Large-scale mapping of gully-affected areas: An approach integrating Google Earth images and terrain skeleton information

Li, Kai; Ding, Hu; Tang, Guoan; Song, Chunqiao; Liu, Yiwen; Jiang, Ling; Zhao, Bangyuan; Gao, Yunfei; Ma, Ronghua

doi:10.1016/j.geomorph.2018.04.011

Cited by 37 publications

(21 citation statements)

References 46 publications

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“…Since the end of 1990s, the emergence and development of digital terrain analysis based on digital elevation models (DEMs) provided important conditions for the research of geomorphology, especially on large scales (Wilson and Gallant 2000;Florinsky 2002;Li et al 2005b;Tang et al 2005;Florinsky 2011;Wilson 2012;Li et al 2016;Luo et al 2020). The key of digital terrain analysis is extraction and geostatistical analysis of the topographic feature elements (including topographic feature point, line, and surface) and topographic factors (such as slope gradient, slope direction, plane curvature, profile curvature, topographic relief, coefficient of elevation variation, surface cutting depth) based on DEMs (Oostwoud Wijdenes et al 2000;Hancock and Evans 2006;McNamara et al 2006;Yang et al 2009;Tang 2014;Torri et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial variation of gully development in the loess plateau of China based on the morphological perspective

Dai

et al. 2020

Earth Sci Inform

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Gullies characterized by frequent material exchange have profoundly affected the loess landforms evolution in the Loess Plateau of China. Accompanying gully development, various types of gullies have shaped the distinctive loess landforms. Thus, the spatial variations of gully morphology map the regional differences of gully development and loess landforms. Based on 6 key test areas with 5 m resolution DEM and 156 evenly distributed test areas with 25 m resolution DEM, a comprehensive gullyshape index system of topographic feature points, lines, and surfaces was constructed to describe spatial differences of the gully development. After comparing gully-shape indexes in the key test areas, an obvious correlation existed between these indexes and loess landform types. The gully development degree was low in Shenmu and Chunhua, high in Suide, Yanchuan, and Ganquan, while Yijun lay in-between. Using the method of Universal Kriging interpolation, a series of spatial interpolation maps of the gully-shape indexes were obtained, which revealed gully development degree in the whole Loess Plateau. Overall, The active areas of gully development were basically distributed along loess hilly ridges, loess hills, loess ridges, and in a few medium mountain areas, while the inactive areas were basically distributed in the thin loess coverage areas, including plains, river terraces, aeolian dunes, and a few loess platforms. Finally, the gully development regionalization was realized, which contained 6 subzones. These results provided significant references for loess landform research and soil erosion management in the Loess Plateau.

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Section: Introductionmentioning

confidence: 99%

Spatial variation of gully development in the loess plateau of China based on the morphological perspective

Dai

et al. 2020

Earth Sci Inform

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“…GE imagery, a type of optical imaging technology, is the only data source used for gully mapping in this paper. Although there is an unquestionable link between topographic attributes and the occurrence of gullies, which helps to improve the extraction of gullies (as suggested by recent studies) [23,25,63], the acquisition and update of high-precision topographic data remain difficult for a vast number of users compared to optical imagery, especially when conducting regional gully erosion surveys and mapping. Hence, this type of data selection allows more people to use the method presented and facilitates large-scale gully erosion investigations at the sacrifice of some accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…With the development of computational statistical models, recent work on the combination of object-oriented analysis and the Random Forest (RF) algorithm has improved the degree of automation and objectivity to a large extent [22][23][24][25]. The RF based on bootstrap aggregation and ensembles of classification trees is a well-accepted performance optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Even though some open-source and downloadable DEM data are available worldwide, such as ALOS DEM (12.5 m resolution), ASTER DEM (the highest resolution 30m), and SRTM DEM (the highest resolution 90 m), the DEM data with a coarse resolution remain too inaccurate for gully extraction. Some studies have attempted to solve this problem by obtaining a terrain skeleton from ASTER DEM [23]. However, all these DEM data were produced before 2010, indicating that they do not reflect the current situation and changes in gully erosion.…”

Section: Introductionmentioning

confidence: 99%

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Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China

et al. 2020

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Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.

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“…Moreover, some DEM-based topographic methods, considering the relationship between topographic attributes and gully distribution, have also been designed for gully feature extraction [40,41]. However, they are also accuracy-limited due to be sensitive to terrain characteristics of study area (e.g., the misestimation of gully area in four study areas in Spain ranged from 13.6% to 24% [40] and gully width was overestimated by up to 30% in the Bleaklow Plateau of the UK [41]), consequently which need to be improved further when applied in different regions [42]. As a result, gully detection and delineation on images is still done visually and manually in most studies because of the convincing accuracy of this way.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment of the Planar Morphology of Valley Bank Gullies Extracted with High Resolution Remote Sensing Imagery on the Loess Plateau, China

Chen

Jiao

Wei

et al. 2019

IJERPH

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Gully erosion is a serious environmental problem worldwide, causing soil loss, land degradation, silting up of reservoirs and even catastrophic flooding. Mapping gully features from remote sensing imagery is crucial for assisting in understanding gully erosion mechanisms, predicting its development processes and assessing its environmental and socio-economic effects over large areas, especially under the increasing global climate extremes and intensive human activities. However, the potential of using increasingly available high-resolution remote sensing imagery to detect and delineate gullies has been less evaluated. Hence, 130 gullies occurred along a transect were selected from a typical watershed in the hilly and gully region of the Chinese Loess Plateau, and visually interpreted from a Pleiades-1B satellite image (panchromatic-sharpened image at 0.5 m resolution fused with 2.0 m multi-spectral bands). The interpreted gullies were compared with their measured data obtained in the field using a differential global positioning system (GPS). Results showed that gullies could generally be accurately interpreted from the image, with an average relative error of gully area and gully perimeter being 11.1% and 8.9%, respectively, and 74.2% and 82.3% of the relative errors for gully area and gully perimeter were within 15%. But involving field measurements of gullies in present imagery-based gully studies is still recommended. To judge whether gullies were mapped accurately further, a standard adopting one-pixel tolerance along the mapped gully edges was proposed and proved to be practical. Correlation analysis indicated that larger gullies could be interpreted more accurately but increasing gully shape complexity would decrease interpreting accuracy. Overall lower vegetation coverage in winter due to the withering and falling of vegetation rarely affected gully interpreting. Furthermore, gully detectability on remote sensing imagery in this region was lower than the other places of the world, due to the overall broken topography in the Loess Plateau, thus images with higher resolution than normally perceived are needed when mapping erosion features here. Taking these influencing factors (gully dimension and shape complexity, vegetation coverage, topography) into account will be favorable to select appropriate imagery and gullies (as study objects) in future imagery-based gully studies. Finally, two linear regression models were built to correct gully area (Aip, m2) and gully perimeter (Pip, m) visually extracted, by connecting them with the measured area (Ams, m2) and perimeter (Pms, m). The correction models were Ams=1.021Aip+0.139 and Pms=0.949Pip+ 0.722, respectively. These models could be helpful for improving the accuracy of interpreting results, and further accurately estimating gully development and developing more effective automated gully extraction methods on the Loess Plateau of China.

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Large-scale mapping of gully-affected areas: An approach integrating Google Earth images and terrain skeleton information

Cited by 37 publications

References 46 publications

Spatial variation of gully development in the loess plateau of China based on the morphological perspective

Spatial variation of gully development in the loess plateau of China based on the morphological perspective

Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China

Accuracy Assessment of the Planar Morphology of Valley Bank Gullies Extracted with High Resolution Remote Sensing Imagery on the Loess Plateau, China

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