Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

Eeckhaut, Miet Van Den; Moeyersons, J; Nyssen, Jan; Zenebe, Amanuel; Poesen, Jean; Haile, Mitiku; Deckers, Jozef

doi:10.1016/j.geomorph.2008.09.027

Cited by 62 publications

(16 citation statements)

References 54 publications

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“…Igwe et al, 2014;Che et al 2012;Che et al, 2011;Claessens et al, 2007;Kitutu et al, 2009;Knapen et al, 2006;Davies, 1996;Davies et al, 2013;Ayonghe et al 1999;Ayonghe et al, 2004;Zogning et al, 2007;Mugagga et al, 2012b;Ngecu et al, 2004;Broothaerts et al, 2012;Van Den Eeckhaut et al, 2009) and our field experience suggest that LS are responsible for acute problems in Equatorial Africa. This is a region characterized by steep slopes, intense rainfalls, deep soil profiles and populations highly vulnerable to geohazards.…”

mentioning

confidence: 51%

Landslide resilience in Equatorial Africa: Moving beyond problem identification!

Kervyn¹,

Jacobs²,

Maes³

et al. 2015

belgeo

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mentioning

confidence: 51%

Landslide resilience in Equatorial Africa: Moving beyond problem identification!

Kervyn¹,

Jacobs²,

Maes³

et al. 2015

belgeo

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“…Several studies have mapped landslides in Ethiopia and have analysed their controlling factors (e.g. Moeyersons et al 2008;Van Den Eeckhaut et al 2009;Broothaerts et al 2012). Although many mass movements have been initiated by natural factors, human activities (i.e.…”

Section: Mass Movementsmentioning

confidence: 99%

Land Degradation in the Ethiopian Highlands

Nyssen

Poesen

Lanckriet

et al. 2015

World Geomorphological Landscapes

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Landforms and landscapes vary enormously across the Earth, from high mountains to endless plains. At a smaller scale, nature often surprises us by creating shapes which look improbable. Many physical landscapes are so immensely beautiful that they received the highest possible recognition-they hold the status of World Heritage Sites. Apart from often being immensely scenic, landscapes tell stories which not uncommonly can be traced back in time for tens of millions of years and include unique geological events such as meteorite impacts. In addition, many landscapes owe their appearance and harmony not solely to the natural forces. For centuries, and even millennia, they have been shaped by humans who have modified hillslopes, river courses and coastlines, and erected structures which often blend with the natural landforms to form inseparable entities.These landscapes are studied by geomorphology-'the science of scenery'-a part of Earth Sciences that focuses on landforms, their assemblages, surface and subsurface processes that moulded them in the past and that change them today. To show the importance of geomorphology in understanding the landscape, and to present the beauty and diversity of the geomorphological sceneries across the world, we have launched a book series World Geomorphological Landscapes. It aims to be a scientific library of monographs that present and explain physical landscapes, focusing on both representative and uniquely spectacular examples. Each book will contain details on geomorphology of a particular country or a geographically coherent region. This volume presents the impressive geomorphic legacy of Ethiopia which hosts many landscapes and landforms of global significance. Examples include the otherworldly Afar Depression with its sun-baked volcanoes, high-elevation basalt plateaus, the East African Rift valley with its splendid lakes, to name just a few. Ethiopia is also a country where geomorphology and people have remained in particularly close association since time immemorial. These relationships can be examined by referring to the past-as demonstrated by the chapter on geoarcheology of Aksum, but perhaps more importantly, with the reference to the present-day environmental problems arising from land use, soil erosion, water resources depletion and settlement growth.

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“…LiDar has proven to be a powerful and promising tool to detect landslides and map features under dense vegetation in many studies [31][32][33][34][35][36][37][38][39][40]. A range of LiDar derivatives, including DTMs, shaded relief, slope, aspect, and surface roughness, have been widely used for qualitative visual interpretation and quantitative statistical analysis of landslides [12,32,[41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

et al. 2015

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For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA) of LiDar data, while few studies have examined (semi-) automated methods and object-based image analysis (OBIA). Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS), were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1) inclusion of holes encircled by the landslide body; (2) removal of isolated segments, and (3) delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1) the filter features of surface roughness were first applied for calculating object features, and proved useful; (2) FS improved OPEN ACCESSRemote Sens. 2015, 7 9706 classification accuracy and reduced features; (3) the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4) compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5) based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

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Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

Cited by 62 publications

References 54 publications

Landslide resilience in Equatorial Africa: Moving beyond problem identification!

Landslide resilience in Equatorial Africa: Moving beyond problem identification!

Land Degradation in the Ethiopian Highlands

Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

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