ABoVE: Hyperspectral Imagery AVIRIS-NG, Alaskan and Canadian Arctic, 2017-2019 V2
简介
该数据集提供机载可见光/红外成像光谱仪-下一代 (AVIRIS-NG) 仪器在 2017 年 6 月至 8 月以及 2018 年和 2019 年 7 月至 8 月飞越北极-北方脆弱性实验 (ABoVE) 区域期间测量的 1 级辐射率和 2 级表面反射率。AVIRIS-NG 以 5 纳米 (nm) 的间隔测量 425 个波段的反射辐射率，间隔为 380 至 2510 nm 的可见光到短波红外光谱范围。测量经过辐射和几何校准，空间分辨率约为 5 米。数据包括 848 条航线，覆盖了阿拉斯加大部分地区和加拿大西部 ABoVE 活动感兴趣的区域。这些数据将使研究人员能够表征生长季节高峰期附近的生态系统结构和功能。该数据集是 11 个不同的飞机团队为 ABoVE 开展的多传感器机载采样活动的一部分。本出版物中的 L2 反射率文件已使用更新的反射率算法重新处理，并取代了此数据.
摘要
Version Number Version Information
2.0 This dataset was revised on November 17, 2022 to include additional flight lines. In addition, data providers reprocessed all L2 ABoVE data from 2017-2019 seasons using an updated reflectance algorithm. As a result, all Level 2 files were replaced with this release. RGB images and boundary shapefiles were moved from companion files to distribution with the main dataset.
1.2 This dataset was revised in May 2021 to include flights from the 2019 ABoVE airborne campaign. There were no changes to the previously provided data from 2017 and 2018 flights.
1.1 This dataset was revised in Feb. 2019 to include flights from the 2018 ABoVE airborne campaign. There were no changes to the previously provided data from 2017 flights.
1.0 Included flights for 2017 from the 2017 ABoVE airborne campaign.
代码
!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassify
import pandas as pd
import leafmap
url = "https://github.com/opengeos/NASA-Earth-Data"
df = pd.read_csv(url, sep="\t")
df
leafmap.nasa_data_login()
results, gdf = leafmap.nasa_data_search(
short_name="ABoVE_Airborne_AVIRIS_NG_V2_2009",
cloud_hosted=True,
bounding_box=(-166.65, 52.16, -103.24, 71.38),
temporal=("2017-06-24", "2017-08-04"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
