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Prediction of land use/cover change in the Bharathapuzha river basin, India using geospatial techniques

机译:使用地理空间技术预测印度Bharathapuzha流域的土地利用/覆盖变化

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The Bharathapuzha river basin, once endowed with dense vegetation and abundant water, has been experiencing acute water shortage and extreme climatic conditions in recent times. To understand the influence of human interventions on the natural environmental conditions, including the problems mentioned above, it is essential to critically examine the changes in land use/cover over these years. The objective of this study is to assess land use/cover change in the Bharathapuzha river basin, Kerala during the period 1990-2017 using LANDSAT series satellite images. The dynamics of land use/cover change were quantified and mapped using geospatial techniques. The multi-temporal LANDSAT images were classified by supervised maximum likelihood method to generate the corresponding land use/cover maps; changes in land use/cover in the river basin were subsequently detected by the post-classification technique. Results of the study revealed a drastic change in land use/cover in the period 1990-2017; the primary causes of this were deforestation and urbanization. The near- and long-term future land use/cover maps of the basin for 2020 and 2035 were generated from the historically retrieved land use/cover change pattern. Multi-Layer Perceptron Neural Network and Markov chain techniques were used to generate future land use/cover maps. These maps reveal that the predominant land use/cover class in the basin will be barren land and about 46.13% of the existing (in 2017) dense vegetation will diminish by 2035. The efficiency of sustainable watershed management activities in the river basin can be improved based on the critical observations from this study.
机译:巴拉萨普扎(Bharathapuzha)流域曾经拥有茂密的植被和丰富的水,近来一直面临着严重的缺水和极端气候条件。为了了解人为干预对自然环境条件(包括上述问题)的影响,至关重要的是,必须认真研究这些年来土地使用/覆盖的变化。这项研究的目的是使用LANDSAT系列卫星图像评估喀拉拉邦Bharathapuzha流域在1990-2017年期间的土地利用/覆盖变化。利用地理空间技术对土地利用/覆盖变化的动态进行了量化和制图。通过监督最大似然法对多时相LANDSAT影像进行分类,以生成相应的土地利用/覆盖图;随后通过后分类技术检测流域土地利用/覆盖的变化。研究结果显示,1990年至2017年期间,土地使用/覆盖面积发生了巨大变化;造成这种情况的主要原因是森林砍伐和城市化。流域近期和长期的土地利用/覆盖图为2020年和2035年,是根据历史检索的土地利用/覆盖率变化模式绘制的。多层感知器神经网络和马尔可夫链技术用于生成未来的土地使用/覆盖图。这些地图显示,流域的主要土地利用/覆盖类型将是贫瘠土地,到2035年,现有(2017年)稠密植被的约46.13%将会减少。流域可持续流域管理活动的效率可以提高基于这项研究的重要观察结果。

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