Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles
Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles
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使用无人机绘制阿拉斯加和加拿大西北部苔原生态系统植物功能类型覆盖度、高度和地上生物量
DOI：
10.1139/as-2021-0044
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发表时间：
2022
期刊：
影响因子：
3.3
通讯作者：
Goetz, Scott J.
中科院分区：
文献类型：
--
作者：
Orndahl, Kathleen M.;Ehlers, Libby P.;Herriges, Jim D.;Pernick, Rachel E.;Hebblewhite, Mark;Goetz, Scott J.
随着气候变暖，北极植被群落正在迅速变化，这影响了野生动物，碳循环和气候反馈。因此，准确监测植被变化至关重要，但实地监测和卫星监测之间的规模不匹配造成了挑战。无人驾驶航空器遥感已成为实地数据与卫星制图之间的桥梁。我们评估了使用高分辨率无人机图像和无人机衍生的运动结构来预测北极植物功能类型（PFT）在一系列植被群落类型中的覆盖、高度和地上生物量（以下简称生物量）的可行性。我们分类PFT图像，估计覆盖和高度，并模拟生物量从无人机衍生的体积估计。将预测值与现场估计值进行比较，以评估结果。覆盖估计的均方根误差（RMSE）为6.29%-14.2%，高度估计的RMSE为3.29-10.5厘米，这取决于PFT。总地上生物量预测的RMSE为220.5 g m−2，每个PFT的RMSE范围为17.14至164.3 g m−2。落叶和万年青灌木生物量预测最准确，其次是地衣，禾本科，和杂类草生物量。我们的研究结果证明了使用无人机绘制PFT生物量的有效性，这为使用地球观测卫星图像改进大面积PFT的绘制提供了一个链接。
Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling, and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assessed the viability of using high-resolution UAV imagery and UAV-derived Structure from Motion to predict cover, height, and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with a root-mean-square error (RMSE) of 6.29%–14.2%, and height was estimated with an RMSE of 3.29–10.5 cm depending on the PFT. Total aboveground biomass was predicted with an RMSE of 220.5 g m−2, and per-PFT RMSE ranged from 17.14 to 164.3 g m−2. Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.