ABoVE: Lake Growing Season Green Surface Reflectance Trends, AK and Canada, 1984-2019
简介
该数据集提供了 1984 年至 2019 年 ABoVE 扩展研究区域内 472,890 个湖泊在生长季（六月和七月）的 Landsat 绿色表面反射率的年度时间序列及其年度趋势。反射率数据来自 Landsat-5、Landsat-7 和 Landsat-8 传感器的绿色波段（中心波长 560 nm）。对超过 270,000 个 Landsat 场景进行了评估，并确保其质量无云且覆盖水面。湖泊选自 HydroLAKES，这是一个至少包含 10 公顷湖泊的全球数据库。湖面反射率是从从湖泊多边形确定的选定 Landsat 场景中的每个湖泊质心为中心的 3×3 像素区域中提取的。该数据集展示了北美北极和北方地区湖泊颜色随时间的变化。颜色对于了解世界上一些湖泊浓度最高的地区中气候变化可能产生重大影响的物理、生态和生物地球化学过程至关重要。
摘要
Table 2. Variables in the data file trends_1984_2019_landsat_ABoVE_lake_greenness.txt.
Column Name Units Description
continent Continent where lake is located.
country Country where lake is located
hylak_id Unique lake identifier from the HydroLAKES dataset
latitude Decimal Degrees Latitude in decimal degrees of the lake centroid
longitude Decimal Degrees Longitude in decimal degrees of the lake centroid
sen_slope Rs per year The Sen slope is the change in unit of reflectance per year calculated by taking the median slope of all observations compared pairwise
mann_kendall_trend Indicates whether the trend is significant at a p-value <0.05 and either increasing or decreasing. (no sig. trend, sig. decreasing, sig. increasing)
trend_significance Significance (p-value) of slope
b2_mean Mean Landsat growing season surface reflectance (Rs) in the green wavelengths averaged over the entire study period (1984-2019)
b2_std_dev Standard deviation of Landsat growing season surface reflectance in the green wavelengths averaged over the entire study period (1984-2019)
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_GrowingSeason_Lake_Color_1866",
cloud_hosted=True,
bounding_box=(-168.1, 49.54, -81.23, 75.0),
temporal=("1984-07-01", "2019-09-01"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
