- author = "Cesar Puente and Gustavo Olague and Stephen V. Smith and Stephen H. Bullock and Alejandro Hinojosa-Corona and Miguel Angel Gonzalez-Botello",
- title = "A Genetic Programming Approach to Estimate Vegetation Cover in the Context of Soil Erosion Assessment",
- journal = "Photogrammetric Engineering \& Remote Sensing",
- year = "2011",
- volume = "77",
- number = "4",
- pages = "363--376",
- month = apr,
- email = "olague@cicese.mx",
- keywords = "genetic algorithms, genetic programming, vegetation indices, remote sensing",
-
URL = "
http://cienciascomp.cicese.mx/evovision/olague-pers-2011.pdf",
-
URL = "
http://digital.ipcprintservices.com/publication/?i=65049&p=65",
- size = "14 pages",
- abstract = "This work describes a genetic programming (GP) approach that creates vegetation indices (VI's) to automatically detect the sum of healthy, dry, and dead vegetation. Nowadays, it is acknowledged that VI's are the most popular method for extracting vegetation information from satellite imagery. In particular, erosion models like the ``Revised Universal Soil Loss Equation'' (RUSLE) can use VI's as input to measure the effects of the RUSLE soil cover factor (C). However, the results are generally incomplete, because most indices recognise only healthy vegetation. The aim of this study is to devise a novel approach for designing new VI's that are better - correlated with C, using field and satellite information. Our approach consists on stating the problem in terms of optimisation through GP learning, building novel indices by iteratively recombining a set of numerical operators and spectral channels until the best composite operator is found. Experimental results illustrate the efficiency and reliability of our approach in contrast with traditional indices like those of the NDVI and SAVI family. This study provides evidence that similar problems related to soil erosion assessment could be analysed with our proposed methodology.",
-
notes = "'improved correlation coefficients with C factor field
samples, and actually it gives reasonable better
performance than widely-used indices such as NDVI and
an alternative to RVI, ... RVI4.'
Todos Santos watershed in Mexico.
0099-1112/11/7704 0363/$3.00/0",
