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Monitoring forest disturbances in Southeast Oklahoma using Landsat and MODIS images

机译:利用Landsat和MODIS影像监测俄克拉荷马州东南部的森林干扰

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Monitoring forest disturbances using remote sensing data with high spatial and temporal resolution can reveal relationships between forest disturbances and forest ecological patterns and processes. In this study, we fused Landsat data at high spatial resolution (30 m) with 8-day MODIS data to produce high spatial and temporal resolution image time-series. The Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) is a simple but effective fusion method. We adapted the STAARCH fusion method to successfully produce a time-series of disturbances with high overall accuracy (89-92%) in mixed forests in southeast Oklahoma. The results demonstrated that in southeast Oklahoma, forest area disturbed in 2011 was higher than it was in 2000. However, two remarkable drops were identified in 2001 and 2006. We speculated that the drops were related to the economic recessions causing reduction in the demand of woody products. The detected fluctuation of area disturbed calls for continuing monitoring of spatial and temporal changes in this and other forest landscapes using high spatial and temporal resolution imagery datasets to better recognize the economic and environmental factors, as well as the consequences of those changes. (C) 2015 Elsevier B.V. All rights reserved.
机译:使用具有高时空分辨率的遥感数据监测森林扰动可以揭示森林扰动与森林生态模式和过程之间的关系。在这项研究中,我们将高空间分辨率(30 m)的Landsat数据与8天的MODIS数据融合在一起,以产生高空间和时间分辨率的图像时间序列。映射反射率变化的时空自适应算法(STAARCH)是一种简单但有效的融合方法。我们采用了STAARCH融合方法,成功地在俄克拉荷马州东南部的混交林中产生了具有较高总体准确度(89-92%)的干扰时间序列。结果表明,俄克拉荷马州东南部2011年的受干扰森林面积高于2000年。但是,在2001年和2006年发现了两个显着的下降。我们推测,下降与经济衰退有关,导致需求下降。木制品。检测到的受区域扰动的波动要求使用高时空分辨率图像数据集来继续监视此森林景观和其他森林景观的时空变化,以更好地识别经济和环境因素以及这些变化的后果。 (C)2015 Elsevier B.V.保留所有权利。

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