Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain: a Geospatial approach

Alam, Nasibul; Saha, Swati; Gupta, Srimanta; Chakraborty, Subha

doi:10.1080/19475683.2020.1870558

Cited by 50 publications

(22 citation statements)

References 44 publications

(44 reference statements)

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“…The accuracy of LULC change and LULC prediction is influenced by many factors, among them, the explanatory variables used. Much worldwide research has diagnosed slope, elevation, roads, water bodies, protected area, soils and precipitation as the most powerful determinants of land cover changes (Leśniewska-Napierała et al 2019, Alam et al 2021, Faisal et al 2021). Our study used some of them; the soil factor was not used due to difficulty in data acquisition.…”

Section: Discussionmentioning

confidence: 99%

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GIS-Based Land Cover Analysis and Prediction Based on Open-Source Software and Data

Dawid

Bielecka

2022

Quaestiones Geographicae

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The study aims at land cover prediction based on cellular automata and artificial neural network (CA-ANN) method implemented in the Methods Of Land Use Change Evaluation (MOLUSCE) tool. The Tricity region and the neighbouring counties of Gdański and Kartuzy were taken as the research areas, and coordination of information on the environment (CORINE Land Cover, CLC, CLMS 2022) data for 2006, 2012 and 2018 were used to analyse, simulate and predict land cover for 2024, the next reference year of the CORINE inventory. The results revealed an increase in artificial surfaces, with the highest value during the period 2006–2012 (86.56 km2). In total, during the period 2006–2018, the growth in urbanised area amounted to 95.37 km2. The 2024 prediction showed that artificial surfaces increased by 9.19 km2, resulting in a decline in agricultural land.

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Section: Discussionmentioning

confidence: 99%

“…The reported accuracy of LULC prediction measured by the maximum kappa value of 0.63 was considered by many (Alam et al 2021, Jayasinghe et al 2021 as favourable. However, as found by Jayasinghe et al ( 2021) and Faisal et al (2021), the platform used is of utmost importance.…”

Section: Discussionmentioning

confidence: 99%

GIS-Based Land Cover Analysis and Prediction Based on Open-Source Software and Data

Dawid

Bielecka

2022

Quaestiones Geographicae

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“…The CA model has been widely applied to simulate the spatial evolution of land use [33]- [35]. The SDG-CA model was implemented herein by comprehensively considering the initial transfer probability of land use transformation, the empirical transfer weights between different land types, the influences of cell neighborhoods, the competition mechanism among various land types, and other constraint conditions.…”

Section: Multiscenario Land Use Simulation Based On the Sdg-ca Modelmentioning

confidence: 99%

Multi-Scenario Simulation of Land Use for Sustainable Development Goals

Cao

Chang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Considering the UN sustainable development goals (SDGs) released in 2015, this article constructed an SDGoriented land use simulation (SDG-LUS) model incorporating SDG-oriented system dynamics (SDG-SD) and SDG-oriented cellular automata (SDG-CA), and utilized it to simulate land use changes in the Yangtze river delta region. The SDG-SD model was developed to predict the land use demands from 2021 to 2030 under the constraints of multiple SDG indicators, including economic indicators (SDG2.3.1 and SDG8.1.1), social indicators (SDG3.c.1, SDG4.1.2, SDG5.b.1, SDG9.C.1, SDG 9.1.2, SDG11.2.1, and SDG11.7.1) and environmental indicators (SDG6.3.1 and SDG11.6.2). Four sustainable development scenarios, including reference, economic development, environmental protection and social progress scenarios, were established by utilizing the index and indicator board of the SDG indicators in 2030. Then, the SDG-CA model was applied to spatialize and simulate the land use evolution from 2021 to 2030 under different sustainable development scenarios. The results validate the applicability of the SDG-LUS model, and confirm that scenario simulations of different sustainability levels are conducive to supporting the formulation of sustainable land use plans.

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“…Landscape shift models help decipher the forces that form landscapes. One justification for modelling is to see what long-term effects extrapolating short-term landscape dynamics can have (Alam et al, 2021). Geo-analysis models for tracking LU/LC status and dynamic change have become one of the most rapid, accurate, and efficient methods available today, thanks to the development of more advanced RS and GIS technologies (Das & Sarkar, 2019).…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Change Detection in the Bhavani Basin of India through Remote Sensing Approach

Manikandan¹,

Rangarajan²

2022

JER is an international, peer-reviewed journal that publishes f

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Using remote sensing data to detect changes in land use and land cover (LULC) is a valuable source of information for various decision support systems. Land use and land cover identification data was used to analyse land conservation, sustainable development, and water resource management. This research aims to determine how the Bhavani basin land use and land cover have changed over the period of time. Land cover changes were detected using Landsat Thematic Mapper (TM) 30 m resolution images in the GIS environment and with image processing techniques for the four years 1999, 2007, 2014, and 2020. The differences in the landuse and land cover classes are described using ERDAS imagine version 2015 and ARC GIS software. The four land cover classes viz. water body, built-up land, barren land, and vegetation were used to classify the region. The accuracy evaluation was assessed separately using the kappa coefficient after carefully examining the image pre-processing and classification. The overall accuracy in the basin was found to be 83.23%, 86.45%, 85.83%, and 88.75 % with a kappa coefficient of 0.79, 0.81, 0.87, 0.85 for the years 1999, 2007, 2014, and 2020 respectively. The Bhavani basin is mostly covered by barren and vegetation. According to the findings, the basin's built-up area has risen by 1.5 percentage to 3.5 percentage in the last 20 years. The increase in the vegetation area and reduction in the barren area may lead to low soil erosion.

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Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain: a Geospatial approach

Cited by 50 publications

References 44 publications

GIS-Based Land Cover Analysis and Prediction Based on Open-Source Software and Data

GIS-Based Land Cover Analysis and Prediction Based on Open-Source Software and Data

Multi-Scenario Simulation of Land Use for Sustainable Development Goals

Land Use and Land Cover Change Detection in the Bhavani Basin of India through Remote Sensing Approach

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