Perceived Human-Induced Causes of Landslide in Chattogram Metropolitan Area in Bangladesh

Ahmed, Zia and Belayeth Hussain, A H M and Ambinakudige, Shrinidhi and Ahmed, Mufti Nadimul Quamar and Alam, Rafiul and Rezoan, Hafiz-Al and Das Dola, Dolan and Rahman, Mohammad Mahbubur and Hassan, Rubaid and Mahmud, Sakib (2022) Perceived Human-Induced Causes of Landslide in Chattogram Metropolitan Area in Bangladesh. Earth Systems and Environment, 6 (2). pp. 499-515.

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Abstract

This study investigates Land Use Land Cover changes in the Chattogram metropolitan area, the second-largest city in Bangladesh. Using a questionnaire survey of 150 local inhabitants, the study explores perceived human-induced causes of landslides. Using time series Landsat images, this study also analyzes Land Use Land Cover changes from 1990 to 2020. The analysis reveals built-up area extended rapidly during 1990–2020. In 1990, total built-up area was 82.13 km2, which in 30 years, stood at 451.34 km2. Conversely, total vegetative area decreased rapidly. In 1990, total vegetation area was 364.31 km2, which reduced to 130.44 km2 in 2020. The survey results show that most of the respondents faced landslide therefore; it is nothing new among them. Respondents were identified several reasons for landslide like extensive rainfall, hill cutting, steep hill, weak soil texture, etc. A large number of local people opined that diverse human activities are causes of landslide in their local area and it has impacted on their livelihood. Chi-square test suggests that there are statistically significant differences between local and non-local inhabitants regarding their opinion on whether excessive hill cutting is alone responsible for landslide and whether deforestation is the sole reason for landslide. This study also used four multinomial logistic regression (MLR) to examine the effects of independent variables like gender, age, level of education, income, housing pattern and experience of facing landslide on their perception of human-induced causes of landslide. Findings show that age and experience of facing landslide are two significant predictors for the first model, explaining excessive hill cutting was alone responsible for landslide. Level of education and experience of facing landslide are found statistically significant for explaining our second model that is building infrastructures solely causes landslide. Moreover, our third model claims only deforestation can be blamed for landslide which is significantly explained by three predictors, namely gender, age and income. Finally, we found our fourth model that is landslide occurs only due to excessive sand collection is significantly explained by participant's gender, level of education, and income.