Abstract
Understanding 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.
Highlights
Remote sensing has been used extensively to map and monitor mangrove environments over the past two decades
This study showed that scale-specific, ecologically relevant information on mangroves can be detected using experimental semi-variogram analysis
The results show that there was a gradual loss in mangrove vegetation structural information, as indicated by mangrove features measured, with increasing pixel size
Summary
Remote sensing has been used extensively to map and monitor mangrove environments over the past two decades It offers some key advantages for mangrove studies, including indirect access to mangrove habitats that are usually hard to access [1,2], extrapolation of observation results at specific sample sites over large areas [3], and delivery of data at specific spatial and temporal scales [4]. The central concept of this theory focuses on the differences in structure and process rate between hierarchical levels Based on these differences, ecosystems are viewed as being stratified into discrete levels of interacting subsystems, with attributes and processes occurring at specific spatial and temporal scales [7,10,11,12]. In order to use satellite or airborne image data to extract information at specific scales or features for mangroves, it is essential to understand the control of mangrove spatial structures on their measurement in an image
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