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Pankaj Singha
Department of Geography, University of Gour Banga, Malda, India

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Article
Published: 25 June 2021 in GeoJournal
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River island (locally Charland) of lower Gangetic plain between Rajmahal and the Farakka barrage in India is the human occupancy of the river bank erosion evicted people, but these areas are prone to numerous problems such as river bank erosion (due to massive channel shifting), flooding, poor accessibility, limited livelihood options etc. This study assessed the livelihood vulnerability using Livelihood Vulnerability Index (LVI) framework, comprising several components and sub-components. Total 640 households were selected and collected information about socio-demographic profile, livelihood strategies, water, health, food and natural disaster status. The contributing factors (exposure, sensitivity and adaptive capacity) were integrated to estimate the livelihood vulnerability index using the LVI and LVI-IPCC approaches. Results suggested that Gadai char households are more vulnerable to water security, food security, health status and natural hazards compared to Bhutni char. But in the case of livelihood strategies, Bhutni char is more vulnerable to Gadai char. Due to low adaptive capacity, both char dwellers are very vulnerable to natural hazards. Based on the LVI score Gadai char is more vulnerable than Bhutni char. The LVI-IPCC vulnerability result also reflects that Gadai char is more vulnerable compared to Bhutni char. The findings will help to frame policies and programs of government and non-government organizations to reduce vulnerability and enhance the adaptive capacity of the rural char community.

ACS Style

Pankaj Singha; Swades Pal. Livelihood vulnerability assessment of the Island (Char) dwellers in the Ganges riparian corridor, India. GeoJournal 2021, 1 -17.

AMA Style

Pankaj Singha, Swades Pal. Livelihood vulnerability assessment of the Island (Char) dwellers in the Ganges riparian corridor, India. GeoJournal. 2021; ():1-17.

Chicago/Turabian Style

Pankaj Singha; Swades Pal. 2021. "Livelihood vulnerability assessment of the Island (Char) dwellers in the Ganges riparian corridor, India." GeoJournal , no. : 1-17.

Journal article
Published: 09 December 2020 in Remote Sensing Applications: Society and Environment
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Landscape structure or fragmentations have important effects on ecosystem services, with a common assumption being that fragmentation effects can reduce ecosystem services provision. Impact of Land use land cover (LULC) change on ecosystem service value is well explored techniques in recent times, but landscape fragmentation effect on ecosystem services value (ESV) is yet not quantitatively explored. The present work has intended to focus on the fragmentation effect on ESV introducing a new approach along with the effect of LULC change on ESV. All the analysis is done considering four times (1991, 2001, 2011, 2019) from Landsat images. Fragmentation analysis in ArcGis software has generated six hierarchic landscape units like patch, edge, perforated, small core, medium core and large core. For showing fragmentation effect ESV is computed for a typical LULC as a whole using Coefficient value (CV) of Costanza, 1997 and 2014 and ESV of the fragmented landscapes using weighted CV based on Analytic Hierarchy Process (AHP). Result has clearly demonstrated that due to fragmentation of forest and water body, a major means of qualitative degradation of eco-region, ESV of the respective lands have reduced from 24.43 to 20.57 million USD/year from 1991 to 2019. Total ESV of agriculture and built up land were respectively 43.62 and 4.15 million USD in 1991 and it was changed to 37.92 and 5.85 million USD in 2019. Computed ESV of forest without considering fragmentation effect is 14.68 million USD/year but it is diminuend only 5.71 million USD/year if fragmentation effect is considered in 2019. The ESV of the water body is six times lower in the fragmented landscape as per 2019. Anthropogenic effects have a paramount role for growing land use change, fragmentation and change of ESV in a natural landscape.

ACS Style

Swades Pal; Pankaj Singha; Kabita Lepcha; Sandipta Debanshi; Swapan Talukdar; Tamal Kanti Saha. Proposing multicriteria decision based valuation of ecosystem services for fragmented landscape in mountainous environment. Remote Sensing Applications: Society and Environment 2020, 21, 100454 .

AMA Style

Swades Pal, Pankaj Singha, Kabita Lepcha, Sandipta Debanshi, Swapan Talukdar, Tamal Kanti Saha. Proposing multicriteria decision based valuation of ecosystem services for fragmented landscape in mountainous environment. Remote Sensing Applications: Society and Environment. 2020; 21 ():100454.

Chicago/Turabian Style

Swades Pal; Pankaj Singha; Kabita Lepcha; Sandipta Debanshi; Swapan Talukdar; Tamal Kanti Saha. 2020. "Proposing multicriteria decision based valuation of ecosystem services for fragmented landscape in mountainous environment." Remote Sensing Applications: Society and Environment 21, no. : 100454.

Review
Published: 02 April 2020 in Remote Sensing
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Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

ACS Style

Swapan Talukdar; Pankaj Singha; Susanta Mahato; Shahfahad; Swades Pal; Yuei-An Liou; Atiqur Rahman. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing 2020, 12, 1135 .

AMA Style

Swapan Talukdar, Pankaj Singha, Susanta Mahato, Shahfahad, Swades Pal, Yuei-An Liou, Atiqur Rahman. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing. 2020; 12 (7):1135.

Chicago/Turabian Style

Swapan Talukdar; Pankaj Singha; Susanta Mahato; Shahfahad; Swades Pal; Yuei-An Liou; Atiqur Rahman. 2020. "Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review." Remote Sensing 12, no. 7: 1135.