Performance evaluation of three DEM‐based fluvial terrace mapping methods

Hopkins, A. J.; Snyder, Noah P.

doi:10.1002/esp.3922

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…In this regard, researchers such as Austin et al [10] have utilized airborne optical detection and ranging (LiDAR) technology to acquire high-resolution digital elevation models (DEMs) for TRAs. Through an in-depth analysis of terrain indices, including slope, aspect, and curvature, along with other land features, they have inferred and interpreted the spatial distribution and morphological characteristics of terraced fields.…”

Section: Introductionmentioning

confidence: 99%

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

Li,

Tian,

et al. 2023

Sustainability

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The Yellow River Basin (YRB) is a crucial ecological zone and an environmentally vulnerable region in China. Understanding the temporal and spatial trends of terraced-field areas (TRAs) and the factors underlying them in the YRB is essential for improving land use, conserving water resources, promoting biodiversity, and preserving cultural heritage. In this study, we employed machine learning on the Google Earth Engine (GEE) platform to obtain spatial distribution images of TRAs from 1990 to 2020 using Landsat 5 (1990–2010) and Landsat 8 (2015–2020) remote sensing data. The GeoDa software (software version number is 1.20.0.) platform was used for spatial autocorrelation analysis, revealing distinct spatial clustering patterns. Mixed linear and random forest models were constructed to identify the driving force factors behind TRA changes. The research findings reveal that TRAs were primarily concentrated in the upper and middle reaches of the YRB, encompassing provinces such as Shaanxi, Shanxi, Qinghai, and Gansu, with areas exceeding 40,000 km2, whereas other provinces had TRAs of less than 30,000 km2 in total. The TRAs exhibited a relatively stable trend, with provinces such as Gansu, Qinghai, and Shaanxi showing an overall upward trajectory. Conversely, Shanxi and Inner Mongolia demonstrated an overall declining trend. When compared with other provinces, the variations in TRAs in Ningxia, Shandong, Sichuan, and Henan appeared to be more stable. The linear mixed model (LMM) revealed that farmland, shrubs, and grassland had significant positive effects on the TRAs, explaining 41.6% of the variance. The random forest model also indicated positive effects for these factors, with high R2 values of 0.984 and 0.864 for the training and testing sets, respectively, thus outperforming the LMM. The findings of this study can contribute to the restoration of the YRB’s ecosystem and support sustainable development. The insights gained will be valuable for policymaking and decision support in soil and water conservation, agricultural planning, and environmental protection in the region.

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Section: Introductionmentioning

confidence: 99%

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

Li,

Tian,

et al. 2023

Sustainability

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“…al., 2007) that was adapted for GIS (Geographic Information System) (Cooley, 2015), Edge Detection in MATLAB (Hopkins 2014,), TerEx Toolbox (Stout and Belmont, 2013) and the improved Demoulin method by Del Val (Del Val, 2015). The evaluation of results (Hopkins and Snyder, 2016) obtained by three methods (Rahnis Method, Feature Classifi cation Algorithm and the TerEx) has shown that the accuracy of terrace mapping generally decreases in the areas of low relief and that all methods overestimate the terrace area. Advantages of the TerEx Toolbox (Stout andBelmont 2013, Hopkins andSnyder, 2016) include its ability to extract the area of the terraces and the elevation above the river channel segment, applicability in more complex settings and relative accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The evaluation of results (Hopkins and Snyder, 2016) obtained by three methods (Rahnis Method, Feature Classifi cation Algorithm and the TerEx) has shown that the accuracy of terrace mapping generally decreases in the areas of low relief and that all methods overestimate the terrace area. Advantages of the TerEx Toolbox (Stout andBelmont 2013, Hopkins andSnyder, 2016) include its ability to extract the area of the terraces and the elevation above the river channel segment, applicability in more complex settings and relative accuracy. Another advantage of the results obtained by the TerEx extension is that they can be improved through manual editing and fi netuning of input parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Another advantage of the results obtained by the TerEx extension is that they can be improved through manual editing and fi netuning of input parameters. The TerEx extension was chosen for this study since it mapped the river terraces in a way that was GIS accessible, allowing correlation of the obtained results with the available geological maps, and has already been tested in river valleys of various sizes and characteristics (Stout and Belmont, 2013;Hopkins and Snyder, 2016). The aim of the conducted research was to extract the extent of Sava Holocene terraces from the acquired high resolution LiDAR dataset using the TerEx procedure and compare the obtained results to an existing geological map.…”

Section: Introductionmentioning

confidence: 99%

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APPLICATION OF GIS PROCEDURE FOR RIVER TERRACE EXTRACTION FROM A LiDAR- BASED DIGITAL ELEVATION MODEL: THE SAVA RIVER VALLEY NW OF ZAGREB, CROATIA

Trenc¹,

Matoš²,

Velić³

et al. 2019

MGPB

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River terraces and fl oodplains indicate channel-sediment dynamics and represent important landform features, which can be used to untangle the evolutionary history of climate and tectonic conditions in a certain area. With the availability of high-resolution topographic data, it has recently become feasible to apply GIS procedures for the identifi cation of these subtle landform features. In this study, the TerEx Toolbox, a GIS extension for semiautomatic terrace extraction was applied on the LiDAR based DEM of the Sava River, west of Zagreb in Croatia. The aim of this research was to compare results obtained by TerEx to the existing geological map from the 1970s, which shows sediments of the recent fl ow (routinely fl ooded area) and two abandoned Holocene river terraces in the study area. The TerEx output was validated using hillshaded DEM, a historical map of the study area, orthophoto imagery, topographic cross sections and fi eld investigations. TerEx has successfully extracted Holocene terraces and risers shown on the geological map but in some parts of the study area, their position and extent was diff erent. The presented study shows that results obtained by TerEx, in particular if used in combination with other data sources, can eff ectively focus fi eldwork and contribute to new unbiased interpretation of terrain morphology.

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“…However, little is known about the magnitude and return frequencies of flows that shape floodplains, and how we may distinguish them from terraces directly from DEM data. Existing approaches based on the geometric features of these flat areas, such as elevations and slopes, are inadequate (Hopkins and Snyder, ). Our hypothesis serves as a basis for addressing these two questions simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Hydrogeomorphological differentiation between floodplains and terraces

Yan

Iwasaki

Stumpf

et al. 2017

Earth Surf Processes Landf

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Floodplains and terraces in river valleys play important roles in the transport dynamics of water and sediment. While flat areas in river valleys can be identified from LiDAR data, directly characterizing them as either floodplain or terraces is not yet possible. To address this challenge, we hypothesize that, since geomorphic features are strongly coupled to hydrological and hydraulic dynamics and their associated variability, there exists a return frequency, or possibly a narrow band of return frequencies, of flow that is associated with floodplain formation; and this association can provide a distinctive signature for distinguishing them from terraces. Based on this hypothesis we develop a novel approach for distinguishing between floodplains and terraces that involves transforming the transverse cross-sectional geometry of a river valley into a curve, named a river valley hypsometric (RVH) curve, and linking hydraulic inundation frequency with the features of this curve. Our approach establishes that the demarcation between floodplains and terraces can be established from the structure of steps and risers in the RVH curves which can be obtained from the DEM data. Further, it shows that these transitions may themselves be shaped by floods with 10-to 100-year recurrence. We additionally show that, when floodplain width and height (above channel bottom) are normalized by bankfull width and depth, the ratio lies in a narrow range independent of the scale of the river valley.

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Performance evaluation of three DEM‐based fluvial terrace mapping methods

Cited by 9 publications

References 8 publications

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

Interpretation and Spatiotemporal Analysis of Terraces in the Yellow River Basin Based on Machine Learning

APPLICATION OF GIS PROCEDURE FOR RIVER TERRACE EXTRACTION FROM A LiDAR- BASED DIGITAL ELEVATION MODEL: THE SAVA RIVER VALLEY NW OF ZAGREB, CROATIA

Hydrogeomorphological differentiation between floodplains and terraces

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