Highlights of Satellite-Based Forest 

Change Recognition and Tracking 

Using the ForWarn System

Steven P. Norman, William W. Hargrove, 

Joseph P. Spruce, William M. Christie, and Sean W. Schroeder
ii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

The Technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Overview of Forest Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

HIGHLIGHTS

Heavy Snowpack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Early Frost. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Tornadoes and Hail. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Hurricanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Drought. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Wildfires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Insect Defoliation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

Development and Deforestation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Table of Contents
Satellite-based remote sensing can assist forest managers with their need to 

recognize disturbances and track recovery. Despite the long standing availability 

of raw imagery, the systematic delivery of spatially continuous, ready-to-use, 

processed products has evaded us until recently. The web-based ForWarn system 

moves us a step forward by generating forest change maps at high frequency 

in a format that is usable to forest managers, planners, and the public. The 

ForWarn system shows change in the Normalized Difference Vegetation Index 

derived from moderate resolution imagery according to a range of baseline 

normals. Expectations of normal derive from previously observed changes in 

seasonal leaf phenology; this adjustment is critical for forests dominated by 

deciduous vegetation that vary in greenness through the year. After these seasonal 

adjustments are made behind the scene, the remaining forest change that 

ForWarn users see may result from an array of climatic and disturbance causes. 

These include insects and disease, wildland fire, wind, hail, human development, 

drought, or variation in the timing of spring and fall. This publication outlines 

the data and methods that underlie this technology, and provides examples that 

illustrate selected capabilities of this system for coarse-scale forest monitoring. 

Keywords: Disturbance, monitoring, phenology, recovery, remote sensing.

Abstract
2 Satellite-Based Forest Change Recognition and Tracking

Forest conditions can change rapidly from violent storms, severe 

fire or human development, but they can also change gradually from the 

progressive effects of altered disturbance regimes, slow-acting stress, or 

the cumulative impact of multiple factors (fig. 1, 2). Forest monitoring is 

difficult because we want to know about rapid change as soon as it occurs, 

while not neglecting the slower changes that can easily go unnoticed.

Satellite-based monitoring has long been used to monitor rapid and 

gradual forest change, but not as systematically as it could be. Regular, high 

frequency observations that are corrected for the confounding effects of 

clouds would provide meaningful insights into how background conditions 

change naturally with climate variation and across seasons. This is 

important for the accurate recognition of change.

Diagnosing the cause or causes of change is the subsequent 

challenge. Fortunately, forest change assessment efforts can build on preexisting efforts. Thanks to Federal and State programs, we have specialized 

knowledge of wildfires, insects and diseases, and climatic stress. What these 

efforts lack is an overarching system for integrated monitoring, assessment, 

tracking and communication for those who need this information.

The highlights of forest change presented in this document reveal 

the capabilities of an important part of just such a system: a satellite-based 

change recognition and tracking system that leverages insights derived from 

these complementary, but specialized efforts.

Introduction

4 Satellite-Based Forest Change Recognition and Tracking

The Technology

The National Early Warning System (EWS) is a coordinated effort 

to bring cutting-edge monitoring and assessment technologies and forest 

professionals together (Hargrove and others 2009

1

). Today, technological 

advances allow us to systematically detect and track forest disturbances from 

space in near-real time, and a network of professionals is required to accurately 

interpret observations and communicate conditions to those who can take 

action, as appropriate. The core technology, ForWarn, is a satellite-based 

change recognition and tracking system developed by the Forest Service, 

USDA’s Threat Assessment Centers,

2 and NASA Stennis Space Center, 

with substantive involvement by the U.S. Geological Survey, Department of 

Energy’s Oak Ridge National Laboratory, and the University of North Carolina, 

Asheville’s National Environmental Modeling and Analysis Center.

ForWarn provides a strategic national overview of potential forest 

change which directs attention to places where forest behavior seems 

unusual or abnormal. These insights can help focus ground and aircraft 

observation efforts (such as those conducted by the Forest Service’s National 

Insect and Disease Detection Survey program or post-disturbance response 

efforts). Operating since January 2010, ForWarn generates national 

disturbance maps covering the conterminous United States every eight 

days, even throughout the winter. It detects all types of forest disturbances, 

including insects, disease, wildfires, frost and ice damage, tornadoes, 

hurricanes, blow-downs, harvest, urbanization, and landslides. It also 

detects drought, flood, and other extreme climate effects and tracks early 

and delayed vegetation development during spring and fall.

ForWarn uses the Normalized Difference Vegetation Index (NDVI) 

to measure vegetational vigor, or relative “greenness.” NDVI measures the 

degree to which solar radiation is differentially absorbed across red and 

infrared wavelengths due to the chlorophyll in plants. Reflectance data are 

obtained from the MODIS

3

sensors aboard NASA’s Terra and Aqua satellites;

1 Hargrove, W.W.; Spruce, J.P.; Gasser, G.E.; Hoffman, F.M. 2009. Toward a national early warning system for forest 

disturbances using remotely sensed canopy phenology. Photogrammetric Engineering & Remote Sensing. 75: 1150-1156. 

http://www.treesearch.fs.fed.us/pubs/33669. [Date accessed: May 15, 2013]

2

 The Eastern Forest Environmental Threat Assessment Center (EFETAC) is located in Asheville, NC, and the Western 

Wildland Environmental Threat Assessment Center (WWETAC) is in Prineville, OR. 

3 Moderate Resolution Imaging Spectroradiometer.
Using the ForWarn System 5

these satellites provide a daily record of the condition of vegetation, when 

the view is not obstructed by smoke or clouds. The data are available at a 

nominal spatial resolution of 250 m, which translates to a map cell size 

of about 13 acres, or 5.4 ha (the equivalent to about 9 football fields each). 

While seemingly coarse compared to the dozens to hundreds of individual 

trees that normally exist within a single hectare of forest, finer resolution 

data do not exist at high frequency, and even with moderate resolution, this 

database grows by billions of data points each year.

ForWarn works by comparing current conditions with the “normal 

greenness” that would be expected for healthy, undisturbed vegetation 

growing at a location during a given time of year. Locations that are currently 

less green than expected are marked as potentially disturbed (as indicated 

by shades of red, orange, and yellow on figure 3a and 3b on pages 6–7). 

Unfortunately, clouds can act to decrease the current observed greenness, 

mimicking the actions of forest disturbance agents. ForWarn overcomes this 

problem by relying on a moving 24-day window of daily satellite observations 

that nearly always provides a cloudless view. The moving window advances 

forward in eight day time steps. ForWarn also includes maps and time series 

graphs of raw NDVI values since 2000. Assessing forest change requires 

a historical baseline or multiple historical baselines of varying durations to 

determine how “normal,” healthy vegetation should appear. ForWarn utilizes 

three baselines—(1) the prior year, (2) the maximum value of the last three 

years, and (3) the entire period of record. The maximum greenness value is 

kept from each of the 46 different 8-day time periods per year. Several of the 

forest change highlights in this document illustrate the insights that come 

from having a range of baselines for comparison. 

Forest change maps generated using ForWarn are available to 

anyone via the Forest Change Assessment Viewer, a Web-based tool that is 

accessible at http://forwarn.forestthreats.org/fcav/. New, near-real time maps 

are available at this Web site, as well as an archive of forest change products 

since 2000. 


8 Satellite-Based Forest Change Recognition and Tracking

Recent years have experienced remarkable forest disturbances 

and climate anomalies across the United States. Their co-occurrence has 

tested ForWarn’s ability to detect disturbances during drought, and to map 

climate stress despite disturbance. For example, in 2011, extreme drought 

dominated much of the south-central and south-eastern United States. This 

drought was particularly strong in Texas where western portions of that 

State experienced extreme and prolonged drought that led to a sharp decline 

in rangeland productivity and a record outbreak of wildfires. Large fires also 

occurred from the ponderosa pine forests of eastern Arizona to the peaty 

forests of the Coastal Plain of Florida, North Carolina, and Virginia in areas 

that were and were not experiencing drought. Despite causing a directionally 

similar decline in greenness, ForWarn successfully distinguished wildfire 

from drought by mapping the relative severity of the decline.

Earlier in 2011, spring was delayed across much of the United States 

by cool temperatures or an abnormally heavy snowpack. In the Southeast, 

the spring of 2011 brought one of the most notable tornado outbreaks in 

memory. Researchers used ForWarn to map tornado scars from these tornado 

outbreaks across northern Mississippi, Alabama, Tennessee, and Georgia. In 

contrast, 2012 experienced a remarkably early spring across the East due to 

early warm temperatures with fewer, yet still destructive tornadoes. ForWarn 

successfully detected these storm effects during both years.

ForWarn has also detected defoliating insects across the United 

States. Particularly noteworthy examples of detected defoliation include 

the bald cypress forests of Louisiana; the ponderosa pine forests of South 

Dakota; the pine and fir forests of Montana, Idaho, and Washington; the 

northern hardwood forests of northwestern Pennsylvania; and the hemlockdominated forests of the Central and Southern Appalachians.

Overview of Forest Changes
H I G H L I G H T S 9

Winter snowpack has a major influence on spring flooding and 

water supplies, and snowpack can vary considerably from year to year. 

While ForWarn does not directly measure snowpack depth, it maps changes 

in snow cover compared to historical cover.

1 Heavy snowpacks can lower 

satellite-based measurements of NDVI either by temporarily obscuring 

evergreen vegetation or by delaying the spring greenup of deciduous 

grasses, herbs, shrubs, or trees. This snow-obscuring effect likely explains 

differences between December 2010’s high elevation forest anomalies in the 

Sierra Nevada and that of 2011 (fig. 4 on page 10).

In high elevation and northern portions of the Northeast, spring 

was delayed by below average winter temperatures and the lingering 

heavy snowpack, despite March and April having normal to above-average 

temperatures

2

(fig. 5 on page 11). 

1 For archival maps of snow cover and modeled snowpack, see the National Weather Service’s National Operational 

Hydrologic Remote Sensing Center Web site located here: http://www.nohrsc.nws.gov. 

2 Monthly temperature and precipitation departures from average for the United States are available on NOAA’s climate 

monitoring Web site: http://www.ncdc.noaa.gov/oa/climate/research/cag.

Heavy Snowpack


12 H I G H L I G H T S

Early Frost

The end of the growing season can be gradual or sudden in 

response to nuances of temperature and precipitation. In the Northeast, 

fall’s onset is sensitive to the first frost. Progressively lower nighttime 

temperatures can lead to an extended fall, but unusually cold days or a series 

of cold days can lead to a rapid decline in observed NDVI measurements. 

These spatial and year to year differences are evident in ForWarn.

The first severe frost in northern Minnesota, Wisconsin, and 

Michigan varies among years. In 2011, northeastern Minnesota experienced 

freezing temperatures on September 14, and the temperature dropped 

several degrees more the following night. In contrast, during 2010, 

widespread frost occurred on October 2, and a broad, killing frost occurred 

at the end of the month.

1 While mid-September frosts have occurred there 

in the past, 2011’s cold temperatures were reached relatively early (fig. 6).

1 RAWS climate data are from Meander, MN. See http://www.raws.dri.edu/.

Joseph O’Brien, USDA Forest Service 














H I G H L I G H T S 27

Figure 19—The grey canopies in the background image are hemlocks that have succumbed to the 

hemlock woolly adelgid. ForWarn captured the rapid and widespread mortality of these evergreen trees 

by this invasive insect across the southern Appalachians in recent years. The time trace of 8-day NDVI 

from 2005-2010 (see inset graph) shows how winter NDVI has progressively fallen for this area in Great 

Smoky Mountains National Park east of Gatlinburg, TN. This decline is due to the selective mortality 

of the evergreen component of this stand, which is most apparent during the winter months. The sudden 

drop in early 2010 was caused by a wet snow that covered branches and understory vegetation for weeks.


30

Summary

ForWarn has successfully detected diverse climate and 

disturbance effects on forest vegetation. Climate effects include delayed 

spring greenup in the East, an early fall in the Midwest, the effects of an 

exceptionally severe drought in Texas, and an unusually heavy snowpack in 

the West. Despite variation in the climate-affected background condition, 

ForWarn successfully detected a range of local disturbances. These 

disturbances included the effects of tornadoes and windstorms in spring, 

variations in the severity of wildfires, the complex effects of a hurricane and 

tropical storm during regional drought, and defoliation and mortality from 

insects. ForWarn also detected deforestation associated with urbanization 

and mining among other drivers of forest change. 

ForWarn’s near-real time and broad spatial approach to monitoring 

allows managers to systematically follow the progression of long-duration 

disturbances and recovery as readily as ephemeral disturbances that are 

detectable for only part of the growing season. This flexibility empowers 

forest managers and researchers to understand the condition and dynamics 

of forests across the United States.
Norman, S.P.; Hargrove, W.W.; Spruce, J.P. [and others]. 2013. Highlights of satellitebased forest change recognition and tracking using the ForWarn System. Gen. 

Tech. Rep. SRS-180. Asheville, NC: U.S. Department of Agriculture Forest 

Service, Southern Research Station. 30 p. 

Satellite-based remote sensing can assist forest managers with their need to 

recognize disturbances and track recovery. Despite the long standing availability 

of raw imagery, the systematic delivery of spatially continuous, ready-to-use, 

processed products has evaded us until recently. The web-based ForWarn system 

moves us a step forward by generating forest change maps at high frequency in a 

format that is usable to forest managers, planners, and the public. The ForWarn

system shows change in the Normalized Difference Vegetation Index derived 

from moderate resolution imagery according to a range of baseline normals. 

Expectations of normal derive from previously observed changes in seasonal leaf 

phenology; this adjustment is critical for forests dominated by deciduous vegetation 

that vary in greenness through the year. After these seasonal adjustments are 

made behind the scene, the remaining forest change that ForWarn users see may 

result from an array of climatic and disturbance causes. These include insects and 

disease, wildland fire, wind, hail, human development, drought, or variation in 

the timing of spring and fall. This publication outlines the data and methods that 

underlie this technology, and provides examples that illustrate selected capabilities 

of this system for coarse-scale forest monitoring. 

Keywords: Disturbance, monitoring, phenology, recovery, remote sensing.

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