National Park Service 

U.S. Department of the Interior

Natural Resource Stewardship and Science

Data Quality Standards for Fire Monitoring in the 

Arctic Network

Natural Resource Report NPS/ARCN/NRR—2020/2099 
ON THE COVER

Reed River fire July 4, 2008 in Gates of the Arctic National Park & Preserve. 

Photograph courtesy of Alaska Fire Service, Bureau of Land Management
Data Quality Standards for Fire Monitoring in the 

Arctic Network

Natural Resource Report NPS/ARCN/NRR—2020/2099 

Jennifer L. Barnes1, Jennifer L. Hrobak1, and Scott D. Miller2 

1

 National Park Service 

Fire Management Program 

4175 Geist Rd. 

Fairbanks, AK 99709

2

National Park Service 

Arctic Inventory & Monitoring Network 

Central Alaska Inventory & Monitoring Network 

240 West 5th Ave 

Anchorage, AK 99501

March 2020 

U.S. Department of the Interior 

National Park Service

Natural Resource Stewardship and Science 

Fort Collins, Colorado
ii

The National Park Service, Natural Resource Stewardship and Science office in Fort Collins, 

Colorado, publishes a range of reports that address natural resource topics. These reports are of 

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This report is available in digital format from the Natural Resource Publications Management 

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assistive technology, please email irma@nps.gov. 

Please cite this publication as:

Barnes, J. L., J. L. Hrobak, and S. D. Miller. 2020. Data quality standards for fire monitoring in the 

Arctic Network. Natural Resource Report NPS/ARCN/NRR—2020/2099. National Park Service, 

Fort Collins, Colorado. 

NPS 953/167646, March 2020 
iii

Contents 

Page

Tables .................................................................................................................................................... iv

Abstract .................................................................................................................................................. v

Introduction ............................................................................................................................................ 1

Protocol Overview .......................................................................................................................... 1

Protocol Activities and Modules .................................................................................................... 1

Sampling Design ............................................................................................................................ 3

Data Quality Objectives ......................................................................................................................... 7

Accuracy of Fire Records and Fire Perimeters ............................................................................. 13

Accuracy of Lightning Data ......................................................................................................... 13

Accuracy of Remotely Sensed Burn Severity .............................................................................. 14

Accuracy of Fire Effects Data ...................................................................................................... 14

Taxonomic Standards .............................................................................................................. 14

Literature Cited .................................................................................................................................... 16
iv

Tables 

Page

Table 1. Protocol activity matrix for Fire Monitoring Protocol for the Arctic Network 

(Barnes et al. 2018). ............................................................................................................................... 4

Table 2. Activity-level sample design matrix for Fire Monitoring Protocol for the Arctic 

Network (Barnes et al. 2018). ................................................................................................................ 6

Table 3. Data Quality Values (DQVs) for Fire Monitoring Protocol for the Arctic 

Network (Barnes et al. 2018). ................................................................................................................ 7

Table 4. Measurement Quality Objectives for Fire Monitoring Protocol for the Arctic 

Network (Barnes et al. 2018). ................................................................................................................ 9

Table 5. Data Protection standards for Fire Monitoring Protocol for the Arctic Network 

(Barnes et al. 2018). ............................................................................................................................. 12

Table 6. Taxonomic standards to be used in Fire Monitoring Protocol for the Arctic 

Network (Barnes et al. 2018). Numbers (left column) match those in the Protocol 

Activity Matrix Table (Table 1). .......................................................................................................... 15
v 

Abstract

The purpose of this report is to document the standards used by the National Park Service (NPS) 

Arctic (ARCN) Inventory and Monitoring Network for activities related to the collection, processing, 

analysis, and publication of the fire extent, severity, and effects monitoring data as described in Fire 

Monitoring Protocol for the Arctic Network (Barnes et al. 2018). This plan also serves as a guide for 

all ARCN personnel who are involved in protocol/program activities and as a resource for identifying 

memoranda, publications, and other literature that describe associated techniques and requirements in 

more detail.
1 

Introduction 

The purpose of this report is to document the standards used by the Arctic Inventory & Monitoring 

Network (ARCN) for activities related to the collection, processing, storage, analysis, and 

publication of monitoring data as described in the Fire Monitoring Protocol for the Arctic Network 

(Barnes et al. 2018). The policies and procedures documented in this quality assurance plan for 

activities complement the quality assurance plans for other monitoring activities conducted by 

ARCN and supplement the National Inventory & Monitoring Division Quality Management Plan. 

The plan also serves as a guide for all ARCN personnel who are involved in protocol/program 

activities and as a resource for identifying memoranda, publications, and other literature that describe 

associated techniques and requirements in more detail. 

Protocol Overview

The National Park Service (NPS) inventory & monitoring networks were established to monitor key 

components of park ecosystems and to provide that information to park managers for use in 

stewardship of park resources. Fire is a dominant ecosystem process that affects almost all of the 

national parks in Alaska. Fire was selected as one of the vital signs to be monitored by the ARCN

(Lawler et al. 2009) because of the landscape-scale change that it can exert on vegetation and other 

ecosystem processes as well as its response to global climate change. 

The subarctic boreal forests and low Arctic tundra biomes of the ARCN parks are subject to periodic 

and sometimes large fires. NPS fire records indicate that between 1955 and 2015 at least 1,968,749 

acres have burned within and adjacent to the ARCN parks. An average of 32,274 acres (SE 9,542) 

have burned per year and 97% of the fires were caused by lightning within the ARCN parks between 

1955 and 2015 (Barnes et al. 2018). Climate change is expected to increase fire frequency, area 

burned, and severity of fires in Alaska due to changes in temperature, precipitation and other factors 

(Kasischke et al. 2010, Flannigan et al. 2013, Young et al. 2016). 

To monitor fire activity in the ARCN, fire and lightning data collected by the NPS Fire Management 

Program and other fire agencies are used to summarize statistics on fire extent, number of fires, and 

lightning strikes within the parks. Burn severity data from remote sensing and ground-based plots are

used to assess changes in burn severity over time. 

Protocol Activities and Modules

Data collected or utilized as a part of the Fire Monitoring Protocol for the Arctic Network (Barnes et 

al. 2018) can be broken into four primary data sources: 

1. Fire records and perimeter data collected by the NPS Fire Management and other fire 

agencies used to summarize statistics on fire extent, number of fires, and timing of fires. 

2. Lightning data collected by lightning detection sensors managed by the Bureau of Land 

Management (BLM) – Alaska Fire Service used to document lightning occurrence within the 

parks. 

3. Burn severity data from remote sensing and ground-based plots used to assess changes in 

burn severity over time. 
2 

4. Fire effects data from ground-based monitoring plots used to assess changes in vegetation 

over time. 

Fire Monitoring Objectives for ARCN (Barnes et al. 2018): 

1. Determine the variability and decadal-scale trends in fire frequency (number of fires/year), 

average fire size, maximum fire size, and total area affected by fire in ARCN network parks. 

2. Detect the variability and decadal-scale trends in number of lightning strikes by park and 

ecoregions for ARCN. 

3. Detect decadal-scale trends and variability in burn severity on fires larger than 1,000 acres in 

ARCN.

4. Detect changes in species composition, vegetation structure, and ground cover due to time 

since fire and burn severity. 

The Fire Monitoring Objectives are achieved by collecting data and incorporating them into a GIS 

database and other database formats for analyses and reporting. 

1. Fire Record Data (Fire Reports). Data associated with individual fires that occur on or 

adjacent to NPS lands. These reports include the following data about individual fires: fire 

name, fire number, fire code, fire type (wildfire, prescribed fire, natural out or false alarm), 

protection type, cause category (natural or human), start or discovery date, declared out date, 

fire origin (latitude/longitude), final fire acreage, acres by land ownership, closest weather 

station, dominant fuel types, topography, aspect, slope class, and elevation of the origin of 

the fire. 

2. Fire Perimeter Data. Statewide fire perimeters are submitted throughout the fire season to the 

BLM Alaska Fire Service and are maintained by the Alaska Interagency Coordination 

Center. The geospatial (GIS) polygon database of fire perimeters is statewide. 

3. Lightning Data. BLM Alaska Fire Service maintains lightning detection sensors throughout 

the state of Alaska. Lightning location data is provided as geospatial point layer and served 

off the Alaska Interagency Coordination Center website. Data includes date, type of strike 

and location coordinates for lightning strikes. 

4. Burn Severity Remote Sensing Data. A method for mapping burn severity with Landsat 

satellite imagery was developed to provide landscape level burn severity maps (Key and 

Benson 2006). The Monitoring Trends in Burn Severity (MTBS) project produces burn 

severity maps nationally for fires larger than 1,000 acres using a metric called the differenced 

Normalized Burn Ratio (Eidenshink et al. 2007). 

5. Burn Severity Field Data. Field data with measurements of burn severity, photos, and site 

information are collected periodically to ground validate remotely sensed burn severity data. 

6. Fire Effects Field Data. Field data with measurements of vegetation cover and composition, 

active layer depths, photos and site information are collected from permanent plots every 10-

15 years to assess changes after fire. 
3 

7. Quality Control Data. Quality control data are produced in the assessment of fire records and 

fire perimeter data. The data are described in the SOPs and are considered temporary files, 

therefore are not addressed in this document. 

Table 1 provides a list of protocol activities for the primary data utilized in the ARCN fire 

monitoring protocol (Barnes et al. 2018). Additional fields may be collected or provided in the data 

sources but the fields provided in Table 1 are pertinent for calculating fire extent and burn severity

statistics in the ARCN fire monitoring protocol. 

Sampling Design

Information regarding the sampling design for the fire monitoring protocol for the ARCN (Barnes et 

al. 2018) is provided in Table 2. Numbers (left column) match those in the Protocol Activity Matrix 

(Table 1).
4 

Table 1. Protocol activity matrix for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018). 

Category

Activity 

# Activity Description

Fire Record Data 

(Fire Reports) (Data acquisition ARCNFire 

SOP 1.1-1.2, QC in ARCNFire SOP 1.3)

1 Year Calendar year that fire occurred.

2 Fire Start Date/Time Date and time that fire was first detected or started.

3 Fire End Date/Time Date and time that fire was declared out.

4 Total Fire Size (Acres) Area that fire burned, most often including unburned inclusions. Data 

recorded in acres.

5 Fire Acres (NPS) Area burned on NPS owned lands and area burned within 

administrative boundaries of the park unit. Data recorded in acres.

6 Cause Category Cause of the fire classified as natural or human.

7 Latitude/Longitude Location of the origin of the fire as determined by fire detection or fire 

perimeter data.

Fire Perimeter Data 

(Data acquisition ARCNFire SOP 1.4, QC in 

ARCNFire SOP 1.3)

8 FireYear Year fire occurred. 

9 PerimDate Date the fire perimeter represents. Day/Month/Year

10 Calculated Acres Area of fire calculated from the geospatial perimeter. Data recorded in 

acres. 

11 Edit Date Date that the fire perimeter was last updated. Some fires are updated 

with satellite imagery or other sources after the fire season. 

12 Comments Notes on how fire perimeter was digitized or mapped and source of data 

for the perimeter.

Lightning Data 

(Data acquisition and QC in ARCNFire SOP 

1.7)

13 LocalDateTime Date and time the lightning strike was detected (Local time zone). 

Day/Month/Year and military time. Continuous data collection. 

14 STROKETYPE Record of ground or cloud-to-cloud lightning strike type. 

15 Latitude/Longitude Location of the lightning strike as determined by the detection system.
5 

Table 1 (continued). Protocol activity matrix for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018).

Category

Activity 

# Activity Description

Burn Severity – Remote Sensed Data 

(Data acquisition and QC in ARCNFire SOP 

2.1 – 2.2)

16 dNBR value Value of the differenced Normalized Burn Ratio, continuous data. GIS 

raster data set.

17 dNBR6 value Categorical value of burn severity as determined by MTBS. Six classes 

of burn severity are coded as: 1- unburned to low, 2- low severity, 3- 

moderate severity, 4- high severity, 5- increased greenness, and 6- nonprocessed areas (due to scan lines, cloud, smoke or other obstructions 

in imagery). GIS raster dataset.

Burn Severity - Field Observation Data 

(Data acquisition in ARCNFire SOP 2.5, 

Data entry/QC ARCNFire SOP 2.6)

18 CBI Value Total value of the composite burn index (CBI) measure of burn severity 

as determined by observer observations. 

19 Latitude/Longitude Location of the plot as determined by GPS units.

20 Vegetation Cover Taxa, height and percent cover estimates made for vascular, nonvascular and ground cover within a 15m radius circle.

21 Photos Photos taken at four cardinal directions from center point of plot.

Fire Effects - Field Observation Data 

(Data acquisition in ARCNFire SOP 3.2, 

Data entry/QC ARCNFire SOP 3.3)

22 Latitude/Longitude Location of the plot as determined by GPS units.

23 Vegetation Cover Taxa, height and percent cover estimates made for vascular, nonvascular and ground cover within 10 1-m x 1-m quadrats for each plot.

24 Active Layer Depth Measured using a soil probe that is 120 cm long, limited by length of soil 

probe.

25 Photos Photos taken from each end of the plot transects.
6 

Table 2. Activity-level sample design matrix for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018).

Category

Activity 

# Activity Sampling Design Revisit Design

Fire Record Data (Fire Reports) 1-7 Data record of individual fires 

including: location, size, start 

date, end date, fire cause.

Census: All fires that occur are 

recorded as detected for each 

year. Data from 1950-Current. 

Continuous data collection, final 

data acquired every 1 year

Fire Perimeter Data 8-12 GIS polygon data of fire 

perimeters

Targeted: Fires over 10 acres are 

required to be mapped and 

digitized (as of 2015). Minimum 

mapping size for fire perimeters 

was higher in prior years. Most 

fires under minimum mapping 

size are not digitized.

Continuous data collection, final 

data acquired every 1 year

Lightning Data 13-15 Record of lightning strikes in 

Alaska 

Census: All lightning strikes that 

are detected for each year. TOA 

lightning data from 2012-Current.

Continuous data collection, final 

data acquired every 1 year

Burn Severity – Remote Sensed Data 16-17 Raster data grid maps of burn 

severity based on Landsat 

imagery 

Targeted: Fires greater than 

1,000 acres and with adequate 

satellite imagery are mapped for 

burn severity. Data from 1984-

Current

Continuous data collection, final 

data acquired every 2 years

Burn Severity - Field Observations 18-21 Plot based ground verification of 

burn severity data

Targeted: Random or targeted 

selection of fires with remotely 

sensed burn severity maps in 

ARNC parks. Ground truth plot 

locations stratified by burn 

severity class and random 

location in clusters for logistical 

access and field work. 

Initial visit only

Fire Effects - Field Observations 22-25 Plot based ground fire effects 

data

Targeted: 16 sites that were 

previously monitored for effects 

of tundra fires. 

Revisit every 10-15 years
7 

Data Quality Objectives

Data quality values and standards for implementation are provided in Table 3 through Table 5. A general overview of the accuracy of the six

activities (Fire Records, Fire Perimeters, Lightning, Burn Severity remote sensing data, Burn Severity field data, and Fire Effects field data) 

are described below.

Table 3. Data Quality Values (DQVs) for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018).

Category Data Quality Value Definition Protocol Considerations

Intrinsic Data Quality Accuracy Measurements reflect the true value of the 

parameter being observed. This applies to 

measures (length, width, position) or classes 

(species, types, or categories). Includes 

components of precision and bias.

Protocol recognizes the limitations and accuracy of 

mapping, detecting fire start locations and 

starting/ending dates of fires in remote areas where 

fires are not staffed. Protocol recognizes the 

limitations of mapping large and small fires of older 

dates. Protocol requirements are designed to 

ensure desired results but take into account 

estimated or expected precision and bias.

Representativeness Measurements represent conditions at a 

permanent sampling location over time. Combined 

with accuracy, leads to robust data collection.

Parameters and methods are chosen such that 

they measure representative conditions. 

Measurements may be direct measures, indexes, 

or indicators of conditions.

Contextual Data Quality Comparability Fire record data are collected using compatible 

data fields across all years. NPS fire records are 

required for all parks and use comparable data 

fields. Data are edited in accordance with 

consistent procedures described in SOPs.

Remotely sensed burn severity data are created 

using comparable algorithms for all dNBR values.

Fire effects data - Vegetation cover estimates are 

comparable to other published protocols (Roland et 

al. 2004). Active layer depths are comparable to 

methods published in other protocols (Swanson 

and Neitlich 2016) 

Protocol recognizes that

• data from early fire records (pre-1990) may not 

have same data quality as more recent fire 

records, due to lack of GPS equipment; 

• alternative methods of remotely sensed burn 

severity data may be developed in the future 

and SOPs would be adjusted accordingly; and

• -tadditional fire effects plots may be added if 

funding were available and SOPs would be 

adjusted accordingly. 

Data management and editing are conducted in 

accordance with standard operating procedures 

that are consistent for all fires. 
8 

Table 3 (continued). Data Quality Values (DQVs) for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018).

Category Data Quality Value Definition Protocol Considerations

Contextual Data Quality

(continued)

Timeliness/Currency Fire data are created upon actual fire events, 

acquisition and data entry are delayed until fire 

season is over. 

Data represents best available information and is 

available and in a format for use at the appropriate 

time in the decision-making process. 

Near real time availability of fire data on agency 

partner websites allows for information on fire 

conditions for park management. These near real 

time data are not used as the certified data set. 

Complete processing of data occurs on an annual 

basis. Certification of data completed on an annual 

basis and archived on Data Store. 

Burn severity maps are not available until 1-3 years 

after the fire has occurred due to MTBS processing 

time.

Fire effects data will be available 1 year after 

collection.

Completeness All data/ measures required to evaluate accuracy 

representativeness are present; incomplete 

datasets (either at a location, across sampling 

locations, or over time) lose utility or relevance. 

Data records contain values as planned across the 

period of record; missing values are left as blank 

values within the time series. 

Data acquisition, methods, measurement intervals, 

and analyses are designed and implemented such 

that they result in a complete dataset across space 

and the planned period of record. 

If improved data become available for past fire 

occurrences, the fire record data will be updated 

and notated.

Representational Data 

Quality

Consistent 

Representation

Use of standard definitions when describing data 

quality or resource quality based on data

Standard definitions of data quality are provided. 

Provisional data indicate data that have been not 

been reviewed and are not certified. Certified data 

are defined as those data that have been reviewed, 

QA/QC checked, and edited in accordance with 

procedures described in ARCNFire SOPs. Data 

are reported and flagged in comparison to 

established management or ecological, or 

thresholds in accordance with procedures 

described in Barnes et al. 2018. 
9 

Table 3 (continued). Data Quality Values (DQVs) for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018).

Category Data Quality Value Definition Protocol Considerations

Data Accessibility Secure Access to data, products, and systems limited to 

appropriate audiences. 

Fire records, lightning data, burn severity and fire 

effects data are public record. 

No protected data will be generated as part of the 

ARCN fire monitoring protocol. 

Table 4. Measurement Quality Objectives for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018). 

Measure Quality Indicator Quality Objective

Fire Origin Locator Measurement Unit Latitude/Longitude

Location Accuracy Variable. Reported as Accurate (known point of origin), General (probable point of origin), 

Uncertain (within fire perimeter), or Other.

Fire Start Date/Time Measurement Units Month/Day/Year; Hour: Minute

Accuracy Variable. Dependent on time of fire detection/discovery. +/- 10 days

Fire End Date/Time Measurement Units Month/Day/Year; Hour: Minute

Accuracy Variable. Dependent on last visible fire growth/activity, fire detection/monitoring availability, 

and/or administrative requirements (holdovers). +/- 30 days

Total Fire Size Measurement Units Acres 

Resolution 0.1 Acres

Accuracy Variable. Dependent on mapping source, source method, and source class resolution (> 1 

km, < 1 km, < 200m, < 90m, or < 10m). 

Range Alaska NPS Region: 0.1 to 463,981 acres. ARCN NPS Parks: 0.1 to 261,909 acres. Data 

from 1946-2017 NPS Fire Records in WFMI as of October 19, 2018. 

Cause Category Category Options Natural – Lightning; Natural – Volcanic; Natural – Other, Known; Natural – Other, Unknown; 

Human
10 

Table 4 (continued). Measurement Quality Objectives for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018). 

Measure Quality Indicator Quality Objective

Fire Perimeter Calculated Acres 

(GIS) 

Measurement Units Acres 

Resolution 0.1 Acres

Accuracy Variable. Dependent on mapping source, source method, and source class resolution (> 1 

km, < 1 km, < 200m, < 90m, or < 10m).

Range Alaska Statewide: 0.1 to 1,366,729 acres. Within NPS Alaska Region Park: 0.1 to 469,185 

acres. Data from 1940-2016 Alaska fire perimeter data.

Lightning Location Measurement Units Latitude/Longitude

Location Accuracy Variable, dependent on triangulation and detection from sensors. 200 to 300 m RMS

Type of Measurement Continuous

Data Reported Continuous

Lightning Type (Cloud to Ground) Type of Measurement Continuous

Data Reported Continuous

Measurement Unit StrokeType 

Remotely Sensed Burn Severity 

(dNBR Landsat)

Assessment Type Initial (immediate post-fire) or extended (approx. 1 year post-fire)

Measurement Units dNBR (differenced normalized burn ratio), numerical value

Range -689 to 1454 (for data analyzed so far, n = 18)

Resolution 30m x 30m pixel

Remotely Sensed Burn Severity 

(dNBR6 Landsat)

Assessment Type Initial (immediate post-fire) or extended (approx. 1 year post-fire)

Measurement Units Threshold classification: 1- unburned to low, 2- low severity, 3- moderate severity, 4- high 

severity, 5- increased greenness, and 6- non-processed areas (due to scan lines, cloud, 

smoke or other obstructions in imagery).

Range -689 to 1454 (for data analyzed so far, n = 18)

Resolution 30m x 30m pixel
11 

Table 4 (continued). Measurement Quality Objectives for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018). 

Measure Quality Indicator Quality Objective

Field Observation – CBI 

(Composite Burn Index) Value

Measurement Units User observed score of burn severity delineated for different surface and vegetation strata 

layers, ranging from 0 to 3. 

Accuracy 90% accuracy (user bias, training occurs prior to field data collection)

Completeness 95% observations completed

Field Observation – 

Vegetation Cover

Taxonomic Resolution 

(Vegetation)

Identifications made to the species level (see taxonomic standards)

Identification Accuracy 

(Vegetation)

90% accuracy

Identification 

Completeness 

(Vegetation)

90% of observations made to species level

Field Observation – 

Site Location

Measurement Units Latitude/Longitude

Location Accuracy Sample location within +/ 4m of established waypoint.

Field Observation – 

Active Layer Depth

Measurement Units Centimeters

Resolution 1.0 Centimeter

Accuracy +/- 1 cm 

Minimum/Maximum 0, 110 cm (based on probe length)
12 

Table 5. Data Protection standards for Fire Monitoring Protocol for the Arctic Network (Barnes et al. 2018). 

Category Type of Data Level of Protection Rules for Dissemination

Fire Record and Fire Perimeter Data Fire Record and Perimeter Data Unprotected Data available on-line as public information

Location Data and Information Permanent Sampling Locations Unprotected Data available on-line as public information

Personally Identifiable Information Non-NPS Staff Information Legally Protected All but first name and last initial redacted from public 

release
13 

Accuracy of Fire Records and Fire Perimeters

The fire records in Alaska have been maintained by the Bureau of Land Management on a regular

basis since the mid-1940s (Todd and Jewkes 2006). Due to the remoteness and size of Alaska, 

detecting and mapping fires has some limitations due to the scale of area covered, lack of GPS 

technology during earlier years, and general limitations of detecting all fires in a 424.5 million acre 

land mass. The fire history data sets in Alaska have varying quality of data (Kasischke et al. 2002). 

During the early 1990s a large effort was put forth to digitize fire perimeter maps to create what was 

called the Alaska large-fire database (Kasischke et al. 2002). The objective was to compile and 

standardize information from historical fire reports and to create a geospatial fire perimeter database 

that contained large fires (greater than 1,000 acres) dating back to 1950. Perimeter map quality varied 

significantly with some reports having only rough, hand sketched boundaries. In addition, many fire 

reports were lost in the 1960s and could not be included in the database. Since the mid-1990s, the 

practice of mapping fire perimeters with a GPS or utilizing satellite imagery to digitize perimeters 

has improved the data quality. The Alaska large-fire database is hosted on the Alaska Interagency 

Coordination Center website and is maintained by the BLM-Alaska Fire Service. 

Between 2007 and 2008 partial funding from a Joint Fire Science Program research study was used 

to update, improve, and digitize missing fire perimeters (Olson et al. 2011). Through this project, 417 

fire perimeters were improved by utilizing Landsat imagery and other sources. Of those perimeters, 

over 112 were located on NPS Alaska lands. With the improved mapping, fire acreages changed for 

many of the fires. It is a continual process to quality check and update the fire datasets. In order to 

adjust for potential discrepancies between data sources and to improve the datasets, a process was 

developed to quality check the data within the fire reporting system and the fire perimeter datasets 

(ARCN Fire SOP 1.3). 

Fire point of origin location data stored in the Alaska NPS fire reporting system database are 

generated from fire records from 1950 until present day, although there are some records that are 

earlier than 1950. The early records of fire locations were often estimated using legal descriptions or 

topographic maps and fire sizes were visually estimated. More recently, fire locations are collected

with a GPS, however due to the remote nature of the parks, fire points are commonly taken in a fixed 

wing aircraft and can affect location accuracy. 

Accuracy of Lightning Data

The BLM-Alaska Fire Service (AFS) owns and maintains the Alaska Lightning Detection Network 

(ALDN). Many transitions have occurred in the last 35 years including: changes and improvements 

in technology, expansion of sensors within the network and the inclusion of data from Environment 

Canada (EC). Each transition has improved detection efficiency (more lightning reported), accuracy 

(strike location) and detection coverage across Alaska (ability to detect at further distance; see 

Fronterhouse 2012). 

Lightning data for Alaska has been available since 1986 through the present time. A major 

replacement of equipment occurred between 2010 and 2011 and the new sensors were operational as 

of 2012. As part of the equipment replacement cycle, AFS purchased DSP-DF200 Multi-Purpose 
14 

Advanced Lightning Sensors and the Advanced Stroke Processor from TOA Systems, Inc. 

Installation of the new sensors began in 2011 with project completion in 2012 (Fronterhouse 2012). 

The data from prior years do not directly compare to the new system. Therefore the lightning data 

utilized for the ARCN fire monitoring program will rely on the TOA data (from 2012 forward), 

unless further research develops a means to compare the older lightning data to the current system. 

Based on the DSP-DF200 Multi-Purpose Advanced Lightning Sensors and the Advanced Stroke 

Processor from TOA sensors capability specification sheet (GPATS 2013), the sensors can monitor 

cloud-to-ground lightning within a distance of 1000 km from the sensor. This provides for accurate 

ground strike location to approximately 200 to 300 m rms (root mean square). 

Accuracy of Remotely Sensed Burn Severity

The accuracy of the remotely sensed burn severity value of dNBR or dNBR6 is dependent on many 

factors including: the spatial georeferencing of the imagery pairs, landsat scene clarity (smoke, cloud, 

scan lines), seasonality of imagery and variations in phenology, slope, and aspect. Additional 

limitations of the data set include the following: 

• Not all fires are mapped due to limited availability of quality imagery. 

• The same dNBR value may not equal the same severity on another fire. Recommend not 

comparing initial and extended assessment values. Further testing and ground truthing are

needed. 

• Severity threshold classifications (dNBR6) are completed by analysts at MTBS and could 

vary by analyst and by fire. 

• Scan line issues in Landsat 7 images result in no data for portions of fires. 

Burn severity ground truth data has shown relatively good agreement between ground and satellite 

derived burn severity in Alaska with linear regression R2

 values ranging from 0.45 to 0.88 for nine 

fires in Alaska parks (Allen and Sorbel 2008). Other studies have found poor correlations when 

sampled at high severity sites between 2004 and 2005 in Alaska (e.g., French et al. 2008). Therefore, 

ground truth verification plots are established sporadically to confirm the values produced by the 

remotely sensed data (dNBR) agree with ground measurements of severity. 

Accuracy of Fire Effects Data

The fire effects plot data are ocular estimates of vegetation cover within 1-m x 1-m quadrats. The 

errors that can occur are incorrect identification of plants, bias, or otherwise incorrect observer 

estimates of percent cover. Training methods are used in the field to minimize differences among 

observers including calibration of what constitutes 1% cover providing a breakdown of what size 

area would equate to 1% cover in a quadrat. Unknown specimens are collected for identification in 

the office or unknowns are recorded as unknowns to not present false data. Accuracy of active layer 

depths are within 1 cm and are limited to the depth of the soil probe which is 110 cm long. 

Taxonomic Standards

Whenever possible, the ARCN fire monitoring program will identify and collect data to published 

identification standards. Taxonomic concepts are essential for data sharing, as the combination of 
15 

taxonomic name and identification reference allow both parties to be sure they agree on the 

underlying circumscription of the taxon or community, even if they call it by a different name (Table 

6). It is also required to ensure data comparability among data collected over time, since data will be 

collected by different personnel (who may be trained in differing identification tools), and taxonomy 

will change as the result of renaming, merging, and splitting of taxa. 

Table 6. Taxonomic standards to be used in Fire Monitoring Protocol for the Arctic Network (Barnes et al. 

2018). Numbers (left column) match those in the Protocol Activity Matrix Table (Table 1). 

Activity 

# Activity Standard to be Used Reference / Authority 

20a Vascular plant cover Field Subject Matter Expert Field crew lead w/ documented 

identification proficiency. 

Publications to standards of Flora of 

North America 2014.

20b Non-vascular plant cover Field Subject Matter Expert (only 

specified species are identified in 

the field).

Field crew lead w/ documented 

identification proficiency. 

The ARCN Fire Monitoring taxonomic data is currently stored in a SQL server database called FFI 

(FEAT/FIREMON Integrated) (Lutes et al. 2018). The database has a look up list of species 

taxonomy based on the USDA PLANTS Database (version 4.0.4, 1996) (USDA 1996). Upon 

publishing the data in a journal publication, species taxonomy will be updated to the current 

nomenclature as identified in Flora of North America (1993+) for vascular plants and current 

nomenclature for non-vascular species.
16 

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