Observing and Monitoring Land Use and Land Cover Change

Thomas R. Loveland

U.S. Geological Survey, EROS Data Center, Sioux Falls, South Dakota

Ruth S. DeFries

Department of Geography and Earth System Science Interdisciplinary Center, University of Maryland at College Park, College Park, Maryland

Understanding the consequences of land use change requires robust documenta­tion on the characteristics of change. Land use change observation and monitoring programs now rely on remotely sensed data coupled with field observations and corroborating information describing the social, economic, and physical dimen­sions of land use and land cover. Remote sensing approaches for observing and monitoring change vary depending on the geographic scope, ecological complexity, and the information required to understand ecosystem interactions. Strategies based on identifying spectral variability are useful for targeting areas of rapid change. Measuring changes in land cover biophysical properties requires a more complex approach, where different dates of remotely sensed data are transformed to such variables as surface imperviousness, canopy structure, and phenology, and then compared. Mapping the conversion of land use and land cover from one category to another (e.g., forest to urban) requires maps of the land use and land cover for two or more periods. These approaches have been used successfully at local, regional, and global scales using a range of remote sensing data (e.g., aerial photography, Landsat Thematic Mapper, Terra MODIS, Space Imaging's IKONOS), field meas­urements, and other supplemental sources. Challenges remain, however, and scientific advances in change detection methods, accuracy assessment procedures, and improved strategies for using land cover to more specifically infer land use are needed so that continued improvements in the types and quality of change measures used to study land use and ecosystem interactions can be realized.

Ecosystems and Land Use Change Geophysical Monograph Series 153 Copyright 2004 by the American Geophysical Union 10.1029/153GM18

1. DETECTING CHANGE AT MULTIPLE SPATIAL AND TEMPORAL SCALES

The previous chapters of this volume address the many dimensions and consequences of land use change and ecosys­tem interactions. The ability to understand change and the associated consequences is predicated on having detailed, timely, and accurate data and information on the various

231

232 OBSERVING AND MONITORING LAND USE AND LAND COVER CHANGE

dimensions of change. Especially important are measures of the rates, types, spatial patterns, and drivers of change that are effective for understanding ecosystem status and the pro­vision of ecosystem goods and services.

Long-term management of the impacts of land use change on ecosystems requires an understanding on how changes in land use modify land cover biophysical characteristics and ecosystem health and sustainability. To understand these rela­tionships, we must ask the following questions:

• How do we monitor the health of the Earth's terrestrial ecosystems?

• How do we assess the cumulative effect on ecosystems of past, present, and anticipated future changes in the land surface?

• Can we understand the ecosystem responses to human-induced changes in the land surface vs. responses to nat­ural variability?

• How do we assess the future availability of ecosystem benefits?

Changes in the land surface are obviously pervasive in both time and space, resulting from human modification of the landscape and from natural events such as climate variability. Understanding the complexity of change requires the contin­ued observation and monitoring of land use and land cover dynamics.

Observation and monitoring programs established to doc­ument the characteristics of landscape change traditionally relied on aerial photography and field-based measures col­lected from sites established via a sampling strategy. Increas­ingly, remotely sensed data collected from aircraft and Earth-orbiting satellites have become a significant part of the global observation and monitoring network. Decisions regard­ing the specific form of observation and monitoring approaches used to characterize change require a clear understanding of the specific nature of change. This is problematic since our understanding of the spatial and temporal dimensions of change is incomplete.

The ability to analyze, understand, and respond to changing landscape conditions requires detailed information on the rates, causes and consequences of land use and land cover change. Whether the issue involves assessing ecosystem health, climate variability, hydrological processes, biogeochemical dynamics, or economic growth, it is imperative that we invest in observation and monitoring capabilities that provide the required data and information on landscape dynamics.

The need for historical and contemporary land use and land cover data is significant, and the need is becoming more urgent as time passes. An improved understanding of the interac­

tions between the landscape and other elements of the Earth's systems has resulted in a clearer recognition of the need for the integration of land use and land cover data into environmen­tal assessments [U.S. Climate Change Science Program, 2003; National Research Council, 2001]. As far back as the late 1970's, James Anderson, the Chief Geographer of the U.S. Geological Survey, said that the need for land use and land cover maps and data were so great at that time that it would be difficult to meet the demand without substantial infusions of resources and technology [Anderson, 1976]. Anderson con­cluded that an operational land use and land cover observation and monitoring program was needed that produced complete land use inventories every 5 to 10 years. Whether considered from a national or global perspective, Anderson's vision has yet to be realized.

2. DEFINING OBSERVATION AND MONITORING

Landscape monitoring is the continuing collection of land observations over time. Monitoring should answer such sim­ple questions as (Robert Unnasch, Nature Conservancy, pers. comm.):

• What was it like in the past?

Land Use and Land Cover Definitions

In the landmark Anderson et al. [1976] document on land use and land cover classification, the following definitions were presented:

Land Use: Human activities on the land which are directly related to the land [Clawson and Steward, 1965].

Land Cover: The vegetation and artificial constructions cov­ering the land [Burley, 1961].

Land Use and Land Cover: A holistic perspective where land cover is used as a surrogate for land use. This perspective is used in remote sensing studies where identification of land cover is used to infer land uses.

There is a complex but linked relationship between land use and land cover. Changes in land use can change land cover, and changes in land cover can change land use. Furthermore, a given land use (e.g., grazing) may associate with several different types of land cover (e.g., grassland, forest land. A given land cover (e.g., forest land) may have several different land uses (e.g., timber production, grazing, recreation). Con­sidering the interrelationship between land use and land cover, remote sensing-based observation and monitoring typically considers land cover as a surrogate for land use.

LOVELAND AND DEFRIES 233

• Why is it the way it is now?

• What will it be like in the future?

Monitoring permits the detection of significant changes in land cover type, condition, configuration, and ecological processes. Land use monitoring provides information on chang­ing uses, paths of intensification, economic status, and so forth. Ultimately, monitoring serves the purpose of providing ongoing information that leads to knowledge on the impacts of management and policy decisions, and the consequences of those decisions on the environment or resource base.

In the context of land use and land cover investigations, observation involves the documentation of the characteristics of land use and land cover. Observation programs typically assess the state and condition of the land surface. The state is the type and structure of land cover (e.g., forest, grassland), use (e.g., grazing), and management (e.g., improvements, rotation cycles, etc.), while condition is the status of the bio­geophysical properties and processes of the surface (e.g., albedo, permeability, moisture). Land cover studies generally focus on observing the physical traits of the landscape such as:

• Land cover type

• Biophysical attributes

• Phenology

• Vegetation structure (e.g., density, leaf area, etc.)

• Surface permeability

• Albedo

• Vegetation condition index

• Moisture index

• Landscape patterns and properties (e.g., fragmentation)

Remote sensing has become an important tool for land cover observation. However, land cover studies also make sig­nificant use of observations and measurements based on more traditional field approaches, such as interviews, in situ data col­lection using a variety of instruments, or simply through visual inspection.

Land use studies typically focus on land use intensity, eco­nomic yield, land rent and value, demographic variables, and other social or economic measures. Mapping and monitoring the types and extent of land use using remote sensing is prob­lematic since remotely sensed images, unless they are of very large scale, portray the land cover rather than use. While the connection is often imperfect, land cover is sometimes used as

a surrogate for land use [Anderson et al, 1976]. For most land use data collection programs, windshield surveys, interviews, public records, and other similar collection strategies have traditionally been used to document land use characteristics.

3. TOOLS FOR CHANGE OBSERVATION AND MONITORING

The process of observing and monitoring change requires the use of a diverse set of tools and approaches. The common tools include remote sensing, aerial photography, field obser­vation, and statistical surveys and interviews. In addition, mapping, sampling, and spatial frameworks (e.g., adminis­trative boundaries, ecoregions, watersheds) are used in con­junction with the common tools. Most studies of change use combinations of tools to achieve their objectives. A basic understanding of the characteristics of these tools is important when considering observation and monitoring strategies.

3.1. Remote Sensing

There are several reasons why remotely sensed data are advantageous for observation and monitoring. First, remote sensing observations from instruments on either Earth-orbit­ing or aircraft platforms provide a synoptic perspective and unique vantage point of the land surface. The ability to view land use and land cover patterns and conditions in a regional context leads to understanding the spatial relationships between different uses of the land. Second, remote sensing can record reflected or emitted energy from throughout the electromag­netic spectrum. The ability to observe reflected infrared energy facilitates study of vegetation condition; short-wave infrared observations contribute to an understanding of vegetation moisture differences; and thermal infrared measures can be used to look at heat-related phenomena. Finally, remotely sensed data when acquired and archived for long-term preser­vation, serve as historical evidence of the state of the land surface at a particular point in time.

For monitoring programs, calibration and data continuity are important considerations. Earth-orbiting observation satellites generally provide repetitive coverage but the qual­ity and comparability of calibration varies from mission to mission. Elaboration on the various Earth-observation mis­sions and their characteristics is beyond the scope of this chapter, and summaries of the salient characteristics of dif­ference sources of remotely sensed data can be found in numerous remote sensing text books [see, for example, Jensen, 2000, or Lillesand et al, 2004]. It is instructive, however, to briefly review the primary remote sensing sys­tems used for the observation and monitoring of land use and land cover change.

234 OBSERVING AND MONITORING LAND USE AND LAND COVER CHANGE

Table 1. Sources and characteristics of common sources of remotely sensed data [from DeFries et ah, in review]. Note that this table is not intended to be complete.

Platform Sensor Spatial resolution at nadir

Date of observations

Coarse-Resolution Satellite Sensors (100 to >1 km) NOAA-TIROS (National Oceanic and Atmospheric Administration Television and Infrared Observation Satellite)

AVHRR (Advanced Very High Resolution Radiometer)

1.1 km (local area coverage); 8 km (global area coverage)

1978-present

SPOT (Satellite Probatoire d'Observation) VEGETATION 1.15km 1998-present ADEOS-II (Advanced Earth Observing Satellite) POLDER (Polarization and

Directionality of the Earth's Reflectances

7 km x 6km 2002-present

SeaStar SeaWIFS (Sea viewing Wide Field of View)

1 km (local coverage); 4 km (global coverage)

1997-present

ADEOS-II (Advanced Earth Observing Satellite) GLI (Global Imager) 250 m-1 km 2002-present EOS AM and PM (Earth Observing System) MODIS (Moderate Resolution

Spectroradiometer) 250-1000m 1999-present

EOS AM and PM (Earth Observing System) MISR (Multi-angle Imaging Spectroradiometer)

275 m 1999-present

Envisat MERIS (Medium-Resolution Imaging Spectroradiometer)

350-1200 m 2002-present

Envisat ASAR (Advanced Synthetic Aperature Radar)

150-1000m 2002-present

Moderate-Resolution Satellite Sensors (10 m-100 m) SPOT (Satellite Probatoire d'Observation) HRV (High Resolution

Visible Imaging System) 20 m; 10 m (panchromatic)

1986-present

ERS (European Remote Sensing Satellite) SAR (Synthetic Aperture Radar)

30 m 1995-present

Radarsat 10-100m 1995-present Landsat (Land Satellite) MSS (Multispectral Scanner) 83 m 1972-1997 Landsat TM (Thematic Mapper) 30 m (120 m thermal-

infrared band) 1984-present

Landsat ETM + (Enhanced Thematic Mapper)

30 m 1999-present

EOS AM and PM ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)

1 5 -9 0m 1999-present

JERS (Japanese Earth Resources Satellite) SAR (Synthetic Aperature Radar)

1 8 m 1992-1998

JERS OPS 1 8 m x 2 4 m 1992-1998 Fine-Resolution Satellite Sensors (<10 m) IKONOS 1 m panchromatic;

4 m multispectral 1999-present

QuickBird 0 .61m panchromatic; 2.44 m multispectral

2001-present

The capabilities of satellite-based observation programs can be organized into three categories: coarse, moderate, and fine resolution. Table 1 summarizes the important properties of the most common sources of remotely sensed data corre­sponding to these three categories and Figure 1 illustrates dif­ferences in geographic coverage and spatial resolution of representative coarse, moderate, and fine resolution satellite imagery.

Coarse resolution remotely sensed data provide complete global coverage on a daily or near-daily basis and gather imagery at resolutions ranging from 250 m by 250 m to 1 km 2 or greater. Coarse resolution data are particularly impor­tant for monitoring and measuring changes in vegetation con­dition and for global hotspot analysis.

The Advanced Very High Resolution Radiometer (AVHRR) instruments on NOAA polar orbiting meteorological satel-

LOVELAND AND DEFRIES 235

Figure 1. A comparison of the geographic coverage and spatial detail of coarse (MODIS), moderate (Landsat ETM+), and

fine (IKONOS) remotely sensed data.

lites have provided complete global coverage at resolutions

from l .l to 16 km since 1983. The unique 20-year global

observation record has been successfully used to characterize

global land cover [DeFries et at., 1995; Loveland et at., 2000].

Because this long-term record is the only data set available with

global coverage, it has been the basis of the only temporally

and spatially consistent estimates of changes in forest cover

despite the disadvantage of the coarse resolution [DeFries et

aI., 2002; Hansen and DeFries, in press].

Since late-1999, a new generation of global imaging began

as the Moderate Imaging Spectrometer (MODIS) started

acquiring images of the earth at 250-m, 500-m, and 1000-m

resolutions. MODIS provides near-daily calibrated imagery

in 36 discrete spectral bands. The improved spatial and spec­

tral resolution and enhanced calibration capabilities are sig­

nificant improvements and increase the utility of MODIS

for global mapping and monitoring of land cover properties

[Friedl et al., 2002; Townshend and Justice, 2002], including

leaf area index [Mynemi et al., 2002], percent tree cover

[M.e. Hansen et at., 2002],[AC3] and vegetation condition

[Huete et al., 2002].

The Systeme Pour l'Observation de la Terre (SPOT)-4 satel­

lite has been providing global daily l -km resolution imagery

since 1998. The SPOT Vegetation sensor, like MODIS, is an

improvement over the AVHRR system with enhanced spectral

and geometric properties.

Moderate-resolution remotely sensed data provide near-global

coverage at resolutions ranging from 10 to 100 ill. Moderate-res­

olution remotely sensed data are critical for earth observation and

monitoring because the longstanding archive, spatial resolu­

tion, and near-global coverage combine into capabilities that

are unique for mapping and quantifying land cover change.

Depending on the program, specific locations can be revisited

from 1 to 26 days. Landsat images have been acquired since

mid-1972, the longest longitudinal Earth observation program.

Multispectral data at 80 m by 80 m (Multispectral Scanner) and

30 m by 30 m (Thematic Mapper and Enhanced Thematic Map­

per Plus) have been collected. With over 30 years of data col-

236 OBSERVING AND MONITORING LAND USE AND LAND COVER CHANGE

lected every 16 or 18 days for much of the globe, Landsat data are key resources for monitoring changes in land use and land cover and ecosystem properties. These data have been the pri­mary means for monitoring tropical deforestation [Institute Nacional de Pesquisas Especiais, 2000; Skole and Tucker, 1993].

SPOT observation missions have imaged the Earth since early 1986. SPOT remotely sensed data are of higher spatial resolution (originally 20 m by 20 m for multispectral data and 10 m by 10 m for panchromatic data) than data from Landsat. While the repeat period of SPOT is 26 days, SPOT instruments can be pointed, which results in repeat coverage capabilities of 1-3 days depending on latitude. With the higher spatial resolution and a potentially shorter revisit period, SPOT data have advantages for mapping and monitoring natural dis­asters and land cover in complex landscapes.

The Indian Remote Sensing Satellite (IRS) has collected mul­tispectral remotely sensed data since 1988. While the IRS pro­gram archives are not as comprehensive as Landsat and SPOT, IRS data are useful for land cover observation and monitoring.

Fine-resolution remotely sensed data include aerial photog­raphy and satellite imagery with spatial resolutions less than 10 m. Aerial photography has been collected over various parts of the world since the 1920s. However, aerial photograph holdings are widely distributed in both government and private archives, and locating specific coverage can be difficult. Recently, the SPOT and IRS programs and several commercial companies have placed instruments into orbit that can acquire very high resolution images. Commercial systems include Space Imag­ing 's Ikonos and Orbimage's Orbview with 1 m (panchromatic) and 4 m (multispectral) imagery, and Digital Globe's Quick-bird with 0.61 m (panchromatic) and 2.44 m (multispectral) imagery. Fine-resolution images provide very detailed views of the landscape but cover a small ground area in comparison to coarse and moderate imagery and are expensive to acquire. As a result, fine-resolution data are excellent for local studies of change and for validation of the accuracy of land cover change observations developed from coarse or moderate-scale remotely sensed data. They are impractical for regional to global studies—unless used within a sampling framework.

The remotely sensed data just described are all from opti­cal systems and measure electromagnetic energy that is either reflected or emitted from the Earth's surface. Active remote sensing systems such as synthetic aperture radar (SAR) that can operate in the day- and nighttime and in nearly any weather conditions are also important for observation and monitor­ing of land use and land cover. SAR data are advantageous for studying change in regions with persistent cloud coverage such as the tropics. The Japanese Earth Resources Satellite (JERS), the European Space Agency's European Remote Sens­ing Satellite (ERS), and the Canadian RADARSAT are exam­ples of SAR missions.

The preceding survey of remote sensing missions repre­sents a subset of the expanding array of experimental and operational missions. Table 1 expands on the discussion with descriptions of additional satellites and sensors but is also incomplete because new capabilities are continuing to be introduced. It is also important to note that remotely sensed data are seldom used without the augmentation of field obser­vations and measurements. Accurate calibration, accuracy assessment, and improved understanding of landscape dynam­ics require the combination of imagery and field inspection.

3.2. Other Monitoring Tools

The objectives of land use and land cover change studies are often too demanding or detailed to be met with remotely sensed data alone. Statistical surveys and inventories are com­monly used to gather demographic, economic, or social data corresponding to land use issues and for assessing the drivers of change [Lambin et al., 2001]. Demographic data are par­ticularly useful for evaluating urban growth issues, employment data are invaluable for understanding the drivers of change, and land value data add understanding to studies of land use change trends and the associated causes [A.J. Hansen et al., 2002; Vesterby and Heimlich, 1991].

Field observations and measurements are traditional and important elements of land use and land cover observation and monitoring. Data calibration, accuracy assessment, measurement of critical ecosystem components, and iden­tification of specific land management treatments are exam­ples of functions associated with field observation and measurement.

Analytical tools used to observe and monitor land use and land cover change include sampling techniques, spatial frame­works, and models. Sampling is commonly used to estimate the properties of land use and land cover and corresponding properties. The National Resources Inventory by the U.S. Nat­ural Resources Conservation Service uses sampling to estimate land use and land management properties on private lands [NRCS, 2000]. The U.S. Forest Service Forest Inventory and Analysis program uses field measurements taken from sam­ple sites to estimate forest characteristics [Gillespie, 1999]. The use of sampling frames to monitor change based on remotely sensed data is less common but has certain advantages in pro­viding relatively low-cost and precise estimates of change [Stehman etal., 2003].

4. MONITORING CHANGE IN LAND COVER

The strategies used for detecting change depend upon the change information required. The common types of change analysis using remotely sensed data include:

LOVELAND AND DEFRIES 237

Identification of Areas with Rapid Change: This type of change information is the most direct and uses differences in multispectral measurements in different dates of remotely sensed images to target areas of potential change. Rapid change identification has the advantage of simplicity and can be effi­ciently applied from local to global scales. The disadvantage is that spectral anomalies may be "false positives" and rep­resent differences in vegetation seasonality, differences in image sources and calibration, or may actually correspond to true land cover changes.

Land Cover Condition Assessment: Changes in biophysical conditions (e.g., canopy density, height, leaf area index, phe­nology) that correspond to the land use intensification, eco­logical succession, biogeochemical variations, or other ecological or social processes can be determined by compar­ing either calibrated spectral values or transformations (e.g., vegetation indices). In order to understand the magnitude of condition change, it is necessary to establish the relationships between image data and the land cover variable of interest. Because condition monitoring uses direct comparisons of image-derived variables, image dates should be from similar calendar dates and be precisely calibrated. Otherwise, the changes may represent variations related to sensor differences or vegetation seasonality.

Land Use and Land Cover Conversions: Changes from one use or cover type to another, such as changes from forest to developed cover, is the goal of this category of change analy­sis. Studies of specific categorical changes require very accu­rate maps of land use or land cover at two or more points in time in order to determine the types of conversions taking place.

Summaries of the various methods used to detect landscape change can be found in Singh [1989] and Sohl [1999].

5. THE CHARACTERISTICS OF CHANGE

The effective use of remote sensing for generating land cover information is highly dependent on the measurable qual­ity of the required information [Congalton and Green, 1999]. All too often, change research using remote sensing has not been driven by the practical information needs of users, nor with consideration of the information content of remotely sensed images [Ryerson, 1989]. To better understand tech­niques used to analyze land cover change from remotely sensed imagery, it is necessary to understand the following charac­teristics of change [summarized from Sohl et al, 2004].

Change in use or cover is a (relatively) rare event: The land­scape is in a constant state of flux due to seasonal changes, veg­etation growth, and ecological succession. However, when considering changes from one land use or cover type to another, in terms of area, change typically covers a very small pro­portion of the total land surface. The percentage of land that

thematically changes during each interval is generally quite small, compared to the total area of the Earth, continent, or country, depending on the ecoregion and the time interval. Longer time intervals can, in effect, increase the percentage of area changed per interval. Use of different classification schemes may also result in higher percentages of overall change per interval. In general, the amount of change reported increases as thematic detail increases.

Change is a local event. Land use and land cover conversions are generally localized, with relatively small patches of con­tiguous changed land. Patch size is somewhat a function of time. Longer intervals between image dates allow more oppor­tunity for change to occur, along with associated clumping of individual changed patches into larger patches. Typically, however, individual, changes occur locally and over relatively small areas. While certain land cover transitions exhibit larger average patch sizes (such as forest to clear-cut and forest to mining), patch sizes of most land cover change are much smaller. The small patch size of many land cover changes has important remote sensing implications. Relatively coarse-scale imagery such as the AVHRR (1 km 2 pixels) and MODIS (250 m 2 pixels) is best suited to the detection of changes in landscape biophysical properties or targeting of locations with large transformations of the landscape associated with events such as the conversion of large tracts for mechanized agri­culture. Higher resolution instruments such as the Landsat Thematic Mapper (TM) with 30 m 2 pixels are suited to the detection of thematic change more typical of urban, subur­ban, or agricultural expansion. In some locations—for exam­ple, expansion of subsistence agriculture in Africa with field sizes far less than 30 m—the TM resolution may still not be adequate to identify change.

Change is spatially variable. Although changes in condi­tion are ubiquitous, there is considerable variability in the geographic distribution of land use and land cover change. The rates, types, and patterns of change can vary substantially from place to place, depending on the driving forces of change, settlement history, and natural resource base. For example, urban transformations are generally clustered around existing cities and towns, while changes in forests or agriculture may vary either uniformly or unevenly in space, depending on such factors as access to markets and land suitability.

Change is temporally variable. Different forms of land­scape transitions occur at different temporal scales. The period of time in which change is measured can have a strong effect on results. A key difficulty with the detection of land surface change is the proper detection and reporting of cyclic change. Unidirectional land cover changes, such as the conversion of an agricultural field or forested area to a developed (urban) use, are less problematic, as the change can occur at any point

238 OBSERVING AND MONITORING LAND USE AND LAND COVER CHANGE

between target date ends. However, the magnitude of cyclic changes such as the timber harvest, replanting, forest regen­eration cycle may be under-tabulated if the temporal window is too wide. The establishment of the temporal window must be based on both geographic and sectoral considerations. Changes in southern U.S. pine plantations, for example, may be as short as 21 years [Gresham, 2002], whereas the optimum cutting and regeneration cycle for forests in other areas may be several times greater due to environmental limitations. Basically, the determination of change rates must consider the local dynamics of change in order to accurately determine the rates of change.

Change can be spectrally ambiguous. Automated detection of land cover change assumes that differences in spectral properties between dates imply a change in land cover or use. Automated change detection results are greatly improved when the remotely sensed data being compared are cali­brated to common reference and physical units (i.e., radi­ance, percent reflectance) and corrections for atmospheric effects are applied. Changes in calibrated spectral values can indicate changing land cover conditions, such as increases or decreases in forest canopy density caused by selective thinning or succession. However, changes in spectral val­ues do not necessarily indicate thematic change. For exam­ple, clear-cut forest patches and fallow or recently harvested agricultural fields may have very similar spectral proper­ties. It is important to note that discretely partitioning land cover is often problematic because the cover is better defined in terms of a continuum.

6. INTEGRATED ANALYSIS OF CHANGE: CASE EXAMPLES AND STUDIES

As the priority for understanding land use and land cover change has increased over the past decade, change studies have increased in number and sophistication. The geographic scope of these studies has also expanded with investigations underway at global [e.g., Ramankutty and Foley, 1999], regional [e.g., Achard et al., 2002; Skole and Tucker, 1993], national [e.g., Loveland et al, 2002; Sohl, 1999], and local [e.g., Turner, 1990; Lo and Yang, 2002] levels.

The strategies used in different land use and land cover studies vary depending on research objectives and geographic scope. Targeting studies are used to identify areas of rapid change, with a secondary objective to determine the direc­tion of change, e.g., degradation of vegetation condition ver­sus improving (increased biomass) vegetation condition. Other studies focus on change characterization that involves quan­tifying and explaining a range of change variables, such as rates of change, types of land use transitions, and the drivers of change. Generally, targeting studies are synoptic, work on

frequent time steps, and are often applied in near real-time. On the other hand, characterization studies are more commonly local to regional in scope and use broader time intervals.

6.1. Targeting Studies

The following case studies illustrate strategies for targeting areas of rapid or long-term change at global, national, and local scales.

6.1.1. Targeting areas of rapid change: millennium assess­ment. Studies of rapid land cover change use a range of data sources and methods and cover a variety of geographic ven­ues. As a contribution to the Millennium Ecosystem Assess­ment [Millennium Ecosystem Assessment, 2003] a systematic comparison and compilation of the literature was undertaken to identify the areas of rapid land cover change occurring in the last 20 years [Lepers et al, submitted]. The types of change included in the synthesis were 1) deforestation and forest degradation, 2) degraded lands in dry- and hyper-arid lands, 3) cropland expansion and abandonment, and 4) urban set­tlements. The synthesis revealed the lack of reliable infor­mation in many parts of the world, most notably changes in 1) tropical and subtropical dry forests, 2) forest cover change caused by selective logging, fires, and insect damage, 3) drainage or other forms of alteration in wetlands, 4) soil degra­dation in croplands, and 5) changes in the extent and pro­ductive capacity of pastoral lands. Of all types of land cover change, tropical deforestation is the most widely documented, though there are still large gaps in some parts of the world and different analyses disagree on the extent of deforestation.

Analysis of satellite data has been pivotal to identifying land cover change at regional and global scales, particularly tropical deforestation. Analyses of both coarse-resolution AVHRR and MODIS data [DeFries et al., 2002] and moder­ate-resolution Landsat data [Achard et al, 2002] provide alter­natives to notoriously inaccurate national statistics for estimating deforestation rates. The satellite-derived estimates indicate generally lower rates of deforestation compared with national-level data reported by the United Nations Food and Agriculture Organization [FAO, 2000].

6.1.2. Mapping changing land cover phenology. One of the earliest applications envisioned using Landsat data was the identification and monitoring of broad-scale variability of vegetation phenology [Dethier et al, 1975]. Changes in phe­nology correspond to variations in inter- and intra-annual veg­etation variability and, when combined with measures of vegetation structure and vigor, can be used to identify distur­bances and successional trends. The use of Landsat for mon­itoring phenology has not advanced as expected, perhaps

LOVELAND AND DEFRIES 239

because of the relatively wide periodicity of Landsat acqui­sitions and because of cloud cover. In addition, data costs make the use of Landsat over large areas prohibitive for many researchers. However, AVHRR and now MODIS are recog­nized as useful sources of phenological time series that can be associated with ecological variables [Mora and Jverson, 1998].

The daily global coverage from AVHRR (1983 to pres­ent) and MODIS (late-1999 to present) provides a 20-year time series for monitoring phenological variables related to ecosystem functioning. Reed et al. [1994, 2003] developed an approach using biweekly composites of AVHRR vegeta­tion index data spanning 1990 through 2002 for identifying the timing and characteristics of key seasonal events, includ­ing the onset and end of the growing season, length of grow­ing season, the time and magnitude (e.g., relative greenness) of peak greenness, time-integrated greenness as a surrogate for annual net ecosystem productivity, and the rates of green­ness and senescence. Reed (pers. comm.) has identified anomalous patterns of both increased and decreased rela­tive productivity using time-integrated vegetation index data for the 1990 to 2002 period. Reed's approach targets areas with trends that may relate to ecosystem degradation. Estab­lishing the factors causing regional anomalies can be com­plex and requires field measurements and other observations to validate the significance and meaning of the potentially degraded areas.

Monitoring phenological variability is emerging as a useful input to regional studies of ecosystem condition. Changes in phenology and productivity variables are particularly valu­able for targeting dynamic areas and documenting the direc­tion of change [Lambin and Strahler, 1994]. Establishing cause and ecological significance, however, will require field evaluation and consideration of corresponding climate and resource inventory measurements.

6.1.3. Targeting urban growth using multi-date impervious surface maps. Automated mapping of urban land use change is uniquely challenging, yet there is a critical need for fre­quent assessments of urban growth rates and patterns. Issues ranging from understanding ecosystem disturbances to doc­umenting flooding potential due to changes in vegetation and topography require periodic maps of urban lands. Urbaniza­tion does not have a unique spectral footprint, and the pat­tern of change often consists of small patches of development spreading from the urban fringe. A number of studies have used Landsat TM data to derive periodic maps of impervious surfaces that, when compared, show areas of anthropogenic change [for example, see Deguchi and Sugio, 1994; Yang et al., 2003]. Remote sensing researchers have demonstrated a number of techniques, including multiple regression, spec­tral unmixing, and classification decision trees for trans­

forming TM spectral data into maps of urban-related imper­vious surfaces [Ridd, 1995; Ji and Jensen, 1999; Yang etal, 2002].

Yang et al. [2003] developed an urban growth monitoring strategy based on the comparison of different percent imper­vious surface maps derived using a regression tree analysis of Landsat TM data. Using high-resolution digital orthophotos as reference for their regression tree analysis, they developed March 1993 and 2003 maps of percent imperviousness for an area in western Georgia (Plate 1). Using independent ref­erence data, they estimated that the error in the two maps was 16.4% for 1993 and 15.3% in 2003. They then determined areas of impervious change by overlaying the two maps. They concluded that areas where the change in imperviousness between 1993 and 2003 was greater than 20% corresponded to actual land use change. They suggest that while changes in imperviousness do not correspond to the total area of change, monitoring changes in sub-pixel imperviousness provides a repeatable and objective framework to rapidly target areas of urban growth.

6.2. Characterizing Land Cover Change

The following studies illustrate local, regional, and national strategies for characterizing land use and land cover change.

6.2.1. Atlanta land cover change. The rapid urban growth of the Atlanta, Georgia, region has stimulated studies directed toward documenting the characteristics and consequences of urban expansion [Lo and Yang, 2002; Lo and Quattrochi, 2003]. Over the past 30 years, Atlanta has experienced sig­nificant growth in terms of population and size. Lo and Yang [2002] used five dates of Landsat MSS and TM data from 1973 to 1999, along with socio-economic data obtained from the U.S. Census Bureau for 1970, 1980, and 1990, to study the drivers of Atlanta's growth. Using automated image classifi­cation procedures, six classes of land cover were mapped: 1) high-density urban cover, 2) low-density urban cover, 3) cul­tivated/exposed land, 4) cropland/grassland, 5) forest, and 6) water. Map accuracies for the five land-use/land-cover maps produced were between 87 and 90. An analysis of the five maps showed that both high- and low-density urban cover increased. Low-density urban cover—mainly suburban hous­ing—increased 268 percent between 1973 and 1999. Cropland declined by 37%), and 27% of the forest land was lost.

Lo and Yang [2002] then established a geostatistical frame­work to identify the driving forces of change. They combined the land use and land cover maps with measures of landscape pattern, topography, population, income, and location within census tracts and administrative boundaries. Simple correla­tion was used to identify the data relationships that corre-

240 OBSERVING AND MONITORING LAND USE AND LAND COVER CHANGE

1993 2001

Plate 1. (a). Subpixel percent imperviousness of northern Atlanta, Georgia, 1993 and 2001 [Yang et al, 2003]. Note a notable development (Perimeter Mall) occurred at the northeastern cross-section by the highway areas along the interstate high­way 285 and the US highway 19 in northern Atlanta (circled and labeled in A), (b). Increase of percent imperviousness, southeastern Atlanta, Georgia, 2001-1993. Significant residential development was evident in this area.

LOVELAND AND DEFRIES 241

sponded to driving forces. They determined that gentle slopes, highways, nodes, and shopping malls generally promoted urban development in the Atlanta metropolitan area as a result of the great demand for suburban housing stimulated by eco­nomic development and population growth.

In a subsequent study, Lo and Quattrochi [2003] used land cover change data derived from 1973 to 1998 Landsat data to study urban heat island issues and the implications of Atlanta's growth on human health. They concluded that land use and land cover change in Atlanta resulted in a reduction of vegetation, which altered the albedo, temperature, and moisture availability characteristics of the Atlanta's land cover. Through an examination of 1987 to 1997 Landsat thermal images, they found that higher temperatures in the inner urban area, spreading out along the highways, coincided with high-density urban cover. Lower temperatures were found in Atlanta's urban-rural fringe. An examination of historical meteorological observations showed that precipitation induced by urban heat island effects tended to occur near high-density urban areas but not in the inner city and the traditional down­town areas. Atlanta's urban boundary layer urban heat island produced a convergence zone that initiated thunderstorms downwind of the city. Lo and Quattrochi then compared the land cover change maps with data on nitrous oxides and volatile organic compounds to see if there was a relationship between change and cardiovascular lower respiratory diseases. They concluded that there was a weak correlation between emissions and chronic lower respiratory diseases but that the spatial patterns of diseases were not distinctive.

6.2.2. Coastal change analysis program. The U.S. National Oceanic and Atmospheric Administration established the Coastal Change Analysis Program (C-CAP) to provide periodic maps, coastal zone changes, including changes in coastal submersed habitats, wetlands, and upland land cover [Dobson et al, 1995]. Maps of land cover change are particularly significant because they provide a basis for understanding some of the key factors affecting coastal zone health. The primary purpose for the peri­odic mapping of change is to provide information to resource managers for use in maintaining the biological integrity and productivity of coastal ecosystems. C-CAP data have been used for applications including conservation and selection, open space protection, estuary management, and assessments of runoff impacts on coastal ecosystems.

Landsat TM data spanning the entire U.S. coastal zone, including the Great Lakes, are interpreted on an approxi­mately five-year cycle to detect changes in land cover, includ­ing wetlands. The C-CAP land cover mapping goal is to map specific land cover transformations occurring between each five-year monitoring period [Dobson et al., 1995]. The empha­sis on mapping categorical change led to a mapping strategy

in which different dates of Landsat TM data (e.g., 1992 and 1997) are independently mapped into C-CAP land cover cat­egories, and then land cover changes are determined by over­laying the two maps [Dobson and Bright, 1991].

As mentioned, C-CAP also monitors changes in submerged habitats. Multi-date aerial photographs are used to detect changes in submerged lands because Landsat TM data lack the spatial resolution needed [Klemas et al., 1993]. The changes in submerged habitat derived through the manual interpretation of aerial photography are digitized so that a comprehensive geospatial view of upland and submerged conditions is available. C-CAP protocols emphasize the use of field surveys to verify the quality of land cover and sub­merged habitat maps.

6.2.3. U.S. land cover trends. Measurement of land use and land cover change characteristics becomes progressively more challenging as the area of investigation increases. Change characteristics vary over time and space, often in complex ways. There is a need for comprehensive periodic mapping of change over large areas. Unfortunately, cost and technical difficulties increase when study objectives require documen­tation on the rates, types, transitions, and causes of land use and land cover change over large areas.

One approach to documenting change for large areas involves the interpretation of remotely sensed images within a stratified probability-sampling framework. The U.S. Geo­logical Survey, with National Aeronautics and Space Admin­istration and U.S. Environmental Protection Agency support, are conducting a study of contemporary land cover change for the conterminous U.S. using a strategy that consists of 1) stratification of the land into 84 ecoregions, 2) selecting a random sample of 20 km by 20 km or 10 km by 10 km sam­ples within each ecoregion, and 3) interpreting 1973, 1980, 1986, 1992, and 2000 Landsat images for the samples to derive estimates of change [Loveland et al, 2002]. The goal of this strategy is to identify within 1% of the actual change at an 0.85 confidence level [Stehman et al, 2003]. Ecoregion estimates of overall percent change, percent change per land cover class, and the primary land cover conversions taking place (e.g., forest cover to developed) are being developed through the pair-wise manual interpretation of the five dates of Landsat images covering all ecoregion samples.

Results from the analysis of 13 eastern U.S. ecoregions reveal a variable quilt of change (Plate 2). First, there are sig­nificant differences in the rates of change between ecore­gions. The ecoregions with southern geographic extents (i.e., Southwestern Appalachians, Southeastern Plains, and Mid­dle Atlantic Coastal Plains) show very high rates of change per period. The high rates are because of the conversion of forests and cropland to cyclic short-rotation plantation forestry. The

242 OBSERVING AND MONITORING LAND USE AND LAND COVER CHANGE

Appalachian Highland ecoregions (North Central Appalachia, Ridge and Valley, Central Appalachia, Western Allegheny, and Blue Ridge) show lower rates of change with more vari­ability in the primary transitions taking place. In Central Appalachia and Western Allegheny, surface mining of coal and mined land reclamation are key changes. The dominant change in the scenic Blue Ridge is urban development. The ecoregions spanning the eastern megalopolis (New England Coastal Zone, Atlantic Pine Barrens, and Northern Piedmont) have relatively low rates of change but the dominant change is the unidirectional expansion of urban land cover.

It is important to separate net from gross land cover change. Consider the case of the Southeastern Plains. From 1973 to 2000, nearly 22% of the land in the ecoregion changed. The dominant change was cyclic forest harvesting and regenera­tion. In 1973, 53.3%) of the ecoregion was forested and 52.3% was forested in 2000. This suggests a relatively unchanged forest base, which is in sharp contrast to the measured over­all land cover change. The dynamic cyclic nature of change in the Southeastern Plains becomes clearer when the lands mapped as disturbed are considered. In 1973, 2.2% of the land was disturbed due to forest clearing, while 4.8%) of the area was in a cleared status in 2000. Piecing together the story of change requires understanding several dimensions of change, including net and gross change rates, key land cover conversions, and the regional drivers of change.

Sampling provides a reduced-cost means for estimating the rates of change, including measures of uncertainty. Ecore­gions provide a framework for localizing change assessments. While ecoregion studies are not as specific as local case stud­ies or "wall-to-wall" change mapping, ecoregions analyses provide a means to look at the rates and characteristics of land cover change efficiently over large areas. The regional characteristics of change within ecoregions are relatively con­sistent and differ from adjoining ecoregions. Within ecore­gions, land uses are evolving to predictable higher value uses based on the region's enabling natural characteristics, con­temporary drivers, and the influences of historical settlement patterns and traditions.

Unchanging land use does not necessarily mean unchanging land cover. Unchanging land cover does not necessarily mean unchanging land use (e.g., extent of cropland can be unchanged but changes in fertilizer use can have large consequences for ecosystems). There is no single profile of change. Instead there are varying pulses affected by clusters of change agents.

7. CHALLENGES

The progress that has been made in land use observation and monitoring suggests that we are in an era of major advances in understanding the dimensions and consequences of change.

While studies such as those we have showcased give rise to optimism, we must recognize that there are still significant research issues that must be overcome, such as 1) improving change detection methods, 2) improving specification of the characteristics of change information needed to study a wider range of environmental consequences, 3) developing accept­able validation protocols, and 4) improving strategies for doc­umenting land use rather than land cover change. The advances needed are interrelated. For example, advanced change meth­ods must be done in parallel with improving the types of infor­mation used to address the consequences of change. More complex change information will challenge the validation process. The problem of data quality, i.e., the accuracy of change measurements, pervades all of these issues. Thus, when addressing the research challenges associated with land observation and monitoring, it is important to consider the interdependence of the various research issues.

Change Detection: As discussed throughout this chapter, the types, timing, and scale of change information are broad. The tools used to identify and characterize change using remotely sensed data are expanding but still incomplete. Tar­geting methods have improved greatly, as have methods for mapping change using coarse-resolution data. Advances in mapping categorical change is still immature and improve­ments are needed. The Holy Grail of change detection in gen­eral, and categorical change in particular, should be a suite of methods that are automated, consistent, and accurate. Achieving this goal may be elusive, but short-term research goals should focus on improving strategies that provide accu­rate results with reduced labor costs.

Improved Change Information: Applications requirements continue to expand as new models emerge and the under­standing of the links between ecosystem functioning and land use and land cover change matures [Lambin et al, 2003]. Improved methods are needed for issues ranging from the identification of land use and land cover types to changes in land cover condition. Assessments of land cover condition must be sufficiently robust to interpret both inter- and intra-annual variability of cover so that the impacts of natural ecosys­tem and climate variability are not confused with other disturbances. The demand for quantitative rather than thematic change information will grow. With the growing need for future land use and land cover projections, information that couples rates of change with the local and regional drivers of change is also increasingly important (see Verburg et al, this volume).

Validation Protocols: Assessing the accuracy of land cover change is difficult and in some cases, impractical [Jensen et al., 1993]. One challenge is that a standard approach for assessing the accuracy of change product has yet to be devel­oped [Foody, 2001]. Research on standards for assessments of change accuracy is urgently needed. Availability of suitable ref-

LOVELAND AND DEFRIES 243

1973 to 2000 Land Cover Change

Plate 2. Variability in the rates and types of land cover change in 13 eastern U.S. ecoregions.

244 OBSERVING AND MONITORING LAND USE AND LAND COVER CHANGE

erence data for comparing maps of change to true conditions is also problematic.

Improving Land Use Mapping: Remote-sensing techniques are suited to mapping land cover. The need for maps of land use over large areas is significant yet land use maps are often unavailable at anything but local venues. Research to com­bine land cover and other geographic data within regional or national contexts is being explored so that land cover can be more than an approximate surrogate for land use [Cihlar and Jans en, 2001].

While the needs for observation and monitoring of land use and land cover continue to grow, progress toward estab­lishing operational capabilities have been slow in coming. Organizational commitments to large-area terrestrial moni­toring are rare. Remote sensing must be a component of any monitoring program. This will require guaranteed access to cal­ibrated global consistent and locally relevant data of suffi­cient resolution and temporal frequency and duration.

8. ADVANCING THE STATE-OF-THE-SCIENCE IN CHANGE OBSERVATION AND MONITORING

The progress made in observing and monitoring the chang­ing landscape has been significant. The capabilities used to characterize change are increasingly accessible and the expert­ise needed to routinely study ecosystem interactions exists. This has resulted in an abundance of examples of studies dealing with the consequences of land use and land cover change on ecosystem health. These studies have contributed to an improved understanding of the critical connections between land use and land cover change and the loss of ecosystem goods and services essential for social, economic, and envi­ronmental well-being. In spite of the increased emphasis on monitoring and improved analytic capabilities, most obser­vation and monitoring activities are still ad hoc in nature and lack the coordination and organizational commitments needed for long-term ecosystem studies.

National and international commitments for operational observation and monitoring are part of the equation for improved understanding and management of the conse­quences of land use change. Part of the commitment must include investments in the science of observation and mon­itoring, with particular emphasis on 1) improving the detec­tion and measurement of all types of change (e.g., thematic conversions, interannual variability, successional trends), 2) advancing the interface of landscape data into ecosys­tem modeling and change forecasting/ecosystem impact studies, 3) improving the measurement of error and uncer­tainty in land use change estimates, and 4) developing an effective geospatial strategy for using land cover as a sur­rogate for land use. Given the attention these issues are now

receiving, s ignif icant and steady progress should be expected.

Institutionalizing observation and monitoring is the next step in ensuring continued measures of landscape change. International initiatives—including the Global Terrestrial Observing System, the Global Observation of Forest Cover/Global Observation of Land Cover Dynamics initia­tive, and the International Geo sphere-Bio sphere Program Land activity—and regional monitoring initiatives such as the Northern Eurasia Earth Science Partnership Initiative are advocating for science-based monitoring. Organizations such as the Committee on Earth Observing Satellites are providing international programmatic coordination for space-based observation and monitoring programs. Brazil [Instituto Nacional de Pesquisas Especiais, 2000] and India [Forest Survey of India, 2001] have successful national land moni­toring programs. The next step is for additional national agen­cies with resource assessment responsibilities to formalize ongoing programs for monitoring landscape dynamics. Pro­grams established for operational observation and monitoring should be:

• Compatible with global land observation and monitor­ing system and protocols while maintaining a focus on national or regional monitoring objectives.

• Based on user requirements and defined objectives. The specific information needed to address the impacts of land use change on ecosystems must be carefully articu­lated and documented.

• Based on hierarchical data-collection strategies. This sug­gests that remotely sensed data be used to provide synoptic coverage, but that traditional surveys, field studies and in situ measurements be integrated into the assessment process.

• Using monitoring strategies and protocols that are vet­ted through peer review processes. This includes the requirement that all data used to determine change char­acteristics have documented accuracy and precision [Estes and Mooneyhan, 1994].

• Coupled with an ongoing research program focused on advancing the quality of data and assessments.

• Sufficiently flexible to permit investigation of new issues.

Expanded investment in programs that ensure long-term data continuity is the bottom line. Without the assurance that long-term field data and remote-sensing data acquisition pro­grams will continue, the potential for not only improving the understanding of the consequences of land use change on ecosystems but also better management of ecosystems in a

LOVELAND AND DEFRIES 245

way that ensures the availability of essential ecosystem goods and services, is destined to failure.

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