Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping
AbstractUnderstanding the relationship between the size of mangrove structural features and the optimum image pixel size is essential to support effective mapping activities in mangrove environments. This study developed a method to estimate the optimum image pixel size for accurately mapping mangrove features (canopy types and features (gaps, tree crown), community, and cover types) and tested the applicability of the results. Semi-variograms were used to characterize the spatial structure of mangrove vegetation by estimating the size of dominant image features in WorldView-2 imagery resampled over a range of pixel sizes at several mangrove areas in Moreton Bay, Australia. The results show that semi-variograms detected the variations in the structural properties of mangroves in the study area and its forms were controlled by the image pixel size, the spectral-band used, and the spatial characteristics of the scene object, e.g., tree or gap. This information was synthesized to derive the optimum image pixel size for mapping mangrove structural and compositional features at specific spatial scales. Interpretation of semi-variograms combined with field data and visual image interpretation confirms that certain vegetation structural features are detectable at specific scales and can be optimally detected using a specific image pixel size. The analysis results provide a basis for multi-scale mangrove mapping using high spatial resolution image datasets.
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Kamal, M.; Phinn, S.; Johansen, K. Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping. Remote Sens. 2014, 6, 984-1006.
Kamal M, Phinn S, Johansen K. Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping. Remote Sensing. 2014; 6(2):984-1006.Chicago/Turabian Style
Kamal, Muhammad; Phinn, Stuart; Johansen, Kasper. 2014. "Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping." Remote Sens. 6, no. 2: 984-1006.