Abstract
Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG) algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time.
Highlights
The areal extents and distributions of different land cover types play a key role in many of the ecological and biophysical processes operating within the Earth system, including the absorption of solar radiation by the Earth surface [1], partitioning of absorbed radiation to latent and sensible heat fluxes [2], hydrologic processes such as infiltration and overland flow [3], biodiversity [4], and the movement of organisms within and among landscapes [5]
AASG1 and AASG2 were generally successful in reproducing the land cover patterns of the National Land Cover Database (NLCD) in both 2006 (Figure 4) and 2011 (Figure 5)
While we explored a simple approach for generating class-specific stable site thresholding parameters, future refinements of adaptive signature generalization (AASG) could implement approaches that optimize the selection of stable sites to maximize agreement with withheld reference data or to minimize overlap of spectral signatures among classes
Summary
The areal extents and distributions of different land cover types play a key role in many of the ecological and biophysical processes operating within the Earth system, including the absorption of solar radiation by the Earth surface [1], partitioning of absorbed radiation to latent and sensible heat fluxes [2], hydrologic processes such as infiltration and overland flow [3], biodiversity [4], and the movement of organisms within and among landscapes [5]. Automated approaches based on signature extension use class spectral libraries to map land cover in multiple images based on a single set of class-specific spectral signatures These approaches attempt to enforce spectral consistency in class definitions between images, but are very sensitive to variation in illumination, sun-sensor geometry, and atmospheric and phenological conditions between images [15,16]. These signature extension approaches depend on time-consuming atmospheric correction procedures as well as anniversary date imagery which may not be available in some regions due to frequent or persistent cloud cover. Unlocking the potential of remote sensing for mapping and monitoring LCLUC hinges upon the development of algorithms that overcome these limitations in a consistent and efficient manner
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