Remote sensing technology for mapping and monitoring vegetation cover (Case study: Semirom-Isfahan, Iran)

Document Type : Original Research Paper

Authors

1 MSc., College of Natural Resources, Isfahan University of Technology, Isfahan, Iran

2 Prof., College of Natural Resources, Isfahan University of Technology, Isfahan, Iran

3 Asst. Prof., College of Natural Resources, Isfahan University of Technology, Isfahan, Iran

4 Assoc., Prof., College of Natural Resources, Isfahan University of Technology., Isfahan, Iran

Abstract

To determine the suitable indices for vegetation cover and production assessment based on the remote sensing data, simultaneous digital data with field data belonging to the spring rangeland of the Semirom-Isfahan province were analyzed. During two years of monitoring the annual, grass, forb, and shrub vegetation cover and the total production data from 86 were collected. The Global Positioning System (GPS) was used to measure the coordinates of plots and transects. Geometric correction and histogram equalization were applied in image processing, and image digital numbers were converted to reflectance numbers. In the next stage, all vegetation indices were calculated from the Advanced Wide Field Sensor (AWiFS) image data and compared with the vegetation cover estimates, at monitoring points, made during field assessments. A linear regression model was used to select suitable vegetation indices. The results showed that there were significant relationships between the satellite data and the vegetative characteristics. Among the indices, the Normalized Difference Vegetation Index (NDVI) consistently showed significant relationships with the vegetation cover. The estimation of the vegetation cover with the NDVI vegetation index was more accurately predicted within rangeland systems. Using the produced model from the NDVI index vegetation crown cover, percentage maps were produced in three class percentages for each image. Generally introduced indices provided accurate quantitative estimation of the parameters. Therefore, it was possible to estimate cover and production as important factors for range monitoring using the AWiFS data. The Remote sensing data and the Geographic Information System are the most effective tools in natural resource management.

Keywords

References

Apan, A.A. (1997). Land cover mapping for tropical forest rehabilitation planning using remotely sensed data, Int. J. Re. Se., 18(5), 1029-1049.
Arzani, H. (1994). Some aspects of estimating Short-term and long-term rangeland carrying capacity, PhD Thesis.University of New South Wales. Australia.
Arzani, H. (1998). Using digital Landsat TM image data for estimate production and vegetation cover. Ira. J. Na. Re., 50(1), 11-21.
Asrar, G., Kanemasu, E.T. and Yoshida, M. (1985). Estimates of leaf area index from spectral reflectance of wheat under different cultural practices and solar angle. Re. Se. of En., 17, 1-11.
Bannari, A., Morin Bonn, D.F. and Huete, A.R. (1995).A review of vegetation indices. Re. Se. Reviews, 13, 95-120.
Boyd, D.S., Foody, G.M., Curran, P.J., Lucus, R.M. and Klonzak, M. (1996). An assessment of radiance in land sat TM middle and thermal infrared ware bands for the detection of tropical forest generation. Int. J. Re. Se., 17(2), 249-261.
Campbell, J.B. (2011). Introduction to remote sensing. 5th Eds. Guilford Press: New York.
Crippen, RE, (1990). Calculating the vegetation index faster. Re. Se. of En., 34, 71-73.
Farzadmehr, H., Arzani, H. Darvishsefat, A. and Jafari, M. (2004). The study of Landsat TM image data for estimate production and vegetation cover in Hanna-Semirom. Ira. J. Na. Res., 57(2), 339-350.
Glenn, E. P., Huete, A. R., Nagler, P.L. and Nelson, S.G. (2008). Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape., Sensor. 8(4), 2136–2160. DOI:10.3390/s8042136.
Goward, S.N., Markham, B. Dye, D.G., Dulaney, W. and Yang, J. (1991). Normalized difference vegetation index measurements from AVHRR. Re. Se. of En., 35, 257–277.
Jianwen, M., Xiuzhen, H., Changlin,W., Yuejin, Z., Jinyi, T., Zhiyu, X. and Deveson, T. (2005). Monitoring East Asian migratory locust plagues using remote sensing data and field investigations, Int. J. Rem. Sens., 26 (3), 629 – 634 http://dx.doi.org/10.1080/01431160310001595019.
Jafari, R., Lewis, M.M. and Ostendorf, B. (2007). Evaluation of vegetation indices for assessing vegetation cover in southern arid lands in South Australia. Rang. J., 29, 39-49. www.publish.csiro.au/journals/trj.
Jin, S. and Sader, S.A. (2005). Comparison of time series tasselled cap wetness and the normalized difference moisture index in detecting forest disturbances. Re. Se. of En., 94, 364-372. DOI: 10.1016/j.rse.2004.10.012.
Khajeddin, S.J. (1995). A survey of the plant communities of the Jazmorian Iran using land sat Mssdata. Ph.D Thesis. University of Reading, UK.
LePrieur, D., Verstraete, M.M. and Pinty, B. (1994). Evaluation of the performance of various vegetation indices to retrieve cover from AVHRR data. Re. Se. Reviews, 10, 265-284.
Mahmodzadeh, H. (2007). Digital Change Detection Using Remotely Sensed Data for Monitoring Green Space Destruction in Tabriz. Int. J. Envi. Re., 1(1), 35-41.
Masoud, A.A. and Koike, K. (2006). Arid Land Sanitization Detected by Remotely-Sensed Land 
Cover Changes: (A Case Study in the Siwa Region NW Egypt). Arid. Env., 66, 151-167.
Moleele, N., Ringose, S. and Arnberg, W. (2001). Assessment of Vegetation Indices Useful for Browse forage prediction In Semi-arid rangelands. Int. J. Re. Se., 22(5), 741-756.
O’Neill, A. L. (1996). Satellite-derived vegetation indices applied to semi-arid shrub lands in Australia. Aust. Geo., 27(2): 185-199. DOI: 10.1080/00049189608703167.
Pearson, R.L. and Miller, L.D. (1972). Remote sensing of standing crop biomass for estimation of the productivity of the short grass prairie, Pawnee national grasslands, Colorado. In: The 8th International Symposium on Remote Sensing of the Environment. Ann Arbor, MI. Committee of the Symposium: Ann Arbor, MI, 1355-1379.
Perry, C.R. and Lautenschlager, L.F. (1984). Functional equivalence of spectral vegetation indices. Re. Se. of En., 14, 169-182.
Pettorelli, N., Vik, J., Mysterud, A., Gaillard, J., Tucker, C.N. and Stenseth, N. (2005). Using the satellite - derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol., 20, 503-510.
Pickup, G., Chewings, V.H. and Nelson, D.J. (1993). Estimating changes in vegetation cover over time in arid rangelands using Landsat MSS data. Re. Se. of En., 43, 243-263. DOI: 10.1016/0034- 4257(93)90069-A.
Qi, J., Chehbouni, A. Huete, A.R. Kerr, Y.H. and Sorooshian, S. (1994). A modified soil adjusted vegetation index. Re. Se. of En., 48, 119-126. DOI: 10.1016/0034 4257(94)90134-1.
Richardson, A.J., Wiegand, C.L., Wajura, D.F., Dusek, D. and Steiner, J.L. (1992). Multisite analyses of spectral- biophysical data for sorghum. Re. Se. of En., 41, 71-82.
Richardson, A.J. and Wiegand, C.L. (1997). Distinguishing vegetation from soil background information.Phot. Eng. and Re. Se., 43, 1541-1552.
Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.W. and Harlan, J.C. (1974). Monitoring the vernal advancement and retrogradation (greenware effect) of natural vegetation. Greenbelt, MD, USA, NASA/GSFCT, Type 3, Final Report.
Rundquist, B.C. (2002). The influence of canopy green vegetation fraction on spectral measurements over native tall grass prairie. Re. Se. of En., 81, 129-135.
Sadeghi, S.H. (2009). The study of vegetation cover of Kalahrud region using ASTER data. M.Sc thesis, Department of Natural Resources, Isfahan University of Tecnology, Isfahan.
Schmidt, H. and Karnieli, A. (2001). Sensitivity of vegetation indices to substrate brightness in hyper arid environment: the Makhtesh Ramon Crater (Israel) case study. Int. J. Re. Se., 22(17), 3503-3520.
Seperhi, A. (2003). Using Vegetation indices for estimate rangeland vegetation cover in Jahannama refuge, Iran. J. Na. Res., 55(2), 20-31.
Sellers, P.J., Berry, J.A., Collatz, G.J., Field, C.B. and Hall, F.G. (1992). Canopy reflectance, photosynthesis and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Re. Se. of En., 42, 187-216.
Thenkabail, P.S., Ward, A.D., Lyon, J.G. and Maerry, C.J. (1994). Thematic Mapper vegetation indices for determining soybean and corn growth parameters. Phot. Eng. and Re. Se., 60, 437-442.
Tucker, C. (1979). Red and photographic infrared linear combination for monitoring vegetation. Re. Se. of En., 8,127–150.
Wallace, J., Behn, G. and Furby, S. (2006). Vegetation condition assessment and monitoring from sequences of satellite imagery. Eco. Mang. and Re., 7, S31-S36. DOI: 10.1111/j.1442- 8903.2006.00289.x.
Wessels, K.J., Prince, S.D., Frost, P.E. and Zyl, D.V. (2004). Assessing the effects of human-induced land degradation in the former homelands of northern South Africa. Re. Se. of En., 91, 47-67. DOI: 10.1016/j.rse.2004.02.005.
Wilson, A.D., Abraham, N.A., Barratt, R., Choate, J., Green, D.R., Harland, R.J., Oxley, R.E. and Stanley, R.J. (1987). Evaluation of methods of assessing vegetation change in the semi-arid rangelands of southern Australia. Aust. Rang. J., 9, 5-13. DOI:10.1071/RJ9870005.
Yang, W., Yang, L. and Merchant, J.M. (1998). An assessment of AVHRR/NDVI eco climatological relations in Nebraska, USA. Int. J. Rem. Sens., 18 (10), 2161–2180.
Yeganeh, H., Khajeddin, S. J. and Soffianian, A. R. (2008). Evaluating the Potentials of Spectral Indices of the MODIS in Estimating the Plant Production in Semirom Pastures, Rangeland., 2(1), 63-77.
Zahedifar, N. (2002). Provision land use map by using satellite data in Bazoft watershed, M.Sc thesis, Department of Agriculture, Isfahan University of Technology, Isfahan.
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Volume 1, Issue 2 - Serial Number 2
April 2015
Pages 165-174

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