大查科美洲地区近期土地利用和土地覆盖变化动态

S. Banchero, D. Abelleyra, S. Verón, M. J. Mosciaro, F. Arévalos, J. Volante
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引用次数: 2

摘要

土地改造是人类对地球表面最重要的变化过程之一。因此,土地利用、土地覆盖时间序列是国家范围内环境监测、自然资源管理、国土规划执行的关键输入。在此,我们利用MapBiomas计划来描述2010年至2017年间大查科的土地利用和土地覆盖(LULC)变化。具体来说,我们试图a)量化主要LULC类别的年度变化;b)确定主要的土地利用价值转变,c)将这些转变与当前的土地利用政策联系起来。在MapBiomas项目中,基于Landsat的年度地图描绘了天然木本植被、天然草本植被、分散的自然植被、农田、牧场、裸地和水域。我们使用随机森林机器学习算法,通过对高分辨率图像的视觉解释产生的样本进行训练。年总体精度在0.73到0.74之间。结果表明,2010 - 2017年间,农业和牧场面积增加了约3.7 Mha,而天然林面积减少了2.3 Mha。从森林到农业的转变占森林砍伐总量的1.14%,而86%与牧场和天然草本植被有关。在阿根廷,森林损失主要发生在《领土规划法》未考虑的地区(39%),其次是中等(33%)、高(19%)和低(9%)保护优先级别。这些结果说明了遥感在描述在扩大的地区和时间框架内发生的复杂的人类与环境相互作用方面的潜在贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recent Land Use and Land Cover Change Dynamics in the Gran Chaco Americano
Land transformation is one of the most significant human changes on the Earth’s surface processes. Therefore, land use land cover time series are a key input for environmental monitoring, natural resources management, territorial planning enforcement at national scale. We here capitalize from the MapBiomas initiative to characterize land use land cover (LULC) change in the Gran Chaco between 2010 and 2017. Specifically we sought to a) quantify annual changes in the main LULC classes; b) identify the main LULC transitions and c) relate these transitions to current land use policies. Within the MapBiomas project, Landsat based annual maps depicting natural woody vegetation, natural herbaceous vegetation, dispersed natural vegetation, cropland, pastures, bare areas and water. We used Random Forest machine learning algorithms trained by samples produced by visual interpretation of high resolution images. Annual overall accuracy ranged from 0,73 to 0,74. Our results showed that, between 2010 and 2017, agriculture and pasture lands increased ca. 3.7 Mha while natural forestry decreased by 2.3 Mha. Transitions from forests to agriculture accounted for 1.14% of the overall deforestation while 86% was associated to pastures and natural herbaceous vegetation. In Argentina, forest loss occurred primarily (39%) on areas non considered by the territorial planning Law, followed by medium (33%), high (19%) and low (9%) conservation priority classes. These results illustrate the potential contribution of remote sensing to characterize complex human environmental interactions occurring over extended areas and time frames.
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