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SPATIO-TEMPORAL DYNAMICS ALONG THE TERRAIN GRADIENT OF DIVERSE LANDSCAPE

机译:多样化地形地形梯度的时空动力学

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摘要

Land use (LU) land cover (LC) information at a temporal scale illustrates the physical coverage of the Earth's terrestrial surface according to its use and provides the intricate information for effective planning and management activities. LULC changes are stated as local and location specific, collectively they act as drivers of global environmental changes. Understanding and predicting the impact of LULC change processes requires long term historical restorations and projecting into the future of land cover changes at regional to global scales. The present study aims at quantifying spatio temporal landscape dynamics along the gradient of varying terrains presented in the landscape by multi-data approach (MDA). MDA incorporates multi temporal satellite imagery with demographic data and other additional relevant data sets. The gradient covers three different types of topographic features, planes; hilly terrain and coastal region to account the significant role of elevation in land cover change. The seasonality is another aspect to be considered in the vegetation dominated landscapes; variations are accounted using multi seasonal data. Spatial patterns of the various patches are identified and analysed using landscape metrics to understand the forest fragmentation. The prediction of likely changes in 2020 through scenario analysis has been done to account for the changes, considering the present growth rates and due to the proposed developmental projects. This work summarizes recent estimates on changes in cropland, agricultural intensification, deforestation, pasture expansion, and urbanization as the causal factors for LULC change.
机译:土地使用(LU)陆地覆盖(LC)占地面积的信息根据其使用说明了地球陆地表面的物理覆盖范围,并提供了有效的规划和管理活动的复杂信息。 LULC的变化将被称为本地和地点特定的,共同担任全球环境变化的驱动因素。理解和预测LULC变化流程的影响需要长期历史修复,并将未来的土地覆盖变更在区域到全球范围内。本研究旨在沿着多数据方法(MDA)在景观中呈现的不同地形的梯度来量化时空时间景观动态。 MDA包含多个时间卫星图像,具有人口统计数据和其他相关数据集。梯度涵盖三种不同类型的地形特征,平面;丘陵地形和沿海地区考虑到陆地覆盖变革中的高度的重要作用。季节性是在植被主导地位的景观中考虑的另一个方面;使用多季节性数据进行核算变化。使用横向度量来识别和分析各种贴片的空间模式,以了解森林碎片。已经完成了2020年通过情景分析的可能变化的预测,以考虑到更改,考虑到当前增长率,并且由于拟议的发展项目。这项工作总结了最近关于农田,农业强化,森林砍伐,牧场扩张和城市化变化的估计,作为LULC变革的因果因素。

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