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Detecting changes in understory and canopy vegetation cycles in West Central Alberta using a fusion of Landsat and MODIS
Applied Vegetation Science ( IF 2.8 ) Pub Date : 2019-11-27 , DOI: 10.1111/avsc.12466
Cameron J. R. McClelland 1 , Nicholas C. Coops 1 , Ethan E. Berman 1 , Sean P. Kearney 1 , Scott E. Nielsen 2 , A. Cole Burton 3 , Gordon B. Stenhouse 4
Affiliation  

AIMS: To model regional vegetation cycles through data fusion methods for creating a 30‐m daily vegetation product from 2000 to 2018 and to analyze annual vegetation trends over this time period. LOCATION: The Yellowhead Bear Management Area, a 31,180‐km² area in west central Alberta, Canada. METHODS: In this paper, we use Dynamic Time Warping (DTW) as a data fusion technique to combine Landsat 5, 7 and 8 satellite data and Moderate Resolution Image Spectroradiometer (MODIS) Aqua and Terra imagery, to quantify daily vegetation using Enhanced Vegetation Index at a 30‐m resolution, for the years 2000–2018. We validated this approach, entitled DRIVE (Daily Remote Inference of VEgetation), using imagery acquired from a network of ground cameras. RESULTS: When DRIVE was compared to start and end of season dates (SOS and EOS respectively) derived from ground cameras, correlations were r = 0.73 at SOS and r = 0.85 at EOS with a mean absolute error of 7.17 days at SOS and 10.76 days at EOS. Results showed that DRIVE accurately increased spatial and temporal resolution of remote‐sensing data. We demonstrated that SOS is advancing at a maximum rate of 0.78 days per year temporally over the 18‐year time period for varying elevation gradients and land cover classes over the region. CONCLUSIONS: With DRIVE, we demonstrate the utility of DTW in quantifying vegetation cycles over a large heterogeneous region and determining how changing climate is affecting regional vegetation. DRIVE may prove to be an important method to determine how carbon sequestration is varying within fine‐scale individual plant communities in response to changing climate and likely will be beneficial to wildlife movement and habitat selection studies examining the varying response of wildlife species to changing vegetation cycles under shifting climatic conditions.

中文翻译:

使用 Landsat 和 MODIS 的融合检测艾伯塔省中西部林下和冠层植被循环的变化

目的:通过数据融合方法对区域植被周期进行建模,以创建 2000 年至 2018 年的 30 米日植被产品,并分析该时期的年度植被趋势。位置:黄头熊管理区,位于加拿大艾伯塔省中西部的一个 31,180 平方公里的区域。方法:在本文中,我们使用动态时间扭曲 (DTW) 作为数据融合技术,将 Landsat 5、7 和 8 卫星数据与中分辨率影像光谱仪 (MODIS) Aqua 和 Terra 影像相结合,使用增强型植被指数量化日常植被2000-2018 年,分辨率为 30 米。我们使用从地面摄像机网络获取的图像验证了这种名为 DRIVE(植被的每日远程推理)的方法。结果:当 DRIVE 与从地面摄像机得出的季节开始和结束日期(分别为 SOS 和 EOS)进行比较时,相关性在 SOS 处为 r = 0.73,在 EOS 处为 r = 0.85,SOS 处的平均绝对误差为 7.17 天,EOS 处为 10.76 天. 结果表明,DRIVE 准确地提高了遥感数据的空间和时间分辨率。我们证明了 SOS 在 18 年的时间段内以每年 0.78 天的最大速度推进,以适应该地区不同的海拔梯度和土地覆盖等级。结论:通过 DRIVE,我们展示了 DTW 在量化大型异质区域的植被周期和确定气候变化如何影响区域植被方面的效用。
更新日期:2019-11-27
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