Improved Change Detection of Forests Using Landsat TM and ETM data

Autor: Vijay Bhagat, Kishor R. Sonawane
Rok vydání: 2016
Předmět:
Zdroj: Remote Sensing of Land. 1:18-40
DOI: 10.21523/gcj1.17010102
Popis: Landsat TM and ETM+ datasets are useful for forest change detection (FCD) at good accuracy level. Classified forest maps have been prepared using NDVI calculated from Landsat-5 TM (2009) and Landsat-7 ETM+ (2002) datasets for FCD using post-classification technique. About 58.59% of reviewed area shows positive changes, 33.69% no-changes and 7.72% negative changes with 77.84% accuracy. This accuracy insists limitations of present FCD analysis. Therefore, improved post-classification technique was formulated for precise FCD using field data and statistical techniques. Information about stable land surface (water bodies, rocky lands, deep forests, etc.) was used for normalisation of exaggerated reflectance in vegetation indices i.e. greenness. About 70.08% land estimated using second approach shows stable vegetation, 23.59% positive changes and 6.33% negative changes. Higher accuracy (95.21%) itself shows improvement in FCD technique and efficient applicability for sustainable land management.
Databáze: OpenAIRE