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

news2024/11/24 17:52:07

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

GroupFieldDescriptionUnits
IDIDUnique polygon identifier
TILETile identifier
GeometryAREA_HAArea of the polygonha
PERIMETER_MLength of polygon’s boundarym
StratificationJURSDICTIONMost represented province/territory
ECOZONEMost represented terrestrial ecozone as defined by Ecological Stratification Working Group (1996)
ECOPROVINCEMost represented ecoprovince as defined by Ecological Stratification Working Group (1996)
ECOREGIONMost represented ecoregion as defined by Ecological Stratification Working Group (1996)
MANAGEMENTMost represented land status from the forest management classification from Stinson et al_ (2019)
Land coverLC_WATERArea covered by water% of polygon area
LC_SNOW_ICEArea covered by snow/ice% of polygon area
LC_ROCK_RUBBLEArea covered by rock/rubble% of polygon area
LC_EXPOSED_BARRENArea covered by exposed/barren land% of polygon area
LC_BRYOIDSArea covered by bryoids% of polygon area
LC_SHRUBSArea covered by shrubs% of polygon area
LC_WETLANDArea covered by wetland% of polygon area
LC_WETLAND-TREEDArea covered by wetland-treed% of polygon area
LC_HERBSArea covered by herbs% of polygon area
LC_CONIFEROUSArea covered by coniferous% of polygon area
LC_BROADLEAFArea covered by broadleaf% of polygon area
LC_MIXEDWOODArea covered by mixedwood% of polygon area
LC_TREEDArea covered by treed vegetation derived from combining the land cover classes% of polygon area
LC_FAO_FORESTArea covered by forest consistent with FAO definitions (Wulder et al_ 2020)% of polygon area
LC_WETLAND_VEGETATIONArea covered by wetlands derived from combining the land cover classes% of polygon area
DisturbancesDISTURB_FIRE_PERCArea impacted by fire disturbances% of polygon area
DISTURB_FIRE_YEARModal year of fire disturbancesyears
DISTURB_FIRE_MAGNITUDE_MINMinimum value of fire magnitudedNBR
DISTURB_FIRE_MAGNITUDE_MAXMaximum value of fire magnitudedNBR
DISTURB_FIRE_MAGNITUDE_AVGAverage value of fire magnitudedNBR
DISTURB_FIRE_MAGNITUDE_SDStandard deviation of fire magnitudedNBR
DISTURB_FIRE_MAGNITUDE_MEDMedian value of fire magnitudedNBR
DISTURB_HARVEST_PERCArea impacted by harvesting disturbances% of polygon area
DISTURB_HARVEST_YEARModal year of harvesting disturbancesyears
RecoveryRECOVERY_FIRE_MINMinimum value of spectral recovery for fire disturbances% of pre-disturbance
RECOVERY_FIRE_MAXMaximum value of spectral recovery for fire disturbances% of pre-disturbance
RECOVERY_FIRE_AVGAverage value of spectral recovery for fire disturbances% of pre-disturbance
RECOVERY_FIRE_SDStandard deviation of spectral recovery for fire disturbances% of pre-disturbance
RECOVERY_FIRE_MEDMedian value of spectral recovery for fire disturbances% of pre-disturbance
RECOVERY_HARVEST_MINMinimum value of spectral recovery for harvesting disturbances% of pre-disturbance
RECOVERY_HARVEST_MAXMaximum value of spectral recovery for harvesting disturbances% of pre-disturbance
RECOVERY_HARVEST_AVGAverage value of spectral recovery for harvesting disturbances% of pre-disturbance
RECOVERY_HARVEST_SDStandard deviation of spectral recovery for harvesting disturbances% of pre-disturbance
RECOVERY_HARVEST_MEDMedian value of spectral recovery for harvesting disturbances% of pre-disturbance
AgeAGE_MINMinimum forest ageyears
AGE_MAXMaximum forest ageyears
AGE_AVGAverage forest ageyears
AGE_SDStandard deviation of forest ageyears
AGE_MEDMedian forest ageyears
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_150Ten-year age class frequency distribution% of treed area in polygon
Forest structureSTRUCTURE_CANOPY_HEIGHT_MINMinimum canopy heightm
STRUCTURE_CANOPY_HEIGHT_MAXMaximum canopy heightm
STRUCTURE_CANOPY_HEIGHT_AVGAverage canopy heightm
STRUCTURE_CANOPY_HEIGHT_SDStandard deviation of canopy heightm
STRUCTURE_CANOPY_HEIGHT_MEDMedian canopy heightm
STRUCTURE_CANOPY_COVER_MINMinimum canopy cover%
STRUCTURE_CANOPY_COVER_MAXMaximum canopy cover%
STRUCTURE_CANOPY_COVER_AVGAverage canopy cover%
STRUCTURE_CANOPY_COVER_SDStandard deviation of canopy cover%
STRUCTURE_CANOPY_COVER_MEDMedian canopy cover%
STRUCTURE_LOREYS_HEIGHT_MINMinimum Lorey’s heightm
STRUCTURE_LOREYS_HEIGHT_MAXMaximum Lorey’s heightm
STRUCTURE_LOREYS_HEIGHT_AVGAverage Lorey’s heightm
STRUCTURE_LOREYS_HEIGHT_SDStandard deviation of Lorey’s heightm
STRUCTURE_LOREYS_HEIGHT_MEDMedian Lorey’s heightm
STRUCTURE_BASAL_AREA_MINMinimum basal aream2 ha−1
STRUCTURE_BASAL_AREA_MAXMaximum basal aream2 ha−1
STRUCTURE_BASAL_AREA_AVGAverage basal aream2 ha−1
STRUCTURE_BASAL_AREA_SDStandard deviation of basal aream2 ha−1
STRUCTURE_BASAL_AREA_MEDMedian basal aream2 ha−1
STRUCTURE_BASAL_AREA_TOTALTotal basal area in polygonm2
STRUCTURE_AGB_MINMinimum aboveground biomasst ha−1
STRUCTURE_AGB_MAXMaximum aboveground biomasst ha−1
STRUCTURE_AGB_AVGAverage aboveground biomasst ha−1
STRUCTURE_AGB_SDStandard deviation of aboveground biomasst ha−1
STRUCTURE_AGB_MEDMedian aboveground biomasst ha−1
STRUCTURE_AGB_TOTALTotal aboveground biomass in polygont
STRUCTURE_VOLUME_MINMinimum gross stem volumem3 ha−1
STRUCTURE_VOLUME_MAXMaximum gross stem volumem3 ha−1
STRUCTURE_VOLUME_AVGAverage gross stem volumem3 ha−1
STRUCTURE_VOLUME_SDStandard deviation of gross stem volumem3 ha−1
STRUCTURE_VOLUME_MEDMedian gross stem volumem3 ha−1
STRUCTURE_VOLUME_TOTALTotal gross stem volume in polygonm3
Tree speciesSPECIES_NUMBER
SPECIES_1Name of the 1st most common leading tree species representing a percentage of treed area in polygon >2_5%
SPECIES_2Name of the 2nd most common leading tree species representing a percentage of treed area in polygon >2_5%
SPECIES_3Name of the 3rd most common leading tree species representing a percentage of treed area in polygon >2_5%
SPECIES_4Name of the 4th most common leading tree species representing a percentage of treed area in polygon >2_5%
SPECIES_5Name of the 5th most common leading tree species representing a percentage of treed area in polygon >2_5%
SPECIES_1_PERCArea covered by the 1st most common leading tree species% of treed area in polygon
SPECIES_2_PERCArea covered by the 2nd most common leading tree species% of treed area in polygon
SPECIES_3_PERCArea covered by the 3rd most common leading tree species% of treed area in polygon
SPECIES_5_PERCArea covered by the 5th most common leading tree species% of treed area in polygon
SPECIES_CONIFEROUS_PERCArea covered by coniferous tree species% of treed area in polygon
SPECIES_CML1Name of the 1st most common tree species based on the class membership likelihood values
SPECIES_CML2Name of the 2nd most common tree species based on the class membership likelihood values
SPECIES_CML3Name of the 3rd most common tree species based on the class membership likelihood values
SPECIES_CML4Name of the 4th most common tree species based on the class membership likelihood values
SPECIES_CML5Name of the 5th most common tree species based on the class membership likelihood values
SPECIES_CML1_PERCDistribution of the class membership likelihood values of the 1st most common tree species% of class membership likelihood from treed pixels in polygon
SPECIES_CML2_PERCDistribution of the class membership likelihood values of the 2nd most common tree species% of class membership likelihood from treed pixels in polygon
SPECIES_CML3_PERCDistribution of the class membership likelihood values of the 3rd most common tree species% of class membership likelihood from treed pixels in polygon
SPECIES_CML4_PERCDistribution of the class membership likelihood values of the 4th most common tree species% of class membership likelihood from treed pixels in polygon
SPECIES_CML5_PERCDistribution 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_PERCProportion of class membership likelihood values of coniferous tree species% of class membership likelihood from treed pixels in polygon
SPECIES_CML_ASSEMBLAGESName of the tree species conforming an assemblage
SPECIES_CML_ASSEMBLAGES_PERCProportion of class membership likelihood values conforming the assemblage% of class membership likelihood from treed pixels in polygon
SymbologySYMB_LAND_BASE_LEVELLand base level classification based on the NFI land cover hierarchy (Wulder et al_ 2008)
SYMB_LAND_COVER_LEVELLand cover level classification based on the NFI land cover hierarchy (Wulder et al_ 2008)
SYMB_VEGETATION_LEVELVegetation level classification based on the NFI land cover hierarchy (Wulder et al_ 2008)
SYMB_DISTURBANCESimplified coding for disturbance type and year
SYMB_RECOVERYSimplified coding for spectral recovery
SYMB_AGESimplified coding for forest age

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=(-165.68, 34.59, -98.1, 71.28),
    temporal=("2017-07-20", "2017-08-08"),
    count=-1,  # use -1 to return all datasets
    return_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 

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