GEE数据——GEDI04_A_和GEDI02_A_002_MONTHLY出现的数据问题

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发布于 2024-03-08 09:23:35

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发布于 2024-03-08 09:23:35

简介

产品介绍

该数据集包含全球生态系统动力学调查（GEDI）第 4A 级（L4A）第 2 版对地上生物量密度（AGBD，单位为兆克/公顷）的预测，以及对每个采样地理定位激光足迹内预测标准误差的估算。在该版本中，颗粒位于子轨道中。模拟波形的高度指标与多个地区和植物功能类型（PFTs）的 AGBD 实地估算值相关联，并对其进行了汇编，以生成一个校准数据集，用于代表世界各地区和植物功能类型组合的模型（即：落叶阔叶树、常绿乔木、常绿灌木、常绿灌木、落叶阔叶树）、针对南美洲的常绿阔叶树，对 GEDI02_A 第 2 版使用的分组选择算法进行了修改，以减少因选择地面高度以上的波形模式作为最低模式而产生的假阳性误差。前言 – 人工智能教程 LARSE/GEDI/GEDI04_A_002_MONTHLY 是原始 GEDI04_A 产品的栅格版本。栅格图像是相应月份各个轨道的月度合成图像。

全球生态系统动态调查 GEDI 任务旨在确定生态系统结构和动态的特征，以便从根本上改进对地球碳循环和生物多样性的量化和了解。GEDI 仪器安装在国际空间站（ISS）上，在北纬 51.6 度和南纬 51.6 度之间收集全球数据，对地球的三维结构进行分辨率最高、密度最大的采样。GEDI 仪器由三个激光器组成，共产生八个光束地面横断面，沿轨道大约每隔 60 米瞬时采样八个约 25 米的脚印。

Dataset Availability

2019-03-25T00:00:00 -

Dataset Provider

Rasterization: Google and USFS Laboratory for Applications of Remote Sensing in Ecology (LARSE) NASA GEDI mission, accessed through the USGS LP DAAC

Collection Snippet

ee.ImageCollection("LARSE/GEDI/GEDI04_A_002_MONTHLY")

Resolution

25 meters

Bands Table

Name	Description	Units
agbd	Predicted aboveground biomass density	Mg/ha
agbd_pi_lower	Lower prediction interval (see "alpha" attribute for the level)	Mg/ha
agbd_pi_upper	Upper prediction interval (see "alpha" attribute for the level)	Mg/ha
agbd_se	Aboveground biomass density prediction standard error	Mg/ha
agbd_t	Model prediction in fit units
agbd_t_se	Model prediction standard error in fit units (needed for calculation of custom prediction intervals)
algorithm_run_flag	The L4A algorithm is run if this flag is set to 1. This flag selects data that have sufficient waveform fidelity for AGBD estimation.
beam	Beam identifier
channel	Channel identifier
degrade_flag	Flag indicating degraded state of pointing and/or positioning information
delta_time	Time since Jan 1 00:00 2018	seconds
elev_lowestmode	Elevation of center of lowest mode relative to reference ellipsoid	m
l2_quality_flag	Flag identifying the most useful L2 data for biomass predictions
l4_quality_flag	Flag simplifying selection of most useful biomass predictions
lat_lowestmode	Latitude of center of lowest mode	deg
lon_lowestmode	Longitude of center of lowest mode	deg
master_frac	Master time, fractional part. master_int+master_frac is equivalent to /BEAMXXXX/delta_time	seconds
master_int	Master time, integer part. Seconds since master_time_epoch. master_int+master_frac is equivalent to /BEAMXXXX/delta_time',	seconds
predict_stratum	Prediction stratum identifier. Character ID of the prediction stratum name for the 1 km cell
predictor_limit_flag	Predictor value is outside the bounds of the training data (0=in bounds; 1=lower bound; 2=upper bound)
response_limit_flag	Prediction value is outside the bounds of the training data (0=in bounds; 1=lower bound; 2=upper bound)
selected_algorithm	Selected algorithm setting group
selected_mode	ID of mode selected as lowest non-noise mode
selected_mode_flag	Flag indicating status of selected_mode
sensitivity	Beam sensitivity. Maximum canopy cover that can be penetrated considering the SNR of the waveform
solar_elevation	Solar elevation angle	deg
surface_flag	Indicates elev_lowestmode is within 300m of Digital Elevation Model (DEM) or Mean Sea Surface (MSS) elevation
shot_number	Shot number, a unique identifier. This field has the format of OOOOOBBRRGNNNNNNNN, where: * OOOOO: Orbit number * BB: Beam number * RR: Reserved for future use * G: Sub-orbit granule number * NNNNNNNN: Shot index
shot_number_within_beam	Shot number within beam
agbd_aN	Above ground biomass density; Geolocation latitude lowestmode	Mg/ha
agbd_pi_lower_aN	Above ground biomass density lower prediction interval	Mg/ha
agbd_pi_upper_aN	Above ground biomass density upper prediction interval	Mg/ha
agbd_se_aN	Aboveground biomass density prediction standard error	Mg/ha
agbd_t_aN	Aboveground biomass density model prediction in transform space	Mg/ha
agbd_t_pi_lower_aN	Lower prediction interval in transform space	Mg/ha
agbd_t_pi_upper_aN	Upper prediction interval in transform space	Mg/ha
agbd_t_se_aN	Model prediction standard error in fit units
algorithm_run_flag_aN	Algorithm run flag-this algorithm is run if this flag is set to 1. This flag selects data that have sufficient waveform fidelity for AGBD estimation
l2_quality_flag_aN	Flag identifying the most useful L2 data for biomass predictions'
l4_quality_flag_aN	Flag simplifying selection of most useful biomass predictions
predictor_limit_flag_aN	Predictor value is outside the bounds of the training data
response_limit_flag_aN	Prediction value is outside the bounds of the training data
selected_mode_aN	ID of mode selected as lowest non-noise mode
selected_mode_flag_aN	Flag indicating status of selected mode
elev_lowestmode_aN	Elevation of center of lowest mode relative to the reference ellipsoid	m
lat_lowestmode_aN	Latitude of center of lowest mode	deg
lon_lowestmode_aN	Longitude of center of lowest mode	deg
sensitivity_aN	Maximum canopy cover that can be penetrated considering the SNR of the waveform
stale_return_flag	Flag from digitizer indicating the real-time pulse detection algorithm did not detect a return signal above its detection threshold within the entire 10 km search window. The pulse location of the previous shot was used to select the telemetered waveform.
landsat_treecover	Tree cover in the year 2010, defined as canopy closure for all vegetation taller than 5 m in height (Hansen et al., 2013) and encoded as a percentage per output grid cell.	%
landsat_water_persistence	The percent UMD GLAD Landsat observations with classified surface water between 2018 and 2019. Values >80 usually represent permanent water while values <10 represent permanent land.	%
leaf_off_doy	GEDI 1 km EASE 2.0 grid leaf-off start day-of-year derived from the NPP VIIRS Global Land Surface Phenology Product.
leaf_off_flag	GEDI 1 km EASE 2.0 grid flag derived from leaf_off_doy, leaf_on_doy, and pft_class, indicating if the observation was recorded during leaf-off conditions in deciduous needleleaf or broadleaf forests and woodlands. 1=leaf-off, 0=leaf-on.
leaf_on_cycle	Flag that indicates the vegetation growing cycle for leaf-on observations. Values are 0=leaf-off conditions, 1=cycle 1, 2=cycle 2.
leaf_on_doy	GEDI 1 km EASE 2.0 grid leaf-on start day- of-year derived from the NPP VIIRS Global Land Surface Phenology product.
pft_class	GEDI 1 km EASE 2.0 grid Plant Functional Type (PFT) derived from the MODIS MCD12Q1v006 product. Values follow the Land Cover Type 5 Classification scheme.
region_class	GEDI 1 km EASE 2.0 grid world continental regions (0=Water, 1=Europe, 2=North Asia, 3=Australasia, 4=Africa, 5=South Asia, 6=South America, 7=North America).
urban_focal_window_size	The focal window size used to calculate urban_proportion. Values are 3 (3x3 pixel window size) or 5 (5x5 pixel window size).	pixel
urban_proportion	The percentage proportion of land area within a focal area surrounding each shot that is urban land cover. Urban land cover was derived from the DLR 12 m resolution TanDEM-X Global Urban Footprint Product.

Product	Description
L2A Vector	LARSE/GEDI/GEDI02_A_002
L2A Monthly raster	LARSE/GEDI/GEDI02_A_002_MONTHLY
L2A table index	LARSE/GEDI/GEDI02_A_002_INDEX
L2B Vector	LARSE/GEDI/GEDI02_B_002
L2B Monthly raster	LARSE/GEDI/GEDI02_B_002_MONTHLY
L2B table index	LARSE/GEDI/GEDI02_B_002_INDEX
L4A Biomass Vector	LARSE/GEDI/GEDI04_A_002
L4A Monthly raster	LARSE/GEDI/GEDI04_A_002_MONTHLY
L4A table index	LARSE/GEDI/GEDI04_A_002_INDEX
L4B Biomass	LARSE/GEDI/GEDI04_B_002

代码：

function main () {
 
  // Demo geometry
  var geometry = ee.Geometry.Point([-121.90178796259104, 44.68086969733805]);
  
  // Load in the GEDI data
  var gedi_agb = ee.ImageCollection("LARSE/GEDI/GEDI04_A_002_MONTHLY")
    .filterBounds(geometry)
    .first()
    .select('sensitivity');
    
  var gedi_height = ee.ImageCollection("LARSE/GEDI/GEDI02_A_002_MONTHLY")
    .filterBounds(geometry)
    .first()
    .select('sensitivity');

  Map.addLayer(gedi_agb, {}, "GEDI L2A");
  Map.addLayer(gedi_height, {}, "GEDI L4A");
  Map.centerObject(geometry, 17);

  return null;
  
}

main();

差异结果

这里4A有数据，但是2A的产品没有数据值，所有的数据值都为0

使用说明

本数据集属于公共领域，使用和分发不受限制。更多信息请参阅[美国国家航空航天局地球科学数据与信息政策](https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-and-information-policy)。

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原始发表：2024-03-07，如有侵权请联系 cloudcommunity@tencent.com 删除

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