Wavelet analysis is chiefly due to the‘adaptive feature’and‘mathematical microtelescope feature’. The 2-D continuous wavelet transform (CWT) is a powerful new tool and has been applied to a number of problems such as astrophysics

aeromagnetic processing

seismic and gravity. It is also used in remote sensing image analysis. This paper focuses on multi-scale analysis at NOAA/AVHRRthermal data (Channels 4 when present). The approach is using an isotropic 2DMexican hatwavelet(DOG m=2)

and studied each componentwithmulti-scale matched onmulti-date data againstthe distributionmap of land-cover classification

which is made up to each pixel on NOAA/AVHRR image withTM image supported

to reveal 2D signals in the temporal variation and spatial patterns. In aword

the result shows the information abouttype of land cover classification and relation

location

and shape in its in small scale as micro-scale analysis and emphasize terraqueous variance by physiognomy

the strengths and features of its trend and structure in large scale as macro-scale observer. The variance of coefficient of different land cover types and the zero-crossing variance of coefficient with scale in 2D CWT discovers the power of signal and the correlation.