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

IF 2.7 Q1 GEOGRAPHY Annals of GIS Pub Date : 2021-01-05 DOI:10.1080/19475683.2020.1870558
Nasibul Alam, Subrata Saha, Srimanta Gupta, S. Chakraborty
{"title":"Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain: a Geospatial approach","authors":"Nasibul Alam, Subrata Saha, Srimanta Gupta, S. Chakraborty","doi":"10.1080/19475683.2020.1870558","DOIUrl":null,"url":null,"abstract":"ABSTRACT Presently, sustainability of floodplain, a diverse element of the riverine landscape, provides an ideal research setting for investigating complex interaction between anthropogenic disturbance and eco-environmental degradation. Nowadays, these floodplains are continually degraded and fragmented on account of unsustainable land use. To analyse the spatial and temporal changes of landuse/landcover, a supervised classification (maximum likelihood algorithm) method has been made for the period 1998 to 2018. Present research simulates and predicts landuse/landcover dynamics of lower stretch of the Ganges river up to 2038 to analyse future riverine landscape dynamics stressed by various natural and socio-economic factors based on Cellular Automata-Artificial Neuron Network (CA-ANN) model clubbed with Modules for Land Use Change Evaluation (MOLUSCE) plugin of QGIS software. Outcome of research reveals that the trend of agriculture land, sand, and inland waterbody areas is reduced by 15.75, 5.71, and 1.95%, whereas, for orchard, agricultural fallow and bare land areas increased by 7.94, 7.92, and 5.69% for the period from 1998 to 2018. The simulation model predicted a continuation of the similar trend till 2038. The significant reduction of agricultural land and sand areas is largely an attribute to floodplain degradation in an altered hydrological regime. Ultimately, hydro-morphological changes, increasing population pressure, and agriculture intensification in floodplain landscape were identified as main driving forces in temporal landuse/landcover changes. The prediction of future forecast indicates that if the present rate of landuse/landcover trend persists in the study stretch of Ganges river without appropriate sustainable development practice, severe floodplain degradation will ensue. This study provides a holistic measure for understanding long-term environmental degradation related to anthropogenic activities and impact of climate changes in floodplain landscape at local and regional scale.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"384 2 1","pages":"299 - 314"},"PeriodicalIF":2.7000,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of GIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19475683.2020.1870558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 25

Abstract

ABSTRACT Presently, sustainability of floodplain, a diverse element of the riverine landscape, provides an ideal research setting for investigating complex interaction between anthropogenic disturbance and eco-environmental degradation. Nowadays, these floodplains are continually degraded and fragmented on account of unsustainable land use. To analyse the spatial and temporal changes of landuse/landcover, a supervised classification (maximum likelihood algorithm) method has been made for the period 1998 to 2018. Present research simulates and predicts landuse/landcover dynamics of lower stretch of the Ganges river up to 2038 to analyse future riverine landscape dynamics stressed by various natural and socio-economic factors based on Cellular Automata-Artificial Neuron Network (CA-ANN) model clubbed with Modules for Land Use Change Evaluation (MOLUSCE) plugin of QGIS software. Outcome of research reveals that the trend of agriculture land, sand, and inland waterbody areas is reduced by 15.75, 5.71, and 1.95%, whereas, for orchard, agricultural fallow and bare land areas increased by 7.94, 7.92, and 5.69% for the period from 1998 to 2018. The simulation model predicted a continuation of the similar trend till 2038. The significant reduction of agricultural land and sand areas is largely an attribute to floodplain degradation in an altered hydrological regime. Ultimately, hydro-morphological changes, increasing population pressure, and agriculture intensification in floodplain landscape were identified as main driving forces in temporal landuse/landcover changes. The prediction of future forecast indicates that if the present rate of landuse/landcover trend persists in the study stretch of Ganges river without appropriate sustainable development practice, severe floodplain degradation will ensue. This study provides a holistic measure for understanding long-term environmental degradation related to anthropogenic activities and impact of climate changes in floodplain landscape at local and regional scale.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
河漫滩可持续管理背景下的河流景观动态预测建模:地理空间方法
目前,河漫滩作为河流景观的一个多元元素,其可持续性为研究人为干扰与生态环境退化之间复杂的相互作用提供了理想的研究环境。如今,由于不可持续的土地利用,这些洪泛平原不断退化和破碎。为了分析1998 - 2018年土地利用/土地覆盖的时空变化,采用监督分类(最大似然算法)方法。本文基于元胞自动机-人工神经网络(CA-ANN)模型,结合QGIS软件的土地利用变化评估模块(MOLUSCE)插件,对恒河下游至2038年的土地利用/覆被动态进行了模拟和预测,分析了各种自然和社会经济因素强调下的未来河流景观动态。研究结果表明,1998 - 2018年,农用地、沙土和内陆水体面积分别减少了15.75%、5.71%和1.95%,果园、农业闲耕地和裸地面积分别增加了7.94%、7.92%和5.69%。模拟模型预测,类似的趋势将持续到2038年。农业用地和沙区的显著减少主要是由于在改变的水文制度下洪泛区退化。结果表明,洪泛平原的水文形态变化、人口压力增加和农业集约化是土地利用/覆被变化的主要驱动力。对未来预测的预测表明,如果恒河研究河段的土地利用/覆被速度保持不变,不采取适当的可持续发展措施,将导致河漫滩严重退化。该研究为了解与人为活动相关的长期环境退化和气候变化对漫滩景观的影响提供了局域尺度和区域尺度的整体测度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Annals of GIS
Annals of GIS Multiple-
CiteScore
8.30
自引率
2.00%
发文量
31
期刊最新文献
Zero watermarking algorithm for BIM data based on distance partitioning and local feature Controlling for spatial confounding and spatial interference in causal inference: modelling insights from a computational experiment Application of GIS and fuzzy sets to small-scale site suitability assessment for extensive brackish water aquaculture Revealing intra-urban hierarchical spatial structure through representation learning by combining road network abstraction model and taxi trajectory data The time- and distance-decay effects of hurricane relevancy on social media: an empirical study of three hurricanes in the United States
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1