Abstract
Today the most popular method for the extraction of vegetation information from remote sensing data is through vegetation indices. In particular, erosion models are based on vegetation indices that are used to estimate the “cover factor” (C) defined by healthy, dry, or dead vegetation in a popular soil erosion model named RUSLE, (“Revised Universal Soil Loss Equation”). Several works correlate vegetation indices with C in order to characterize a broad area. However, the results are in general not suitable because most indices focus only on healthy vegetation. The aim of this study is to devise a new approach that automatically creates vegetation indices that include dry and dead plants besides healthy vegetation. For this task we propose a novel methodology based on Genetic Programming (GP) as summarized below. First, the problem is posed as a search problem where the objective is to find the index that correlates best with on field C factor data. Then, new indices are built using GP working on a set of numerical operators and bands until the best composite index is found. In this way, GP was able to develop several new indices that are better correlated compared to traditional indices such as NDVI and SAVI family. It is concluded with a real world example that it is viable to synthesize indices that are optimally correlated with the C factor using this methodology. This gives us confidence that the method could be applied in soil erosion assessment.
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References
Brady, N.C., Weil, R.R.: The Nature and Properties of Soils, 4th edn. Pearson Prentice Hall, New Jersey (2008)
Ryerson, R.A. (ed.): Manual of Remote Sensing, 3rd edn., vol. 3. John Wiley & Sons in co-operation with the ASPRS, USA (1999)
de Jong, S.M.: Applications of Reflective Remote Sensing for Land Degradation Studies in a Mediterranean Environment. Universiteit Utrecht, Utrecht, Nederlands (1994)
Smith, S.V., Bullock, S.H., Hinojosa-Corona, A., Franco-Viscaíno, E., Escoto-Rodríguez, M., Kretzschmar, T.G., Farfan, L.M., Salazar-Cesena, J.M.: Soil Erosion and Significance for Carbon Fluxes in a Mountainous Mediterranean-Climate Watershed. Ecol. App. 17(5), 1379–1387 (2007)
Asis, A.M., Omasa, K.: Estimation of Vegetation Parameter for Modeling Soil Erosion using Linear Spectral Mixture Analysis of Landsat ETM data. ISPRS J. of Phot. & R. S. 62, 309–324 (2007)
Wischmeier, W.H., Smith, D.D.: Predicting Rainfall Erosion Losses: A Guide to Conservation Planning. Agricultural Handbook Number, vol. 537. Government Printing Office, Washington (1978)
Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K., Yoder, D.C.: Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation. Agricultural Handbook Number, vol. 703. Government Printing Office, Washington (1997)
Wang, G., Wente, S., Gertner, G.Z., Anderson, A.: Improvement in Mapping Vegetation Cover Factor for the Universal Soil Loss Equation by Geostatistical Methods with Landsat Thematic Mapper Images. Int. J. of R. S. 23(18), 3649–3667 (2002)
Gilabert, M.A., Garcia-Haro, F.J., Melia, J.: A mixture modeling approach to estimate vegetation parameters for heterogeneous canopies in remote sensing. R. S. of Env. 72(3), 328–345 (2000)
Ray, T.W.: A FAQ on Vegetation in Remote Sensing, ftp://kepler.gps.caltech.edu/pub/terrill/rsvegfaq.txt
Zhao, D., Huang, L., Li, J., Qi, J.: A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy. ISPRS J. of Phot. & R. S. 62, 25–33 (2007)
Matsuchita, B., Yang, W., Chen, J., Onda, Y., Qiu, G.: Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects. Sensors 7, 2636–2651 (2007)
Khana, S., Palacios-Orueta, A., Whiting, M.L., Ustin, S.L., Riaño, D., Litago, J.: Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination. R. S. of Env. 109, 154–165 (2007)
Poli, R., Langdon, W., McPhee, N.F.: A field guide to genetic programming. Published via and freely (2008), http://lulu.com , http://www.gp-field-guide.org.uk
Silva, S.: GPLAB. A Genetic Programming Toolbox for MATLAB (2007), http://gplab.sourceforge.net/index.html
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Puente, C., Olague, G., Smith, S.V., Bullock, S.H., González-Botello, M.A., Hinojosa-Corona, A. (2009). A Novel GP Approach to Synthesize Vegetation Indices for Soil Erosion Assessment. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2009. Lecture Notes in Computer Science, vol 5484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01129-0_42
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DOI: https://doi.org/10.1007/978-3-642-01129-0_42
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