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首页> 外文期刊>Applied Geography >Mapping long-term land use and land cover change in the central Himalayan region using a tree-based ensemble classification approach
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Mapping long-term land use and land cover change in the central Himalayan region using a tree-based ensemble classification approach

机译：使用基于树的集成分类方法绘制喜马拉雅中部地区的长期土地利用和土地覆被变化图

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Forest cover and its change analysis along with nexus between other land cover types are often seen as insufficient data quality for operational applications in the Himalayan region. Despite extensive documentation reporting rapid demographic, socio-economic and environmental changes in this region, we lack comprehensive detailed assessments of spatial distribution of land use/land cover (LULC) change over an extended period of time. In this study, we overcame this limitation by producing annual maps of change among forests and other LULC classes in the Kumaon division in the state of Uttarakhand, India. This is the first attempt to develop a database for this region using public domain Landsat images and replicable mapping techniques. To deal with high spatial and temporal variability as well as complex multi-signature classes, this study uses a tree-based ensemble classification approach. The central premise of the approach is to exploit multi-seasonal information using characteristic temporal signatures in several spectral regions along with various environmental variables to identify twenty (20) LULC classes spanning three decades, focussing on distinguishing geographically dominant forest types. The maps were combined into seven LULC classes with reference to global databases. Random forest (RF) classifier was used to create seasonal maps, and knowledge-based decision level fusion was used to produce annual composite maps. Overall accuracies were equal to 82% (kappa = 0.75), 87% (kappa = 0.81), 87% (kappa = 0.82), and 88% (kappa = 0.83) for 1990,1999, 2009 and 2014, respectively, while detailed maps had moderately high (similar to 70%) overall accuracies. As forests in the Himalayan region represent the most widespread vegetation structure, development of such time series analysis in this region can be potentially used for national and regional resource management efforts. This study, therefore, gives an insight on the potential of using a tree-based ensemble classification approach to provide a baseline database, which can aid in developing practical field inventories and forest conservation policies. (C) 2016 Elsevier Ltd. All rights reserved.
机译：森林覆盖率及其变化分析以及其他土地覆盖类型之间的联系经常被视为喜马拉雅地区业务应用的数据质量不足。尽管有大量文献报道了该地区人口，社会经济和环境的快速变化，但我们仍缺乏对土地使用/土地覆被（LULC）长期变化的空间分布的全面详细评估。在这项研究中，我们通过制作印度北阿坎德邦州库马恩省森林和其他LULC类之间的年度变化图，克服了这一限制。这是使用公共领域的Landsat图像和可复制的映射技术为该区域开发数据库的首次尝试。为了处理高时空变异性以及复杂的多重签名类别，本研究使用了基于树的集成分类方法。该方法的中心前提是利用几个光谱区域中的特征性时标以及各种环境变量来利用多季节信息，以识别跨越三十年的二十（20）个LULC类，重点在于区分地理优势林类型。参照全球数据库，将地图分为七个LULC类。随机森林（RF）分类器用于创建季节性地图，而基于知识的决策水平融合用于生成年度复合地图。 1990年，1999年，2009年和2014年的总体准确度分别等于82％（kappa = 0.75），87％（kappa = 0.81），87％（kappa = 0.82）和88％（kappa = 0.83），详细情况地图具有较高的总体准确度（大约70％）。由于喜马拉雅地区的森林代表着最广泛的植被结构，因此在该地区开展这种时间序列分析可潜在地用于国家和地区的资源管理工作。因此，本研究对使用基于树的集成分类方法提供基准数据库的潜力提供了见解，该基准数据库可帮助制定实用的实地调查清单和森林保护政策。 （C）2016 Elsevier Ltd.保留所有权利。

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