A study of the performance of the MSR vegetation index, using probabilistic and geostatistical methods

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Published: Jan 1, 2007
DOI: https://doi.org/10.12681/bgsg.17297
Keywords:
distribution histogram signal to noise ratio autocorrelogram NDVI
Aim. G. Skianis
National and Kapodistrian University of Athens, Faculty of Geology and Geoenvironment, Remote Sensing Laboratory
D. Vaiopoulos
National and Kapodistrian University of Athens, Faculty of Geology and Geoenvironment, Remote Sensing Laboratory
K. Nikolakopoulos
I.G.M.E.
Abstract

In the present paper is studied the effect of the MSR (Modified Soil Ratio) vegetation index on multispectral digital images, with the aid of probability theory and geostatistics. Using proper distributions to describe the histograms of the image at the red and infrared band zones, an analytical expression of the distribution g of the MSR values is deduced. The study of the behaviour of g shows that the ratio of the standard deviation to the mean value of the MSR image is higher than that of the NDVI vegetation index, which is quite often used. This means that the MSR vegetation index produces images with a good contrast. It was also observed that the MSR image has a better signal to noise ratio than that of the NDVI image. Finally, the autocorrelograms of the MSR and NDVI images showed that the tonality differences between adjacent pixels of the MSR image are slightly stronger than those of the NDVI image. The general conclusion is that the MSR vegetation index produces images with a good contrast and a high signal to noise ratio, which could aid in making a reliable mapping of the vegetation cover of the area under study

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  • New Technologies in Geophysical and Geological Research
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References
Baret, F., and Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment, Remote Sensing of Environment 35, 161-173.
Baret, F., Guyot, G., and Major, D.J., 1989. TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation, Proc. IGARSS'89 and 12th Canadian Symposium on Remote Sensing. Vancouver, Canada, 1355-1358.
Brown, L., Jin, M.C., Lablanc, S.G., and Cihlar, J., 2000. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: An image and model analysis, Remote Sensing of Environment, 71, 16-25.
Burgan, R.E., 1996. Use of Remotely Sensed Data for Fire Danger Estimation, Earsei Advances in Remote Sensing. Remote Sensing and GIS applications for Forest Fire Management, 4(4), 1-8.
Chen, J., 1996. Evaluation of vegetation indices and modified simple ratio for boreal applications, Canadian. Journalof Remote Sensing, 22, 229-242.
Chen, J., and Cihlar, J., 1996. Retrieving leaf area index of boreal conifer forests using Landsat Thematic Mapper, Remote Sening of Environment, 55, 153-162.
Coulibaly, L., and Goita, K., 2006. Evaluation of the potential of various spectral indices and textural features derived from satellite images for surficial deposits mapping, International Journal of Remote Sensing 27(20), 4567-4584.
Deering, D.W., Rouse, J.W., Haas, R.H., and Schell, J.A., 1975. Measuring Forage Production of Grazing Units from Landsat MS S Data, lCfh Internatonal Symposium on Remote Sensing of Environment 2, 1169-1178.
Fassnacht, K.S., Gower, S.T., Mackenzie, M.D., Nordheim, E.V., and Lillesand, T.M., 1997. Estimating the leaf area index of north central Wisconsin forest using Landsat Thematic Mapper. Remote Sening of Environment, 61, 229-245.
Faust, N.L., 1989. Image Enhancement. In Allen Kent and James G. Williams (eds), Encyclopedia of Computer Science and Technology, vol. 20, Supplement 5. Marcel Dekker Inc.
Fernandes, R.A., Miller, J.R., Chen, J.M., and Rubinstein, LG., 2004. Evaluating image-based estimates of leaf area index in boreal conifer stands over a range of scales using highresolution CASI imagery, Remote Sensing of Environment 89, 200-216.
Goel, N.S., 1988. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data, Remote Sens. Rev., 4, 1-212.
Goel, N.S., 1989. Inversion of canopy reflectance models for estimation of biophysical parameters from reflectance data. In G. Asrar (ed.), Theory and applications of optical remote sensing (205-251).
Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B., 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture, Remote Sensing of Environment 90, 337-352.
Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment 25, 295-309.
Jensen, R.J., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall. 316pp.
Liang, S., 2004. Quantitative Remote Sensing, Wiley. 534pp.
Nemani, R., Pierce, L., Running, S., and Band, L., 1993. Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed leaf area index estimates, International Journal of Remote Sensing, 14, 2519-2534.
Qi, J., Chehbouni, Α., Huete, A.R., Kerr, Y.H., and Sorooshian, S., 1994. A modified soil adjusted vegetation index, Remote Sensing of Environment 48(2), 119-126.
Rahman, M.M., Csaplovics, E., and Koch, B., 2005. An efficient regression strategy for extracting forest biomass information from satellite sensor data, International Journal of Remote Sensing, 26(7), 1511-1519.
Rondeaux, G., Steven, M., and Baret, F., 1996. Optimization of soil-adjusted vegetation indices, Remote Sensing of Environment, 55, 95-107.
Rouse, J.W., Haas, R.H., Schell, J.Α., and Deering, D.W., 1973. Monitoring vegetation systems in the Great Plains with ERTS, 3rd ERTS Symposium, vol. 1, 48-62.
Skianis, G. Aim., Vaiopoulos, D., and Nikolakopoulos, K., 2004a. Assesment of the TVI vegetation index with the aid of probability theory, Proceedings of the l(f International Congress of the Geological Society of Greece, Thessaloniki, vol. 3, 1338-1346.
Skianis, G. Aim., Vaiopoulos, D., and Nikolakopoulos, K, 2004b. A study of the behavior of vegetation index SAVI, based on probability theory, Proceedings of the 7th Panhellenic Congress of the Hellenic Geographical Society, Mytilini, Greece, vol. 1, 41-48.
Spanner, M.A., Pierce, L.L., Peterson, D.L., and Running, S.W., 1990. Remote sensing of temperate coniferous forest leaf area index. The influence of canopy closure understory vegetation and background reflectance, Inernational Journal of Remote Sensing, 11, 95-111.
Spiegel, M.R., 1977. Probability and Statistics, McGraw-Hill New York, ESPI Athens, 384pp.
Vaiopoulos, D., Skianis, G. Aim., and Nikolakopoulos, K, 2004. The contribution of probability theory in assessing the efficiency of two frequently used vegetation indices, International Journal of Remote Sensing 25(20), 4219-4236.
Verhoef, W., 1998. Theory of radiative transfer models applied in optical remote sensing of vegetation canopies, Wageningen: Grafish Service Centrum Van Gils.
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