Quantifying temporal changes in gully erosion areas with object oriented analysis

Shruthi, R.; Kerle, N.; Jetten, Victor; Laouina, Abdellah; Machmach, Issam

doi:10.1016/j.catena.2014.01.010

Cited by 81 publications

(46 citation statements)

References 31 publications

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“…In contrast to other approaches [36], the absence of terrain data is considered an advantage from the authors' point of view, due to the fact that optical input data is more readily available for a broad range of users. Moreover, the rather dynamic geomorphology of gullies leads to a constant change of shape, extent and volume.…”

Section: Discussionmentioning

confidence: 99%

Detection of Gully-Affected Areas by Applying Object-Based Image Analysis (OBIA) in the Region of Taroudannt, Morocco

et al. 2014

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This study aims at the detection of gully-affected areas by applying object-based image analysis in the region of Taroudannt, Morocco, which is highly affected by gully erosion while simultaneously representing a major region of agro-industry with a high demand of arable land. As high-resolution optical satellite data are readily available from various sensors and with a much better temporal resolution than 3D terrain data, an area-wide mapping approach to extract gully-affected areas using only optical satellite imagery was developed. The methodology additionally incorporates expert knowledge and freely-available vector data in a cyclic object-based image analysis approach. This connects the two fields of geomorphology and remote sensing. The classification results show the successful implementation of the developed approach and allow conclusions on the current distribution of gullies. The results of the classification were checked against manually delineated reference data incorporating expert knowledge based on several field campaigns in the area, resulting in an overall classification accuracy of 62%. The error of omission accounts for 38% and the error of commission for 16%, respectively. Additionally, a manual assessment was carried out to assess the quality of the applied classification algorithm. The limited error of omission contributes with 23% to the overall error of omission and the limited error of commission contributes with 98% to the overall error of commission. This assessment improves the results and confirms the high quality of the developed approach for area-wide mapping of gully-affected areas in larger regions. In the field of landform Remote Sens. 2014, 6 8288 mapping, the overall quality of the classification results is often assessed with more than one method to incorporate all aspects adequately.

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Section: Discussionmentioning

confidence: 99%

Detection of Gully-Affected Areas by Applying Object-Based Image Analysis (OBIA) in the Region of Taroudannt, Morocco

et al. 2014

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“…However, achieving the satisfactory result was time consuming and required a certain expertise. According to previous studies, with respect to high spatial resolution satellite images (such as Pleiades-1A), although the visual interpretation of gully erosion with field verification achieved much higher accuracies [8,46], (semi-)automatic mapping of gullies at a regional scale or larger scales showed great potential for obtaining a general statement of the distribution of gullies to a wider extent [43,47,48].…”

Section: Visual Pleiades Imagery Interpretation For Gully Erosion Mapmentioning

confidence: 99%

Gully Erosion Mapping and Monitoring at Multiple Scales Based on Multi-Source Remote Sensing Data of the Sancha River Catchment, Northeast China

Wang

Zhang

et al. 2016

IJGI

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This research is focused on gully erosion mapping and monitoring at multiple spatial scales using multi-source remote sensing data of the Sancha River catchment in Northeast China, where gullies extend over a vast area. A high resolution satellite image (Pleiades 1A, 0.7 m) was used to obtain the spatial distribution of the gullies of the overall basin. Image visual interpretation with field verification was employed to map the geometric gully features and evaluate gully erosion as well as the topographic differentiation characteristics. Unmanned Aerial Vehicle (UAV) remote sensing data and the 3D photo-reconstruction method were employed for detailed gully mapping at a site scale. The results showed that: (1) the sub-meter image showed a strong ability in the recognition of various gully types and obtained satisfactory results, and the topographic factors of elevation, slope and slope aspects exerted significant influence on the gully spatial distribution at the catchment scale; and (2) at a more detailed site scale, UAV imagery combined with 3D photo-reconstruction provided a Digital Surface Model (DSM) and ortho-image at the centimeter level as well as a detailed 3D model. The resulting products revealed the area of agricultural utilization and its shaping by human agricultural activities and water erosion in detail, and also provided the gully volume. The present study indicates that using multi-source remote sensing data, including satellite and UAV imagery simultaneously, results in an effective assessment of gully erosion over multiple spatial scales. The combined approach should be continued to regularly monitor gully erosion to understand the erosion process and its relationship with the environment from a comprehensive perspective.

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“…Despite their lower resolutions compared to UAS imagery, satellite and aerial images as data sources have also been studied for fissure extraction by various researchers [23,[34][35][36][37][38]. Youssef et al [34] demonstrated the use of high-resolution satellite images using QuickBird imagery, acquired on 2 June 2007 (0.61 m spatial resolution), for detailed mapping of recent developments and slope instability hazard zones.…”

Section: Introductionmentioning

confidence: 99%

“…Shruthi et al [35] developed The Random Forests model from an object-oriented analysis using medium resolution ASTER images as the source of imagery information, combined with topographic information extracted from a DEM. In 2015, Shruthi et al [36] made use of a semi-automatic object-oriented analysis to quantify changes in gully erosion areas over a period of eight years (2001 to 2009). Shruthi et al [37] also investigated the use of object-oriented image analysis to extract erosion features, using a combination of topographic, spectral, shape (geometric) and contextual information obtained from satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

Akçay

2015

IJGI

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Unmanned Aerial Systems (UAS) are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS) values of the image coordinates of the Ground Control Points (GPCs) varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER) detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS) and a Logistic Regression (LR) were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.

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Quantifying temporal changes in gully erosion areas with object oriented analysis

Cited by 81 publications

References 31 publications

Detection of Gully-Affected Areas by Applying Object-Based Image Analysis (OBIA) in the Region of Taroudannt, Morocco

Detection of Gully-Affected Areas by Applying Object-Based Image Analysis (OBIA) in the Region of Taroudannt, Morocco

Gully Erosion Mapping and Monitoring at Multiple Scales Based on Multi-Source Remote Sensing Data of the Sancha River Catchment, Northeast China

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

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