Wetland Mapping Using Remote Sensing Imagery and ModelMap

1. Wetland Mapping Using Remote Sensing Imagery and ModelMap Xuan Zhu Monash University, Australia

2. Remote Sensing of Wetlands Some wetland mapping studies have successfully utilised supervised rule-based or classification tree-based methods Typical classification tree-based methods include classification tree analysis (CTA), Random Forests (RF) and Stochastic Gradient Boosting (SGB) Large-area satellite remote sensing of wetlands is based on computer-based classification of multispectral image data. Classification techniques generally fall into one of two camps, supervised or unsupervised. Some wetland mapping studies have successfully utilised supervised rule-based or classification tree-based methods with the reported overall accuracy of over 70% (Sader et al. 1995; Baker et al., 2006). Typical classification tree-based methods include classification tree analysis (CTA), Random Forests (RF) and Stochastic Gradient Boosting (SGB). Large-area satellite remote sensing of wetlands is based on computer-based classification of multispectral image data. Classification techniques generally fall into one of two camps, supervised or unsupervised. Some wetland mapping studies have successfully utilised supervised rule-based or classification tree-based methods with the reported overall accuracy of over 70% (Sader et al. 1995; Baker et al., 2006). Typical classification tree-based methods include classification tree analysis (CTA), Random Forests (RF) and Stochastic Gradient Boosting (SGB).

3. Classification Tree Analysis To determine a set of if-then logical (split) conditions that permit accurate prediction or classification of cases via tree-building algorithms In image classification, recursively parsing the training observations in a form of binary partitioning based on the values of the selected explanatory variables such as spectral responses and ancillary data