C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
No free lunch for cross-validation
Neural Computation
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Forest succession is an important ecological process that has profound biophysical, biological and biogeochemical implications in terrestrial ecosystems. Therefore, information on forest successional stages over an extensive forested landscape is crucial for us to understand ecosystem processes, such as carbon assimilation and energy interception. This study explored the potential of using Forest Inventory and Analysis (FIA) plot data to extract forest successional stage information from remotely sensed imagery with three widely used predictive models, linear regression (LR), decision trees (DTs) and neural networks (NNs). The predictive results in this study agree with previous findings that multitemporal Landsat Thematic Mapper (TM) imagery can improve the accuracy of forest successional stage prediction compared to models using a single image. Because of the overlap of spectral signatures of forests in different successional stages, it is difficult to accurately separate forest successional stages into more than three broad age classes (young, mature and old) with reasonable accuracy based on the age information of FIA plots and the spectral data of the plots from Landsat TM imagery. Given the mixed spectral response of forest age classes, new approaches need to be explored to improve the prediction of forest successional stages using FIA data.