AmeriFlux

BADM Group Overview

BADM variables in this group are organized as shown below. The overview highlights what variables are required per group. It also indicates which variables cannot be specified together ( OR ) in the same group entry. See BADM Basics for more details.

Multiple entries of this BADM group can be reported per site. However, combinations of Ⓒ variables must be unique. Read more:

Required Variable
Optional Variable
Ⓒ Combinations
Examples

Required

Optional

Leaf Area Index

LAI

LAI_TYPE Ⓒ

LAI_CANOPY_TYPE Ⓒ

LAI_STATISTIC Ⓒ

LAI_DATE Ⓒ

LAI_DATE_START Ⓒ

LAI_DATE_END

LAI_SPP Ⓒ

LAI_VEGTYPE Ⓒ

LAI_STATISTIC_METHOD Ⓒ

LAI_STATISTIC_NUMBER

LAI_CLUMP

LAI_METHOD Ⓒ

LAI_APPROACH

LAI_DATE_UNC

LAI_COMMENT

BADM variables: Definitions, Units, Requirements

See Overview tab or BADM Basics for explanation of Required and Optional variables.

Multiple entries of this BADM group can be reported per site. However, combinations of Ⓒ variables must be unique. Read more:

Required Variable
Optional Variable
Ⓒ Combinations
Examples

Variable
Requirements

Units

Description

LAI
Required

m2 m-2

Vegetation (Plant, Leaf, Green) Area Index
Leaf Area Index (or other Vegetation Area Index as indicated in LAI_TYPE) at the tower site. Calculated as one half the total leaf area (the one-sided leaf area) per unit ground surface area.

LAI_TYPE
Ⓒ Required

LIST(LAI_TYPE) Show

Vegetation (Plant, Leaf, Green) Area Index type
Use predefined list to indicate type of Vegetation Area Index. Note that Leaf Area Index is one type of Vegetation Area Index type. See predefined list for definitions.

LAI_TYPE Options×

Option	Description
LAI	Leaf Area Index (includes all the leaves)
PAI	Plant Area Index (includes leaves, stems and other parts of the plants)
GAI	Green Area Index (includes only green leaves)

LAI_CANOPY_TYPE
Ⓒ Required

LIST(LAI_CANOPY_TYPE) Show

Vegetation (Plant, Leaf, Green) Area Index canopy type
Use predefined list to indicate the type of canopy being described by the LAI measurement. Use Total for non-forest ecosystems. For forest ecosystems, use Total if not reporting separate Overstory and Understory LAI measurements.

LAI_CANOPY_TYPE Options×

Option	Description
Total	Total canopy. Use for non-forest ecosystems or for forest ecosystem if not reporting Overstory and Understory measurement.
Overstory	Overstory canopy
Understory	Understory canopy

LAI_SPP
Ⓒ Optional-a

Scientific name

Vegetation (Plant, Leaf, Green) Area Index species (taxa)
Vegetation species being described by the measurement. To enter a plant species, use the scientific name (Genus species Author or Genus Author). Please refer to www.theplantlist.org for current scientific names and authors. If the measurement includes all vegetation present, do not enter a value. Do not use SPP if reporting VEGTYPE.

LAI_VEGTYPE
Ⓒ Optional-a

LIST(VEGTYPE) Show

Vegetation (Plant, Leaf, Green) Area Index vegetation type
Use the predefined list to indicate the type of vegetation being described by the measurement (e.g., Grass, Evergreen Tree, Deciduous Tree, etc). If the measurement includes all vegetation present, do not enter a value. If vegetation type is not in predfined list, enter "Other/Not in list" and describe briefly in Approach. Do not use VEGTYPE if reporting SPP.

VEGTYPE Options×

Option	Description
Grass
C3 Grass
C4 Grass
Grass / Graminoid	Grasses, Sedges, Rushes, and other grass-like monocots (member of order Poales)
Crop
Herbaceous Crop
Woody Crop
Tree
Deciduous Tree	Trees that shed their entire canopy seasonallly. Drought or cold avoidence are common reasons for deciduousness.
Evergreen Tree	Trees that do not seasonally shed their full canopies.
Shrub
Herb / Forb
Annual Herb / Forb
Perennial Herb / Forb
Aerenchymatous Plants
Liana / vine
Succulant	Cactus, Euphorbs, including CAM
Non-vascular	Moss, lichen
Other/Not in list	Only use this option if a major vegetation group is not included already included in the predefined list. If an existing vegetation group is not specific enough, use the existing vegetation option and add details in Approach (rather than using the "Other/Not in list" option).
No vegetation	Bare ground, water, or rock. Use only with SPP_PERC_O and SPP_PERC_U
Water	Water (perennially submerged ground)

LAI_STATISTIC
Ⓒ Required

LIST(STATISTIC) Show

Vegetation (Plant, Leaf, Green) Area Index statistic
The statistic for the measurement reported. Use predefined list (e.g., mean, min / max, standard deviation, etc).

STATISTIC Options×

Option	Description
Mean	Average (mean) value of sample population
Standard Deviation	Standard deviation may be reported from a sample population that consists of individual or aggregated samples (observations). If the distinction is important, specify in STATISTIC_METHOD or Comments.
Minimum	Minimum value
1st Percentile	Quantile at 1% of distribution
5th Percentile	Quantile at 5% of distribution
10th Percentile	Quantile at 10% of distribution
25th Percentile	Quantile at 25% of distribution
Median - 50th Percentile	Median - Quantile at 50% of distribution
75th Percentile	Quantile at 75% of distribution
90th Percentile	Quantile at 90% of distribution
95th Percentile	Quantile at 95% of distribution
99th Percentile	Quantile at 99% of distribution
Maximum	Maximum value
Single observation	Single observation that is not a calculated statistic of replicates either to get a robust estimate or for spatial variability analysis. For example a single biomass observation may be the vegetation harvested in a single 1 x 1 meter area.
Measurement Uncertainty	Report uncertainty as a plus or minus value in the measurement units. For example, enter 1.5 for +/- 1.5 units. Uncertainty may be reported from the instrument's specifications, determined empirically, or estimated by the tower team. Please describe such details in Approach. For uncertainty values that are better described by a range, a percent, or other, please enter information in Comments.

LAI_STATISTIC_METHOD
Ⓒ Optional

LIST(STATISTIC_METHOD) Show

Vegetation (Plant, Leaf, Green) Area Index statistic method
Method used to generate the reported statistic (e.g., aggregate of individuals, aggregate of sample aggregates) from observations representing the same time period. Use predefined list. The aggregation method is not meant to describe temporal aggregations for example in calculations of higher frequency observations (e.g., sub-minute) to lower frequency observations (e.g., hourly) at a single location.

STATISTIC_METHOD Options×

Option	Description
Aggregate of individual observations	The statistic is aggregated from individual observations located within the site. Observations may be grouped into sample areas (e.g., plots) within the site but those groupings are ignored. Statistics generated from individual observations directly yield information on the site generally. The statistic may represent spatial characteristics of the measurement within the site (e.g., spatial heterogeneity) and/or characteristics due to other factors (e.g., population variability).
Aggregate of sample aggregates	The statistic is aggregated from aggregated individual observations located in sample areas within the site. For example, individual observations are made in 5 sample plots within a site. "Aggregate of sample aggregates" is used if a plot statistic (e.g., Mean) is first calculated, and then the plot values are aggregated to calculate the site statistic. Statistics generated by this approach are often used to highlight the spatial characteristics within the site (i.e., the spatial heterogeneity of measurement within the site).
Expert estimate	Estimate made by expert familiar with site

LAI_STATISTIC_NUMBER
Optional

integer number

Number of observations used to determine Vegetation (Plant, Leaf, Green) Area Index statistic
Number of observations (samples / replicates) used to calculate the STATISTIC for the reported measurement.

LAI_CLUMP
Optional

decimal number

Foilage element clumping index for Vegetation (Plant, Leaf, Green) Area Index measurement
Foliage element clumping index is used to correct optical data estimates of LAI. Use this variable only if the method specified in LAI_METHOD supports clumping correction. For conifers, use needle clumping within shoot.

LAI_METHOD
Ⓒ Optional

LIST(LAI_METHOD) Show

Vegetation (Plant, Leaf, Green) Area Index methodology
Method used to quantify the Vegetation (Plant, Leaf, Green) Area Index. Use predefined list. If method is not present, select "Other" and give additional details in LAI_APPROACH.

LAI_METHOD Options×

Option	Description
ACCUPAR	AccUPAR from Decagon Devices
Litterfall	Collection of leaves in litterfall traps
SUNSCAN	SunScan from Delta-T Devices
SPECTRAL	Relation between spectral reflectance and direct measurements
Direct
Hemispherical photo	Analog or digital hemispherical photography (DHP)
LAI_2000	LI-COR LI-2000
LAI_2200	LI-COR LI-2200
Lidar	Use LAI_APPROACH to describe details of Lidar method used
Other	Use LAI_APPROACH to describe the method used

LAI_APPROACH
Optional

free text

Vegetation (Plant, Leaf, Green) Area Index measurement approach
Report additional details used to quanity Vegetation (Plant, Leaf, Green) Area Index.

LAI_DATE
Ⓒ Required-b

YYYYMMDDHHMM

Vegetation (Plant, Leaf, Green) Area Index measurement date
Please report the date at the precision known. Allowed reporting precisions are YYYY, YYYYMM, YYYYMMDD, and YYYYMMDDHHMM. For measurements conducted during a campaign or for temporal averages, use DATE_START and DATE_END instead of DATE.

LAI_DATE_START
Ⓒ Required-b

YYYYMMDDHHMM

Start date of Vegetation (Plant, Leaf, Green) Area Index measurment
Start date of a measurement campaign or start date of a temporal average. Please report the date at the precision known. Allowed reporting precisions are YYYY, YYYYMM, YYYYMMDD, and YYYYMMDDHHMM. Do not use DATE_START if DATE is used.

LAI_DATE_END
Required

YYYYMMDDHHMM

End date of Vegetation (Plant, Leaf, Green) Area Index measurment
End date of a measurement campaign or end date of a temporal average. Please report the date at the precision known. Allowed reporting precisions are YYYY, YYYYMM, YYYYMMDD, and YYYYMMDDHHMM. Do not use DATE_END if DATE is used.

LAI_DATE_UNC
Optional

days

Uncertainty in the Vegetation (Plant, Leaf, Green) Area Index measurement date, start date, and/or end date
If uncertainty in the start date differs than end date, report the uncertainty for start date here in DATE_UNC and the uncertainty for end date in Comments.

LAI_COMMENT
Optional

free text

Vegetation (Plant, Leaf, Green) Area Index comments

BADM Examples

Choose a variable marked with to show examples of how to submit and interpret these BADM. See BADM Basics for more details.

Combinations of Ⓒ variables must be unique. Read more: .

Required Variable
Optional Variable
Ⓒ Combinations
Examples

Leaf Area Index

LAI

LAI_TYPE Ⓒ

LAI_CANOPY_TYPE Ⓒ

LAI_SPP Ⓒ

LAI_VEGTYPE Ⓒ

LAI_STATISTIC Ⓒ

LAI_STATISTIC_METHOD Ⓒ

LAI_STATISTIC_NUMBER

LAI_CLUMP

LAI_METHOD Ⓒ

LAI_APPROACH

LAI_DATE Ⓒ

LAI_DATE_START Ⓒ

LAI_DATE_END

LAI_DATE_UNC

LAI_COMMENT

Click a variable marked with to see examples.

STATISTIC Variables

Many BADM groups have a required and several optional STATISTIC variables. Specific examples of their use are given after an overview the variables basics.

STATISTIC Basics

BADM typically describe site-level descriptions and observations. The STATISTIC variables allow for full characterization of the reported information if desired. BADM groups, such as canopy height, LAI, soil chemistry, phenology, and biomass, contain the following STATISTIC variables:

var_STATISTIC
Required

The type of value reported.

Options:
Mean, Minimum, Maximum, Percentiles
Standard Deviation, Measurement Uncertainty
Single observation

var_STATISTIC_METHOD
Optional

The method of aggregation used to generate the statistic.

Options:
Aggregate of individual observations
The statistic is aggregated from individual observations located within the site. Observations may be grouped into sample areas (e.g., plots) within the site but those groupings are ignored.

Statistics generated by this approach may represent spatial characteristics of the measurement within the site (e.g., spatial heterogeneity) and/or characteristics due to other factors (e.g., population variability).

Aggregate of sample aggregates
The statistic is aggregated from aggregated individual observations located in sample areas within the site. For example, individual observations are made in 5 sample plots within a site. “Aggregate of sample aggregates” is used if a plot statistic (e.g., Mean) is first calculated, and then the plot values are aggregated to calculate the site statistic.

Statistics generated by this approach are often used to highlight the spatial characteristics within the site (i.e., the spatial heterogeneity of measurement within the site).

Expert estimate
Estimate made by expert familiar with site

See the Examples for more details.

var_STATISTIC_NUMBER
Optional

The number of observations used in calculating the statistic.

STATISTIC Examples

Example 1: DBH calculated from a single sampling area
Example 2: DBH calculated from 8 plots
Example 3: DBH calculated from randomly selected trees within the site
Example 4: Biomass calculated from 8 plots each with 5 sub-plots
Example 5: Soil carbon calculated from replicate samples at 10 locations

Example 1: DBH calculated from a single sampling area

For DBH observations of individual trees in a single sample area at the site:

STATISTIC* = Mean, Minimum, Maximum, Percentiles, or Standard Deviation
STATISTIC_METHOD = Aggregate of individual observations
STATISTIC_NUMBER = # of individual samples

* Minimum, Maximum, and Percentiles should only be calculated if the sample size is adequately large.

Example 2: DBH calculated from 8 plots

For DBH observations of individual trees in 8 sample plots at the site:

If the individual DBH observations are first aggregated at the plot level and then the plot values are are used to calculate the site-level STATISTICs to highlight spatial variability:

STATISTIC* = Mean, Minimum, Maximum, or Standard Deviation
STATISTIC_METHOD = Aggregate of sample aggregates
STATISTIC_NUMBER = 8

If the individual DBH observations are aggregated across all plots to calculate the site-level STATISTIC:

STATISTIC* = Mean, Minimum, Maximum, Percentiles, or Standard Deviation
STATISTIC_METHOD = Aggregate of individual observations
STATISTIC_NUMBER = # of individual samples

* Minimum, Maximum, and Percentiles should only be calculated if the sample size is adequately large.

Example 3: DBH calculated from randomly selected trees within the site

For DBH observations of individual trees randomly selected at the site:

STATISTIC* = Mean, Minimum, Maximum, Percentiles, or Standard Deviation
STATISTIC_METHOD = Aggregate of individual observations
STATISTIC_NUMBER = # of individual samples

* Minimum, Maximum, and Percentiles should only be calculated if the sample size is adequately large.

Example 4: Biomass calculated from 8 plots each with 5 sub-plots

For Biomass observations collected from 5 sub-plots located in each of 8 sample plots at the site:

In many cases, the sub-plot biomass observations are first aggregated at the plot level. Then the plot values are are used to calculate the site-level STATISTICs:

STATISTIC* = Mean, Minimum, Maximum, or Standard Deviation
STATISTIC_METHOD = Aggregate of sample aggregates
STATISTIC_NUMBER = 8

If pseudo-replication or spatial autocorrelation is not an issue, the sub-plot observations may be aggregated across all plots to calculate the site-level STATISTIC:

STATISTIC* = Mean, Minimum, Maximum, Percentiles, or Standard Deviation
STATISTIC_METHOD = Aggregate of individual observations
STATISTIC_NUMBER = 40

* Minimum, Maximum, and Percentiles should only be calculated if the sample size is adequately large.

Example 5: Soil carbon calculated from replicate samples at 10 locations

For replicate soil carbon observations at 10 randomly-selected points within the site:

To calculate Mean, Minimum, Maximum, Percentiles, and Standard Deviation, the replicates at each location should first be averaged. Then the average values at each location can be used to calculate the STATISTIC:

STATISTIC* = Mean, Minimum, Maximum, Percentiles, or Standard Deviation
STATISTIC_METHOD = Aggregate of sample aggregates
STATISTIC_NUMBER = 10

* Minimum, Maximum, and Percentiles should only be calculated if the sample size is adequately large.

The average difference between the replicates can be used to estimate the Measurement Uncertainty:

STATISTIC = Measurement Uncertainty
STATISTIC_METHOD = Aggregate of individual observations
STATISTIC_NUMBER = 10

Customize and Download CSV for BADM Submission

After selecting your desired variables, download the customized CSV file for submission of BADM. For additional submission details, see BADM Submission Instructions. See BADM Basics for general BADM details.

Multiple entries of this BADM group can be reported per site. However, combinations of Ⓒ variables must be unique. Read more: . See BADM Basics for more details.

Open/Close

Select

Leaf Area Index

Required

Optional

LAI

LAI_TYPE Ⓒ

LAI_DATE Ⓒ

LAI_DATE_END

Both

LAI_SPP Ⓒ

LAI_VEGTYPE Ⓒ

Both

Neither

LAI_STATISTIC_NUMBER

LAI_CLUMP

LAI_METHOD Ⓒ

LAI_APPROACH

LAI_DATE_UNC

LAI_COMMENT

Submit completed CSV file at Upload Data using the BADM option (login required).

BADM Group: LAI

BADM Group Overview

BADM variables: Definitions, Units, Requirements

BADM Examples

SPP and VEGTYPE Variables

STATISTIC Variables

STATISTIC Basics

STATISTIC Examples

Example 1: DBH calculated from a single sampling area

Example 2: DBH calculated from 8 plots

Example 3: DBH calculated from randomly selected trees within the site

Example 4: Biomass calculated from 8 plots each with 5 sub-plots

Example 5: Soil carbon calculated from replicate samples at 10 locations

DATE Variables

DATE or DATE_START / DATE_END

DATE_UNC

Customize and Download CSV for BADM Submission