Prediction of shrub forms production by integrating statistical and remote sensing data (a case study: Chehelgazi watershed of Sanandaj-Iran)
Paper Details
Prediction of shrub forms production by integrating statistical and remote sensing data (a case study: Chehelgazi watershed of Sanandaj-Iran)
Abstract
Rangelands vegetation cover is the main resource production of protein in Iran. Inappropriate usage and misknowing of species combination, is the agent of decreasing of valuable species in rangelands. Annual production definition based on growth form or possibly could base on species is the major factor of accurate rangelands management specially grazing programing and natural or intentional fire prevention. This study applied double date TM imagery to estimate production of shrubberies forms of rangelands of Qeshlaq dam watershed. The images were processed by ERDAS IMAGINE software. The rangeland yield clipping and weighing system applied to measure green herbage biomass from ground truth sites by means of 300 medium plots (5m2). Ground truth sites were selected to represent five rangeland types and four sites were sampled by systematic random method in each type to calibrate the relationship between satellite-derived Wavebands, vegetation indices and green yields. These yield data were compared with yields estimated by 6 main wavebands also 4 synthetic bands of 2 scenes of TM data in corresponding time and go through linear multivariate regression processing to make the model. Remote sensing yield estimation model were also analyzed for their precision and checked by actual 10 percent measured yields on four ground truth sites. Results showed that relationship between shrubberies growths is meaningful with, ND53 and TM5/TM3 bands. Resulted model have higher accuracy in estimating shrub forms growth production in order to permanent management of rangelands in comparison with traditional models.
Arzani H, King G, Forster B. 1998. The application of the Landsat TM data to vegetation cover and yield measurement. Iranian Journal of Natural Resource. Vol. 1, No. 50, pp. 3-21.
Arzani H, Noori S, Kaboli H, Moradi R, Ghelichnia H. 2009. Determination of suitable indices for vegetation cover assessment in summer rangelands in south of Mazandaran. Journal of the Iranian Natural Res., Vol. 61, No. 4, pp. 997-1016.
Arzani H. 1994. Some aspects of estimating Short term and long-term rangeland carrying capacity. PhD Thesis. University of New South Wales. Australia.
Bilbrough CJ, Richards JH. 1993. Growth of sagebrush and bitterbrush following simulated winter browsing: mechanisms of tolerance. Ecology 74, pp. 481-492.
Bing XU, Peng G, Ruliang P. 2003. Crown closure estimation of oak Savanah in a dry season with Landsat TM imagery: comparison of various indices through correlation analysis. International Journal of remote sensing. 24(9). Pp.1811-1822.
Bradley BA. 2010. Assessing ecosystem threats from global and regional change: hierarchical modeling of risk to sagebrush ecosystems from climate change, landuse, and invasive species in Nevada, USA. Ecography 33, 198-208. doi: 10.1111/j.1600-0587.2009.05684.x.
Bonham C D.1989. Measurements for Terrestrial Vegetation. New York, NY, USA: John Wiley and Sons. 352 p.
Lu D, Paul M, Eduardo B, Emilio M. 2002. Above-Ground Biomass Estimation og Successional and Mature Forests using TM Images in the Amazon Basin, 10th International Symposium on spatial Data Handling, pp. 183-196.
Duncan J, StowD, Franklin J, Hope A. 1993. Assessing the Relationship between spectral vegetation indices and shrub cover in the Yornada Basin, New Mexico. International Journal of Remote sensing, 14, 3395-3416.
Farzadmehr DJ, Arzani H, Darvishsefat AA, Jafari M. 2004. Investigation in Estimating Vegetation Cover and Phytomass Production, Using Enhanced Landsat Data in A semi-arid Region, Journal of the Iranian Natural Res., Vol. 57, No. 1, pp. 339-351.
Jin S, Sader SA. 2005. Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote sensing of environment. 94, 364-372. DOI: 10.1016/j.rse.2004.10.012.
Lawrence R, Hurst R, Weaver T, Aspinall R. 2006. Mapping prairie pothole communities with multitemporal Ikonos satellite imagery, Photogrammetric Engineering and Remote Sensing. 72(2), 169-174.
Jakubauskas ME, Legates DR, Kastens JH. 2002. Crop identification using harmonic analysis of time-series AVHRR NDVI data. Computers and Electronics in Agriculture. 37, 127-139.
Okin GS, Roberts DA, Murray B, Okin WJ. 2001. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment 77, 212-225.
Ripple WJ, Wang S, Isaacson DL, Paine DP. 1991. A preliminary comparison of Landsat TM and Spot-1 HRV multispectral data for estimating coniferous forest volume, International Journal of remote sensing, 12(9), 1971-1977.
Roy PS, Shirish AR. 1996. Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest, Journal of Bioscience. 21(4), pp 535-561.
Santi E, Tarantino C, Amici V, Bacaro G, Blonda P, Borselli L, Rossi M, Tozzi S, Torri D. 2014. Fine-scale spatial distribution of biomass using satellite images. Journal of Ecology and the natural Environment. Vol. 6(2), pp. 75-86. DOI: 10.5897/JENE2013.0416.
Seperhi A. 2003. Using Vegetation indices for estimate rangeland vegetation cover in Jahan nama refuge, Iranian Journal of Natural Resource. 55(2), 20-31.
Underwood E, Ustin S, Dipietro D. 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sensing of Environment. 86, pp. 150–161.
Ren X, Wei X, Smith A. 2012. Remote Sensing, Crop Yield Estimation and Agricultural Vulnerability Assessment: a Case of Southern Alberta. The Open Hydrology Journal, 2012, 6, 68-77.
Zahedi S, Ghasriani F. 2014. Tolerence of Festuca ovina to different severities of simulated grazing effects in semi-arid Rangelands, Kurdistan, Iran. Journal of Biodiversity and Environmental Sciences (JBES). Vol.4. No.2, 369-377.
Salahudin Zahedi, Kaka Shahedi (2014), Prediction of shrub forms production by integrating statistical and remote sensing data (a case study: Chehelgazi watershed of Sanandaj-Iran); JBES, V5, N1, July, P249-257
https://innspub.net/prediction-of-shrub-forms-production-by-integrating-statistical-and-remote-sensing-data-a-case-study-chehelgazi-watershed-of-sanandaj-iran/
Copyright © 2014
By Authors and International
Network for Natural Sciences
(INNSPUB) https://innspub.net
This article is published under the terms of the
Creative Commons Attribution License 4.0