author =   {Eric A. Lehmann and Peter A. Caccetta and Zheng-Shu Zhou and Stephen J. McNeill and Xiaoliang Wu and Anthea L. Mitchell},
  title =    {Joint processing of Landsat and ALOS-PALSAR data for forest mapping and monitoring},
  journal =  {IEEE Transactions on Geoscience and Remote Sensing},
  volume =   {50},
  number =   {1},
  pages =    {55--67},
  month =    {January},
  year =     {2012},
  abstract = {Recent technological advances in the field of radar remote sensing have allowed the deployment of an increasing number of new satellite sensors. These provide an important source of Earth observation data which adds to the currently existing optical datasets. In parallel, the development of robust methods for global forest monitoring and mapping is becoming increasingly important. As a consequence, there is significant interest in the development of global monitoring systems that are able to take advantage of the potential synergies and complementary nature of optical and radar data. This paper proposes an approach for the combined processing of Landsat and ALOS-PALSAR data for the purpose of forest mapping and monitoring. This is achieved by incorporating the PALSAR data into an existing, operational Landsat-based processing system. Using a directed discriminant technique, a probability map of forest presence/absence is first generated from the PALSAR imagery. This SAR classification data is then combined with a time-series of similar Landsat-based maps within a Bayesian multi-temporal processing framework, leading to the production of a time series of joint radar–optical maps of forest extents. This approach is applied and evaluated over a pilot study area in north-eastern Tasmania, Australia. Experimental outcomes of the proposed joint processing framework are provided, demonstrating its potential for the integration of different types of remote sensing data for forest monitoring purposes.}