Abstract
Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution. Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu’s method followed by the random walker algorithm. Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas. Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop.
Highlights
Under the limitations of long-term available data and computation abilities of personal computers, image segmentation optimized for delineation of tree density demonstrated in this study provided an alternative framework for vegetation mapping, instead of data intensive analyses
Because species associations are correlated with forest density in the study area, image segmentation optimized for delineation of tree density could be labeled by specific species associations, even though the third-step procedure of detailed species segmentations did not work well at identifying individual species segmentation, due to coarse spatial resolutions and less attributes
With the evaluation by the Kruskal-Wallis test, this step, especially the aspect factor was proved effective to reduce image variances in local digital number (DN) and texture and more importantly, characterize similar components of tree covers within strata
Summary
Vegetation mapping is required for biological conservation and forest inventory; especially time series mapping is commonly used to detect transformations of species or suitable habitats (e.g., [1,2,3]). Species distribution models (SDMs, called habitat suitability models), which correlate environment variables with species sampling data to map species occurrences [6], have been used for vegetation mapping. SDMs are viewed as static and equilibrium models capturing species-environment relations at large scales, but ignoring dynamic biological interactions, such as dispersal, migration, facilitation, competition, mutualism, and predation at local scales [8]. Mapping species directly using very high resolution imagery (1 to 2 m), and tracking vegetation transformations over time through repeated mapping provided the potential for building more dynamic vegetation mapping
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