Multivariate Differencing Techniques for Land Cover Change Detection: the Normalized Difference Reflectance Approach

The importance of the dynamic side of natural and man-made phenomena has become an urgent need when trying to mitigate the human impact on environment. Remote Sensing is one of the most effective way to quantify and map the changes of environmental conditions on our planet: the tools used for this purpose are called Change Detection Techniques. Techniques among which an important role is played by those methodologies based on multi-spectral remote sensing data and exploiting multivariate analysis derived methodologies, also demonstrating their capabilities through some test cases, covering flood events and urban growth studies. Multi-temporal and multi-spectral techniques for Change Detection exist in a wide variety of approaches, often far too sector oriented and not straightforward. Compression and decorrelation techniques, on the other side, tend not to exploit the whole spectral content of remotely sensed data. The Normalized Difference Reflectance (NDR) here introduced is a general approach for bi-temporal land cover change mapping and detection that exploits the whole spectral capabilities of panchromatic, multi-spectral or hyper-spectral images. NDR is a general and simple measure that can be used in the frame of what are called Normalized Difference Change Detection Techniques (NDCD), which starts using as a input the NDR derived results. This Chapter includes a large test case which is a good benchmark for NDR approach, using Minimum Noise Fraction implementation of NDCD for mapping Hurricane Katrina aftermaths over the city of New Orleans, U.S., thus fusing together urban and flood change applications. The purpose of the chapter is to give an overview of multivariate difference-based techniques for land cover change mapping using multispectral remote sensing data, and to introduce and demonstrate the Normalized Difference Reflectance approach in the frame of Normalized Difference Change Detection techniques. Two examples of NDCD results are given as a complement to theoretical aspects of the methodology, and an application study has been used as benchmark for the technique performances evaluation, in comparison with other established Change Detection techniques.