Abstract
In recent years the use of remote sensing imagery to classify and map vegetation over different spatial scales has gained wide acceptance in the research community. Many national and regional datasets have been derived using remote sensing data. However, much of this research was undertaken using multispectral remote sensing datasets. With advances in remote sensing technologies, the use of hyperspectral sensors which produce data at a higher spectral resolution is being investigated. The aim of this study was to compare the classification of selected vegetation types using both hyperspectral and multispectral satellite remote sensing data. Several statistical classifiers including maximum likelihood, minimum distance, mahalanobis distance, spectral angular mapper and parallelepiped methods of classification were used. Classification using mahalanobis distance and maximum likelihood methods with an optimal set of hyperspectral and multispectral bands produced overall accuracies greater than 80%.
Highlights
Sensed data are commonly used for the classification and mapping of vegetation over large spatial scales, replacing traditional classification methods, which require expensive and time-intensive field surveys
The mahalanobis distance and the maximum likelihood methods most accurately classified the vegetation classes used in this study, achieving overall accuracies above 80%
The classification and mapping of the different genera and species selected in this study were strongly affected by seasonal physiological changes in vegetation as illustrated using the multispectral Proba CHRIS remote sensing imagery, the range of spectral bands used in the classification and, the use of different statistical classifier methods
Summary
Sensed data are commonly used for the classification and mapping of vegetation over large spatial scales, replacing traditional classification methods, which require expensive and time-intensive field surveys. Since the early 1960s, multispectral airborne and satellite remote sensing technologies have been used as a common source for the remote classification of vegetation (Landgrebe, 1999). Multispectral remote sensing technologies, in a single observation, collect data from three to six spectral bands from the visible and near-infrared region of the electromagnetic spectrum. This crude spectral categorization of the reflected and emitted energy from the earth is the primary limiting factor of multispectral sensors. Over the past 2 decades, the development of airborne and satellite hyperspectral sensor technologies has overcome the limitations of multispectral sensors. Over the past 2 decades, the development of airborne and satellite hyperspectral sensor technologies has overcome the limitations of multispectral sensors. Govender et al (2007) have reviewed the application of hyperspectral imagery in the classification and mapping of land use and vegetation and, in particular, in water resource studies
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