Land cover maps are tools that provide vital information about the Earth's land use and cover patterns. They aid policy development, urban planning, and forest and agricultural monitoring.[1][2]
The systematic mapping of land cover patterns, including change detection, often follows two main approaches:
Image pre-processing is normally done through radiometric corrections, while image processing involves the application of either unsupervised or supervised classifications and vegetation indices quantification for land cover map production.
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.
Several machine learning algorithms have been developed for supervised classification.
Unsupervised classification is a system of classification in which single or groups of pixels are automatically classified by the software without the user applying signature files or training data. However, the user defines the number of classes for which the computer will automatically generate by grouping similar pixels into a single category using a clustering algorithm. This system of classification is mostly used in areas with no field observations or prior knowledge on the available land cover types.
Vegetation indices classification is a system in which two or more spectral bands are combined through defined statistical algorithms to reflect the spatial properties of a vegetation cover.
Most of these indices make use of the relationship between red and near-infrared (NIR) bands of satellite images to generate vegetation properties. Several vegetation indices have been developed; scientists apply these via remote sensing to effectively classify forest cover and land use patterns.
These spectral indices use two or more bands to accurately acquire surface reflectance of land features, thereby improving classification accuracy.[18][19]
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