Unpiloted aerial system (UAS) LiDAR snow depth and static variable maps (New Hampshire; Cho et al., 2021)

This resource is a repository of the Unpiloted Aerial System (UAS) lidar-based maps of snow depth, local gradient of snow depth, and static variables (1-m spatial resolution) over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States (N 43.10892°, W 70.94853°, 35 m above sea level). Snow surface elevations were collected on January 23rd, 2019 and December 4th, 2019. The respective bare earth baseline elevations were collected following snowmelt on April 11th, 2019 and March 18th, 2020. The total area surveyed was approximately 0.11 sqkm, of which 0.7 sqkm was open field and 0.4 sqkm was mixed deciduous and coniferous forest. The static variables include plant functional type (0 = fields, 0.1 = deciduous needleleaf, and 0.2 = evergreen broadleaf) roughness (cm), slope (%), shadow hours (hours), aspect (degree), inter-pixel variability of lidar returns (STD; m), topographic compound index (TCI; unitless), and total local gradient of snow-off condition (LG; cm). Please see Cho et al. (2020) in Journal of Hydrology for full details.

Map Metadata (+proj=utm +zone=19 +datum=WGS84 +units=m +no_defs)

Preferred citation:
Cho, E., Hunsaker, A. G., Jacobs, J. M., Palace, M., Sullivan, F. B., & Burakowski, E. A. (2021). Maximum Entropy Modeling to Identify Physical Drivers of Shallow Snowpack Heterogeneity using Unpiloted Aerial System (UAS) Lidar. Journal of Hydrology, 126722. https://doi.org/10.1016/j.jhydrol.2021.126722

Corresponding author: Eunsang Cho ([e-mail])

本数据集为美国新罕布什尔州新罕布什尔大学汤普森农场研究天文台（地理坐标：北纬43.10892°，西经70.94853°，海拔35米）开阔地带与林区的无人航空系统（Unpiloted Aerial System, UAS）激光雷达（lidar）积雪深度、积雪深度局地梯度及静态变量数据集，空间分辨率为1米。积雪表面高程数据分别采集于2019年1月23日与2019年12月4日；对应的裸土基准高程分别于融雪后采集，时间为2019年4月11日与2020年3月18日。总调查面积约0.11平方千米，其中开阔农田约0.7平方千米，针阔混交林约0.4平方千米。本数据集包含的静态变量包括：植物功能型（0代表农田，0.1代表落叶针叶林，0.2代表常绿阔叶林）、粗糙度（单位：厘米）、坡度（百分比）、阴影时长（单位：小时）、坡向（单位：度）、激光雷达回波像元间变异度（标准差STD，单位：米）、地形复合指数（Topographic Compound Index, TCI，无量纲）以及无雪条件下的局地总梯度（Local Gradient, LG，单位：厘米）。详细研究细节请参阅《水文学杂志》发表的Cho等人（2020）的研究成果。

地图元数据：+proj=utm +zone=19 +datum=WGS84 +units=m +no_defs

首选引用：
Cho, E., Hunsaker, A. G., Jacobs, J. M., Palace, M., Sullivan, F. B., & Burakowski, E. A. (2021). 利用无人航空系统（UAS）激光雷达识别浅雪层异质性物理驱动因子的最大熵模型. 《水文学杂志》, 126722. https://doi.org/10.1016/j.jhydrol.2021.126722

通讯作者：曹恩相（Eunsang Cho），邮箱：[e-mail]