Spaceborne Multispectral Sensors for Vegetation Mapping and Change Analysis
In Comprehensive Everglades Restoration Plan, vegetation maps or Land Cover Land Use data are mainly produced using aerial photography through a manual interpretation procedure. Efforts have been made to apply satellite imagery for vegetation mapping via digital image classification techniques. This chapter introduces an application of Landsat time series data to reveal the vegetation change during the period 1996 to 2016 in Water Conservation Area-2A by developing object-based change analysis techniques and producing more vegetation maps based on the classification system of Vegetation Classification for South Florida Natural Areas. A machine learning classifier support vector machine is then applied to classify the 2003 base image and normalized images, resulting in a time series of vegetation maps from 1996 to 2016. Promising time series vegetation maps were achieved by combining object-based Image Analysis, a machine learning classifier, and the training sample selection approach.