remote sensing for mapping and monitoring land-cover and land-use change—an introduction

Remote sensing for mapping and monitoring

land-cover and land-use change—an introduction

Paul Treitz

Department of Geography, Queen’s University, Kingston, Ont., Canada K7L 3N6

John Rogan

Clark School of Geography, Clark University, 950 Main Street, Worcester, MA 01610, USA

Received 7 July 2003; accepted 7 July 2003

Introduction

Remote sensing has long been an important component of urban and regional planning

for applications ranging from rural–urban fringe change detection (e.g. Treitz et al., 1992;

Bahr, 2001) to monitoring change of natural forest landscapes (e.g. Collins and Woodcock,

1996; Coppin and Bauer, 1996; Franklin, 2001). Since the launch of the first Earth

Resources Technology Satellite in 1972 (ERTS-1, later renamed Landsat 1), there has been

significant activity related to mapping and monitoring environmental change as a function

of anthropogenic pressures and natural processes. A significant component of change

detection methods using remote sensing is related to the characterization of both natural and

urban ecosystem structure and function at synoptic scales (Prenzel and Treitz, 2003). As

these methods mature, there is an increased need for remote sensing data and associated

analysis techniques in detecting and monitoring change, particularly for resource

management and planning. With the parallel expansion of computer processing capabilities

and software, specifically developed to handle image and spatially explicit data, (i.e. image

analysis systems [IAS] and geographic information systems [GIS]), spatial data products

have become more widely accepted outside the remote sensing community.

Information derived from remote sensing data has often been used to assist in the

formulation of policies and provide insight into land-cover and land-use patterns, and

0305-9006/$ - see front matter q 2003 Elseiver Ltd. All rights reserved.

doi:10.1016/S0305-9006(03)00064-3

Progress in Planning 61 (2004) 269–279

www.elsevier.com/locate/pplann

E-mail address: [email protected] (P. Treitz).

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multi-temporal trends. Interpretation of aerial photographs continues to be a standard tool

for mapping and monitoring land-cover and land-use change (Loveland et al., 2002).

Furthermore, as technologies have improved, so too has the range and opportunity for

remote sensing of ecosystem structure, dynamics and processes (Lunetta, 1998). These

aspects have received attention for resource management and planning. However, it

should be noted that remote sensing applications for urban analysis have not, as yet, been

met with widespread acceptance within the planning community (Donnay, 1999).

Although the potential was greatly enhanced in the late 1980s with the launch of the SPOT

series of satellites (e.g. Baraldi and Parmiggiani, 1990; Treitz et al., 1992) there remains

scepticism as to the operational capacity (i.e. robustness, reliability) of these data for urban

applications (Donnay et al., 2001). This limitation can in part be linked to sensor spatial

resolution. For instance, Welch (1982) identified spatial resolution as the single most

important issue for urban remote sensing. As a result, it can be postulated that there has

been increasing acceptance of remote sensing data for urban analysis with each new

generation of satellite equipped to collect high-spatial resolution data.

There has been an evolution in the manner in which remote sensing, associated

technologies, and analysis techniques are being used to map land-cover and land-use

change at local, landscape, regional and continental scales. Today, remote sensing

imagery from satellite and airborne platforms provide digital data at scales of observation

that meet various mapping criteria for characterizing anthropogenic and natural surfaces.

Regional and continental-scale land cover and land use can be mapped operationally, and

high spatial detail local- to landscape-scale analysis has great potential because satellites

currently provide scales of information comparable to aerial photographs. For example,

the most recent generation of remote sensing satellites provide very high-spatial resolution

data (i.e. IKONOS [1 m] and Quickbird [0.60 m]). These data are now amenable to

meeting the mapping and monitoring needs of municipal (and regional) planning agencies.

In particular, as spatial resolution of remote sensing satellites improves, there is increased

focus on applications for urban analysis (Forster, 1983; Fritz, 1999). High-spatial

resolution data assist in the examination of less ‘planned’ urban cores of older cities

(Ridley et al., 1997) and the expanding ‘edge cities’ of developing nations (Donnay et al.,

2001; Prenzel and Treitz, 2003).

Remote sensing of land-cover and land-use change is a diverse area of study and

application, with different meanings to different users and practitioners. The goal of this

monograph is to provide a current assessment of remote sensing technology and methods

(Chapters 2 and 3), and case studies at different scales of observation (Chapters 4–6). The

purpose of this chapter is to provide a general introduction to remote sensing for mapping

and monitoring land cover and land use at various scales of observation as well as to

provide a context for subsequent chapters.

Land-cover and land-use mapping and monitoring

Barnsley et al. (2001: p. 116) refer to land cover as “the physical materials on

the surface of a given parcel of land (e.g. grass, concrete, tarmac, water),” and land use as

“the human activity that takes place on, or makes use of that land (e.g. residential,

P. Treitz, J. Rogan / Progress in Planning 61 (2004) 269–279270

commercial, industrial)”. Land use can consist of varied land covers, (i.e. a mosaic of

biogeophysical materials found on the land surface). For instance, a single-family

residential area consists of a pattern of land-cover materials (e.g. grass, pavement,

shingled rooftops, trees, etc.). The aggregate of these surfaces, and their prescribed

designations (e.g. park) determines landuse (Anderson et al., 1976). Landuse is an abstract

concept, constituting a mix of social, cultural, economic and policy factors, which have

little physical importance with respect to reflectance properties, and hence has a limited

relationship to remote sensing. Remote sensing data record the spectral properties of

surface materials, and hence, are more closely related to land cover. In short, land use

cannot be measured directly by remote sensing, but rather requires visual interpretation or

sophisticated image processing and spatial pattern analyses to derive land use from

aggregate land-cover information and other ancillary data (Cihlar and Jansen, 2001).

Integrated analyses within a spatial database framework (i.e. IAS and/or GIS) are often

required to assign land cover to appropriate land-use designations.

Success in land-cover and land-use change analysis using multi-temporal remote sensing

data is dependent on accurate radiometric and geometric rectification (Schott et al., 1988; Dai

and Khorram, 1998). These pre-processing requirements typically present the most

challenging aspects of change detection studies and are the most often neglected, particularly

with regard to accurate and precise radiometric and atmospheric correction (Chavez, 1996).

For change to be identified with confidence between successive dates, a consistent

atmosphere between dates must be modelled so that variations in atmospheric depth (i.e.

visibility) do not influence surface reflectance to the extent that land-cover change is detected

erroneously. This is particularly important in biophysical remote sensing where researchers

attempt to estimate rates of primary productivity and change in total above ground biomass

(Coppin and Bauer, 1996; Treitz and Howarth, 1999; Franklin, 2001; Peddle et al., 2003).

Where change is dramatic, (i.e. conversion of agricultural land to residential), the ‘change

signal’ is generally large compared to the atmospheric signal. Here, the accuracy and

precision of geometric registration influences the amount of spurious change identified.

Where accurate and precise registration of one date to the other is achieved, identified surface

changes can be confidently attributed to land conversion. Inaccuracy and imprecise co-

registration can lead to systematic overestimation of change, although methods have been

developed to compensate for these effects (e.g. spatial reduction filtering).

Research continues to focus on the potential for digital image processing of high-

resolution imagery for detecting, identifying and mapping areas of rapid change (Longley

et al., 2001). The methodological aspects for implementing change detection strategies are

outlined by Rogan and Chen (Chapter 2) and Prenzel (Chapter 3). It has been noted that the

utility of per-pixel classification of spectral reflectance for identifying areas of land

modification, or land conversion is limited, as a result of various sources of error or

uncertainty that are present in areas of significant landscape heterogeneity (e.g. rural–

urban fringe, forest silvicultural thinning, etc.). For urban areas, the complex mosaic of

reflectance creates significant confusion between land-use classes that possess reflectance

characteristics similar to those of land-cover types. Typically, the quality (i.e. precision

and accuracy) of automated per-pixel classifications in urban areas using remote sensing

are poor, compared to non-urban areas. Also, urban areas present the problem of

having logical correspondence between spectral classes and functional land-use classes

P. Treitz, J. Rogan / Progress in Planning 61 (2004) 269–279 271

(Prenzel and Treitz, 2003). Improvements in traditional per-pixel classifications have been

developed over the last decade and include (i) the extraction and use of a priori

probabilities or a posteriori processing (Barnsley and Barr, 1996; Mesev et al., 2001); (ii)

texture processing (Haralick, 1979; Møller-Jensen, 1990); (iii) artificial neural networks

(Halounova, 1995; Abuelgasim et al., 1999); (iv) fuzzy set theory (Foody, 1996; Zang and

Foody, 1998; Abuelgasim et al., 1999; Foody, 1999); (v) frequency-based contextual

approaches (Gong and Howarth, 1992); (vi) knowledge-based algorithms (Wang, 1992;

Huang and Jensen, 1997); (vii) image segmentation (Conners et al., 1984; Bahr, 2001);

and the incorporation of ancillary data (Forster, 1985; Treitz et al., 1992; Harris and

Ventura, 1995; Treitz and Howarth, 2000). These approaches are necessary to

accommodate the more complex spatial structures arising from heterogeneous spectral

signatures, particularly in urban environments, but also for fragmented and heterogeneous

canopies common in areas of secondary growth and human influence.

Research into sophisticated spatial analytical methods for land-cover and land-use

classification continues through the integration of land-use morphology regarding

configuration, syntax, structure, and function with the inherent characteristics of remote

sensing data (Curran et al., 1998; Barnsley, 1999; Longley et al., 2001). For urban areas,

research has focused on (i) empirical/statistical kernel-based techniques (Wharton, 1982;

Barnsley and Barr, 1996); (ii) knowledge-based texture models (i.e. relating spatial

variations in detected spectral response to dominant land-use, using explicit spatial models of

urban structure as opposed to empirical models) (Barnsley et al., 2001); and (iii) structural

pattern-recognition techniques (Barnsley and Barr, 1997). It remains difficult to map point

and linear features, particularly digitally, due to the fact that they are not always recognizable

at the spatial resolution of the data, nor are they represented at their ‘true’ location due to

sensor and panoramic distortions inherent in satellite data collection. It has also proven

difficult to digitally separate linear features such as road networks from surrounding land-

cover and land-use (Wang and Zhang, 2000). This is largely due to the complexity of pattern

recognition procedures required for tracing specific cultural edge features.

Reporting of land-cover and land-use change—accuracy assessment

Accuracy assessment is an important feature of land-cover and land-use mapping, not

only as a guide to map quality and reliability, but also in understanding thematic

uncertainty and its likely implications to the end user (Czaplewski, 2003). Prior to image

classification, calibration data must be sampled from appropriate areas, at an appropriate

support size (Stehman and Czaplewski, 1998). However, sampling for change detection is

more challenging than that found in single-date approaches (Biging et al., 1998).

Typically, a first step in this process is to highlight areas of change vs. no-change. This can

be accomplished using an optimal threshold value based on similar spectral band

comparisons between dates, vegetation indices or texture measures (Lunetta, 1998). To

ensure appropriate sampling of no-change areas, the stratified adaptive cluster sampling

(SACS) approach has been recommended (Thompson and Seber, 1996; Biging et al.,

1998; Brown and Manly, 1998). SACS has particular utility for sampling disturbed

locations (changed landcover and landuse) because they usually represent a minor portion


of the target population (most of the land area has not changed) and are often clustered

(Rogan et al., 2003).

Following classification, the accuracy of the land-cover and land-use change maps must

be assessed. The total error in a thematic map can be the sum of the following: (i) reference

data errors; (ii) sensitivity of the classification scheme to observer variability; (iii)

inappropriateness of the mapping process or the technological interpolation method; and

(iv) general mapping error (Congalton and Green, 1999). General (total) map error

conveys map quality, or ‘fitness for use’ by end users (Chrisman, 1991). The conventional

method of communicating ‘fitness of use’ for map users is the confusion or error matrix

(Richards, 1996). The error matrix summarizes results by comparing a primary reference

class label to the map land-cover or land-use class for the sampling unit and presents errors

of inclusion (commission errors) and errors of exclusion (omission errors) in a

classification. The Kappa statistic (also known as a measure of ‘reproducibility’) is

a discrete multi-variate technique used in accuracy assessment (Congalton, 1988). A

standard overall accuracy for land-cover and land-use maps is set between 85 (Anderson

et al., 1976) and 90% (Lins and Kleckner, 1996). However, no such standard accuracy

exists for change-detection scenarios, although 80–85% appears to be a reasonable limit,

depending on complexity of the mapping study (Rogan et al., 2003).

Although the error matrix provides a global summary of map accuracy, it does not

describe the range and variation of accuracy across the change-map (Stehman and

Czaplewski, 1998). Error matrices are location-independent (i.e. global) measures of

spatial data quality, and therefore cannot display much-needed information such as the

location of areas where map-class labels on the ground are most likely misclassified by

image-derived variables, or where acquisition of additional data could improve the

accuracy of the land-cover and land-use change maps (i.e. local) (Steele et al., 1998;

Kyriakidis and Dungan, 2001).

Recent approaches to analyzing spatial variation in mapping error are presented by

Fisher (1994), Steele et al. (1998) and Kyriakidis and Dungan (2001). Fisher (1994)

proposed a visual method of displaying image classification errors via animation. Steele

et al. (1998) presented a method of estimating misclassification probabilities at calibration

site locations in order to interpolate these misclassification probability estimates for the

generation of a contour accuracy map. Kyriakidis and Dungan (2001) used stochastic

simulation of misclassification probabilities to generate multiple alternative realizations of

map error. It must be emphasized that accuracy assessment and reporting represent a

necessary component of the overall change analysis protocol in order to render these

technologies useful and repeatable for mapping and monitoring change.

Integrated spatial analysis

Regional and municipal planners require up-to-date information to effectively manage

land development and plan for change. In urban areas, particularly at the rural–urban

fringe, this change is typically very rapid. As a result, it is difficult to maintain up-to-date

information on new housing and industrial/commercial developments. This is particularly

true for regional municipalities whose jurisdictions cover large areas. Regional planners of


metropolitan areas devote large amounts of time, human expertise, and resources to update

land-cover and land-use maps to maintain timely information. An integrated approach to

land-cover and land-use change analysis is optimal for providing the land-use planner with

the maximum information content and benefit. While remote sensing data provide a means

of monitoring the rate of change with respect to land-cover conversion or systematic

change in health or productivity, other forms of digital data provide the positional

reference for new and existing land-covers and land-uses. Through the integration of

varied datasets, the land-use planner is able to make responsible decisions based on

existing information within the digital database, as well as create new information through

various spatial analysis techniques. Here, we emphasize the importance of timely and

spatially consistent remote sensing data for systematic analysis of landscape change (i.e.

local to regional scales) over space and time.

Remote sensing data, IAS and GIS provide opportunities for integrated analysis of

spatial data and product development. The interactions of these components have been

described by Wilkinson (1996) in the following three ways:

1. Remote sensing data can be used as input data for analysis within a GIS.

2. GIS data can provide ancillary data for improved remote sensing data analysis for

discrimination of land-cover and land-use classes.

3. The application of remote sensing data and other spatial data within a GIS for

combined modelling and analysis.

Often, classified remote sensing data, particularly for change detection within a

monitoring context, are used within a GIS. As when working with any spatial data, it is

important to have a good understanding of the accuracy of the input data (i.e. classified

remote sensing data) as well as a complete documentation of the lineage of the results of

any further analysis of those data (Baudot, 2001). Hord and Brooner (1976) identified

three components that determine the quality of a thematic map product. These are errors in

boundary location, map geometry and classification. These types of errors in source

documents are compounded during overlay and other forms of spatial analysis within a

GIS. Hence, two types of errors affect the accuracy of products generated by a GIS. These

are inherent errors, or errors present in the source data, and operational errors that arise

from data capture and manipulation within the GIS. Operational errors may further be

categorized as positional and identification errors, and in combination are a component of

every thematic overlay. Significant research is still required in the area of accuracy

assessment where a variety of data sources are integrated to create new information. This

is of particular importance when information extraction from the source documents is

selective, rather than complete. Accuracy measures must be made available for source

data, as well as for new information created through spatial analysis techniques.

At the beginning of this chapter we alluded to the preconception that there is reluctance

among planning agencies to adopt remote sensing methods and applications for change

detection and mapping of urban areas. However, there is real opportunity for optimism

because high-spatial resolution data, comparable to aerial photographs, are now widely

available from satellite sensors. In addition, remote sensing data and image analysis

algorithms are converging with GIS applications (Atkinson and Tate, 1999). In fact, it is


becoming more difficult to distinguish these two ‘technologies’ as they become more

integrated in software and application development. Longley et al., (2001: p. 245) go so

far as to state that “there are now very real prospects that ‘RS–GIS’ can provide a near

seamless software environment for urban analysis.” Planning is one of many disciplines

that stand to benefit from the frequent, large-area land surface information that can be

derived using remote sensing, particularly as we move through this current era of high-

spatial resolution satellite data, and adopt new processing and spatial analysis techniques

for integrated database systems.

Monograph outline

In Chapter 2, Prenzel provides an overview of methods used to extract quantitative

land-cover and land-use change information from remote sensing data, with particular

reference to current and potential applications in planning. The chapter first outlines

important considerations for conducting remote sensing change analysis in planning, and

then uses two planning contexts to illustrate two representative types of change analysis.

Rogan and Chen (Chapter 3) discuss how remote sensing technology has developed

over the last three decades, with major developments in: (i) sensor design; (ii) data

quality, volume, and availability; (iii) improved data processing methods; and (iv)

widespread applications. Advancements in medium- and high-spatial resolution sensors,

high-spectral resolution sensors, and active microwave sensors have provided for

significant improvements in mapping and monitoring urban, rural and natural

environments. They go on to describe the major technical considerations of land-

cover and land-use monitoring using remote sensing data, and specifically, the key

methodological considerations of a change-detection study (i.e. geometric correction,

radiometric correction and normalization, change enhancement, and classification).

Chapters 4–6 present case studies of land-cover and land-use mapping projects that

have relied on remote sensing data and analysis techniques. These studies are conducted at

a wide variety of scales (local, regional and continental), and have applications for urban

planning, environmental monitoring and assessment, and national policy formulation. In

the first instance, Langevin and Stow (Chapter 4) illustrate the extent to which image

processing techniques have evolved. They describe a neural network classification

approach for mapping urban land-use change in a rapidly expanding area of southern

California. These are among the most sophisticated classification algorithms currently

employed and adapted specifically to deal with high-resolution digital remote sensing

data, and incremental change.

A landscape-scale case study is presented in Chapter 5 (Prenzel and Treitz) whereby a

‘hybrid’ method for extracting thematic land surface change information is described for a

human-dominated tropical landscape in Sulawesi, Indonesia. SPOT satellite data were

obtained on anniversary dates in 1990 and 1999 and used in conjunction with ground,

terrain and ancillary information to conduct a nine-year change analysis. Results support

those of other studies in that the ‘hybrid’ method was shown to be effective for isolating

change, and increasing the overall accuracy of the final change analysis. The potential


utility of these types of analyses for environmental planning in Sulawesi, Indonesia, are

also discussed.

The third case study (Wulder, Kurz and Gillis—Chapter 6) presents a program for

updating and monitoring forests in Canada in response to an increased demand for

verifiable, current, and credible information on a range of forest indicators that have arisen

as a function of increased appreciation of the forests’ ecological, economic, and social

functions. Canada is implementing, in co-operation with provincial and territorial resource

management agencies, a new National Forest Inventory and a satellite-based forest

mapping and monitoring program. According to Wulder, Kurz and Gillis, the new

plot-based forest inventory will provide a statistically valid estimate of the current forest

conditions and their changes over time. The satellite-based forest cover information will

be used to extend and update some of the inventory attributes. These programs are

designed to address various current and future information and reporting needs. One

specific application described is the National Forest Carbon Accounting Framework. It

combines data from these (and other) sources to estimate forest carbon stocks and stock

changes. Information from these three integrated national programs will support

international reporting requirements and will assist in the development of policies

aimed at the sustainable development of Canada’s forest resources.

The discussions presented below represent a sample of the many activities taking place

in the area of remote sensing for change detection and monitoring. For further discussions,

the reader is referred to Lunetta and Elvidge (1998), Jensen (1996, 2000) and Donnay et al.

(2001), as well as the many references cited.

Acknowledgements

Dr Treitz and Dr Rogan gratefully acknowledge the support of the Natural Sciences and

Engineering Research Council (NSERC) of Canada and the National Aeronautics and

Space Administration (NASA) (Grant #LCLUC99-0002-0126) respectively.

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remote sensing for mapping and monitoring land-cover and land-use change—an introduction

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