Identifying ephemeral gullies from high-resolution images and DEMs using flow-directional detection

Dai, Wen; Hu, Guanghui; Yang, Xin; Yang, Xianwu; Cheng, Ying; Xiong, Liyang; Strobl, Josef; Tang, Guoan

doi:10.1007/s11629-020-6084-5

Cited by 13 publications

(9 citation statements)

References 60 publications

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“…Ephemeral gully is too small to be extracted by DEM with 5 m resolution, but it exhibits clear linear features in high-resolution images [45]. Thus, high-resolution remote sensing images are applied to recognize and extract ephemeral gully by the combination of our previous method and visual interpretation and manual digitizing [45]. Bank and valley gullies are mainly extracted by our proposed method [41] In morphology, hillslope ephemeral gullies are generally 20-50 cm deep and 30-50 cm wide and are smaller than the bank gully, which are mainly distributed in the area above the shoulder line [40].…”

Section: Gully System Extraction and Classificationmentioning

confidence: 99%

“…Bank and valley gullies are mainly extracted by our proposed method [41] In morphology, hillslope ephemeral gullies are generally 20-50 cm deep and 30-50 cm wide and are smaller than the bank gully, which are mainly distributed in the area above the shoulder line [40]. Ephemeral gully is too small to be extracted by DEM with 5 m resolution, but it exhibits clear linear features in high-resolution images [45]. Thus, high-resolution remote sensing images are applied to recognize and extract ephemeral gully by the combination of our previous method and visual interpretation and manual digitizing [45].…”

Section: Gully System Extraction and Classificationmentioning

confidence: 99%

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An Exploration of Loess Landform Development Based on Population Ecology Method

Yang

2022

IJGI

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The study of gully characteristics is one of the most effective ways to explore the loess landform development in the Loess Plateau of China. However, current studies mostly focus on gullies’ overall characteristics and ignore the different composition of the whole gully system. Therefore, a new perspective is provided in this paper for exploring loess landform development from the population characteristics of the gully system. Firstly, different types of gullies were extracted based on DEM and high-resolution images in three sample watersheds, including hillslope ephemeral gully, bank gully and different-level valley gully. Secondly, population characteristics from the amount, length, age structure and convergent relationship were calculated and analyzed by referring to the biological population in ecology. Finally, the development stages of loess landform in three watersheds were explored based on their population characteristics. The results showed that: (1) The population characteristics, including number density, length density, age structure and convergence, were obviously different in three sample watersheds. (2) The development differences of three watersheds were obtained by synthesizing all population characteristics: Linjiajian was the most developed and oldest watershed, followed by Yangjiaju and then Wangjiagou. (3) The comparison based on the existing soil erosion intensity map and predisposing factors proved that the findings of this paper were more reasonable than that of the traditional hypsometric integral. This research provides a new quantitative-based approach to explore the development degree of loess landform from the gully population, and is a beneficial attempt to combine geomorphology and ecology, further supplementing and improving the study of loess landform development.

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Section: Gully System Extraction and Classificationmentioning

confidence: 99%

Section: Gully System Extraction and Classificationmentioning

confidence: 99%

An Exploration of Loess Landform Development Based on Population Ecology Method

Yang

2022

IJGI

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“…Available remote sensing data on Google Earth™ consist of satellite and aircraft-based imagery with three bands of red, green, and blue (RGB), whereas the available resolution varies for the different regions of the Earth with the data provider of the imagery. Nevertheless, Google Earth™ has been used in previous studies to map objects such as gullies in the context of soil erosion [34][35][36]. Boardman et al [37] pointed out the untapped potential of Google Earth™ imagery, which can significantly expand the existing database in the field of soil erosion based on the free availability of data and allows a retrospective view of historical events.…”

Section: Google Earth™ Remote Sensing Datamentioning

confidence: 99%

Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring

Scholand

Schmalz

2021

Land

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The P-factor for support practice of the Universal Soil Loss Equation (USLE) accounts for soil conservation measures and leads to a significant reduction in the modelled soil loss. However, in the practical application, the P-factor is the most neglected factor overall due to high effort for determining or lack of input data. This study provides a new method for automatic derivation of the main cultivation direction from seed rows and tramlines on agricultural land parcels using the Fast Line Detector (FLD) of the Open Computer Vision (OpenCV) package and open remote sensing data from Google Earth™. Comparison of the cultivation direction with the mean aspect for each land parcel allows the determination of a site-specific P-factor for the soil conservation measure contouring. After calibration of the FLD parameters, the success rate in a first application in the low mountain range Fischbach catchment, Germany, was 77.7% for 278 agricultural land parcels. The main reasons for unsuccessful detection were problems with headland detection, existing soil erosion, and widely varying albedo within the plots as well as individual outliers. The use of a corrected mask and enhanced parameterization offers promising improvements for a higher success rate of the FLD.

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“…However, large-area field surveys are needed to accurately grasp the spatial distribution characteristics of ephemeral gully morphological features, requiring a large workload with low efficiency. In recent decades, the spatial resolutions, revisiting times, and detail of remote sensing data and digital elevation models (DEM) have significantly increased, providing crucial data support for accurately identifying the spatial position and morphological parameters of ephemeral gullies (Arabameri et al, 2019;Cao et al ., 2020;Dai et al ., 2020;Karydas and Panagos, 2020;King et al ., 2005;Liu et al ., 2022;Vallejo Orti et al, 2019;Yermolayevet al ., 2020). Ephemeral gully volume is an important morphological parameter of ephemeral gullies and an indicator of the contribution of ephemeral gully erosion to sediment yield (Kompia-Zare et al, 2011;Woodward, 1999).…”

Section: Introductionmentioning

confidence: 99%

Evolution and Development of Ephemeral Gully Erosion in Hilly and Gully Region of the Loess Plateau in China

Liu

Zhang

Yin

et al. 2023

Preprint

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Ephemeral gully erosion is a primary mode of soil erosion that is highly visible, affecting soil productivity and restricting land use. Watershed is the basic unit of soil erosion control; existing research has focused on several typical ephemeral gullies or slopes, which do not fully display changes in ephemeral gullies at a watershed scale. This study analyzed the spatial-temporal evolution and development rate of ephemeral gully erosion at the watershed scale on the Loess Plateau from 2009 to 2021 using remote sensing images (0.5 m resolution), unmanned aerial vehicles (UAV), and field investigations. The results revealed that: (1) most ephemeral gullies occurred in southwestern parts of the watershed, with many hills and large slope gradients; (2) average growth rates of each ephemeral gully frequency, length, density, dissection degree, and width were 2.87 km y , 1.66 m y , 0.12 km km y , 0.0125% y , and 0.04 m y , respectively; (3) ephemeral gully erosion volume ( V) and length ( L) had a good power function relationship: V = 0 . 0842 L 1 . 1932 ( R 2 = 0 . 80 ) . The root mean square error (RMSE) and coefficient of determination (R ) between the measured and predicted ephemeral gully volumes suggest that the V–L relationship has a good predictive ability for ephemeral gully volume. Thus, the V–L model was used to evaluate the development rate of ephemeral gully erosion volume in small watersheds from 2009 to 2021, revealing an average value of 743.20 m y . This study proposed a feasible model for assessing ephemeral gully volume and volume changes at a watershed scale using high-resolution remote sensing images, providing a reference for understanding the development of ephemeral gully erosion in small watersheds over time.

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Identifying ephemeral gullies from high-resolution images and DEMs using flow-directional detection

Cited by 13 publications

References 60 publications

An Exploration of Loess Landform Development Based on Population Ecology Method

An Exploration of Loess Landform Development Based on Population Ecology Method

Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring

Evolution and Development of Ephemeral Gully Erosion in Hilly and Gully Region of the Loess Plateau in China

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