ABoVE：加拿大西北地区火灾后和未燃烧植被群落及实地数据，2017 年

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此星光明 发表于 2025/07/15 12:20:34 2025/07/15
【摘要】 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2017简介该数据集提供了 2017 年从 ABoVE 研究区域内的 11 个研究地点收集的植被群落特征、土壤湿度和生物物理数据。这 11 个研究区域包含 2014 年和 2015 年遭受野火烧毁的 28 个地点和加拿大西北地区 (N...

ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2017

简介

该数据集提供了 2017 年从 ABoVE 研究区域内的 11 个研究地点收集的植被群落特征、土壤湿度和生物物理数据。这 11 个研究区域包含 2014 年和 2015 年遭受野火烧毁的 28 个地点和加拿大西北地区 (NWT) 的 10 个未烧毁地点。烧毁地点包括泥炭地和高地。这些实地数据包括烧毁严重程度评估、植被清单、地面覆盖、树木和灌木的直径和高度、幼苗和发芽覆盖、土壤湿度以及未冻土深度。地块大小为 10 米 x 10 米，并设有较小的子地块用于选定的测量。2015 年至 2019 年期间为这些地点收集了类似的数据，可在相关的单独数据集中找到。实地数据以 CSV 格式提供。该数据集包括采样地点植被状况的数码照片（JPEG 格式）。

摘要

File Name Description
NWT_Wildfires_Field_data_summary_2017.csv This file provides a summary of the field data collected at each site. It includes geographic coordinates, date of sampling, and types of data collected at 42 sites in 11 study areas. Site names that include "VAL" indicate unburned, validation sites for this study.
NWT_Wildfires_Burn_severity_Regrowth_2017.csv Data for burn severity, the cover of woody and herbaceous vegetation, mosses, and liverworts along with measurements of peat, depth of unfrozen soil, and soil pH at 28 sites burned in 2014-2015 wildfires. The file lists the file names of photographs taken at these sites.
NWT_Wildfires_Biophysical_2017.csv Data on ground, shrub, and canopy cover and ecosystem characteristics.
NWT_Wildfires_Biophysical_Inventory_2017.csv Data on trees and shrubs.
NWT_Wildfires_Seedling_Sprouting_2017.csv Data on seedlings/sproutings in areas affected by wildfires.
NWT_Wildfires_SoilMoisture_HydrosenseI_2017_BurnedSites.csv Soil moisture and temperature data for sites affected by wildfires.
NWT_Wildfires_SoilMoisture_HydrosenseI_2017_UnburnedSites.csv Soil moisture and temperature data for unburned, validation sites.
NWT_Wildfires_Unburned_validation_2017.csv Data on topography, depth of unfrozen soil, ground cover, woody vegetation, and peat depth for unburned validation sites. The file lists file names of photographs taken at these sites.

Table 2. Names of study areas and sites. Study areas with "Val" in the name are unburned, validation sites.

Study Area Sites
SS3 SS3-28, SS3-29, SS3-64, SS3-65, SS3-200
SS50 SS50-3, SS50-200
ZF14 ZF14-3, ZF14-200, ZF14-203, ZF14-207, ZF14-208, ZF14-210, ZF14-220
ZF17 ZF17-6, ZF17-7, ZF17-9, ZF17-40, ZF17-200, ZF17-203, ZF17-206, ZF17-207
ZF20 ZF20-3, ZF20-10, ZF20-26, ZF20-47
ZF28 ZF28-201
ZF46 ZF46-14
SS3-Val SS3-Val205
ZF14-Val ZF14-Val200, ZF14-Val-2R, ZF14-Val6, ZF14-Val7R
ZF20-Val ZF20-Val200, ZF20-Val201, ZF20-Val202, ZF20-Val210
ZF46-Val ZF46-Val200

Table 3. Variables names and descriptions in the file NWT_Wildfires_Field_data_summary_2017.csv.

Variable Unit Description
Site Unique name given to a field site
Plot Plot number for sampling location (1 to 6)
Site_Name Site name followed by plot number
Date YYYY-MM-DD
Latitude degrees_north
Longitude degrees_east
BurnSeverity Yes/No/YYYY Presence of Burn Severity Data / other year of collection provided if applicable
Seedling/Sprouting Presence of seedling/Sprouting Data: Yes or No
Biophysical Yes/No/YYYY Presence of Biophysical Data / other year of collection provided if applicable
Soil Moisture Presence of Soil Moisture Data: Yes or No
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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_NWT_2017_Field_Data_1771",
    cloud_hosted=True,
    bounding_box=(-117.38, 60.52, -111.37, 62.58),
    temporal=("2015-07-13", "2017-08-10"),
    count=-1,  # use -1 to return all datasets
    return_gdf=True,
)
 
 
gdf.explore()
 
#leafmap.nasa_data_download(results[:5], out_dir="data")

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