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Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited.
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Evaluating Landscape Complexity and the Contribution of Non‐Locality to Geomorphometry
Abstract A long‐standing question in geomorphology concerns the extent that statistical models of terrain elevations have adequate characteristics with respect to the known scaling properties of landscapes. In previous work, it has been challenging to ascribe statistical significance to metrics adopted to measure landscape properties. Here, we use a recently developed surrogate data algorithm to generate synthetic surfaces with identical elevation values to the source data set, while also preserving the value of the Hölder exponent at any point (the underpinning characteristic of a multifractal surface). Our primary source data are from a laboratory experiment on landscape evolution. This allows us to examine how the statistical properties of the surfaces evolve through time and the extent to which they depart from the simple (multi)fractal formalisms. We show that there is a strong departure that is driven by the diffusive processes in operation. The number of sub‐basins of a given channel order (for orders sufficiently small relative to the basin order) exhibits a clear increase in complexity after a steady‐state for sediment flux is established. We also study elevation data from Florida and Washington States, where the relative departure from simple multifractality is even more strongly expressed but is similar for two very different locations. Our results show that at the very least, the minimum complexity for a stochastic model for terrain statistics with appropriate geomorphic scalings needs to incorporate a conditioning between the pointwise Hölder exponents and elevation.
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- PAR ID:
- 10387030
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Earth Surface
- Volume:
- 126
- Issue:
- 4
- ISSN:
- 2169-9003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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