ABoVE: Landsat Vegetation Greenness Trends, Boreal Forest Biome, 1985-2019
简介
这个数据集涵盖了1985年到2019年的时间范围内，在北部森林生物群系中，利用Landsat卫星测量的植被绿度趋势。数据集收集了关于植被覆盖变化的信息，可以帮助研究人员了解在这一时期内，北部森林地区植被的生长和变化情况。
摘要
Table 1. File names and descriptions.
File Name Units Description
boreal_greenness_median_percent_change_BBBB_CCCC.tif percent Median percent change in annual maximum vegetation greenness for time period across all sample locations within each ecological land unit. Percent change was computed as the change in vegetation greenness during the time period divided by initial vegetation greenness and then multiplied by 100.
boreal_greenness_percent_browning_BBBB_CCCC.tif percent Percent of sample locations in each ecological land unit that had a significant (α=0.10) negative trend in annual maximum vegetation greenness for the time period.
boreal_greenness_percent_greening_BBBB_CCCC.tif percent Percent of sample locations in each ecological land unit that had a significant (α=0.10) positive trend in annual maximum vegetation greenness for the time period.
boreal_sample_frame.tif 1 Binary raster identifying grid cells that were part of the boreal forest sampling frame.
boreal_ecounits.tif 1 Numerical identifier for each ecological land unit in the boreal sampling frame.
boreal_greenness_trend_summary.csv - Tabular data including trends in annual maximum vegetation greenness for sample locations during two time periods derived using an ensemble of spectral vegetation indices.
See Table 2 for variables and descriptions.
Data File Details
Each GeoTIFF has a spatial domain covering the circum-hemispheric distribution of the boreal forest biome between 45 to 70 degrees north at 300 m spatial resolution in the North Pole Lambert Azimuthal Equal Area (LAEA) spatial projection (EPSG:3571).
Table 2. Variables in the file boreal_greenness_trend_summary.csv. Each trend metric includes a best-estimate (50th percentile) as well as a lower bound (2.5th percentile) and upper bound (97.5th percentile) of a 95% confidence interval derived from Monte Carlo simulations.
Variable Units Description
site - Unique alphanumeric identifier for each sample location.
latitude degree_north Latitude in decimal degrees of site; WGS84 datum.
longitude degree_east Longitude in decimal degrees of site; WGS84 datum.
ecounit 1 Numerical identifier for the Ecological Land Unit in which each site is located.
trend.period - Time period over which the trend in vegetation greenness was assessed (“1985 to 2019” or “2000 to 2019").
tau.p025 1 Mann-Kendall’s tau statistic (2.5th percentile).
tau.p500 1 Mann-Kendall’s tau statistic (50th percentile).
tau.p975 1 Mann-Kendall’s tau statistic (97.5th percentile).
percent.change.p025 percent Percent change in vegetation greenness (2.5th percentile).
percent.change.p500 percent Percent change in vegetation greenness (50th percentile).
percent.change.p975 percent Percent change in vegetation greenness (97.5th percentile).
编辑
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/raw/main/nasa_earth_data.tsv"
df = pd.read_csv(url, sep="\t")
df
leafmap.nasa_data_login()
results, gdf = leafmap.nasa_data_search(
short_name="ABoVE_ASCENDS_XCO2_2050",
cloud_hosted=True,
bounding_box=(-180.0, 45.0, 180.0, 72.0),
temporal=("1985-01-01", "2019-12-31"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
引用
Berner, L.T., and S.J. Goetz. 2022. ABoVE: Landsat Vegetation Greenness Trends, Boreal Forest Biome, 1985-2019. ORNL DAAC, Oak Ridge, Tennessee, USA.
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