Volume XXXIX-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 335-340, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-335-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 335-340, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-335-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Aug 2012

01 Aug 2012

SEMI-AUTOMATED CLOUD/SHADOW REMOVAL AND LAND COVER CHANGE DETECTION USING SATELLITE IMAGERY

A. K. Sah, B. P. Sah, K. Honji, N. Kubo, and S. Senthil A. K. Sah et al.
  • PASCO Corporation, 1-1-2 Higashiyama, Meguro-ku, Tokyo 153-0043, Japan

Keywords: Satellite, Forestry, Land Cover, Classification, Change Detection, Semi-automation

Abstract. Multi-platform/sensor and multi-temporal satellite data facilitates analysis of successive change/monitoring over the longer period and there by forest biomass helping REDD mechanism. The historical archive satellite imagery, specifically Landsat, can play an important role for historical trend analysis of forest cover change at national level. Whereas the fresh high resolution satellite, such as ALOS, imagery can be used for detailed analysis of present forest cover status. ALOS satellite imagery is most suitable as it offers data with optical (AVNIR-2) as well as SAR (PALSAR) sensors. AVNIR-2 providing data in multispectral modes play due role in extracting forest information.

In this study, a semi-automated approach has been devised for cloud/shadow and haze removal and land cover change detection. Cloud/shadow pixels are replaced by free pixels of same image with the help of PALSAR image. The tracking of pixel based land cover change for the 1995-2009 period in combination of Landsat and latest ALOS data from its AVNIR-2 for the tropical rain forest area has been carried out using Decision Tree Classifiers followed by un-supervised classification. As threshold for tree classifier, criteria of NDVI refined by reflectance value has been employed. The result shows all pixels have been successfully registered to the pre-defined 6 categories; in accordance with IPCC definition; of land cover types with an overall accuracy 80 percent.