An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping

Rabby, Yasin Wahid; Li, Yingkui

doi:10.1007/s10346-018-1107-9

Cited by 48 publications

(57 citation statements)

References 16 publications

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“…In recent years, the Google Earth platform has been widely used in landslide investigation [33,44,45]. Google Earth contains available Landsat imagery (30 m or 15 m pan-sharpened), orthophotos (0.5-2 m), and high-resolution commercial satellite imagery (SPOT, FORMOSAT-2: 0.5-8 m; World View-1 and World View-2, 0.5-2.5 m) [35,46]. The satellite images are 100% coverage of the whole study area and mainly available from Feb 2009 to Jan 2018 (mainly including Feb 2009, Mar 2010, Oct 2012, Oct 2014, Nov 2017 and Jan 2018).…”

Section: Landslide Inventory Mapmentioning

confidence: 99%

Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China

Shao

et al. 2020

Sustainability

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Inventorying landslides in mountainous areas is of great importance for prevention of geologic hazards. This study aimed to establish a detailed landslide inventory of Baoshan City, Yunnan Province, China, based on a large set of high-resolution satellite images from Google Earth. The landslides of this region were divided into two groups, i.e., recent landslides and old landslides. The spatial distribution and geometric characteristics of the two kinds of landslides were analyzed, respectively. Results show that 2427 landslides are present in the study area, including 2144 recent landslides and 283 old landslides with a total area of 7.2 km2 and 97.6 km2, respectively. The recent landslides occurred primarily at steep slopes with higher elevation, while old landslides took place at gentle terrains. For the slope position, most landslides, whether old or recent, cluster near ridges. The lower boundary of the recent landslides is far away from the valley, while the accumulation area of the old landslide is closer to the valley. The H/L (height to length) ratios are basically the same for all landslides, ranging from 0.2 to 0.5. Old landslides have larger mobility, as their travel distances are longer than recent landslides at the same height. The results would be helpful for further understanding the development and spatial distribution of the landslides in Southwest China, and also provide essential support for the subsequent landslide susceptibility mapping and geologic hazard assessment in this area.

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Section: Landslide Inventory Mapmentioning

confidence: 99%

Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China

Shao

et al. 2020

Sustainability

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“…They mainly occur on steep slopes, especially in the high hills. Heavy rainfall-induced landslides and soil erosions are dominant on the hills [7,13,35]. Given its geomorphological, geological, and climatic characteristics, and human interventions such as hill cutting and deforestation, CMA has become highly vulnerable to landslides.…”

Section: Study Area Profilementioning

confidence: 99%

“…Landslides are recognized as the third type of most important natural hazard induced disaster worldwide [2]. Asia, in particular India, Nepal, China, and Japan, are most vulnerable to rainfall-induced landslide disasters [3][4][5][6][7]. For example, at least 200 people were killed and six million were evacuated due to landslides and flooding triggered by historic levels of torrential rainfall in Japan in July 2018 [8].…”

Section: Introductionmentioning

confidence: 99%

Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh

Ahmed

Rahman

Islam

et al. 2018

IJGI

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This article aims to develop a Web-GIS based landslide early warning system (EWS) for the Chittagong Metropolitan Area (CMA), Bangladesh, where, in recent years, rainfall-induced landslides have caused great losses of lives and property. A method for combining static landslide susceptibility maps and rainfall thresholds is proposed by introducing a purposely-build hazard matrix. To begin with, eleven factor maps: soil permeability; surface geology; landcover; altitude; slope; aspect; distance to stream; fault line; hill cut; road cut; and drainage network along with a detailed landslide inventory map were produced. These maps were used, and four methods were applied: artificial neural network (ANN); multiple regressions; principal component analysis; and support vector machine to produce landslide susceptibility maps. After model validation, the ANN map was found best fitting and was classified into never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960–2017) revealed consecutive 5-day periods of rainfall of 71–282 mm could initiate landslides in CMA. Later, the threshold was classified into three rainfall rates: low rainfall (70–160 mm), medium rainfall (161–250 mm), and high rainfall (>250 mm). Each landslide was associated with a hazard class (no warning vs. warning state) based on the assumption that the higher the susceptibility, the lower the rainfall. Finally, the EWS was developed using various libraries and frameworks that is connected with a reliable online-based weather application programming interface. The system is publicly available, dynamic, and replicable to similar contexts and is able to disseminate alerts five days in advance via email notifications. The proposed EWS is novel and the first of its kind in Bangladesh, and can be applied to mitigate landslide disaster risks.

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“…A landslide inventory is a dataset of various information associated with landslides including the absolute and relative location, date, type, size, distribution, casualties, and triggers of landslides [1]. Several methods have been used for landslide inventory mapping including field mapping and visual interpretation of aerial and satellite images [5]. The first step to creating a landslide inventory is to map the exact location of landslides and then construct a dataset of landslides [6].…”

Section: Introductionmentioning

confidence: 99%

“…Sifa et al [11] and Comprehensive Disaster Management Plan (CDMP) Phase_II [12] have published landslide inventories in three cities in this area: Cox's Bazar, the Teknaf municipalities, and the Chittagong Metropolitan Area (CMA). Our recent work [5] mapped the landslides of the whole region. This inventory can be used for landslide susceptibility mapping of the entire area, which is very important for land use planning and policymaking.…”

Section: Introductionmentioning

confidence: 99%

Landslide Inventory (2001–2017) of Chittagong Hilly Areas, Bangladesh

Rabby

2019

Data

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Landslides are a frequent natural hazard in Chittagong Hilly Areas (CHA), Bangladesh, which causes the loss of lives and damage to the economy. Despite this, an official landslide inventory is still lacking in this area. In this paper, we present a landslide inventory of this area prepared using the visual interpretation of Google Earth images (Google Earth Mapping), field mapping, and a literature search. We mapped 730 landslides that occurred from January 2001 to March 2017. Different landslide attributes including type, size, distribution, state, water content, and triggers are presented in the dataset. In this area, slide and flow were the two dominant types of landslides. Out of the five districts (Bandarban, Chittagong, Cox’s Bazar, Khagrachari, and Rangamati), most (55%) of the landslides occurred in the Chittagong and Rangamati districts. About 45% of the landslides were small (<100 m2) in size, while the maximum size of the detected landslides was 85202 m2. This dataset will help to understand the characteristics of landslides in CHA and provide useful guidance for policy implementation.

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An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping

Cited by 48 publications

References 16 publications

Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China

Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China

Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh

Landslide Inventory (2001–2017) of Chittagong Hilly Areas, Bangladesh

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