالش های زیست‌محیطی در جهان فرسایش خاک است که دشواری‌های پرشمار اقتصادی و نبود توسعه‌ی پایدار را به‌دنبال دارد. هدف از این پژوهش به‌کاربردن روش‌های نوین در پژوهش‌های خاک‌شناسی با روی‌کرد به روزرسانی شرح خدمات در زیرحوزه‌های زوجی دهگین بود. برای انتخاب جا‌های نمونه‌برداری از خاک و خاک‌رخ‌‌ها روش ابر مکعب لاتین مشروط، با اطلاعات محیطی کمکی (مدل رقومی بلندی) برگرفته از نقشه برداری هوایی با پهپاد فانتوم4پرو و مولتی‌روتر به‌کار برده‌شد. نتیجه نشان داد که 2 نوع اصلی زمین شامل تپه و فلات و پادگانه های بالایی در این جا هست. تکامل خاک‌رخ در منطقه بیش‌تر در تاثیر از پستی‌بلندی است، و خاک‌های واحد تپه ارتباطی مستقیم با جنس مواد مادری و شیب دارد. پایداری خاک‌دانه در مرتع‌های درخت‌دار و منطقه‌های بافت لومی‌سیلتی بیش‌تر بود، زیرا متغیرهای کمکی از ویژگیهای مهم و تاثیرگزار در نقشه‌ی پایداری خاک‌دانه است. برای مرحله‌ی ارزیابی نقشه اندازه‌های RMSE و R2 به‌ترتیب 0/32 و 0/26 بود. ظرفیت تبادل کاتیونی و نیتروژن خاک در هر دو زیرحوزه‌ی شاهد و نمونه کم بود، و بیشینه‌ی اندازه‌های کربن آلی خاک در زیرحوزه‌ی نمونه بیش‌تر از زیرحوزه‌ی شاهد بود. کاربرد روش‌های نوین مانند کاربرد پهپاد در تصویربرداری و روش‌های دقیق نمونه‌برداری با متغیرهای محیطی در نقشه‌برداری رقومی خاک به کاهش تعداد نمونه‌ی خاک و افزایش دقت نقشه‌های خروجی منجر میشود و نتیجه‌ی دقیق‌تری از برآورد اندازه‌ی فرسایش می دهد، که در مقایسه با روش‌های پیشین در زمان و هزینه صرفهجویی میشود. بنابراین کاربرد این روش‌ها در حوزه‌های هم‌سان با شرایط طبیعی و اقلیمی منطقه‌ی پژوهش توصیه میشود. | ||
| کلیدواژه‌ها | ||
| ابر مکعب لاتین؛ پهپاد؛ خاک‌رخ؛ فتوگرامتری؛ فرسایش خاک | ||
| عنوان مقاله [English] | ||
| New Methods in Soil Science Studies in Paired Watersheds with an Approach to Updating Service Description in Paired Watersheds of Dehgin, Hormozgan Province | ||
| نویسندگان [English] | ||
| Sedigheh Maleki1؛ Khadijeh Khermandar2؛ Mohsen Hosseinalizadeh3؛ Abbas Goli Jirandeh4؛ Aiding Kornejadi4؛ Hamid Reza Pourghasemi5 | ||
| 1Postdoc researcher, Dept. of Soil Science, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| 2Ph.D. Student, Dept. of Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 3Associate Professor, Dept. of Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 4Spatial Sciences Innovators Consulting Engineering Company, Tehran, Iran | ||
| 5Professor, Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran | ||
| چکیده [English] | ||
| One of the most critical environmental problems globally is soil erosion, which leads to several problems in the field of economy and sustainable development. This study aims to use new methods in soil science studies of the paired sub-catchments to update service descriptions in Dehgin, Hormozgan Province. For this purpose, to select sampling sites of pedons and soil samples, the conditional Latin Hypercube sampling (cLHS) method was used using environmental covariates (digital elevation model), which derivate from unmanned aerial vehicles (UAV), phantom4 pro, and multirotor. The results showed two main land types in the study area, including hills, plateaus, and high terraces. Soil development in the region is mainly influenced by elevation, and the soils of the hill lands are directly related to the type of parent material and slope percentage. In the areas containing wooded pasture and areas with silt loam texture class, the aggregate stability values are higher, which are auxiliary covariates essential and influential parameters in the aggregate stability map. The RMSE and R2 values are 0.32 and 0.26, respectively, for the evaluation map criteria. The values of cation exchange capacity (CEC) and nitrogen in both control and sample sub-catchments are small and the maximum amounts of soil organic carbon in the sample sub-catchment are higher than in the control sub-catchment. The use of new methods including UAV, accurate sampling methods using high-resolution environmental covariates, and digital soil mapping can reduce the number of soil samples, increase the accuracy of output maps, and provide more accurate erosion estimation results, which has reduced time and cost compared to the old methods. | ||
| کلیدواژه‌ها [English] | ||
| Latin hypercube sampling, pedons, photogrametry, soil erosion, unmanned aerial vehicles | ||
| مراجع | ||
|
Abbaszadeh Afshar F, Ayoubi SH, Jafari A. 2018. The extrapolation of soil great groups using multinomial logistic regression at regional scale in arid regions of Iran. Geoderma, 315: 36–48. Abu-Hamdeh NH, Abo-Qudais SA, Othman AM. 2005. Effect of soil aggregate size on infiltration and erosion characteristics. European Journal of Soil Science, 57 (5): 609–616 Adhikari K, Kheir RB, Greve MB, Bocher PK, Malone BP, Minasny B, McBratney AB, Greve MH. 2013. High-resolution 3-D mapping of soil texture in Denmark. Soil Science Society of America Journal, 77(3): 860–876. Akbari S, Vaezi AR. 2015. Investigating aggregates stability against raindrops impact in some soils of a semi- arid region, north west of zanjan. Water and soil science (Agricultural science), 25(2): 65–77. (In Persian). Bare A, Kainz M, Veihe A. 2010. The spatial variability of erodibility and its relation to soil type, a study from northern Ghana. Geoderma, 106(1–2): 101–120. Barthes B, Roose E. 2002. Aggregate stability as an indicator of soil susceptibility to runoff and erosion: validation at several levels. Catena, 47(2): 133–149. Belaid H, Habaieb H. 2015. Soil aggregate stability in a Tunisian semi-arid environment with reference to fractal analysis. J. Soil Sci. Environ. Manage, 6(2): 16–23. Boehner J, Antonic O. 2009. Land-surface parameters specific to topo-climatology. In: Hengl, T. and Reuter, H. (Eds.) Elsevier. 'Geomorphometry– Concepts, Software, Applications'. Developments in Soil Science, pp. 195–226. Breiman L, Cutler A. 2004. Random Forests homepage. Retrieved April 23 rd. Breiman L. 2001. Random forests. Mach. Learn, 45(1): 5–32. Burger IA, Kelting DL, 1999. Using soil quality indicators to assess forest stand management.. Forest Ecology and Management, 122: 155–156. Chapman HD. 1965. Cation exchange capacity. In Black C.A.. Editors. Methods of Soil Analysis. American Society of Agronomy. Madison. pp. 891–901. Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Bohner J. 2015. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8(7): 1991–2007. Fathizad H, Ardakani MA, Sodaiezadeh H, Kerry R, Taghizadeh-Mehrjardi R. 2020. Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran. Geoderma, 365:114233. Gallant JC, Dowling TI. 2003. A multi resolution index of valley bottom flatness for mapping depositional areas. Water Resour. Water Resources Research, 39(12): 1347–1360. Gee GW, Bauder JW. 1986. Particle size analysis. In: Klute, A. (Eds.), Methods of soil analysis. American Society of Agronomy Madison, WI. Part 1, pp. 383–411. Hengl T. 2006. Finding the right pixel size. Comput. Geosci, 32 (9): 1283–1298. Hosseinalizade M, Ahmadi H, Feiznia S, Rivaz F. 2017. Latin hypercube sampling in natural resources using. Journal of Conservation and Utilization of Natural Resources, 6 (1):1–14.(In Persian). Iqbal J, Read JJ, Thomasson AJ, Jenkins JN. 2005. Relationships between Soil-Landscape and Dryland Cotton Lint Yield. Soil Science Society of America, 69(3): 1–11. Kariminejad N, Hosseinalizade M, Pourghasemi HR. 2020. A Review of Spatial Monitoring of Piping Collapse Using Unmanned Aerial Vehicle in Loess-Derived Soils in the Golestan Province. Watershed Management Research, 33(3):53–69.(In Persian). Kemper WD, Rosenau RC. 1986. Aggregate stability and size distribution. In: Klute, A. (Ed.), Methods of Soil Analysis. Part I: Physical Analysis. SSSA. Madison, WI, pp. 425–442. Khaledian Y, Miller BA. 2020. Selecting appropriate machine learning methods for digital soil mapping. Applied Mathematical Modelling, 81(1–2):401–418. Liang Z, Chen S, Yang Y, Zhou Y, Shi Zh. 2019. High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling. Science of the Total Environment, 685(2): 480–489. Loch RJ, Foley JL. 1994. Measurement of aggregate breakdown under rain: comparison with tests of water stability and relationships with field measurements of infiltrations. Australian Journal of Soil Research, 32(4): 701–720. Lozano-García B, Parras-Alcántara L, Brevik EC. 2016. Impact of topographic aspect and vegetation (native and reforested areas) on soil organic carbon and nitrogen budgets in Mediterranean natural areas. Science of the Total Environment, 544(4): 963–970. Maleki S, Khormali F, Chen S, Pourghasemi HR, Hosseinalizade M. 2021. Digital soil mapping of organic carbon at two depths in loess hilly region of Northern Iran. In: Pourghasemi HR. Editors. Computers in Earth and Environmental Sciences,Elsevier (EDS.).pp. 467–475. Maleki S, Khormali F, Mohammadi J, Bogaert P, Bagheri Bodaghabadi M. 2020a. Effect of the accuracy of topographic data on improving digital soil mapping predictions with limited soil data: An application to the Iranian loess plateau. Catena, 195( 104810): 1–13. Maleki S, Khormali F, Kariminejad N, Hosseinalizade M. 2020b. Modeling of the spatial point pattern of hillside and stream using unmanned aerial vehicle in a part of loess plateau, Golestan Province. Journal of Water and Soil Conservation, 27(1): 91–107.(In Persian). Maleki S, Khormali F, Karimi AR. 2014. Introducing different flow direction algorithms to map topographic wetness index and soil organic carbon in a loess hillslope of Toshan area, Golestan Province, Iran. J. of Water and Soil Conservation, 21(1): 145–162. McBratney AB, Mendonc Santos ML, Minasny B. 2003. On digital soil mapping. Geoderma, 117(1–2): 3–52. Minasny B, McBratney AB. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences, 32(9): 1378–1388. Minasny B, McBratney AB. 2007. Latin hypercube sampling as a tool for digital soil mapping. In: Lagacherie P, McBratney AB, Voltz M. (Eds.), Digital Soil apping: An introductory perspective. Elsevier, Amsterdam, pp. 153–165. Moore ID, Gessler P, Nielsen G, Peterson G. 1993. Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57(2): 443–452. Moreira Furlan L, AugustoMoreira C, Guilhermede Alencar P, Rosolen V. 2021. Environmental monitoring and hydrological simulations of a natural wetland based on high-resolution unmanned aerial vehicle data (Paulista Peripheral Depression, Brazil). Environmental Challenges, 4( 100146):1–8. Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD. 2004. An introduction to decision tree modeling. Journal of Chemometrics, 18(6): 275–285. Nelson DW, Sommers LE. 1982. Total carbon, organic carbon, and organic matter. In: Page AL, Miller RH, Keeney DR. Editors. Methods of soil analysis. Part 2. 2nd ed. Agron. Monogr. 9. Madison, WI. pp. 539–577. Olaya VF. 2004. A gentle introduction to Saga GIS. The SAGA User Group e.V, Göttingen, Germany, 208 p. Page AL, Miller RH, Keeney DR. 1982. Methods of soil analysis. Part 2. In: Chemical and Microbiological Properties No. 9, 2nd edn. ASA, SSSA, CSSA, Madison, Wisconsin, USA, pp. 595–623. Pahlavan-Rad MR, Dahmardeh K, Brungard C. 2018. Predicting soil organic carbon concentrations in a low relief landscape, eastern Iran. Geoderma Regional, 15(e00195): 1–7. Pahlavan Rad MR, Toomanian N, Khormali F, Brungard CW, Komaki CB, Bogaert P. 2014. Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Geoderma, 232–234:97–106. Pahlavan Rad MR, Toomanian N, Khormali F. 2016. Introduce digital soil mapping. Land Management Journal, 4 (2): 97–114. (In Persian). Poorshademan A, Taghizadeh-Mehrjardi R, Tazeh M, Nabiollahy K. 2019. The prediction of spatial variability of soil erodibility index using digital mapping technique in Kanisef Region, Baneh. Soil Applied Research, 6(2):15–26. (In Persian). RDCT-R Development Core Team .2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Available at https://www.R-project.org/ Roudier P, Brugnard C, Beaudette D, Louis B. 2020. Conditioned latin hypercube sampling. R Package \ Available at https://github.com/pierreroudier/clhs/ Soil Survey Staff. 2014. Keys to soil taxonomy (12th ED), U.S. Department of Agriculture, Natural Resources Conservation Service, pp. 1–372. Sokouti Oskoui R, Arabkhedri M. 2018. Developing a Suitable Method for Determining Soil Loss Tolerance in Iran. Journal of land Management (Soil and Water Science), 6(1):1–19. (In Persian). Sreenivas K, Dadhwal VK, Kumar S, Harsha GS, Mitran T, Sujatha G, Janaki Rama Suresh G, Fyzee MA, Ravisankar T. 2016. Digital mapping of soil organic and inorganic carbon status in India. Geoderma, 269(1): 160–173. Stoorvogel JJ, Kempen B, Heuvelink GBM, Bruin S. 2009. Implementation and evaluation of existing knowledge for digital soil mapping in Senegal. Geoderma, 149(1): 161–170. Taghizadeh-Mehrjardi R, Minasny B, Sarmadian F, Malone B. 2014. Digital mapping of soil salinity in Ardakan region, Central Iran. Geoderma, 213:15–28. Teka K, Nyssen J, Teha N, Haile M, Deckers J. 2015. Soil, land use and landform relationship in the Precambrian lowlands of northern Ethiopia. Catena, 131: 84–91. Thomas GW. 1996. Soil pH and Soil Acidity. In: Sparks D.L. Editors. Methods of Soil Analysis. Part 3 Chemical Methods, Soil Science Society of America. pp. 475–490. Topp GC, Reynolds WD, Cook FJ, Kirkby JM, Carter MR. 1997. Physical attributes of soil quality. In: Gregorich EG and Carter MR (eds.). Soil Quality for Crop Production and Ecosystem Health, Elsevier Science Amesterdam, Netherlands. pp. 21–58. Tucker BB, Kurtz LT. 1961. Calcium and magnesium determinations by EDTA titrations. Soil Science Society of America Journal, 25 (1): 27–29. Vasu D, Kumar Singh S, Kumar Ray S, Duraisami VP, Tiwary P, Chandran P, Nimkar AM, Anantwar SHG. 2016. Soil quality index (SQI) as a tool to evaluate crop productivity in semiarid Deccan plateau, India. Geoderma, 282:70–79. Wang SH, Jin X, Adhikari K, Li W, Yu M, Bian ZH, Wang Q. 2018. Mapping total soil nitrogen from a site in northeastern China. Catena, 166(13): 134–146. Weiss AD. 2001. Topographic position and landforms analysis, in Proceedings of the ESRI User Conference, 9-13 July, San Diego, CA, USA. pp. 4345–4360. WRB- World Reference Base for Soil Resources . 2014. Update 2015. Food and Agriculture Organization of the United Nations, pp. 1–204. Young F, Hammer R. 2000. Defining geographic soil bodies by landscape position, soil taxonomy, and cluster analysis. Soil Science Society of America, 64(12): 989–998. Zeraatpisheh M, Ayoubi SH, Jafari A, Finke P. 2017. Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran. Geomorphology, 285(15): 186–204. Zeraatpisheh M, Ayoubi S, Jafari A, Tajik S, Finke P. 2019. Digital mapping of soil properties using multiple machine learning in a semi- arid region, Central Iran. Geoderma 338(15): 445–452. Zhenghong Y, Zheng Y, Zhang J, Zhang C, Ma D, Chen L, Caid T. 2020. Importance of soil interparticle forces and organic matter for aggregate stability in a temperate soil and a subtropical soil. Geoderma, 362(15) :114088. Zhenghong Y, Zhang J, Zhang C, Xin X, Li H. 2017. The coupling effects of soil organic matter and particle interaction forces on soil aggregate stability. Soil and Tillage Research, 174(1): 251–260. Zhi J, Zhang G, Yang F, Yang R, Liu F, Song X, Zhao Y, Li D. 2017. Predicting mattic epipedons in the northeastern Qinghai-Tibetan plateau using random forest. Geoderma Regional, 10(1): 1–10. Zhu AX, Yang L, Li B, Qin CH, English E, Burt JE, Zhou CH. 2008. Purposive Sampling for Digital Soil Mapping for Areas with Limited Data. In: Digital Soil Mapping with Limited Data, Part 12. Springer Science, pp. 223–245. | ||
|
آمار تعداد مشاهده مقاله: 602 تعداد دریافت فایل اصل مقاله: 764 |
||