Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine
Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine
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DOI：
10.3390/rs10081226
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发表时间：
2018-08-01
期刊：
影响因子：
5
通讯作者：
Streletskiy, Dmitry A.
中科院分区：
文献类型：
--
作者：
Nyland, Kelsey E.;Gunn, Grant E.;Streletskiy, Dmitry A.
由于区域扩大，气候变暖正以前所未有的速度在北极发生，有可能加速土地覆盖的变化。由于持续存在的云和雪覆盖问题以及在光谱上相似的土地覆盖类型，利用光学遥感测量和监测北极的土地覆盖变化一直具有挑战性。谷歌地球引擎(GEE)是一个强大的工具，可以使用大量可用的光学图像来有效地研究这些变化。这项工作研究了北极西伯利亚中部叶尼塞河下游地区的土地覆盖变化，并举例说明了GEE在1985年至2017年32年期间使用随机森林分类算法对Landsat密集堆栈的应用，总共参考了1641张图像。这里介绍的半自动方法利用该地区现有的完整陆地卫星记录，仅从受云层和降雪影响最小的像素进行分类，以像素为基础对研究区域进行分类。在研究区域自然环境中观察到的气候变化显示出统计上显著的稳定绿化(21,000公里(2)从冻土带过渡到泰加山脉)和大型湖泊丰度的轻微减少(700公里(2))，这表明永久冻土显著退化。研究成果为多年冻土区遥感提供了一种有效的半自动分类策略和地图产品，可应用于叶尼塞河下游地区未来的区域环境模拟。
Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area's natural environments show a statistically significant steady greening (21,000 km(2) transition from tundra to taiga) and a slight decrease (700 km(2)) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.