Regional landscape mapping through a method of chain standardization of Landsat images

• Regional landscape analysis is made by developing a novel method of imagery chain standardization (ICS).
• Images are standardized at the level of a reference image located around the center of a large area by using ICS.
• Landscape visualization and land cover classification of 66-scene images for Louisiana and Arkansas are processed and assessed.
• The ICS provides a simple and promising method for landscape scientists to do regional landscape ecological modeling and analysis.

Large area landscape analysis and modeling are often based on the data of land cover categories, which represent the interface between natural conditions and human impacts over time and across space. This study proposes a method of imagery chain standardization, which is straightforward and simple for landscape scientists to do large area land cover analysis with commonly available Landsat data. Unlike image normalization methods using complicated atmospheric correction and radiometric conservation, chain standardization proposes that for a large area, one Landsat image locating around the center of this area is used as the reference image. The reference image is first used to standardize its nearest neighboring images by applying a simple linear regression equation without intercept, which is developed based on the overlap imagery areas. Likewise, the firstly standardized images are used as new reference images to standardize their neighboring images further away from the study area center, and till all images covering a study area are transformed to the standard of the reference image. We test imagery chain standardization by using support vector machine classifier to classify the land cover of the states of Louisiana and Arkansas, USA, into agriculture, barren lands, forest, urban, water, and wetlands. 66-scene commonly available Landsat TM images are standardized, which results in promising mosaics and classification performance with overall accuracy values about 90% and Kappa coefficients close to 1. The imagery chain standardization is a promising method for landscape scientists to do regional landscape mapping and modeling.