Understanding the political ecology of forced migration and deforestation through a multi-algorithm classification approach: the case of Rohingya displacement in the southeastern border region of Bangladesh

Ahmed, Nizam; Islam, Mashnoon; Hasan, Mahmudul; Motahar, Tamanna; Sujauddin, Mohammad

doi:10.1080/24749508.2018.1558025

Cited by 25 publications

(17 citation statements)

References 40 publications

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“…The Classification and Regression Tree (CART) (Breiman et al 2017) predictive model was used for the analysis. CART has been utilised for similar purposes in recent times (Ahmed et al 2019). Since assignment of pixels to disjoint classes translates into the target variable taking discrete values, the model is more specifically addressed as a classification tree which is a specialised form of decision trees.…”

Section: Algorithmmentioning

confidence: 99%

Modelling Spatio-Temporal Changes of Tropical Forest Cover in the North-Eastern Region of Bangladesh: Context of Traditional and Co-Management Paradigms

Ahmed¹,

Mahbub²,

Hossain³

2020

JTFS

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Forests in Bangladesh are losing their land footprint mainly due to anthropogenic onslaught. Not much is known about the changes in tropical forest cover in Sylhet Forest Division (SFD) of Bangladesh in the backdrop of mixed managerial paradigms (i.e. traditionally managed and co-managed). This study was aimed at (1) modelling changes in forest cover over the temporal domain 1988-2018, (2) projecting future gain/loss of forest cover, and (3) comparing changes between areas under co-management and traditional management. The study was based on satellite image pixel classification through machine learning, area interpolation, regression-based modelling for forest cover and future projections. SFD, comprising 18 forests, has been grouped into 10 forest patches for analysis. Mean forest cover area was 121,533 ha in 1988-1997, which declined to 120,216 ha in 1998-2007. Comparison between the area values of the two time intervals showed that the forest cover area has increased from 120,216 ha in 1998-2007 to 124,556 ha in 2007-2018. In the managerial perspective, the study showed aggregated loss in patches under co-management and aggregated gain in patches under traditional forest management. This study has legislative implications from managerial perspective of forest cover changes.

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Section: Algorithmmentioning

confidence: 99%

Modelling Spatio-Temporal Changes of Tropical Forest Cover in the North-Eastern Region of Bangladesh: Context of Traditional and Co-Management Paradigms

Ahmed¹,

Mahbub²,

Hossain³

2020

JTFS

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“…This algorithm is rarely used for the classification of remote sensing data [55], especially for multi-class classification. However, it is a well-known method in studies of species distributions and habitat models [53,56,57], and it has been reported to perform better in one-class or binary classification applications than other algorithms, such as the support vector machine method [58].…”

Section: Supervised Classificationmentioning

confidence: 99%

Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data

Arjasakusuma

Kusuma

Saringatin

et al. 2021

Land

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Coastal regions are one of the most vulnerable areas to the effects of global warming, which is accompanied by an increase in mean sea level and changing shoreline configurations. In Indonesia, the socioeconomic importance of coastal regions where the most populated cities are located is high. However, shoreline changes in Indonesia are relatively understudied. In particular, detailed monitoring with remote sensing data is lacking despite the abundance of datasets and the availability of easily accessible cloud computing platforms such as the Google Earth Engine that are able to perform multi-temporal and multi-sensor mapping. Our study aimed to assess shoreline changes in East Java Province Indonesia from 2000 to 2019 using variables derived from a multi-sensor combination of optical remote sensing data (Landsat-7 ETM and Landsat-8 OLI) and radar data (ALOS Palsar and Sentinel-1 data). Random forest and GMO maximum entropy (GMO-Maxent) accuracy was assessed for the classification of land and water, and the land polygons from the best algorithm were used for deriving shorelines. In addition, shoreline changes were quantified using Digital Shoreline Analysis System (DSAS). Our results showed that coastal accretion is more profound than coastal erosion in East Java Province with average rates of change of +4.12 (end point rate, EPR) and +4.26 m/year (weighted linear rate, WLR) from 2000 to 2019. In addition, some parts of the shorelines in the study area experienced massive changes, especially in the deltas of the Bengawan Solo and Brantas/Porong river with rates of change (EPR) between −87.44 to +89.65 and −18.98 to +111.75 m/year, respectively. In the study areas, coastal erosion happened mostly in the mangrove and aquaculture areas, while the accreted areas were used mostly as aquaculture and mangrove areas. The massive shoreline changes in this area require better monitoring to mitigate the potential risks of coastal erosion and to better manage coastal sedimentation.

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“…Random forest Random Forest (Breiman, 2001) is also an ensemble model, where the prediction is an aggregation of the outputs from the individual decision trees. The algorithm has been used for land cover classification of the southeastern border region of Bangladesh (Ahmed, Islam, Hasan, Motahar & Sujauddin, 2019;Hassan, Smith, Walker, Rahman, & Southworth, 2018). As shown in Equation 11, the outputs of the individual weak learners are averaged.…”

Section: Preprocessingmentioning

confidence: 99%

Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure

Ahmed

Firoze

Rahman

2020

Journal of Information and Telecommunication

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Since the dawn of human civilization, forced migration scenarios have been witnessed in different regions and populations, and is still present in the twenty-first century. The current largest population of stateless refugees in the world, the Rohingya people, reside in the southeastern border region of Bangladesh. Due to rapid expansion of refugee camps and lack of suitable locations, a large proportion of the infrastructure are at risk of landslides. This study aims to use machine learning for predicting landslide risk of camp infrastructure using geospatial features. Four supervised classification algorithms have been employed viz., (i) Logistic Regression (LR), (ii) Multi-Layer Perceptron (MLP), (iii) Gradient Boosted Trees (GBT) and (iv) Random Forest (RF) and applied on preprocessing varied versions of features. Results show that RF achieves accuracy of 76.19% and AUC of 0.76 on un-scaled features which is higher than all other algorithms. The applications of the study reside in refugee management and landslide susceptibility mapping of Rohingya camps, which can both potentially save refugee lives and serve as a case study for global applications.

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Understanding the political ecology of forced migration and deforestation through a multi-algorithm classification approach: the case of Rohingya displacement in the southeastern border region of Bangladesh

Cited by 25 publications

References 40 publications

Modelling Spatio-Temporal Changes of Tropical Forest Cover in the North-Eastern Region of Bangladesh: Context of Traditional and Co-Management Paradigms

Modelling Spatio-Temporal Changes of Tropical Forest Cover in the North-Eastern Region of Bangladesh: Context of Traditional and Co-Management Paradigms

Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data

Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure

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