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Analysis and Prediction of Land Use Changes Related to Invasive Species and Major Driving Forces in the State of Connecticut

机译:康涅狄格州与入侵物种和主要驱动力有关的土地利用变化的分析和预测

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Land use and land cover (LULC) patterns play an important role in the establishment and spread of invasive plants. Understanding LULC changes is useful for early detection and management of land-use change to reduce the spread of invasive species. The primary objective of this study is to analyze and predict LULC changes in Connecticut. LULC maps for 1996, 2001 and 2006 were selected to analyze past land cover changes, and then potential LULC distribution in 2018 was predicted using the Multi-Layer Perceptron Markov Chain (MLP_MC) model. This study shows that the total area of forest has been decreasing, mainly caused by urban development and other human activity in Connecticut. The model predicts that the study area will lose 5535 ha of deciduous forest and gain 3502 ha of built-up area from 2006 to 2018. Moreover, forests near built-up areas and agriculture lands appear to be more vulnerable to conversion. Changes in LULC may result in subtle spatial shifts in invasion risk by an abundant invasive shrub, Japanese barberry ( Berberis thunbergii ). The gain of developed areas at the landscape scale was most closely linked to increased future invasion risk. Our findings suggest that the forest conversion needs to be controlled and well managed to help mitigate future invasion risk.
机译:土地利用和土地覆盖(LULC)模式在入侵植物的建立和传播中起着重要作用。了解LULC的变化对于早期发现和管理土地利用变化以减少入侵物种的扩散很有用。这项研究的主要目的是分析和预测康涅狄格州的LULC变化。选择1996年,2001年和2006年的LULC地图来分析过去的土地覆盖变化,然后使用多层感知器马尔可夫链(MLP_MC)模型预测2018年的LULC分布。这项研究表明,森林总面积一直在减少,这主要是由于康涅狄格州的城市发展和其他人类活动所致。该模型预测,从2006年到2018年,研究区域将失去5535公顷的落叶林,而增加3502公顷的建成区。此外,建成区附近的森林和农业用地似乎更容易转化。 LULC的变化可能会导致大量侵袭性灌木小bar(小ber(Berberis thunbergii))侵袭风险的空间变化。景观规模上发达地区的增加与未来入侵风险的增加最密切相关。我们的发现表明,森林转化必须受到控制和妥善管理,以帮助减轻未来的入侵风险。

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