Abstract:
Characterisation and mapping of land cover/land use within forest areas over
long-multitemporal intervals is a complex task. This complexity is mainly due to
the location and extent of such areas and, as a consequence, to the lack of full
continuous cloud-free coverage of those large regions by one single remote
sensing instrument. In order to provide improved long-multitemporal forest
change detection using Landsat MSS and ETM in part of Mt. Kenya
rainforest, and to develop a model for forest change monitoring, wavelet
transforms analysis was tested against the ISOCLUS algorithm for the derivation
of changes in natural forest cover, as determined using four simple ratio-based
Vegetation Indices: Simple Ratio (SR), Normalised Difference Vegetation Index
(NDVI), Renormalised Difference Vegetation Index (RDVI) and modified simple
ratio (MSR). Based on statistical and empirical accuracy assessments, RDVI
presented the optimal index for the case study. The overall accuracy statistic of
the wavelet derived change/no-change was used to rank the performances of the
indices as: RDVI (91.68%), MSR (82.55%), NDVI (79.73%) and SR (65.34%).
The integrated discrete wavelet transform ISOCLUS (DWT ISOCLUS) result
was 42.65% higher than the independent ISOCLUS approach in mapping the
change/no-change information. The methodology suggested in this study presents
a cost-effective and practical method to detect land-cover changes in support of
decision-making for updating forest databases, and for long-term monitoring of
vegetation changes from multisensor imagery. The current research contributes to
Digital Earth with regards to geo-data acquisition, data mining and representa-
tion of one forest systems.
