Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method

Several previous studies have shown that the inclusion of the LST (Land Surface Temperature) parameter to a NDVI (Normalized Difference Vegetation Index) based classification procedure is beneficial to classification accuracy. In this work, the Yearly Land Cover Dynamics (YLCD) approach, which is based on annual behavior of LST and NDVI, has been used to classify an agricultural area into crop types. To this end, a time series of Landsat-5 images for year 2009 of the Barrax (Spain) area has been processed: georeferenciation, destriping and atmospheric correction have been carried out to estimate NDVI and LST time series for year 2009, from which YLCD parameters were estimated. Then, a maximum likelihood classification was carried out on these parameters based on a training dataset obtained from a crop census. This classification has an accuracy of 87% (kappa = 0.85) when crops are subdivided in irrigated and non-irrigated fields, and when cereal crops are aggregated in a single crop, and performs better than a similar classification from Landsat bands only. These results show that a good crop differentiation can be obtained although detailed crop separation may be difficult between similar crops (barley, wheat and oat) due to similar annual NDVI and LST behavior. Therefore, the YLCD approach is suited for vegetation classification at local scale. As regards the assessment of the YLCD approach for classification at regional and global scale, it will be carried out in a further study.