Geomorphic trajectory and landform analysis using graph theory: A panel data approach to quantitative geomorphology

Abstract

Comparing successive datasets of GIS polygons derived from remote-sensing data is a common approach to quantify morphological change. GIS-derived datasets capture instantaneous observations or “snapshots” of the state of a system at a given time but do not explicitly capture the temporal sequences needed to characterize system processes. Comparisons between these “temporally-naive” datasets can be used to infer properties and trends of the landscape as a whole, but tracking changes in the characteristics of individual landforms (e.g. sandbars, dunes, or other surface features of interest) across snapshots is labor-intensive and infeasible for large or irregular datasets. Using traditional computer-based procedural methods to compare sequences of datasets without knowledge of temporal trajectories introduces several challenges and data artifacts that complicate analysis. We propose a graph-theory approach for processing sequential spatial data to automatically identify and track distinct groups of related landforms or “geomorphic units” across fully or partially overlapping snapshots. This approach allows tracking even in cases where landforms fragment, merge, migrate, or become temporarily obstructed from view. The method promotes new panel data analysis opportunities and overcomes three critical limitations of traditional procedural methods of assessing landscape change from spatial data: (1) it can generate landscape metrics based on geomorphic units, rather than the arbitrary geographic units of the underlying spatial datasets, (2) it distinguishes missing or obstructed observations from changes in the characterization of landforms due to environmental conditions, and (3) it automatically generates panel datasets and discriminates between within-landform change and across-landform variation. The panel datasets can be used to upscale feature-level information to system-level metrics and analysis. Furthermore, a graph-theory approach can yield insight on geomorphic change through analysis of the graph structure, and offers a promising approach for geomorphological analyses which retain information on the spatial configuration of geomorphic units. We demonstrate the method with examples from emergent sandbars on the Missouri River.

The Review of Ecological Network Indicators in Graph Theory Context: 2014-2019

“…One of the tools of ecological network analysis is graph theory. Graph theory greatly facilitates the analysis of functional (Iswoyo, Dariati, Vale, & Bryant, 2018) and structural (Koohafkan & Gibson, 2018) connections of the components of ecological networks. This theory can be used to simplify the analysis of relationships in the study of different animals and different ecosystems.…”

Geochemical Analysis of Metal Minerals on Kuantan Singingi Regency, Riau Province as a Connection part of Bukit Barisan Mountain, West Sumatera, Indonesia

“…3) Geomorphological analysis: Geomorphology is defined as a study that describes landforms and processes and looks for the relationships between landforms and spatial arrangement processes [18]- [23]. The formation of an area's landform is the result of a geomorphological process caused by an endogenous and exogenous force.…”

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

“…False-color Sentinel-2 imagery (bands B11, B8, B4) acquired January 06, 2018 (dry season) and September 18, 2018 (wet season) for the Abra River between (a) Bucay and Carsuan (17 36 0 22.9 00 N, 120 40 0 12.1 00 E) and (b) Luba and Bucay (17 26 0 35.0 00 N, 120 42 0 47.3 00 E). Flow direction is east to west in (a) and south to north in (b) This can potentially misrepresent landscape characteristics when prevailing conditions such as seasonal vegetation or hydrodynamic effects ( Figure 2) influence the geomorphic identification and characterization (Koohafkan & Gibson, 2018), with implications for delineation of channel and flow boundaries (Güneralp, Filippi, & Hales, 2014). These issues raise concerns for the suitability of snapshot analyses of dynamic systems, particularly without explicit consideration for the geomorphic processes in operation, their functioning timescales and the time dependence effects associated with process rate estimation.…”

“…Here it is important to consider what could be absent from these “snapshots” or “endpoints,” which can reveal gross change in river planform and time‐averaged lateral erosion rates, but mask compensatory changes in the intervening period (Boruah, Gilvear, Hunter, & Sharma, 2008; Kondolf & Piégay, 2016). Information on the temporal continuity or discontinuity of the system is missing (Koohafkan & Gibson, 2018). The issue is further complicated where features of interest typically have amorphous boundaries in space and time (Karpatne, Ebert‐Uphoff, Ravela, Babaie, & Kumar, 2019), with fluvial systems adjusting to reflect the complex interplay of nonstationary anthropogenic, sediment, and climatic influences (Slater, Khouakhi, & Wilby, 2019).…”

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