Gatha Cognition®
Perception, Learning and Reasoning

Article Title :

Improved Change Detection of Forests Using Landsat TM and ETM plus data

Remote Sensing of Land

1 (2017)

1

18-40

Landsat-5 TM , Landsat-7 ETM+ , NDVI , LST , Tasseled Cap Coefficient , Forest change detection , Stable land objects

Gatha Cognition Free Publication
Cross Referance

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.

Reliable change detection of forest using remote sensing data is challenging task.

Ground information about stable land objects is useful for correction of target image.

Statistical approach is useful for robust correction and estimations.

Improved change detection technique for forest achieves precise estimations.

The technique can be useful for precise change detection of land.

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