نشریه علمی - پژوهشی مرتع و آبخیزداری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد مرتعداری دانشگاه شهرکرد

2 دکترای مرتعداری،عضو هیئت علمی گروه مرتع و آبخیز دانشگاه شهرکرد

3 دکترای زمین شناسی، عضو هیئت علمی گروه مرتع و آبخیز دانشگاه شهرکرد

چکیده

در این تحقیق سه روش سنجش از دوری، فیزیوگرافیکی و ژئومورفولوژیکی برای تهیۀ نقشۀ پوشش گیاهی مورد بررسی قرار گرفت. در روش سنجش از دور، علاوه بر تصاویرسنجندۀ IRS-LISSIII از نقشۀ مدل رقومی ارتفاعی زمین[1] و شاخص گیاهی تفاضلی نرمال شده[2] به­عنوان داده­های کمکی استفاده و برای طبقه­بندی آنها از روش نظارت­شدۀ حداکثر احتمال استفاده شد. بررسی دقت نقشه­های تولیدی، نشان داد زمانی که تنها از داده­های سنجش از دور استفاده شود، میزان دقت و ضریب کاپای حاصل به ترتیب 82% و 43/79% و دقت و ضریب کاپای به همراه داده­های کمکی به ترتیب 93% و 63/90% می­باشد. در روش فیزیوگرافی، پس از تعیین مهم­ترین عوامل فیزیوگرافیکی شامل شیب، جهت شیب، ارتفاع از سطح دریا، میانگین سالانۀ بارش، درجۀ حرارت و میزان تابش خورشیدی به­عنوان عوامل تعیین­کنندۀ پوشش گیاهی و رابطۀ این عوامل با پوشش گیاهی مورد آزمون قرار گرفت. بدین منظور، با استفاده از مدل رگرسیون لجستیک چند­جمله­ای نقشۀ پوشش گیاهی با دقت 08/47% پیش­بینی شد. در روش ژئومورفولوژ‍ی نیز نقشه­های سنگ­شناسی، شکل پستی و بلندی و رخساره­های ژئومورفولوژی تعیین و جهت پیش­بینی نقشۀ پوشش گیاهی از روش شبکۀ عصبی مصنوعی استفاده گردید. این روش دقتی برابر با 1/39% را نشان داد. تفاوت فاحشی که در دقت تصاویر حاصل از دو روش فیزیوگرافیکی و ژئومرفولوژیکی با روش سنجش از دور مشاهده می­گردد، بیانگر این است که روش سنجش از دوری دقت قابل توجه بیشتری برای پیش­بینی پوشش گیاهی در مقایسه با دو روش دیگر حتی در صورت استفاده نکردن از لایه­های کمکی دارد.




[1] DEM


[2] NDVI

کلیدواژه‌ها

عنوان مقاله [English]

Comparison of three methods of vegetation/land cover mapping, including remote sensing, Physiographic and Geomorphologic

نویسندگان [English]

  • shahrebanoo rahmani 1
  • Ataollah Ebrahimi 2
  • alireza davoudian 3

1 u

2 u

3 u

چکیده [English]

In this study, three methods were evaluated for vegetation mapping. For remote sensing method, in addition to IRS data of LISSIII, Ddigital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) were used for classification of 14 classes of land covers mostly vegetation types using a maximum likelihood algorithm. After comparing of produced vegetation maps, overall accuracy and Kappa index were 82% and 79.43% respectively when only the IRS were used. Whereas, the overall accuracy and Kappa index were increased to 93% and 90.63% respectively, when ancillary data of DEM and NDVI were added.
Slope, slope direction, elevation above sea level, annual precipitation, temperature, and sun radiation were selected as the main physiographic after a broad literature review. Then the relationship between of these six factors with vegetation types was evaluated. so a multivariate logistic regression was used to draw vegetation map of the study area based on the sixth independent variables. The result showed a predicted vegetation map of 47.08% accuracy.Finally, in the morphological method, relationship between three maps of lithology, undulating form of geomorphology and faces with vegetation/land cover were determined using a neural network synthetic approach and predict vegetation map was drawn as the output. The accuracy of resulted map was 39.1%. Comparison of accuracy of vegetation mapping by three methods of RS, physiographic and geomorphological methods revealed that RS method of vegetation/land cover mapping is significantly promising due to a meaningfully higher accuracy even without using ancillary data such as DEM and NDVI in this method.

کلیدواژه‌ها [English]

  • Vegetation mapping
  • Rangelands vegetation mapping
  • remote sensing
  • geomorphology
  • Physiographic
  • Sabzkouh
[1]       Abdul Ali Zadeh, Z.(2010). Study of changes in the surface of the land over the past three decades and the prediction of the future situation in Sabzkouh province. Master's Degree, Faculty of Natural Resources, Shahrekord University.
[2]       Asadi Borujeni, A. (1990). An Investigation of the Ecology of Vegetation Communities in Sabzkouh Region of Chaharmahal & Bakhtiari Province with regard to Soil and Geomorphology Units. Master thesis, Faculty of Natural Resources, Tarbiat Modarres University.
[3]       Azarnivand, H. (1989). Study of vegetation and soil in relation to geomorphologic units in Damghan. Master thesis of Rangeland, Faculty of Natural Resources, Tarbiat Modarres University
[4]       Blackard, J.A. and Dean, D.J. (1999). Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture, 24, 131-151.
[5]       Blesius, L.and Weirich, F. (2005). The use of the minnaert correction for land-cover classification in mountainous terrain. International Journal of Remote Sensing, 26, 3831–3851.
[6]       Brown, D.G. (1994). Predicting vegetation types at treeline using topography and biophysical disturbance variables. Journal of Vegetation Science, 5,642-656.
[7]       Burke, A. (2001). Classification and ordination of plant communities of the Nauklaft mountain, Namibia. Journal of Vegetation Science, 12, 53-60.
[8]       Clements, F.E. (1916). Plant succession: an analysis of the development of vegetation. Carnegie Institute, Publication242, Washington, D.C.
[9]       Consulting company Warzbum. (2002). Management Plan for Sabzkouh Protected Area. Environmental Protection Agency
[10]    DorrenL, K. A., Maier, B.and Seijmonsbergen, A.C. (2003). Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology Management, 183, 31–46.
[11]    Ematati Noush Abadi, A.( 1994). Geobetonic study of watershed of Cham Rud Kashan. Master thesis of Rangeland, Faculty of Natural Resources, University of Tehran
[12]    Franklin, J., McCullough, P. and Gray, C. (2000). Terrain variables used for predictive mapping of vegetation communities in Southern California. In Terrain Analysis: Principles and Applications, Wilson J.P.and Gallant J.C. (Eds.), John Wiley and Sons, New York.
[13]    Giti, A., Ahmadi, H., Mashhadi, N. and Reyahi, A.( 2001). A Survey and Comparison of Geomorphologic Facies Boundary Adaptation and Land Unit Components with Vegetation Type Borders, Case Study of Watershed Ardehal Mashhad. Journal of Natural Resources, Volume 54 (Issue 2)
[14]    Guisan, A. and Zimmermann, N.E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135,147–186
[15]    Jafarian Jaludar, Z. (2003). Preparation of vegetation map with two geomorphologic and physiographic units. Master thesis of Rangeland, Faculty of Natural Resources, University of Tehran.
[16]    Javanshir, K. (1986). Vegetation Study and Park Mapping, Khajir and Sorkhe Hesar Parking Design
[17]    Kessell, S.R. (1976). Gradient modeling: a new approach to fite modeling and wilderness resource management. Enviromental Management, 1, 39-48.
[18]    Kilpelainen, P. and Tokola, T. (1999). Gain to be achieved from stand delineation in LANDSAT TM image-based estimates of stand volume. Forest Ecology and Management, 124,105-111.
[19]    Levine, E.R., Kimes, D.S. and Sigillito, V.G. (1996). Classifying soil structure using neural networks. Ecological modelling, 92,101–108.
[20]    Moshaver 1 company. (2000). Comprehensive Plan for the Recovery and Development of Agriculture and Natural Resources in Chaharmahal va Bakhtiari Province
[21]    Paruelo, J.M. and Tomasel, F. (1997). Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecological modelling, 98,173-186.
[22]    Reddy, M. B. and Blah, B. (2009). Topographic normalization of satellite imagery for image classification in northeast India. Progress in Physical Geography, 33(6), 815-836.
[23]    Roy, P.S., Ranganath, B.K., Diwakar, P.G., Vohra, T.P.S., Bhan, S.K. and Singh, I.J. (1991). Tropical forest type mapping and monitoring using remote sensing. International Journal of Remote Sensing, 12, 2205-2228.
[24]    Saha, A.K. Arora, M.K., Csaplovics, E. and Gupta, R.P. (2005). Land Cover Classification Using IRS LISS III Image and DEM in a Rugged Terrain: A Case Study in Himalayas. Geocarto International, 20(2), 33-40.
[25]    Tan, S.S.and Smeins, F.E. (1996). Predicting grassland community changes with an artificial neural network model. Ecological modelling, 84, 91–97.
[26]    Tokola, T. Sarkeala, J.and Van der Linden, M. (2001). Use of topographic correction in Landsat TM-based forest interpretation in Nepal. International Journal of Remote Sensing, 22,551–563.
[27]    Welch, R., Madden, M. and Jordan, T. (2002). Photogrammetric and GIS techniques for the development of vegetation databases of mountainous areas: Great Smoky Mountains National Park. ISPRS Journal of Photogrammetry and Remote Sensing, 57, 53– 68.
[28]    Whittaker, R.H. (1991). A criticism of the plant association and climatic climax concepts. Northwest Scientist, 25, 17-31.
[29]    Woodcock, C.E., Collins, J.B., Gopal, S., Jakabhazy, V.D. Li X., and Macomber, S. (1994). Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model. Remote Sensing of Environment, 50,
240-254.
[30]    Wulder, M.A., Dechka, J.A., Gillis, M.A., Luther, J.E., Hall, R.J. and Beaudoin, A. (2003). Operational mapping of the land cover of the forested area of Canada with Landsat data, EOSD land cover program. Forestry Chronicle, 79, 1075-1083.