Regional forest and non-forest mapping using Envisat ASAR data

F. Ling, Z.Y. Li, E.X. Chen, Y.P. Huang, X. Tian, C. Schmullius, R. Leiterer, J. Reiche, S. Maurizio

Research output: Contribution to journalArticleAcademicpeer-review


Envisat Advanced Synthetic Aperture Radar (ASAR) dual-polarization data are shown to be effective for regional forest monitoring. To this scope, an automatic SAR image preprocessing procedure was developed using SRTM DEM and Landsat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-forest classif ication method was then proposed based on the HH (horizontal transmit and horizontal receive) to HV (horizontal transmit and vertical receive) polarization intensity ratio and HV images of ASAR data at single acquisition time in winter. The developed method was applied to forest and non-forest mapping in Northeast China. The overall accuracy, the user’s accuracy and the producer’s accuracy of forest were 83.7%, 85.6% and 75.7%, respectively. These results indicate that the proposed method is promising for operational forest mapping at regional scale.
Original languageEnglish
Pages (from-to)1101-1114
JournalJournal of Remote Sensing (China)
Issue number5
Publication statusPublished - 2012

Fingerprint Dive into the research topics of 'Regional forest and non-forest mapping using Envisat ASAR data'. Together they form a unique fingerprint.

Cite this