Uncrewed aerial vehicle remote sensing imagery of postfire vegetation in Siberian larch forests 2018-2019

This data set contains remote sensing imagery collected using uncrewed aerial vehicles at a series of fire perimeters in larch forests located in northeastern Siberia in 2018 and 2019. Images were collected using visible sensors (blue, green, and red wavelengths) and multispectral sensors (green, red, red-edge, and near-infrared wavelengths). The data were collected perpendicular to fire perimeter boundaries in order to characterize variation vegetation composition and structure between burned and burned forests, and as a function of distance from the unburned forest edge. The resulting images are co-located with field observations of ecosystem properties collected as part of this project that are posted in a related data set (Alexander et al, 2018).
Heather Alexander, Jennie DeMarco, Rebecca Hewitt, Jeremy Lichstein, Michael Loranty, et al. 2018. Fire influences on forest recovery and associated climate feedbacks in Siberian Larch Forests, Russia, June-July 2018. Arctic Data Center. urn:uuid:a5de1514-78d3-449f-aad1-2ff8f8d0fb27.

本数据集收录了2018至2019年于西伯利亚东北部落叶松林的多处火灾边界处，通过无人驾驶航空飞行器（Uncrewed Aerial Vehicles, UAV）采集的遥感影像。影像采集使用两类传感器：可见光传感器（覆盖蓝、绿、红波段）与多光谱传感器（覆盖绿、红、红边及近红外波段）。数据采集方向垂直于火灾边界，旨在表征过火与未过火森林间植被组成、结构的差异，以及植被特征随距未过火林缘距离的变化规律。所获影像与本项目配套采集的生态系统属性野外观测数据空间匹配，该野外观测数据已发布于关联数据集（Alexander等，2018）。
希瑟·亚历山大、珍妮·德马科、丽贝卡·休伊特、杰里米·利奇斯坦、迈克尔·洛兰蒂等. 2018. 俄罗斯西伯利亚落叶松林火灾对森林恢复及相关气候反馈的影响，2018年6-7月. 北极数据中心. urn:uuid:a5de1514-78d3-449f-aad1-2ff8f8d0fb27.