Arzani, Hossein

Ghelichnia, H.

Kaboli, S. H.

Moradii, H. R.

Noori, S.

English

University of Kentucky

University of Kentucky UKnowledge International Grassland Congress Proceedings 21st International Grassland Congress / 8th International Rangeland Congress Application of Digital Data for Grassland Monitoring in Northern Iran Hossein Arzani University of Tehran, Iran S. Noori Tarbiyat Modares University, Iran S. H. Kaboli University of Tehran, Iran H. R. Moradii Tarbiyat Modares University, Iran H. Ghelichnia Research Center of Agriculture and Natural Resource of Mazandaran Province, Iran Follow this and additional works at: https://uknowledge.uky.edu/igc  Part of the Plant Sciences Commons, and the Soil Science Commons This document is available at https://uknowledge.uky.edu/igc/21/5-1/10 The 21st International Grassland Congress / 8th International Rangeland Congress took place in Hohhot, China from June 29 through July 5, 2008. Proceedings edited by Organizing Committee of 2008 IGC/IRC Conference Published by Guangdong People's Publishing House This Event is brought to you for free and open access by the Plant and Soil Sciences at UKnowledge. It has been accepted for inclusion in International Grassland Congress Proceedings by an authorized administrator of UKnowledge. For more information, please contact UKnowledge@lsv.uky.edu. 瞯 ]650　 瞯 　 Multifunctional Grasslands in a Changing World 　 Volume Ⅰ 　Grasslands/Rangelands Resources and Ecology ——— Application of Information Technology in Monitoring and Managing Grasslands/Rangelands ResourcesApplication of digital data for grassland monitoring in northern IranA rz ani , .H 1 , S . Noori2 , S .H . K aboli3 , H .R . Moradii4 , and H .Ghelichnia51 University o f Tehran , harz ani＠ ut .ac .ir 2 Tarbiyat Modares university , 3 University o f Tehran , 4 Tarbiyat ModaresUniversity ,5 Research Center o f A griculture and Natural Resource o f Maz andaran Prov inceKey words : remote sensing , vegetation cover , production , vegetation index .Introduction Rangeland resource management is dependent on increasing scientific capability and remote sensing can contributeinformation for range management applications ( Tueller , １９８９) . The capability of remote sensing for providing information ofchanges in cover and biomass has been discussed by previous authors ( eg . Arzani ２００５) . Selection of suitable vegetation indiceswas the objective of the present study .Material and methods Simultaneous digital and field data of summer rangeland south of Mazandaran , were analyzed . During ３years of monitoring , annual grass , forb and shrub cover , and total yield data of six ty １ m２ plots were collected . Aftercorrection of images , digital numbers were converted to reflectance numbers . Intrinsic indices , soil‐line related indices andatmospheric corrected indices were generated . Suitable indices were determined using linear regression .Results and discussion There were significant relationships between digital data and vegetative characteristics . Among indicesVI１０ with annual cover , VI６ , VI１０ , MIRV１ with grass cover , Pd３１２ , VI９ , M IRV１ , GVI indices with forb cover , MIRV２ , VI６ ,VI１０ with shrub cover , VI１２ , M IRV２ , VI５ , fuse２ with total cover and MIRV１ ,M IRV２ , VI５ ,Tgr with total production , showedsignificant relationships in different sites ( e .g . Table １ for the site in Vaz rangeland) . Jackson et al . ( １９８３ ) argued that anideal vegetation index should have the characteristics of high sensitivity to vegetation , insensitivity to soil background changesand be only slightly affected by atmospheric path radiance .Table 1 Correlation obtained between vegetation indices and vegetation parameters in V az grassland .VegetationparametersVegetationindices Correlation Regressionequation Correlationcoefficient ( R) SE SignificantlevelG . cover％ MIRV１  ４９ Ζ２８ ＋ ２６３MIRV１ ０ .８９ ３ 抖.３０ 倡 倡F . cover％ MIRV１  ５１ Ζ１９  .３ ＋ １２９MIRV２ ０ .５８ ５ 抖.４０ 倡 倡S . cover％ VI６ n７５ Ζ‐２２ *.５ ＋ ５０ .８VI６ ０ .７４ １ 抖.１０ 倡 倡T . cover％ VI５ n８１ Ζ１７４‐１２５VI５ l０ .９２ ９ 抖.２０ 倡 倡T . yield kg / ha VI５ n８２ Ζ７５‐５５ 唵.７VI５ ０ .８２ １ 抖.３０ 倡 倡倡 倡 Significant at P ＜ ０ .０１ , G ＝ Grasses , F ＝ Forbs , S ＝ Shrubs , T ＝ Total .Conclusions Generally , introduced indices , presented accurate estimation of the quantitative parameters . Therefore , it ispossible to estimate cover and range production for range monitoring using Landsat ETM ＋ data .ReferencesArzani , H . (２００５) . Vegetation indices for vegetation parameters measurements using remote sensing data in arid areas . FirstCon f erence Remote Sensing in K uwait , ２６‐２８ sep .２００５ .Jackson , R .D . , Slater , P .N . and Pinter , P .Jr . ( １９８３ ) , Discrimination of grow th and water stress in wheat by variousvegetation indices through clear and turbid atmosphers , Remote Sensing o f Env ironment , Vol . １３ , pp . １８７‐２０８ .Tueller P . T . ( １９８９ ) , Remote sensing technology for rangeland management applications , Journal o f Range Management ,Vol . ４２ , No . ６ , pp . ４４２‐４５３ .

Application of Digital Data for Grassland Monitoring in Northern Iran

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Application of Digital Data for Grassland Monitoring in Northern Iran

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