N: 54 S: -54 E: 180 W: -180
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The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.
The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.
The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.
The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.
Known Issues
- Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
- Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers.
- Known Issues: Section 8 of the User Guide provides additional information on known issues.
Version Description
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
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File Naming Convention
The file name begins with the Product Short Name (GEDI02_A), followed by the Julian Date and Time of Acquisition designated as YYYYDDDHHMMSS (2024333190440), the Orbit Number starting with the letter O (O33777), the Sub-Orbit Granule Number (03), Track Number (T03689), the Positioning and Pointing Determination System type where 00 is predict, 01 rapid, 02 and higher is final (02), the Product Generation Executables Version (004), the Granule Production Version (02), the Version Number (V002), and the Data Format (h5).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach | Kamusoko, Courage | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, LIDAR WAVEFORM, Evergreen Vegetation, Deciduous Vegetation, Biomass, Shrubland/Scrub, Forests, Grasslands | |
| Ecological Systems Classification: Integrating Machine Learning | Sunde, Michael, Diamond, David, Elliott, Lee | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| EVALUATING URBAN BIODIVERSITY: EFFECTIVENESS OF CITIZEN SCIENCE DRIVEN | Buhrs, Malte, Zepp, Harald, Schmitt, Thomas | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Evaluation of GEDI footprint level biomass models in Southern African Savannas using airborne LiDAR and field measurements | Li, Xiaoxuan, Wessels, Konrad, Armston, John, Duncanson, Laura, Urbazaev, Mikhail, Naidoo, Laven, Mathieu, Renaud, Main, Russell | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), LIDAR WAVEFORM, VIEWING GEOMETRY, Terrain Elevation, Evergreen Vegetation, Deciduous Vegetation, Biomass, Shrubland/Scrub, Forests, Grasslands | |
| Coupling GEDI LiDAR and Optical Satellite for Revealing Large-Scale | Zhang, Qiang, Zhang, Geli, Zhang, Yao, Xiao, Xiangming, You, Nanshan, Li, Zhichao, Tang, Hao, Yang, Tong, Di, Yuanyuan, Dong, Jinwei | Reflectance, Anisotropy, Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| DeltaDTM: A global coastal digital terrain model | Pronk, Maarten, Hooijer, Aljosja, Eilander, Dirk, Haag, Arjen, de Jong, Tjalling, Vousdoukas, Michalis, Vernimmen, Ronald, Ledoux, Hugo, Eleveld, Marieke | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, LIDAR WAVEFORM, Terrain Elevation | |
| Correcting SAR-derived DEMs with ICESat-2 using deep learning | Guenther, Eric, Neuenschwander, Amy, Magruder, Lori, Maze-England, Donald | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| POLITICAL GRAFFITI IN PRAGUE AS A REACTION TO THE RUSSIAN INVASION OF | Hana, David, Dresler, Alexandra, Sel, Jan | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Predicting forest parameters through generalized linear mixed models using GEDI metrics in a temperate forest in Oaxaca, Mexico | Ortiz-Reyes, Alma Delia, Barrera-Ortega, Daisy, Velasco-Bautista, Efrain, Romero-Sanchez, Martin Enrique, Correa-Diaz, Arian | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation | |
| Pre-and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural ... | Chou, Tsung-Chi, Zhu, Xuan, Reef, Ruth | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| NEON-SD: A 30-m Structural Diversity Product Derived from the NEON | Wang, Jianmin, Choi, Dennis H., LaRue, Elizabeth, Atkins, Jeff W., Foster, Jane R., Matthes, Jaclyn H., Fahey, Robert T., Fei, Songlin, Hardiman, Brady S. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Monthly Monitoring of Inundated Areas and Water Storage Dynamics in | Chen, Yongzhe, Wang, Yiming, Li, Luoqi, Cui, Yanhong, Duan, Xingwu, Long, Di | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Maximum tree height in European Mountains decreases above a climate-related elevation threshold | Gelabert, P. J., Rodrigues, M., Coll, L., Vega-Garcia, C., Ameztegui, A. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps | |
| Multi-resolution gridded maps of vegetation structure from GEDI | Burns, Patrick, Hakkenberg, Christopher R., Goetz, Scott J. | Canopy Characteristics, Biomass, Vegetation Height, Forest Composition/Vegetation Structure, LIDAR WAVEFORM, Terrain Elevation, Plant Phenology, Vegetation Cover, Lidar, Topography, Digital Elevation/Terrain Model (DEM), Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Grasslands, VIEWING GEOMETRY, Terrestrial Ecosystems | |
| Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management | Leite, Rodrigo V., Amaral, Cibele, Neigh, Christopher S. R., Cosenza, Diogo N., Klauberg, Carine, Hudak, Andrew T., Aragao, Luiz, Morton, Douglas C., Coffield, Shane, McCabe, Tempest, Silva, Carlos A. | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Terrestrial Ecosystems, Biomass, LIDAR WAVEFORM, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Forests, Grasslands | |
| Characterizing the structural complexity of the Earth's forests with | de Conto, Tiago, Armston, John, Dubayah, Ralph | Land Use/Land Cover Classification, Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, LIDAR WAVEFORM, Evergreen Vegetation, Deciduous Vegetation, Biomass, Shrubland/Scrub, Forests, Grasslands, Terrestrial Ecosystems, Forest Composition/Vegetation Structure | |
| Quantification of GEDI Geolocation Error and Its Influence on Elevation | Yang, Cancan, Peng, Daoli, Deng, Kai, Jiang, Ling, Zhao, Mingwei, Zeng, Weisheng, Shao, Yakui, Wang, Ni | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Quantifying and correcting geolocation error in spaceborne LiDAR forest canopy observations using high spatial accuracy data: A Bayesian model approach | Shannon, Elliot S., Finley, Andrew O., Hayes, Daniel J., Noralez, Sylvia N., Weiskittel, Aaron R., Cook, Bruce D., Babcock, Chad | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Land Use/Land Cover, Forests, Carbon, Biomass | |
| Repeat GEDI footprints measure the effects of tropical forest disturbances | Holcomb, Amelia, Burns, Patrick, Keshav, Srinivasan, Coomes, David A. | Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area, Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM | |
| State-wide forest canopy height and aboveground biomass map for New York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data | Tamiminia, Haifa, Salehi, Bahram, Mahdianpari, Masoud, Goulden, Tristan | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Use of GEDI signal and environmental parameters to improve canopy height estimation over tropical forest ecosystems in Mayotte Island | Lahssini, Kamel, Baghdadi, Nicolas, Le Maire, Guerric, Dupuy, Stephane, Fayad, Ibrahim | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Unveiling Anomalies in Terrain Elevation Products from Spaceborne | Jiang, Hailan, Li, Yi, Yan, Guangjian, Li, Weihua, Li, Linyuan, Yang, Feng, Ding, Anxin, Xie, Donghui, Mu, Xihan, Li, Jing, Xu, Kaijian, Zhao, Ping, Geng, Jun, Morsdorf, Felix | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| The Roles of Winter Versus Summer Precipitation in Supplying | Kesting, Helen M., Allen, Scott T. | Plant Phenology, Enhanced Vegetation Index (EVI), Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height | |
| Algorithm theoretical basis document for GEDI footprint aboveground biomass density | Kellner, James R., Armston, John, Duncanson, Laura | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Forests, Evergreen Vegetation, Deciduous Vegetation, Shrubland/Scrub, Biomass, Grasslands, LIDAR WAVEFORM, Terrestrial Ecosystems | |
| A spatially varying model for small area estimates of biomass density across the contiguous United States | May, Paul, McConville, Kelly S., Moisen, Gretchen G., Bruening, Jamis, Dubayah, Ralph | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Terrestrial Ecosystems, Biomass, LIDAR WAVEFORM, Forests, Carbon |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| /BEAM0000/rx_1gaussfit/ancillary/rx_constraint_gloc_lower | Lower allowable limit for the rx Gaussian fit location | ns | float64 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/rx_constraint_gloc_upper | Upper allowable limit for the rx Gaussian fit location | ns | float64 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/rx_constraint_gwidth_lower | Lower allowable limit for the rx Gaussian fit width. | ns | float64 | N/A | 0 to 1000 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/rx_constraint_gwidth_upper | Upper allowable limit for the rx Gaussian fit width. | ns | float64 | N/A | 0 to 1000 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/rx_estimate_bias | If set to 1, a bias was estimated as part of the Gaussian fit. Set to 0 otherwise. | N/A | int32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/rx_mean_noise_level | If amplitude is less than this value, no gaussian fitting is performed to the rxwaveform. | counts | float64 | N/A | 0 to 350 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/ancillary/rx_smoothwidth | Smoothing width to apply to waveforms before Gaussian fitting. | ns | float64 | N/A | 0 to 50 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gamplitude | Amplitude of single gaussian fit to the rxwaveform | counts | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gamplitude_error | Error in ampltiude estimate for single gaussian fit to the rxwaveform | counts | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gbias | Bias estimated in fitting a single gaussian to the rxwaveform | counts | float32 | N/A | 0 to 350 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gbias_error | Error on rx_gbias parameter estimate | counts | float32 | N/A | 0 to 350 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gchisq | Chi-squared value of gaussian fit to the rxwaveform | N/A | float32 | N/A | 0 to 10000 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gflag | Gaussian fit status flag. 1=convergence in chi2 value, 2=convergence in parameter value, 3=convergence in chi2 and parameter values, 4=convergence in orthogonality, 5=maximum number of iterations reached, 6=ftol too small (no further improvement), 7=xtol too small (no further improvement), 8=gtol too small (no further improvement). | N/A | uint8 | N/A | 0 to 9 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_giters | Number of iterations to converge Gaussian fit to rxwaveform. | N/A | uint16 | N/A | 1 to 100 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gloc | Location (mean) of the Gaussian fit to the rxwaveform. | ns | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gloc_error | Error on rx_gloc parameter estimate | ns | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gwidth | Width (1 sigma) of the gaussian fit to the rxwaveform | ns | float32 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_1gaussfit/rx_gwidth_error | Error on rx_gwidth parameter estimate | ns | float32 | N/A | 0 to 300 | N/A | N/A |
| /BEAM0000/rx_assess/ancillary/rx_ampbounds_ll | Lower limit used for pulse amplitude flag | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_assess/ancillary/rx_ampbounds_ul | Upper limit used for pulse amplitude flag | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_assess/ancillary/rx_clipamp | Ampltiude above which clipping may occur | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_assess/ancillary/rx_pulsethresh | Amplitude used to flag low amplitude return waveforms | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_assess/ancillary/rx_ringthresh | Multipler on noise stddev to detect presence of ringing below the mean in the pulse | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_assess/ancillary/smoothing_width_locs | Used in debugging only | ns | float64 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_assess/mean | Mean noise estimate used in rx waveform interpretation algorithm | counts | float32 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_assess/mean_64kadjusted | Average amplitude within 10km search window with energy from rxwaveform removed | counts | float32 | N/A | 0 to 1000000 | N/A | N/A |
| /BEAM0000/rx_assess/ocean_calibration_shot_flag | Flag indicating return suitablefor use in sensor parameter estimation? | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_assess/quality_flag | Flag indicating likely invalid waveform (1=valid, 0=invalid) | N/A | uint8 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_assess/rx_assess_flag | Flags indicating various error conditions possible in rxwaveform | N/A | uint16 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_assess/rx_clipbin0 | location of first waveform sample exceeding clip amplitude | bins | uint16 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_assess/rx_clipbin_count | number of consecutive waveform samples affected by clipping | bins | uint16 | N/A | 0 to 1420 | N/A | N/A |
| /BEAM0000/rx_assess/rx_energy | total energy of rxwaveform, mean noise removed | counts | float32 | N/A | -1000 to 1000000 | N/A | N/A |
| /BEAM0000/rx_assess/rx_maxamp | maximum amplitude of rxwaveform relative to mean noise level | counts | float32 | N/A | N/A | N/A | N/A |
| /BEAM0000/rx_assess/rx_maxpeakloc | Waveform sample where maximum amplitude of waveform occurs | counts | uint16 | N/A | 1 to 1420 | N/A | N/A |
| /BEAM0000/rx_assess/sd_corrected | noise standard deviation, corrected for odd/even digitizer bin errors based on pre-launch calibrations | counts | float32 | N/A | 0 to 20 | N/A | N/A |
| /BEAM0000/rx_assess/shot_number | Shot number | N/A | uint64 | N/A | N/A | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/ampval_limit2 | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/ampval_limit3 | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/amp_thresh | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 4096 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/botlocdist_limit1 | Final ground return pulse selection parameter | ns | float64 | N/A | 0 to 50 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/botlocdist_limit2 | Final ground return pulse selection parameter | ns | float64 | N/A | 0 to 50 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/botlocdist_limit3 | Final ground return pulse selection parameter | ns | float64 | N/A | N/A | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/cumulative_energy_minimum | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/cumulative_energy_thresh | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/enable_select_mode | Final ground return pulse selection parameter | N/A | int32 | N/A | 0 to 1 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/energy_thresh | Final ground return pulse selection parameter | counts | float64 | N/A | 0 to 1000 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/preprocessor_threshold | Initial search threshold multiplier to detect signal start and end | N/A | float64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/pulse_sep_thresh | Final ground return pulse selection parameter | ns | float64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/rx_back_threshold | If use_fixed_threshold=0, this is the noise stddev multiplier used to detect the lowest elevation signal. If use_fixed_threshold is non_zero, use the value as the threshold | N/A | float64 | N/A | 0 to 100 | N/A | N/A |
| /BEAM0000/rx_processing_a1/ancillary/rx_front_threshold | If use_fixed_threshold=0, this is the noise stddev multiplier used to detect the highest elevation signal. If use_fixed_threshold is non_zero, use the value as the threshold | N/A | float64 | N/A | 0 to 100 | N/A | N/A |