GEE数据集：加拿大卫星森林资源调查 (SBFI)-2020 年加拿大森林覆盖、干扰恢复、结构、物种、林分年龄以及 1985-2020 年林分替代干扰的信息

目录

简介

数据集后处理

数据下载链接

矢量属性

代码

代码链接

引用

许可

网址推荐

0代码在线构建地图应用

机器学习

---

加拿大卫星森林资源调查 (SBFI)

简介

卫星森林资源清查（SBFI）提供了 2020 年加拿大森林覆盖、干扰恢复、结构、物种、林分年龄以及 1985-2020 年林分替代干扰的信息。 SBFI 多边形代表了与战略森林资源清查中划定的林分相似的同质森林状况。 使用多分辨率分割算法对 2020 年大地遥感卫星表面反射 BAP 复合影像（30 米空间分辨率）、火灾年份和采伐年份图层进行了划分，这些图层是使用 C2C 方法从大地遥感卫星上获取的。 最小地图单位为 0.45 公顷（5 像素），用于定义多边形。 整个加拿大的森林生态系统都使用相同的数据、属性和时间表示方法进行测绘，从而形成了加拿大约 6.5 亿公顷森林生态系统的通用植被清查系统。 鉴于加拿大森林面积大且种类繁多，SBFI 的优势在于使用一致的数据源和方法，跨越管辖边界、管理和非管理林区，从而能够一致地生成综合、空间明确的信息输出。 此处包含的数据基于免费开放的卫星数据和信息产品，并遵循既定的交流方法。

数据集后处理

为便于使用，瓦片数据集被合并为一个单一的特征集合。 网格文件保留原样，以便用户了解网格是如何创建的。

数据下载链接

https://opendata.nfis.org/downloads/forest_change/CA_Forest_Satellite_Based_Inventory_2020.zip

矢量属性

Group	Field	Description	Units
ID	ID	Unique polygon identifier
	TILE	Tile identifier
Geometry	AREA_HA	Area of the polygon	ha
	PERIMETER_M	Length of polygon’s boundary	m
Stratification	JURSDICTION	Most represented province/territory
	ECOZONE	Most represented terrestrial ecozone as defined by Ecological Stratification Working Group (1996)
	ECOPROVINCE	Most represented ecoprovince as defined by Ecological Stratification Working Group (1996)
	ECOREGION	Most represented ecoregion as defined by Ecological Stratification Working Group (1996)
	MANAGEMENT	Most represented land status from the forest management classification from Stinson et al_ (2019)
Land cover	LC_WATER	Area covered by water	% of polygon area
	LC_SNOW_ICE	Area covered by snow/ice	% of polygon area
	LC_ROCK_RUBBLE	Area covered by rock/rubble	% of polygon area
	LC_EXPOSED_BARREN	Area covered by exposed/barren land	% of polygon area
	LC_BRYOIDS	Area covered by bryoids	% of polygon area
	LC_SHRUBS	Area covered by shrubs	% of polygon area
	LC_WETLAND	Area covered by wetland	% of polygon area
	LC_WETLAND-TREED	Area covered by wetland-treed	% of polygon area
	LC_HERBS	Area covered by herbs	% of polygon area
	LC_CONIFEROUS	Area covered by coniferous	% of polygon area
	LC_BROADLEAF	Area covered by broadleaf	% of polygon area
	LC_MIXEDWOOD	Area covered by mixedwood	% of polygon area
	LC_TREED	Area covered by treed vegetation derived from combining the land cover classes	% of polygon area
	LC_FAO_FOREST	Area covered by forest consistent with FAO definitions (Wulder et al_ 2020)	% of polygon area
	LC_WETLAND_VEGETATION	Area covered by wetlands derived from combining the land cover classes	% of polygon area
Disturbances	DISTURB_FIRE_PERC	Area impacted by fire disturbances	% of polygon area
	DISTURB_FIRE_YEAR	Modal year of fire disturbances	years
	DISTURB_FIRE_MAGNITUDE_MIN	Minimum value of fire magnitude	dNBR
	DISTURB_FIRE_MAGNITUDE_MAX	Maximum value of fire magnitude	dNBR
	DISTURB_FIRE_MAGNITUDE_AVG	Average value of fire magnitude	dNBR
	DISTURB_FIRE_MAGNITUDE_SD	Standard deviation of fire magnitude	dNBR
	DISTURB_FIRE_MAGNITUDE_MED	Median value of fire magnitude	dNBR
	DISTURB_HARVEST_PERC	Area impacted by harvesting disturbances	% of polygon area
	DISTURB_HARVEST_YEAR	Modal year of harvesting disturbances	years
Recovery	RECOVERY_FIRE_MIN	Minimum value of spectral recovery for fire disturbances	% of pre-disturbance
	RECOVERY_FIRE_MAX	Maximum value of spectral recovery for fire disturbances	% of pre-disturbance
	RECOVERY_FIRE_AVG	Average value of spectral recovery for fire disturbances	% of pre-disturbance
	RECOVERY_FIRE_SD	Standard deviation of spectral recovery for fire disturbances	% of pre-disturbance
	RECOVERY_FIRE_MED	Median value of spectral recovery for fire disturbances	% of pre-disturbance
	RECOVERY_HARVEST_MIN	Minimum value of spectral recovery for harvesting disturbances	% of pre-disturbance
	RECOVERY_HARVEST_MAX	Maximum value of spectral recovery for harvesting disturbances	% of pre-disturbance
	RECOVERY_HARVEST_AVG	Average value of spectral recovery for harvesting disturbances	% of pre-disturbance
	RECOVERY_HARVEST_SD	Standard deviation of spectral recovery for harvesting disturbances	% of pre-disturbance
	RECOVERY_HARVEST_MED	Median value of spectral recovery for harvesting disturbances	% of pre-disturbance
Age	AGE_MIN	Minimum forest age	years
	AGE_MAX	Maximum forest age	years
	AGE_AVG	Average forest age	years
	AGE_SD	Standard deviation of forest age	years
	AGE_MED	Median forest age	years
	AGE_0_10, AGE_10_20, AGE_20_30, AGE_30_40, AGE_40_50, AGE_50_60, AGE_60_70, AGE_70_80, AGE_80_90, AGE_90_100, AGE_100_110, AGE_110_120, AGE_120_130, AGE_130_140, AGE_140_150, AGE_GT_150	Ten-year age class frequency distribution	% of treed area in polygon
Forest structure	STRUCTURE_CANOPY_HEIGHT_MIN	Minimum canopy height	m
	STRUCTURE_CANOPY_HEIGHT_MAX	Maximum canopy height	m
	STRUCTURE_CANOPY_HEIGHT_AVG	Average canopy height	m
	STRUCTURE_CANOPY_HEIGHT_SD	Standard deviation of canopy height	m
	STRUCTURE_CANOPY_HEIGHT_MED	Median canopy height	m
	STRUCTURE_CANOPY_COVER_MIN	Minimum canopy cover	%
	STRUCTURE_CANOPY_COVER_MAX	Maximum canopy cover	%
	STRUCTURE_CANOPY_COVER_AVG	Average canopy cover	%
	STRUCTURE_CANOPY_COVER_SD	Standard deviation of canopy cover	%
	STRUCTURE_CANOPY_COVER_MED	Median canopy cover	%
	STRUCTURE_LOREYS_HEIGHT_MIN	Minimum Lorey’s height	m
	STRUCTURE_LOREYS_HEIGHT_MAX	Maximum Lorey’s height	m
	STRUCTURE_LOREYS_HEIGHT_AVG	Average Lorey’s height	m
	STRUCTURE_LOREYS_HEIGHT_SD	Standard deviation of Lorey’s height	m
	STRUCTURE_LOREYS_HEIGHT_MED	Median Lorey’s height	m
	STRUCTURE_BASAL_AREA_MIN	Minimum basal area	m2 ha−1
	STRUCTURE_BASAL_AREA_MAX	Maximum basal area	m2 ha−1
	STRUCTURE_BASAL_AREA_AVG	Average basal area	m2 ha−1
	STRUCTURE_BASAL_AREA_SD	Standard deviation of basal area	m2 ha−1
	STRUCTURE_BASAL_AREA_MED	Median basal area	m2 ha−1
	STRUCTURE_BASAL_AREA_TOTAL	Total basal area in polygon	m2
	STRUCTURE_AGB_MIN	Minimum aboveground biomass	t ha−1
	STRUCTURE_AGB_MAX	Maximum aboveground biomass	t ha−1
	STRUCTURE_AGB_AVG	Average aboveground biomass	t ha−1
	STRUCTURE_AGB_SD	Standard deviation of aboveground biomass	t ha−1
	STRUCTURE_AGB_MED	Median aboveground biomass	t ha−1
	STRUCTURE_AGB_TOTAL	Total aboveground biomass in polygon	t
	STRUCTURE_VOLUME_MIN	Minimum gross stem volume	m3 ha−1
	STRUCTURE_VOLUME_MAX	Maximum gross stem volume	m3 ha−1
	STRUCTURE_VOLUME_AVG	Average gross stem volume	m3 ha−1
	STRUCTURE_VOLUME_SD	Standard deviation of gross stem volume	m3 ha−1
	STRUCTURE_VOLUME_MED	Median gross stem volume	m3 ha−1
	STRUCTURE_VOLUME_TOTAL	Total gross stem volume in polygon	m3
Tree species	SPECIES_NUMBER
	SPECIES_1	Name of the 1st most common leading tree species representing a percentage of treed area in polygon >2_5%
	SPECIES_2	Name of the 2nd most common leading tree species representing a percentage of treed area in polygon >2_5%
	SPECIES_3	Name of the 3rd most common leading tree species representing a percentage of treed area in polygon >2_5%
	SPECIES_4	Name of the 4th most common leading tree species representing a percentage of treed area in polygon >2_5%
	SPECIES_5	Name of the 5th most common leading tree species representing a percentage of treed area in polygon >2_5%
	SPECIES_1_PERC	Area covered by the 1st most common leading tree species	% of treed area in polygon
	SPECIES_2_PERC	Area covered by the 2nd most common leading tree species	% of treed area in polygon
	SPECIES_3_PERC	Area covered by the 3rd most common leading tree species	% of treed area in polygon
	SPECIES_5_PERC	Area covered by the 5th most common leading tree species	% of treed area in polygon
	SPECIES_CONIFEROUS_PERC	Area covered by coniferous tree species	% of treed area in polygon
	SPECIES_CML1	Name of the 1st most common tree species based on the class membership likelihood values
	SPECIES_CML2	Name of the 2nd most common tree species based on the class membership likelihood values
	SPECIES_CML3	Name of the 3rd most common tree species based on the class membership likelihood values
	SPECIES_CML4	Name of the 4th most common tree species based on the class membership likelihood values
	SPECIES_CML5	Name of the 5th most common tree species based on the class membership likelihood values
	SPECIES_CML1_PERC	Distribution of the class membership likelihood values of the 1st most common tree species	% of class membership likelihood from treed pixels in polygon
	SPECIES_CML2_PERC	Distribution of the class membership likelihood values of the 2nd most common tree species	% of class membership likelihood from treed pixels in polygon
	SPECIES_CML3_PERC	Distribution of the class membership likelihood values of the 3rd most common tree species	% of class membership likelihood from treed pixels in polygon
	SPECIES_CML4_PERC	Distribution of the class membership likelihood values of the 4th most common tree species	% of class membership likelihood from treed pixels in polygon
	SPECIES_CML5_PERC	Distribution of the class membership likelihood values of the 5th most common tree species	% of class membership likelihood from treed pixels in polygon
	SPECIES_CML_CONIFEROUS_PERC	Proportion of class membership likelihood values of coniferous tree species	% of class membership likelihood from treed pixels in polygon
	SPECIES_CML_ASSEMBLAGES	Name of the tree species conforming an assemblage
	SPECIES_CML_ASSEMBLAGES_PERC	Proportion of class membership likelihood values conforming the assemblage	% of class membership likelihood from treed pixels in polygon
Symbology	SYMB_LAND_BASE_LEVEL	Land base level classification based on the NFI land cover hierarchy (Wulder et al_ 2008)
	SYMB_LAND_COVER_LEVEL	Land cover level classification based on the NFI land cover hierarchy (Wulder et al_ 2008)
	SYMB_VEGETATION_LEVEL	Vegetation level classification based on the NFI land cover hierarchy (Wulder et al_ 2008)
	SYMB_DISTURBANCE	Simplified coding for disturbance type and year
	SYMB_RECOVERY	Simplified coding for spectral recovery
	SYMB_AGE	Simplified coding for forest age

代码

!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassifyimport pandas as pd
import leafmapurl = "https://github.com/opengeos/NASA-Earth-Data/raw/main/nasa_earth_data.tsv"
df = pd.read_csv(url, sep="\t")
dfleafmap.nasa_data_login()results, gdf = leafmap.nasa_data_search(short_name="ABoVE_ASCENDS_XCO2_2050",cloud_hosted=True,bounding_box=(-165.68, 34.59, -98.1, 71.28),temporal=("2017-07-20", "2017-08-08"),count=-1,  # use -1 to return all datasetsreturn_gdf=True,
)gdf.explore()#leafmap.nasa_data_download(results[:5], out_dir="data")

代码链接

https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CA-SBFI

引用

Wulder, Michael A., Txomin Hermosilla, Joanne C. White, Christopher W. Bater, Geordie Hobart, and Spencer C. Bronson. "Development and
implementation of a stand-level satellite-based forest inventory for Canada." Forestry: An International Journal of Forest Research (2024): cpad065.

Wulder, M.A., Hermosilla, T., White, J.C., Bater, C.W., Hobart, G., Bronson, S.C., 2024. Development and implementation of a stand-level
satellite-based forest inventory for Canada. Forestry: An International Journal of Forest Research. https://doi.org/10.1093/forestry/cpad065

许可

本作品采用加拿大开放式政府许可协议（Open Government Licence - Canada）进行许可，并向公众免费开放。 创作者：Wulder et al： Wulder et al. 2024 在 GEE 中策划： ： Samapriya Roy 主要作品： 大地遥感卫星、土地覆盖、变化探测、森林结构、生物量；NFI 在 GEE 中的最新更新时间： 2024-08-29 

网址推荐

0代码在线构建地图应用

https://www.mapmost.com/#/?source_inviter=CnVrwIQs

机器学习

https://www.cbedai.net/xg