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Remote Sensing Techniques for Accurate and Consistent Detection of Small-Scale Changes in a Tropical Forest:Exploring Details of Forest Cover Dynamics Using Multi-Temporal Landat Imagery
Remotely sensed information plays key role in detection and monitoring forest cover changes. While several advanced image analysis techniques are developed and described in the literature, remote sensing of forest cover changes often suffers from lack of accuracy and consistency of estimates. In this study a sequential combination of decision tree and machine learning algorithms has been applied to improve accuracies. Six Landsat images were acquired at approximately 5 years intervals between years 1986 and 2012. First, images were classified into vegetation and novegetation categories based on threshold value obtained from kernel density distribution of Normalized Difference Vegetation Index (NDVI). Non-vegetated categories were classified into barren and other cover types applying a bareness index. Support Vector Machine (SVM) was used to further classify forest into dense, medium and low canopy (>30%, 10%-30% and <10% canopy) classes. Using this approach, minimum and maximum overall accuracy of 86.3% and 92.9%, and kappa coefficients of 0.82 and 0.90 were, respectively, achieved. Between years 1986 and 2012, annual losses of dense forest (canopy cover of >30%) was 1.1%. During the same time span, about 14% net gain in dense forest was shown in steep sloping terrains. However, magnitude of losses, gains and persistence of forest cover varied in time and spaces. Results presented in this study are useful for planning and implementing locally appropriate management interventions and policy strategies in order to halt the rapid rate of forest destruction in Belete and other similar forest ecosystems.
Accuracy, Classification, Forest Cover Change, Landsat, Remote Sensing, Land Use, Support Vector Machine.
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