A genetic programming approach to estimate vegetation cover in the context of soil erosion assessment

Photogrammetric Engineering and Remote Sensing

Volumn 77, Issue 4, 2011, Pages 363-376

  Puente, Cesar ^a   Olague, Gustavo ^a   Smith, Stephen V ^a   Bullock, Stephen H ^a   Hinojosa Corona, Alejandro ^a   González Botello, Miguel A ^b  

^a CENTRO DE INVESTIGACIÓN CIENTÍFICA Y DE EDUCACIÓN SUPERIOR DE ENSENADA   (Mexico)

^b Facultad de Ciencias   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

EROSION; GENETIC ALGORITHMS; ITERATIVE METHODS; SATELLITE IMAGERY; SEDIMENT TRANSPORT; SOILS; VEGETATION;

COMPOSITE OPERATORS; EFFICIENCY AND RELIABILITY; EROSION MODELS; REVISED UNIVERSAL SOIL LOSS EQUATIONS; SATELLITE INFORMATION; SPECTRAL CHANNELS; VEGETATION COVER; VEGETATION INDEX;

GENETIC PROGRAMMING;

ASSESSMENT METHOD; CARBON; CORRELATION; ESTIMATION METHOD; EXPERIMENTAL STUDY; GENETIC ANALYSIS; NDVI; NUMERICAL MODEL; OPTIMIZATION; SATELLITE IMAGERY; SOIL EROSION; VEGETATION COVER;

EID: 79957756144     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.77.4.363     Document Type: Article

References (45)

1. Agnelli, D., A. Bollini, and L. Lombardi, 2002. Image classification: An evolutionary approach, Pattern Recognition Letters, 23:303-309.
2. Asis, A.M., and K. Omasa, 2007. Estimation of vegetation parameter for modeling soil erosion using linear spectral mixture analysis of Landsat ETM data, ISPRS Journal of Photogrammetry and Remote Sensing, 62:309-324.
3. Asner, G.P., and D.B. Lobell, 2000. A biogeophysical approach for automated SWIR unmixing of soils and vegetation, Remote Sensing of Environment, 74:99-112
4. Bauer, H.L., 1943. The statistical analysis of chaparral and other plant communities by means of transect samples, Ecology, 24:45-60.
5. Bhanu, B., and Y. Lin, 2003. Genetic algorithm based feature selection for target detection in SAR images, Image and Vision Computing, 21:591-608.
6. Brady, N.C., and R.R. Weil, 2008. The Nature and Properties of Soils, Fourth edition, Pearson Prentice Hall, New Jersey.
7. Chander, G., and B. Markham, 2003. Revised LANDSAT-5 TM radiometric calibration procedures and post-calibration dynamic ranges, IEEE Transactions on Geoscience and Remote Sensing 41(3):2674-2677.
8. Crist, E.P. and R.C. Cicone, 1984. Application of the tasseled cap concept to simulated Thematic Mapper data, Photogrammetric Engineering & Remote Sensing, 50(3):343-352.
9. Daida, J.M., J. Hommes, T. Bersano-Begey, S. Ross, and J. Vesecky, 1996. Algorithm Discovery using the Genetic Programming Paradigm: Extracting Low-contrast Curvilinear Features from SAR Images of Arctic Ice, MIT Press, Cambridge, Massachusetts.
10. Folly, A., M.C. Bronsveld, and M. Clavaux, 1996. A knowledgebased approach for C-factor mapping in Spain using Landsat TM and GIS, International Journal of Remote Sensing, 17(2):2401-2415.
11. Gao, B., 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, 58:257-266.
12. Guerschman, J.P., M.J. Hill, L.J. Renzullo, D.J. Barrett, A.S. Marks, and E.J. Botha, 2009. Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors, Remote Sensing of Environment, 113:928-945.
13. Harvey, N., J. Thelier, S. Brumby, S. Perkins, J. Szymanski, J. Bloch, R. Porter, M. Galassi, and A. Young, 2002. Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction, IEEE Transactions on Geoscience and Remote Sensing 40(2):393-404.
14. Howard, D., S.C. Roberts, and R. Brankin, 1999. Target detection in SAR Imagery by Genetic Programming, Advances in Engineering Software, 30:303-311.
15. Huete, A.R., 1988. A Soil-Adjusted Vegetation Index (SAVI), Remote Sensing of Environment, 25:295-309.
16. Inoue, Y., J. Peñuelas, A. Miyata, and M. Mano, 2008. Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice, Remote Sensing of Environment, 112(1):156-172.
17. Inoue, Y., and A. Olioso, 2006. Estimating dynamics of ecosystem CO2 flux and biomass production in agricultural field by synergy of process model and remotely sensed signature, Journal of Geophysical Research D, Atmospheres, 111:D24S91, doi:10.1029/2006JD007469.
18. de Jong, S.M., 1994. Applications of Reflective Remote Sensing for Land Degradation Studies in a Mediterranean Environment, Ph.D. dissertation, Universiteit Utrecht, Nederlands.
19. Jordan, C.F., 1969. Derivation of leaf area index from quality of light on the forest floor, Ecology, 50:663-666.
20. Khana, S., A. Palacios-Orueta, M.L. Whiting, S.L. Ustin, D. Riaño, and J. Litago, 2007. Development of angle indexes for soil moisture estimation, dry matter detection and land-cover discrimination, Remote Sensing of Environment, 109:154-165.
21. Koza, J.R., 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press Cambridge, Massachusetts.
22. Koza, J.R., 2010. Human-competitive results produced by genetic programming, Genetic Programming and Evolvable Machines, 11(3-4):251-284.
23. Lin, C.Y., W.T. Lin, and W.C. Chou, 2002. Soil erosion prediction and sediment yield estimation: The Taiwan experience, Soil and Tillage Research, 68(2):143-152.
24. Lu, H., I.P. Prosser, C.J. Moran, J.C. Gallant, G. Priestly, and J.G. Stevenson, 2003. Predicting sheetwash and rill erosion over Australian continent, Australian Journal of Remote Sensing, 41(6):1037-1062.
25. Makkeasorn, A., N. Chang, and J. Li, 2009. Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed, Journal of Environment Management, 90:1069-1080.
26. Matsuchita, B., W. Yang, J. Chen, Y. Onda, and G. Qiu, 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density cypress forest, Sensors, 7:2636-2651.
27. Moran, M.S., Y. Inoue, and E.M. Barnes, 1997. Opportunities and limitations for image-based remote sensing in precision crop management, Remote Sensing of Environment, 61:319-346.
28. Olague, G., 2002. Automated photogrammetric network design using genetic algorithms, Photogrammetric Engineering & Remote Sensing, 68(5):423-431.
29. Pinty, B., and M.M. Verstraete, 1992. GEMI: A non-linear index to monitor global vegetation from Satellites, Vegetation, 101:15-20.
30. Poli, R., W. Langdon, and N. Freitag, 2008. A field guide to genetic programming, URL: http://www.gp-field-guide.org.uk (last date accessed: 21 January 2011).
31. Rauss, P., J. Daida, and S. Chaudhary, 2000 Classification of spectral imagery using genetic programming, Proceedings of Genetic and Evolutionary Computation Conference, 08-12 July, Las Vegas, Nevada, pp. 726-733.
32. Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder, 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation, Agricultural Handbook, Number 703, U.S. Government Printing Office, Washington, D.C.
33. Ross, B.J., A.G. Gualtieri, F. Fueten, and P. Budkewitsch, 2005. Hyperspectral image analysis using genetic programming, Applied Soft Computing, 5:147-156.
34. Rouse, J.W., R.H. Haas, J.A. Schell, and D.W. Deering, 1973. Monitoring vegetation systems in the great plains with ERTS, Proceedings of the Third ERTS Symposium, NASA SP-351, 1:309-317.
35. Ryerson, R.A. (editor), 1999. Manual of Remote Sensing Third edition, Volume 3: Remote Sensing for the Earth Sciences, American Society for Photogrammetry and Remote Sensing, John Wiley & Sons.
36. Sabins, F.F., 1997. Remote Sensing: Principles and Interpretation, Third edition, Freeman, New York, 496 p.
37. Silva, S., 2007 GPLAB - A GP Toolbox for MATLAB, URL: http://gplab.sourceforge.net/index.html (last date accessed: 21 January 2011).
38. Smith, S. V., S.H. Bullock, A. Hinojosa-Corona, E. Franco-Viscaíno, M. Escoto-Rodríguez, T.G. Kretzschmar, L.M. Farfan, and J.M. Salazar-Cesena, 2007. Soil erosion and significance for carbon fluxes in a mountainous Mediterranean-climate watershed, Ecological Applications, 17(5):1379-1387.
39. Streck, N.A., D. Rundquist, and J. Connot, 2002. Estimating residual wheat dry matter from remote sensing measurements, Photogrammetric Engineering & Remote Sensing, 68(11):1193-1201.
40. Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 8:127-150.
41. Wang, G., S. Wente, G. Gertner, and A. Anderson, 2002. Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper images, International Journal of Remote Sensing, 23(18):3649-3667.
42. Weltz, M.A., K.G. Renard, and J.R. Simanton, 1987. Revised universal soil loss equation for western rangeland, Proceedings of the Symposium of Strategies for Classification and Management of Native Vegetation for Food Production in Arid Zones, USDA-GTR, RM-150, Tucson, Arizona, pp. 104-111.
43. Wischmeier, W.H., and D.D. Smith, 1978. Predicting Rainfall Erosion Losses: A Guide to Conservation Planning, Agricultural Handbook, Number 537, U.S. Government Printing Office, Washington, D.C.
44. Vincent, R.K., 1997. Fundamentals of Geological and Environmental Remote Sensing, Prentice Hall, Upper Saddle River, New Jersey, 370 p.
45. Zippin, D.B., and J.M. Vanderwier, 1994. Scrub community descriptions of the Baja California Peninsula, Mexico, Madroño, 41:85-119.