Forestry Department
Food and Agriculture Organization of the United Nations
FRA 2000
FOREST COVER MAPPING
& MONITORING WITH
NOAA-AVHRR
& OTHER COARSE SPATIAL
RESOLUTION SENSORS
Rome, 2000
Forest Resources Assessment Programme
Working Paper 29
Rome 2000
The Forest Resources Assessment Programme
Forests are crucial for the well-being of humanity. They provide foundations for life on earth through ecological
functions, by regulating the climate and water resources, and by serving as habitats for plants and animals.
Forests also furnish a wide range of essential goods such as wood, food, fodder and medicines, in addition to
opportunities for recreation, spiritual renewal and other services.
Today, forests are under pressure from expanding human populations, which frequently leads to the
conversion or degradation of forests into unsustainable forms of land use. When forests are lost or severely
degraded, their capacity to function as regulators of the environment is also lost, increasing flood and erosion
hazards, reducing soil fertility, and contributing to the loss of plant and animal life. As a result, the sustainable
provision of goods and services from forests is jeopardized.
FAO, at the request of the member nations and the world community, regularly monitors the world’s forests
through the Forest Resources Assessment Programme. The next report, the Global Forest Resources
Assessment 2000 (FRA 2000), will review the forest situation by the end of the millennium. FRA 2000 will
include country-level information based on existing forest inventory data, regional investigations of land-cover
change processes, and a number of global studies focusing on the interaction between people and forests.
The FRA 2000 report will be made public and distributed on the world wide web in the year 2000.
The Forest Resources Assessment Programme is organized under the Forest Resources Division (FOR) at
FAO headquarters in Rome. Contact persons are:
Robert Davis
FRA Programme Coordinator
[email protected]
Peter Holmgren
FRA Project Director
[email protected]
or use the e-mail address: [email protected]
DISCLAIMER
The Forest Resources Assessment (FRA) Working Paper Series is designed to reflect the activities
and progress of the FRA Programme of FAO. Working Papers are not authoritative information sources –
they do not reflect the official position of FAO and should not be used for official purposes. Please refer to the
FAO forestry website (www.fao.org/fo) for access to official information.
The FRA Working Paper Series provides an important forum for the rapid release of preliminary FRA
2000 findings needed for validation and to facilitate the final development of an official quality-controlled FRA
2000 information set. Should users find any errors in the documents or have comments for improving their
quality they should contact either Robert Davis or Peter Holmgren at [email protected].
Table of Contents
ABSTRACT......................................................................................................................................................................... 6
1
INTRODUCTION ..................................................................................................................................................... 7
2
UTILITY OF NOAA-AVHRR FOR FOREST MAPPING/CHANGE DETECTION........................................ 8
2.1
2.2
2.3
AREA OF COVERAGE, IMAGE AVAILABILITY, TEMPORAL RESOLUTION .................................................................. 8
SPECTRAL RESOLUTION........................................................................................................................................... 9
SPATIAL AND GEOMETRIC CHARACTERISTICS ....................................................................................................... 10
3
MAPPING CONSIDERATIONS........................................................................................................................... 12
4
CLASSIFICATION METHODOLOGIES .......................................................................................................... 14
4.1
4.2
4.3
4.4
4.5
4.6
5
SINGLE IMAGE VISUAL INTERPRETATION .............................................................................................................. 14
SINGLE-IMAGE RADIANCE OR BRIGHTNESS TEMPERATURE THRESHOLDING ........................................................... 14
SINGLE-IMAGE DIGITAL SUPERVISED OR UNSUPERVISED CLASSIFICATION ............................................................. 14
MULTI-TEMPORAL, MULTI-SPECTRAL CLASSIFICATION ......................................................................................... 15
REGRESSION ANALYSIS AND MIXTURE MODELLING ............................................................................................. 16
FOREST MONITORING AND CHANGE ANALYSIS CONSIDERATIONS ........................................................................ 16
CASE STUDIES ...................................................................................................................................................... 18
5.1 GENERAL ............................................................................................................................................................... 18
5.2 IGBP-DISCOVER: A GLOBAL LAND COVER CLASSIFICATION AT 1KM RESOLUTION ............................................. 18
5.2.1 Purpose............................................................................................................................................................. 18
5.2.2 Legend .............................................................................................................................................................. 18
5.2.3 Data Sources .................................................................................................................................................... 20
5.2.4 Classification and Mapping Methodology........................................................................................................ 20
5.2.5 Validation and Accuracy .................................................................................................................................. 21
5.2.6 Coverage, Resolution and Availability ............................................................................................................. 22
5.2.7 Problems Or Constraints in Use....................................................................................................................... 22
5.3 FAO FRA 2000 GLOBAL FOREST COVER MAP .................................................................................................... 23
5.3.1 Purpose............................................................................................................................................................. 23
5.3.2 Legend .............................................................................................................................................................. 23
5.3.3 Data Sources .................................................................................................................................................... 23
5.3.4 Classification and Mapping Methodology........................................................................................................ 23
5.3.5 Validation and Accuracy .................................................................................................................................. 24
5.3.6 Coverage, Resolution and Availability ............................................................................................................. 25
5.3.7 Problems Or Constraints In Use ...................................................................................................................... 25
5.4 TREES (TROPICAL ECOSYSTEM ENVIRONMENT OBSERVATION BY SATELLITES).................................................. 25
5.4.1 Purpose............................................................................................................................................................. 25
5.4.2 Legend .............................................................................................................................................................. 25
5.4.3 Data Sources .................................................................................................................................................... 26
5.4.4 Classification and Mapping Methodology........................................................................................................ 26
5.4.5 Validation and Accuracy .................................................................................................................................. 26
5.4.6 Coverage, Resolution and Availability ............................................................................................................. 26
5.4.7 Problems Or Constraints In use ....................................................................................................................... 27
5.5 FIRS – FOREST INFORMATION FROM REMOTE SENSING: A FOREST INFORMATION SYSTEM FOR THE ENTIRE
EUROPEAN CONTINENT........................................................................................................................................... 27
5.5.1 Purpose............................................................................................................................................................. 27
5.5.2 Legend .............................................................................................................................................................. 27
5.5.3 Data Sources .................................................................................................................................................... 27
5.5.4 Classification and Mapping Methodology........................................................................................................ 27
5.5.5 Validation and Accuracy .................................................................................................................................. 28
5.6 OTHER FOREST COVER MAPS ................................................................................................................................ 28
5.7 SUMMARY ............................................................................................................................................................. 28
3
6
COMBINING HIGH AND LOW SPATIAL RESOLUTION CLASSIFICATIONS........................................ 30
7
BIBLIOGRAPHY.................................................................................................................................................... 32
FRA WORKING PAPERS .................................................................................................................................................. 42
Paper drafted by:
Dr. Giles D'Souza, Director Geographic Data Support Limited
In cooperation with the staff of the FAO FRA 2000 Programme
Editorial production: Patrizia Pugliese, FAO FRA-Programme
4
Abbreviations
BEF
Biomass Expansion Factor
BV
Biomass of inventoried volume
CATIE
Centro Agronómico Tropical de Investigación y Enseñanza
Cirad
Centre de coopération internationale en recherche agronomique pour le développement
EDC
Eros Data Centre
FAO
Food and Agricultural Organization of the United Nations
FORIS
Forest Resources Information System
FRA
Forest Resources Assessment
GIS
Geographic Information System
NOAA -
National Oceanic and Atmospheric Agency – Advanced Very High Resolution
AVHRR
Radiometer
SNU
Sub National Unit(s)
UN-ECE
United Nations Economic Commission for Europe
VOB
Volume Over Bark
WD
Wood Density
WCMC
World Conservation Monitoring Centre
5
Abstract
This paper represents a review of the use of coarse spatial resolution satellite data, mainly NOAAAVHRR, for forest cover mapping and monitoring all over the globe. Information about the sensor’s
utility in respect of this application, appropriate scales, differentiation of forest classes, coverage
(global, tropical, regional, national, etc.), accuracy of the results, integration with high spatial
resolution results and problems/constraints in use, are all discussed.
Other programmes that have used this instrument for forest mapping and monitoring (i.e. TREES,
IGBP, FAO and other regional programmes) are reported on, and a brief review of the applicability
of new coarse spatial resolution data (e.g. VEGETATION, ATSR) is also presented.
6
1
Introduction
Forests are crucial for the well-being of humanity, and the need to map and monitor their extent and
condition in a timely fashion at local, national, regional and global scales is growing increasingly
urgent. However, forests are very complex and difficult biomes to classify and map, even
conventionally (eco-floristically). Because of the urgent need to map forest extent and condition,
several organisations have turned to remote-sensing methods from which to derive their information.
And because the areas that need to be mapped are typically large, many have turned to coarse spatial
resolution data obtained from sensors flown onboard polar-orbiting satellites. In this paper we will
review the use of coarse spatial resolution data from the U.S. National Oceanic and Atmospheric
Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor in studies
of forest mapping and monitoring.
Although specifically designed for meteorological applications, data from the NOAA-AVHRR in
their various forms, have also been widely used for a variety of land applications, for example crop
monitoring in the Nile Delta (Tucker et. al., 1984a), grassland monitoring in Senegal (Tucker et. al.,
1983) and the Sahel (Prince et. al., 1990), continental or global-scale vegetation classification
(Townshend et. al., 1991), forest and bush fire mapping and monitoring (Gregoire, 1995), and
tropical deforestation monitoring in South East Asia and South America (Malingreau et. al.,1989).
Among the reasons cited for their use in land applications are:
•
•
•
•
•
the suitability of their spatial, spectral and temporal resolutions,
the large area of coverage offered,
the operational and guaranteed nature of the system (guaranteeing continuity of data in the same
format to users from 1981 to at least 2005 (NOAA,1998),
the relative cheapness and accessibility of the data, and
the manageable data rates for use over large areas.
For the forest mapping and monitoring over large areas at national or regional scales as required by
programmes such as the FAO Forest Resources Assessment 2000, these reasons are equally
applicable.
7
2
Utility of NOAA-AVHRR for forest mapping/change detection
2.1
Area of Coverage, Image Availability, Temporal Resolution
In its rawest full-resolution form, known as High Resolution Picture Transmission (HRPT) or Large
Area Coverage (LAC), a single swath of data from the AVHRR sensor spans about 2700km in the
cross-track direction, and a single recording of 10 minutes covers more than 3600 kilometres in the
along-track direction. So, for example, the whole of the Amazon Basin could be covered by only two
such scenes. These would have a spatial resolution ranging from 1.1 by 1.1km at centre swath (nearnadir) to about 2.4 by 6.9 km at the edge of swath, i.e. at the AVHRR's most extreme view angles of
55.4°=off-nadir (Belward, 1991). Assuming the spatial and spectral resolutions were detailed enough
for the purpose at hand this would offer considerable time and cost advantages over the purchase and
processing of over 200 Landsat TM or 1800 SPOT images to cover the same area.
Although the orbital characteristics of the NOAA series of satellites are very similar to those of the
Landsat series (i.e. 14.1 orbits per day, sun-synchronous, near-polar orbit at 99.1° orbital
inclination), the higher altitude (nominally 833km), the wider swath-width of the AVHRR sensor,
and the fact that the NOAA series of satellites usually operate in complementary pairs, together
permit a much higher theoretical frequency of image acquisition for the same area (albeit with
different look angles). Potentially one image per day may be acquired if afternoon overpasses only
are considered, or 4 images per 24-hour period if night-time and early morning overpasses are also
suitable (for example in applications requiring thermal waveband detection only). Even considering
just afternoon images that are reasonably near-nadir, at least one image every 5 days should be
possible (Goward et. al., 1991).
This high image repeatability, or temporal resolution, of the data is very important in tropical areas
where cloud cover is high and/or where intra-seasonal or phenological variations in time have to be
monitored. For most land applications, the images acquired by early morning overpasses of the
NOAA satellites are not very suitable since at that time (about 0730 local solar time), the light levels
are too low and the thermal contrast between differing land cover types is not sufficiently developed.
The light levels and thermal contrast are more suitable at the time of the NOAA satellite afternoon
overpass time (about 1430 local solar time). However, this is normally also a time of intense local
convection in tropical areas and there is therefore also an increased likelihood of cloud and haze
contamination at that time. In sub-tropical or more extreme latitudes, frontal cloudiness and lower
light levels for longer periods of the year provide other restrictions to suitable image acquisition
(Kasischke and French, 1997).
In practice, therefore, the theoretical rate of daily image acquisition is rarely achieved so a large
number of daily AVHRR images have to be examined and/or composited to obtain sufficiently
cloud-free scenes for forest mapping and monitoring (Malingreau et. al., 1989). In the past, the
potential daily acquisition for certain areas may also have been hampered by lack of data capture and
archiving facilities, but the number and distribution of local and regional receiving and archiving
facilities for NOAA data acquisition has continued to grow and improve. So have the facilities for
exchange and transmission of data acquired all over the world, particularly with the IGBP-DIS
project – a concerted global effort to collect full-resolution AVHRR data for all land surfaces on a
daily basis for over 54 months in 1992 – 6 (Townshend, 1992; Belward et. al., 1999)
Another characteristics of the NOAA-AVHRR systems that should be noted, is the marked orbital
drift which causes the Equator crossing time of the orbit to change by several hours during a three or
8
four year period (Price, 1991; Privette et. al., 1995). This together the gradual degradation of the
sensor in the visible channels over time (D’Souza, 1996; Rao and Chen, 1999) should be borne in
mind when examining long quantitative time series of data from the AVHRR sensor.
2.2
Spectral Resolution
All of the AVHRR sensors have had four to five channels, sensing data in the visible red, nearinfrared, shortwave-infrared and thermal channels. In AVHRR land applications the first two
channels of the radiometer, Channel 1 (0.58-0.68 µm ) sensitive to red reflected light, and Channel 2
(0.725-1.10µm), sensitive to infra-red reflected light, have been the most widely used.
Photosynthetically-active vegetation will typically yield a low reflectance at red wavelengths
(chlorophyll pigment absorption) and a high reflectance at near-infrared wavelengths (caused by
scattering of leaf surface and internal structure). Because it is the only material on the Earth’s surface
that will exhibit this contrast, the two channels are useful for discriminating and monitoring the
condition of vegetation.
Often the reflectances from the two channels are combined mathematically to form a spectral
vegetation index. Spectral vegetation indices attempt to discriminate between vegetated and nonvegetated areas by enhancing the spectral contribution of green vegetation whilst minimising the
contribution of the background. Numerous spectral vegetation indices have been developed based
upon combinations of the first two channels. The normalised difference vegetation index (NDVI) is
the most commonly used AVHRR vegetation index and is defined as the difference between nearinfrared and red reflectances divided by their sum (e.g. Curran, 1983). The NDVI has been
demonstrated to be a robust and sensitive vegetation measure and is the only vegetation index that
has been used widely for continental and global-scale vegetation dynamics examination (i.e. Tucker
et. al., 1984; Justice et. al., 1985, etc.., and see box 4). The widespread use of the NDVI is, in part,
due to its “ratioing” properties, which cancel out a large proportion of signal variations attributed to
changing irradiance conditions.
However, the uncalibrated wide-wavelength bands used for the first two channels of the AVHRR are
particularly sensitive to the smoke, water vapour, aerosols and other atmospheric particles that are
prevalent in the atmosphere in tropical regions in particular. Canopy structure and background (soil,
snow, wetness, litter), thin and sub-pixel cloud, illumination and viewing geometry effects have also
been shown to effect the AVHRR NDVI values. Furthermore, near infrared/red vegetation indices are
often more sensitive to leaf-area than to standing biomass (Sellers, 1986; Goward et. al., 1991; Los
et. al., 1994; Meyer et. al., 1995; Walter-Shea et. al., 1997). Therefore dense forest areas may yield
similar (saturated) vegetation indices to recently regenerated or agricultural areas although in terms
of standing biomass these two are vastly different (Tucker et. al., 1984b). Furthermore, the NDVI
and other vegetation indices have been found to be particularly insensitive in dense forest areas or
areas of complex canopies with several layers of shadow interactions and effects (Singh, 1987).
Depending on the understorey, closed conifer cover often shows an inverse relationship with NDVI
(Ripple, 1994; Ripple et. al., 1991, Spanner et. al., 1990).
On some single date NOAA-AVHRR images, channel 3 (3.5-3.9µm), sensitive to reflected and
emitted shortwave-infrared light, has been found to be more useful for forest/ non-forest delineation
(Kerber & Schutt, 1986). This channel has advantages (at least when used in conjunction with the
visible and near-infrared channels) because:
•
this wavelength is not so contaminated by aerosols and smoke particles (Bird, 1984);
9
•
•
it is sensitive to emitted (thermal) energy, where non-forested areas are characterised by a higher
temperature than the surrounding cool forest because of differences in the sensible-latent heat
balance;
it is also sensitive to reflected energy, where non-forested areas are normally characterised by a
higher albedo than the forested areas where the inter-canopy shadow effects act to trap visible
light.
Thus the presence of dense forest areas work to reduce both the reflected and the emitted parts of the
signal in channel 3 while non-forested areas generally yield increased components of both. One of
the disadvantages of channel 3 is that the signal obtained in this wavelength can be prone to noise,
especially in earlier satellites (NOAA-7 and NOAA-9) (Warner, 1989) and the mixed
emitted/reflected signal is also occasionally difficult to interpret.
Some work carried out on splitting the channel 3 response to its reflected and emitted parts (Holben
and Simabukuro, 1993; Kaufman and Kendall, 1994) has showed some promise, but has been
hampered by the need to know the thermal emissivity of the surface features being examined before
reliable splits can be achieved.
Channel 3 is also very sensitive to the presence of very hot areas within pixels, either from active or
recent fires (Gregoire et. al., 1988; Malingreau et. al., 1985). The occurrence of fire, especially
within dense forested areas is a good indicator of human activities (e.g. forest clearing for
cultivation), and is often used as "circumstantial evidence" of forest degradation activities
(Malingreau, 1990).
The fully-calibrated channels 4 (10.5 – 11.3µm) and 5 (11.5 – 12.5µm) (the thermal sensitive bands)
of the AVHRR have also been used for fire detection and characterisation (Matson, 1987; Matson &
Holben, 1987). Although they show some sensitivity to forest/non-forest areas (Kerber, 1983; Kerber
& Schutt, 1986), these channels are also often heavily contaminated by the water vapour which is
ever present in the tropical atmosphere. They have therefore been little used for characterisation or
classification of tropical forest areas on single-date AVHRR images. However, the seasonal variation
of surface temperature as estimated from AVHRR channels 4 and 5 in multitemporal data sets has
been shown to be closely related to the land cover type in West Africa (Achard & Blasco, 1989). It
has also been used in conjunction with NDVI time series for land cover classification over Africa
(Lambin and Ehrlich, 1996) and for forest classification over Europe (Roy et. al., 1997).
The other use for channels 4 and 5 in land applications has been in masking out areas of cloud or
haze (Saunders & Kriebel, 1990).
2.3
Spatial and Geometric Characteristics
Raw NOAA-AVHRR images are characterised by a very distorted geometry (Emery et. al., 1989).
They have a nominal spatial resolution of approximately 1.1 by 1.1 km. However, because of the
large swath width, the very off-nadir viewing angles, and the Earth's curvature, the spatial resolution
decreases to about 2.4km (in the along-track direction) by 6.9 km at the most off-nadir viewing angle
of the sensor. The varying pixel resolution compounded by the over sampling of the pixels in the
across-track direction (Mannstein and Gessell, 1991; Breaker, 1990) makes it difficult to use the data
in any detailed spatial or textural pattern analyses (Belward and Lambin, 1990), but if the use of the
data is restricted to that acquired at less than 25° off-nadir the problem is considerably reduced
(Goward et. al., 1991; Vogt, 1992).
10
Much literature refers to NOAA AVHRR data as 1.1km resolution, and several products are made at
a nominal pixel cell size of 1km. In practice, because of the spatial characteristics of the imagery, the
orbital characteristics of the NOAA satellite and the subsequent processing which may include
automated remapping of the data, the actual spatial resolution of the data is somewhat coarser. Even
with the most advanced geometric correction methods of the data, the locational accuracy of
remapped data, especially if carried out over large areas, is probably closer to 1.5 – 2.0 km (D’Souza
and Sandford, 1995). Where several images are composited (i.e. for multi-temporal analyses), the
individual image mis-registration is compounded, and the effective pixel size of a multi-date
composite could be as high as 5.1km, assuming a view zenith angle cutoff of 57 degrees and a
misregistration root mean square of 1km (Cihlar et. al., 1996).
However, despite the geometrical problems and the somewhat poor spatial resolution compared with
that of other Earth Resources satellites, the imagery has been found to be suitable for the detection of
a number of features relevant to forest monitoring, especially where single-date , good quality images
are used (see box 3). Many useful maps and datasets have been published at scales ranging from 1: 1
million to 1 : 5 million, and these will be described in detail later.
11
3
Mapping considerations
Remotely sensed images offer a new perspective on the land surface and environment, and a trained
image interpreter can draw an enormous amount of information from even a qualitative examination
of an enhanced image. However, the demand for statistical measures, and for comparisons with
conventionally-derived maps normally leads to a process of classification of remotely-sensed data
into a thematic map. It is widely recognised that a thematic map is in itself a considerable
condensation of the information available from a survey (Goodchild, 1995). One of the major
challenges in the use and adoption of remotely sensed data is to reconcile the type of observations
made from satellite-based sensors with classifications that have been derived from direct groundbased eco-floristic classifications. Additionally, eco-floristic definitions and classifications often vary
across national or regional boundaries.
The problem is probably more marked with coarser spatial resolution data. Ground-based
classification schemes are often based on direct detailed observation and physical measurement of
eco-floristic characteristics over quite small and widely distributed sample areas or transects, often
just a few metres wide. Conventional vegetation cover maps are often produced by interpolation and
generalisation of point or transect data into polygons, with abrupt and distinct boundaries shown
between different cover types, i.e. forest to non-forest. Contrast this to a complete coverage of large
but remote and indirect samples of light from 1.1km squares that form the basis of NOAA AVHRR
data. Satellite-based data are continuous in space, but indirect measures of what is on the ground,
averaged to the pixel size sample of measurement. Both datasets have their advantages and
disadvantages, and the real benefit is to use the two systems synergistically.
NOAA AVHRR data are normally resampled to a grid with a nominal cell size of 1km (pixel). The
classification of these data then involves an attempt to assign a category to this 1km pixel, according
to some predetermined legend. Statistics can then be generated by summing the number of pixels
assigned to this category and thematic maps can be produced. Some classification methodologies
assign more than one value to a pixel, for example, a category, and a reliability, or proportion. The
methods used for the categoric assignment are discussed in the next section. However, classification
techniques applied to remotely-sensed data, especially to those of coarse spatial resolution such as
NOAA AVHRR, have the inherent problem that pixels seldom contain exclusively one distinct
ground cover class (e.g. Foody et. al., 1992). Furthermore, classification techniques are not well
suited for the estimation of forest cover variables, which are more continuous than discrete in nature.
Numerous studies have shown that the accuracy of classification of NOAA AVHRR data is directly
related to the purity of the ground cover in the AVHRR size pixels (Cihlar et. al., 1996).
Accuracy assessments of digital land cover classifications are typically based on contingency tables,
or confusion matrices, where accuracy is expressed in terms of errors of omission and commission, or
in terms of agreement analysis using the Kappa test statistic (Stehman, 1996a; Congalton, 1991;
Rosenfeld and Fitzpatrick-Lins, 1986). The contingency table is created by comparing on a class by
class basis the land cover classification with an independent data source - field observations, existing
maps, higher resolution imagery - collected using a statistically valid sampling strategy (Robinson et.
al., 1983; Stehman, 1996b; Rosenfeld, 1986). Whilst such methods are well established they have
generally only been applied to local scale classifications, occasionally to regional scale work and
only in isolated instances on scales greater than these (Hay, 1988; Manshard, 1993; Estes and
Mooneyhan, 1994; Jeanjean et. al., 1996). The general method of validating NOAA AVHRR-based
classifications by employing classification of higher spatial resolution remotely-sensed data is now
well established (Fitzpatrick-Lins, 1980; Rosenfield et. al., 1981) and has been employed extensively
12
at local and regional scales (Borella et. al., 1982; Estes et. al., 1987). However, it is often based on
the assumption that the results derived from the high spatial resolution imagery is completely
accurate and reliable. This is obviously not the case – they themselves will include some imprecision
and perhaps mis-classification, especially when seasonal patterns are prevalent but the purchase of
multi-date high resolution imagery is prohibitive. In general though, it is generally felt that data
derived from the higher spatial resolution imagery should be more reliable than those derived from
coarser spatial resolution data, at least for the detailed spatial representation of the different land
cover patterns. Furthermore, it is much more convenient to carry out field checking with higher
spatial resolution imagery (say at scales of 1: 25,000) than with coarse spatial resolution data. Errors
in the AVHRR-based classifications due to scale differences are often measured by intercomparisons of the two co-registered sets of data (see section 6 below).
Because the satellite-based data are in a digital and “raster” form, they could in theory be reproduced
at any scale. However, it is generally accepted that the nominally 1km-resolution data are generally
suitable for reproduction of paper maps at one to one million scale. Most paper-based products of
NOAA AVHRR data tend to be at smaller scales than this, typically 1: 5 million scale (see for
example Stone et. al., 1993).
13
4
Classification methodologies
Several image classification methods for large-area forest delineation from AVHRR data are found in
the literature and these may be grouped into four main categories.
4.1
Single Image Visual Interpretation
This is the simplest and most flexible approach, since the eye-brain system can make allowances for
the inevitable large variations in feature appearance that occur with different viewing angles and
differential bidirectional reflectance of various surfaces. At the same time local variations can be
detected for improved feature discrimination. Visual interpretations made by trained people familiar
with the relevant area and image data characteristics, can be accurate in many cases. However, the
delineation of boundaries is also time-consuming, difficult and somewhat subjective. If the
interpretations are made on a hard-copy image rather than on-screen image displays, then it often
requires digitisation for input into a GIS. A further drawback of visual interpretation of hard-copy
images is that it can be based on an image representation of, at most, three spectral bands at any one
time, and the results can be quite dependent on the contrast-stretch or enhancement used. However,
this method is easy to understand and teach, and it requires no specialised hardware. Examples of the
successful use of this method are provided in Tucker et. al. (1984), Malingreau and Tucker (1990)
and Malingreau et. al. (1989).
4.2
Single-image radiance or brightness temperature thresholding
Single-image radiance or brightness temperature thresholds applied to channels 3 and/or 4 have been
found to delineate quite well the forest/non-forest boundary, at least in areas where the transition is
very sharp, e.g. Malingreau and Tucker 1988, Malingreau et. al.1989, Stone et. al. 1991; Woodwell
et. al.1986, Amaral 1992, Cross 1990, Cross 1991). The technique is simple to apply and understand,
but requires visual interaction with the data. Also, even in the cases cited, the thresholds used have
differed both in time and space, and the method is not reliable in areas of diffuse or complex
forest/non-forest interfaces.
4.3
Single-image digital supervised or unsupervised classification
This technique offers an improvement on the previous methods, because it is said to be more
quantitative, objective and can be based on more than three channels at a time. Examples of its
successful application are provided in Cross (1990), Stone et. al. (1991), Paivinen and Witt (1988)
and Horning and Nelson (1993). Normally, a supervised classification requires recourse to quite
detailed geo-located reference data that define the locations and extent of representative land cover
units for classification training and accuracy assessment procedures. An unsupervised approach can
be more automatic in the first stage, but requires significant human interaction and reference data to
assign meaningful classes to the resultant spectral clusters. In some cases, however, supervised and
unsupervised approaches have been found to yield quite similar results, as in the mapping of
deforestation in Rondonia state in Brazil (Skole 1993).
14
Because NOAA AVHRR images cover such large areas, the quality of classification may vary across
the image, or some parts of some images may be obscured by haze or cloud. In these cases, a
successful approach by D’Souza et. al. (1995) was to carry out several image classifications for
overlapping areas, and assign a reliability value at each location. The classification with the most
reliable indicator was then retained in the final product.
4.4
Multi-temporal, multi-spectral classification
This method as used by Achard et. al. (1993) involves the examination of the behaviour of a
particular radiometric characteristic (e.g. vegetation index or surface temperature) through time. This
is the most sophisticated and best method for making a seasonal/evergreen forest discrimination.
However, it relies on a good set of high quality (cloud and haze-free) imagery throughout the season
and is therefore normally only practical in tropical areas where there is a marked dry season, or when
a large series of data is available. This technique usually involves the reduction of the AVHRR time
series into weekly, 10-day or monthly maximum value composites (Holben, 1996), and until recently
were performed on very low spatial (nominally 8km) sampled NOAA AVHRR data (e.g. Townshend
et. al., 1987).
With the recent improvements in computing power, and in the global acquisition and processing of
NOAA AVHRR data (see D’Souza et. al., 1995), 10-day maximum value composites have recently
been developed at continental and global scales for the full spatial resolution data. Loveland et. al.,
(1991) showed that such monthly full spatial resolution NDVI composites could be used to
characterise land cover types at continental scales and a resolution of nominally 1km. In their work
NDVI monthly composites were used to develop a land cover database for the conterminous United
States. Eight monthly NDVI composites were used in an automatic unsupervised classification
scheme to produce 70 spectral temporal “seasonally-distinct” classes. These were then refined and
used with expert knowledge and a wide variety of ancillary data to subjectively assign class labels to
each pixel. The labels assigned were taken from the USGS land cover classification. This work is
continuing under the auspices of the International Global Biosphere Programme (IGBP). See
Belward and Loveland (1995) and section later.
Other work that uses multitemporal NDVI composites seeks to extract phyto-phenological measures
or characteristics rather than categoric land cover classes from the data (see Lloyd 1990). Such
measures included maximum photosynthetic activity, the length of growing season, and mean daily
NDVI. DeFries et. al. (1995) also concluded that decision-tree classification of phyto-phenological
measures proved to be more accurate than straight classification of monthly composited NDVI values
alone. This method of classification is probably more reliably repeatable because it describes and
characterises land cover types according to their radiometric behaviour through time, rather than the
forcing a correspondence to a classification scheme developed on some other bases. However, it has
not achieved widespread acceptance because the final classes do not correspond to the wellestablished land cover classifications.
It has also been suggested that the multi-temporal examination surface temperature together with a
spectral index may increase the discrimination of regional land cover classes (e.g. Running et. al.,
1994). Generally, surface temperature is observed to be inversely proportional to the amount of
vegetation canopy due to a variety of factors including latent heat transfer through evapotranspiration
and the lower heat capacity and thermal inertia of vegetation compared with that of soil (Nemani et.
al., 1993). Multitemporal NDVI and surface temperature have been used together to produce land
cover classifications of the conterminous U.S. (Nemani and Running, 1997), a forest/non-forest
15
classification of Europe (Roy, 1997) and land cover change maps of Africa at 8km resolution
(Lambin and Ehrlich, 1996)
4.5
Regression Analysis and Mixture Modelling
Regression analyses and mixture-modelling approaches may assign several classes in different
proportions to a single pixel. The results of regression analysis or mixture modelling are usually a set
of images showing the concentrations of a different class component (called endmembers) in each
image. Although they do not provide any more detailed about the location of forest/non-forest
boundaries and sub-pixel forest locations, the overall accuracy of total forest cover estimation is often
improved. The mixture modelling approaches assume some form of scene reflectance model (implicit
or explicit) that describes the mixing relationship between the different scene components. Iverson et.
al. (1989) used an implicit model based on forest/non-forest estimates made from a sample of
overlapping Landsat TM and NOAA AVHRR images. The percentage forest cover estimates made
from TM classifications were related to the red and near-infrared AVHRR pixel values by linear
multiple regression. Then the resulting regression coefficients were used to estimate the percentage
forest area over the rest of the AVHRR imagery. This approach has been used to make regional forest
cover maps of the U.S. (e.g. Zhu and Evans, 1992; Iverson et. al., 1994; Ripple, 1994), of central
Africa (DeFries et. al., 1997) and of the conterminous U.S. (Zhu and Evans, 1994). Regression
analysis has also proven to be effective for biomass estimation of coniferous forests, but less
successful where the target area includes conifers and broad leaves trees (Hame et. al., 1997).
Cross et. al. (1991) produced tropical forest proportion images by linear mixture modelling four
channels of an AVHRR sub-image. The endmembers were derived by examination of the principal
components analysis of the AVHRR data. The resultant forest and non-forest proportion images were
compared with supervised classifications of co-registered Landsat TM images. Cross et. al. (1992)
concluded that 60–70% of an AVHRR pixel must be forested before it is recognised as such. Holben
and Simabukuro (1993) presented a method to extract the reflected component of the mid-infrared
AVHRR channel and used it with the red and near-infrared AVHRR channels to generate vegetation,
soil and shade proportion images using a linear mixture model.
The attraction of mixture modelling and regression analysis approaches with NOAA AVHRR data is
that they appear to present a more realistic case of what exists on the ground. Over a 1km area, there
is likely to be a mix of vegetation types, and the assignment of just one class (e.g. forest) to the whole
1km area seems unrealistic. The assignment of a proportion (e.g. 70% forest) is more satisfying.
However, the application of pure mixture modelling techniques is not straightforward. The initial
derivation of endmembers is crucial to the accuracy of the mixture modelling approach, but it is
complex and not well understood. It is generally recognised that for successful mixture modelling
much more spectral information is required where a complex mix of land cover types exist.
Regression analysis has been used to create an AVHRR-based forest probability map of the PanEuropean area (Hame et. al., 1999). However, such techniques have a requirement for a large amount
of independent, reliable and geo-coded reference data, and a set of very clean noise-free imagery.
4.6
Forest Monitoring and Change Analysis Considerations
If image classification is reliable and repeatable, then the change in land cover can be identified
between classifications made at different times. However, in nearly all of the reported forest
16
classification exercises using NOAA AVHRR, the objective has been to achieve the classification.
Very few studies have looked at or made repeated classifications at different time periods. One
notable exception is the work of Cross (1991), in which he tested image difference, image division,
image regression and edge difference techniques and applied them to AVHRR images of Rondonia
and Mato Grosso from 1984, 1988 and 1990. Large scale changes such as the establishment of new
“fazendas” and other ranching and “fish-bone” and other forest clearances for agriculture were
clearly determinable from the examination of the individual channels on single-date AVHRR
imagery.
Because of the lack of image classification repeatability tests in the literature, it is very difficult to
assess the utility of NOAA AVHRR for forest extent or condition monitoring or change detection.
However, it appears that where changes are abrupt, marked and larger than 1km square (e.g.
denudations of dense forest and conversion to agriculture or bare soil) they are easily discernible
from individual, good quality single-date full-resolution AVHRR images. However, land conversions
which are more gradual (e.g. degradation or reduction of forest cover percentage) or of a smaller
extent would not be easily discernible, even with recourse to multi-temporal image examination. For
monitoring of such areas, some suggestions have been made as to the utility of AVHRR for
circumstantial or qualitative evidence gathering (e.g. GOFC 1999) to identify deforestation
“hotspots”, either independently or in conjunction with a multi-level approach using NOAA AVHRR
together with higher spatial resolution imagery and ground survey for measurement of the nature or
amount of the observed change (see section later and, for example, Jeanjean and Achard 1997).
17
5
Case studies
5.1
General
In the last decade, several major initiatives have been launched concerning the use of coarseresolution data for mapping and monitoring of forest cover extent and condition. Because they had
different objectives, time-scales and resources, the legends and methodologies adopted varied
considerably. In the section we will provide a brief synopsis of each relevant project. In each, we will
list the legend, data sources, methodology and validation procedures adopted. We will also attempt to
outline any major problems or constraints in the use of the results from the various projects.
5.2
IGBP-DISCover: A Global land cover classification at 1km resolution
5.2.1 Purpose
The International Global Biosphere Programme (IGBP) global land cover classification was made to
support a number of IGBP initiatives. Their stated objective was to provide a global land cover
dataset that was more current, of known accuracy and that had higher spatial resolution and greater
internal consistency than any other existing datasets (Loveland and Belward, 1997). Thus its primary
focus was on fast delivery of a global product for use in other IGBP initiatives.
5.2.2 Legend
The legend was chosen to be exhaustive, so that every part of the Earth’s surface was assigned to a
class; exclusive, so that classes did not overlap; and structured so that classes were equally
interpretable with 1km data, higher resolution remotely-sensed imagery, or ground observation. The
categories were chosen so that they embraced the climate-independence and canopy component
philosophy presented by Running et. al. (1994) but modified to be compatible with classification
schemes currently used for environmental modelling to provide, where possible, land use
implications and to represent landscape mosaics (Belward 1996). The legend comprises 17 so-called
DISCover classes and these are defined in Table1.
18
Table 1. IGBP DISCover Data Set Land Cover Classification System
CLASS
CLASS NAME
DESCRIPTION
1
Evergreen
Needleleaf Forests
2
Evergreen
Broadleaf Forests
3
Deciduous
Needleleaf Forests
4
Deciduous
Broadleaf Forests
5
Mixed Forests
6
Closed Shrublands
7
Open Shrublands
8
Woody Savannas
9
Savannas
10
Grasslands
11
Permanent
Wetlands
12
Cropland
13
Urban and Built-up
14
15
16
Cropland/Natural
Vegetation Mosaics
Snow and Ice
Barren
17
Water Bodies
Lands dominated by trees with a percent canopy cover >60% and height
exceeding 2 meters. Almost all trees remain green all year. Canopy is
never without green foliage.
Lands dominated by trees with a percent canopy cover >60% and height
exceeding 2 meters. Almost all trees remain green all year. Canopy is
never without green foliage.
Lands dominated by trees with a percent canopy cover >60% and height
exceeding 2 meters. Consists of seasonal needleleaf tree communities with
an annual cycle of leaf-on and leaf-off periods.
Lands dominated by trees with a percent canopy cover >60% and height
exceeding 2 meters. Consists of seasonal broadleaf tree communities with
an annual cycle of leaf-on and leaf-off periods.
Lands dominated by trees with a percent canopy cover >60% and height
exceeding 2 meters. Consists of tree communities with interspersed
mixtures or mosaics of the other four forest cover types. None of the forest
types exceeds 60% of landscape.
Lands with woody vegetation less than 2 meters tall and with shrub
canopy cover is >60%. The shrub foliage can be either evergreen or
deciduous.
Lands with woody vegetation less than 2 meters tall and with shrub
canopy cover is between 10-60%. The shrub foliage can be either
evergreen or deciduous.
Lands with herbaceous and other understorey systems, and with forest
canopy cover between 30-60%.The forest cover height exceeds 2 meters.
Lands with herbaceous and other understorey systems, and with forest
canopy cover between 10-30%.The forest cover height exceeds 2 meters.
Lands with herbaceous types of cover. Tree and shrub cover is less than
10%.
Lands with a permanent mixture of water and herbaceous or woody
vegetation that cover extensive areas. The vegetation can be present in
either salt, brackish, or fresh water.
Lands covered with temporary crops followed by harvest and a bare soil
period (e.g., single and multiple cropping systems. Note that perennial
woody crops will be classified as the appropriate forest or shrub land
cover type.
Land covered by buildings and other man-made structures. Note that this
class will not be mapped from the AVHRR imagery but will be developed
from the populated places layer that is part of the Digital Chart of the
World.
Lands with a mosaic of croplands, forest, shrublands, and grasslands in
which no one component comprises more than 60% of the landscape.
Lands under snow and/or ice cover throughout the year.
Lands exposed soil, sand, rocks, or snow and never has more than 10%
vegetated cover during any time of the year.
Oceans, seas, lakes, reservoirs, and rivers. Can be either fresh or salt water
bodies
19
5.2.3 Data Sources
Under the joint auspices of the Committee on Earth Observations Satellites, the U. S. Geological
Survey (USGS), National Aeronautics and Space Administration (NASA), National Oceanic and
Atmospheric Administration (NOAA) and the European Space Agency (ESA), a concerted effort was
launched to capture and process full-resolution daily NOAA AVHRR data for every part of the
globe’s land surface between April 1992 and September 1996 (with a gap between September 1994
to February 1995 when there was no operational afternoon-pass NOAA satellite). Over 4.4
Terrabytes of 1km resolution AVHRR data from 30 receiving stations were collected, assembled and
processed into a coherent data set (Eidenshink and Faundeen, 1994). This dataset consists of ten
processed channels: including the individual calibrated five channel data and the NDVI.
5.2.4 Classification and Mapping Methodology
The IGBP Land Cover Working Group (LCWG) adapted the methods of the USGS EROS Data
Center (EDC) and the University of Nebraska-Lincoln (ULN) (Loveland et. al., 1991; Brown et. al.,
1993). The classification stages include:
1. The 10-day NDVI data from April 1992 to March 1993 were converted to monthly maximum
value composites, which essentially depict “greenness” classes.
2. Artefacts in the composites due to mis-registration, mis-calibration, geometric distortions, and
atmospheric effects were identified, and affected data were either excluded form the analysis or
re-processed (Zhu and Yang 1996).
3. “Water Bodies” pixels were identified from the AVHRR Channel 2, and the “Snow and Ice” and
“Barren Or Sparsely Vegetated” pixels were determined from their low values in a 12-month
maximum value NDVI composite. “Urban and Built-Up” pixels, typically very difficult to
classify from remotely-sensed data, were separated by reference to the “urban” layer information
from the Digital Chart of the World (Danko, 1992). Loveland, et. al., (1999) provides full details
of the classification methodology.
4. The remaining data were segmented into greenness classes using an unsupervised classification
clustering algorithm described in Kelly and White (1993). This identifies clusters of NDVI with
similar temporal properties. Clustering was controlled by predetermined parameters for numbers
of iterations and classes, and for measures of inter- and intra-cluster distance. The clusters were
defined by channel mean vectors and covariance matrices. For each continent the number of
clusters was based on empirical evaluation of several clustering trials.
5. Each cluster was then assigned a preliminary cover type label. Class labelling involves the
inspection of spatial patterns and spectral or multi-temporal statistics of each class, comparing
each class with ancillary data, and making a final decision concerning cover type(s). A minimum
of three independent interpreters label each class to avoid interpreter bias (McGwire 1992).
Where differences exist, interpreters compare decision histories to arrive at a consensus.
6. Preliminary clusters with two or more cover types are then split into “single category” land cover
classes; at least 60 per cent of the greenness classes typically represent multiple land cover types
(Running et. al., 1995). These can be subdivided on the basis of the relation between the mixed
greenness classes and selected ancillary data sets. Elevations, ecoregions, and climate data have
proved to be the most useful for sub-dividing mixed greenness classes (Brown et. al., 1993).
Occasionally, clustering of individual AVHRR spectral channels in the IGBP-DIS dataset may be
used to refine mixed clusters. Analyst-defined polygons are used as a last resort and call for
extremely careful documentation.
20
7. Attributes for each “single category” class are determined. These include descriptive land cover
information, NDVI and elevation statistics, and class relations to other land cover legends.
8. Related “single category” classes are then grouped under DISCover labels using a convergence of
evidence approach, in which all available documentation, including the “single category” class
attributes, image maps, atlases and existing land cover/vegetation maps are compared with the
spatial distribution of each DISCover class. Three interpreters are again used to avoid bias.
5.2.5 Validation and Accuracy
Considerable effort has been made to validate the IGBP global land cover classification (known as
DISCover 1.0). Initial phases of validation included peer review of the datasets published on the
Internet. Subsequent phases of validation using high spatial resolution imagery are reported in a
special edition of the Journal of Photogrammetric Engineering and Remote Sensing (from which
most of the following results have been taken).
A stratified random sample design was adopted for identifying validation sites with a goal of
identifying 25 samples per DISCover class, for which to obtain high spatial resolution images for
validation. A total of 379 Landsat TM or SPOT images were obtained, processed and interpreted to
the same IGBP legend as used in the global land cover classification. Then at a series of workshops,
three regional Expert Image Interpreters independently verified each sample and a majority decision
rule was used to determine sample accuracy. For the 15 DISCover classes validated (excluding Water
and Snow And Ice classes), the average class accuracy was 59.4% with accuracies ranging between
40.0% and 100%. The overall area weighted accuracy of the data set was determined to be 66.9%.
When only samples which had a majority interpretation for errors as well as for correct classification
were considered, the average class accuracy of the data set was calculated to be 73.5%.
The highest individual class accuracies were established in Class 16 (Barren; 1.00), Class 2
(Evergreen Broadleaf Forests; .840), and Class 7 (Open Shrublands; .778). Classes 2 and 16 met the
accuracy goal established by IGBP for DISCover 1.0 of .85 accuracy (at 95% confidence). The
accuracies for Class 4 (Deciduous Broadleaf Forests) and Class 9 (Savannas) were the lowest of the
15 classes verified. Class 3 (Deciduous Needleleaf Forests) and Class 11 (Permanent Wetlands) also
had low accuracy, but the number of samples validated for this class was well below the minimum 25
samples specified in the validation protocol. Not surprisingly, larger and less fragmented classes had
higher thematic accuracy (Scepan 1999, Scepan et. al., 1999)
21
Table 2. IGBP DISCover 1.0 Overall Accuracy (from Scepan, 1999)
DISCover Class
1 - Evergreen
Needleleaf Forests
2 - Evergreen
Broadleaf Forests
3 - Deciduous
Needleleaf Forests
4 - Deciduous
Broadleaf Forests
5 - Mixed Forests
6 - Closed
Shrublands
7 - Open Shrublands
8 - Woody Savannas
9 - Savannas
10 - Grasslands
11 - Permanent
Wetlands
12 - Cropland
13 - Urban and
Built-up
14 - Cropland/
Natural Vegetation
Mosaics
16 - Barren
Verified
Samples
26
Samples
Verified
Correct
15
User’s
Accuracy
.58
Confidence
Interval
Percent
(.95)
Cover
0.38 - 0.77 .0482
25
21
.84
0.69 - 0.99
.0916
11
5
.46
0.15 - 0.76
.0123
25
10
.40
0.20 - 0.60
.0284
27
27
15
15
.56
.56
0.36 - 0.75
0.36 - 0.75
.0471
.0198
27
31
26
26
17
21
18
11
15
5
.78
.58
.42
.58
.29
0.62 - 0.94
0.40 - 0.76
0.23 - 0.62
0.39 - 0.77
0.07 - 0.52
.1489
.0750
.0755
.0830
.0075
28
30
18
16
.64
.53
0.46 - 0.82
0.35 - 0.72
.1028
.0032
26
13
.50
0.30 - 0.70
.1114
27
27
1.00
0.87 - 1.00
.1452
Overall
DISCover
Accuracy
.669
5.2.6 Coverage, Resolution and Availability
The IGBP land cover classification covers the whole of the Earth’s land surface. It is at a nominal
spatial resolution of 1km, but because it is based on multi-temporal composites its geometric
accuracy is more likely to be closer to 2 – 5km. The intermediate and final datasets are made
available on the Internet for download and/or validation on:
http://edcwww.cr.usgs.goc/landdaac/
http://www.cnrm.meteo.fr:800/igbp/dis_home/html
5.2.7 Problems Or Constraints in Use
Because the IGBP land cover classification is based on multitemporal composites, the results are
subject to all the image compositing errors (e.g. the geometric accuracy is probably of the order of 2
– 5 km). Also, because the classification is based on an unsupervised classification of a whole year of
monthly maximum value composites of NDVI, it is very difficult to repeat or amend the
classification for any change detection or monitoring purposes. The assignment of classes to the
automatically-determined clusters relies heavily on ancillary datasets, so these too must be kept up to
date and validated themselves. Although there was an attempt to remove interpreter subjectivity by
22
using at least three expert interpreters would the same ones have to be employed for subsequent
reclassifications?
5.3
FAO FRA 2000 Global Forest Cover Map
5.3.1 Purpose
The global FRA 2000 global forest cover mapping effort builds on the full IGBP/USGS seasonal
database, refining forest classes to reflect forest density classification. Its main objectives are to:
1) develop a general, actual forest cover map linking to the IGBP/USGS land cover database;
2) map broad forest cover categories that can be consistently extracted from AVHRR 1 km
composite data; and
3) use the map to support the FAO year 2000 global forest survey.
As a complete enumeration of forest cover in the world, it may be used to supplement regions lacking
recent, reliable forest inventory (FAO, 1995). As a new addition to the USGS land cover database,
the effort should help improve applications of the database when information about forest cover is
needed.
5.3.2 Legend
Six land cover categories are initially targeted:
1.
2.
3.
4.
5.
6.
closed forest,
open forest,
fragmented forest,
woody savannah,
other land cover, and
water.
These categories follow, to the extent possible, canopy density definitions that have been suggested
by FAO Forestry as required for global assessment using remotely sensed data (1995). The first three
categories are considered to be forested land cover.
5.3.3 Data Sources
The same data as used for the IGBP global land cover classification are used. Note that the source
data used for the IGBP land cover classification were data acquired in the period from March 1992 to
April 1993, thus in order for the global FAO forest cover map to be used consistently for FRA 2000
purposes, the global land cover map may have to be updated with source data from a later period in
the 1990s.
5.3.4 Classification and Mapping Methodology
As described in the previous section, vegetation classification in the IGBP land cover database are
built on characteristics of vegetation seasonality determined in terms of AVHRR NDVI. The
magnitude of integrated NDVI over the length of the temporal period helped separate successively
decreasing vegetation biomass, from dense forestlands to sparse land cover. On the other hand,
23
seasonal variations were investigated to partially support identification of vegetation physiognomy
(e.g. separating deciduous from evergreen forests).
Because the IGBP seasonal land cover database was not intended to optimise for forest cover, no
direct relationship exits to enable a simple conversion of the seasonal land cover classes to the six
FAO classes. Rather, a two-step methodology has been implemented which allows certain
interactive flexibility in deriving and correcting for the FAO classes (Zhu et. al., 1999).
Adapting the IGBP seasonal land cover classes to the FAO classification. Zhu et. al. (1999)
report that the full IGBP seasonal land cover classes are used as the baseline data, on the continentby-continent basis (presumably the unsupervised classes). Then, refinement methods similar to those
used in producing the IGBP classes are used to fit the derived unsupervised classes to FAO
definitions. These refinement methods depend on local conditions of land cover and rely on a careful
study of all available evidence. The country-level forest database maintained by FAO is also used as
a general reference for country level forest classification.
Estimating percent forest cover using two techniques. The first is what Zhu et. al. (1999) refer to
as spectral unmixing, apparently applied to the bright pixels in AVHRR bands 2 (infrared) and 1
(visible) spectral space, which were found to be primarily mixtures of forest, cropland, and bare soil
having high reflectance in these bands. For the dark pixels in AVHRR bands 1 and 2 a scaled NDVI
was found to be a more representative indicator or amount of forest. Negative relationships of
amount to NDVI were found for closed forests, open forest and woody savannah areas. It is not clear
in Zhu et. al. (1999) whether the spectral unmixing and scaled NDVI approach was applied to
monthly or the initial 10-day composites.
To provide the least atmospherically affected result, final percent forest cover was determined over
the course of the year based on the maximum monthly forest cover value achieved, regardless of the
methods chosen (mixture analysis or scaled NDVI). In addition to the derived forest cover map,
results also include look-up tables linking the USGS seasonal land cover classes to the six-category
map legend. Comparisons with the FAO forest resource information system are also made at the
country level to guide the mapping work. However, it was not the objective of this effort to calculate
forest area statistics based on the digital map, recognizing that, with limited spatial and spectral
resolution, the data are best suited for showing where major forested areas are at small scales, rather
than quantity of forests.
5.3.5 Validation and Accuracy
Most of the seasonal land cover classes from the IGBP database were translated well into the FAO
categories. For South America, for example, only 34 out of 167 seasonal classes needed further
processing under the second step. Accuracy assessment has been conducted for the IGBP land cover
database at the IGBP 17-class level based on a set of Landsat and SPOT scenes, as reported in the
previous section. Validation of the FAO forest cover map is believed to be forthcoming, and should
be of a higher accuracy since there are fewer classes used in the separation (this author’s opinion
only)
The IGBP global land cover database is a convenient mapping framework, from which a new, and
different, global forest cover map is derived. Factors affecting quality of the derived global forest
cover map include misclassification errors from the IGBP product, errors of translation to the FAO
legend, data quality problems, and accuracy of the linear unmixing approach.
24
5.3.6 Coverage, Resolution and Availability
Presumably as with the IGBP land cover classification.
5.3.7 Problems Or Constraints In Use
Since the FAO global forest cover map is derived from the same data and largely with the same
methodology as employed in the IGBP land cover classification, the caveats associated with that data
and methodology also apply here.
5.4
TREES (Tropical Ecosystem Environment observation by Satellites)
5.4.1 Purpose
The TREES project has several objectives and goals, including:
-
The compilation of a comprehensive pan-tropical forest map at about 1: 1million scale (or
1km resolution);
The study of seasonality effects in tropical forests as evidenced in relatively long-term series
of satellite-derived information;
The modelling of deforestation processes and dynamics;
The investigation of the usefulness of new sensors for topical forest mapping and monitoring,
in particular the potential of Synthetic Aperture Radar (SAR) and the Along-Track Scanning
Radiometer (ATSR) flown onboard the ERS-1 satellite.
Of most interest in this context is the global tropical forest inventory from NOAA AVHRR at a
nominal spatial resolution of 1km.
5.4.2 Legend
After detailed reviews and preliminary examinations of spatial, spectral and temporal characteristics
of NOAA AVHRR imagery over tropical forest areas, it was felt that a meaningful, appropriate and
useful legend for the pan-tropical map should be based essentially on two parameters that were felt to
be determinable with a high degree of reliability for all tropical areas: the amount of forest cover
within 1km pixels, and the phonological character of the forest area overall. Based on these two
criteria, the following five classes, on which the global TREES legend is based, were identified:
-
Intact evergreen forest; to include areas of more than 70% forest cover mainly of an evergreen
nature;
Fragmented evergreen forest; to include areas of between 40% and 70% forest cover, mainly
of an evergreen nature;
Intact seasonal forest; to include areas of more than 70% forest cover mainly of a seasonal
nature;
Fragmented seasonal forest; to include areas of between 40% and 70% forest cover, mainly of
a seasonal nature;
Non-Forest; to include all other areas of less than 40% forest cover.
Fore some areas, under certain conditions, more detailed classes can sometimes be determined (e.g.
mixed forest/savannah in Africa, mixed deciduous or semi-evergreen and dry dipterocarp forests in
25
continental South-EastAsia, etc.). For continental-level maps published at 1:5million scale, the
legend above is merged with other vegetation maps to provide more detailed classes (see Eva et. al.,
1999; Mayaux et. al., 1999 and Achard et. al., 1999).
5.4.3 Data Sources
In the TREES project, the selection, processing and subsequent classification of the AVHRR imagery
was considered in a convenient number of “image windows”, which provided a crude first level
stratification based on consideration of the prevailing ecological and climatological conditions which
ultimately control the type of existing vegetation and likely acquisition of good quality imagery.
In all areas, channels 1 – 4 of the full spatial resolution NOAA AVHRR imagery were used. About
300 single-date images for South-East Asia, 150 – 200 for Central and West Africa and over 150
images for South and Central America were used in the first round of classifications. Considerable
time was spent at local receiving stations examining archives and only selecting images which had
the least cloud and atmospheric contamination. Where possible images that were as close to nadir as
possible, and which allowed a representative sample of data over dry seasons where these occurred
were chosen in priority (Malingreau et. al., 1996). Most of the data were collected during 1992 –
1994.
5.4.4 Classification and Mapping Methodology
In most cases individual images or parts of images were classified using 3 – 4 channels of data and
NDVI in an unsupervised approach. The resulting spectral classes were then assigned to a class on
the legend after detailed comparison with ancillary data such as (i) high spatial resolution imagery;
(ii) published vegetation maps; (iii) ecoregions and other published information, held in the
supporting Tropical Forest Information System (TFIS). In areas of seasonal forest formations, multitemporal classification was used.
5.4.5 Validation and Accuracy
The TREES project has adopted a multi-scale, multi-sensor approach for validation of the AVHRRderived classification, and subsequent calibration of area measurements therein. For this purpose, a
number of classifications based on higher spatial resolution imagery (Landsat TM or SPOT) were
commissioned at a sample number of locations. The process of validation as defined in the TREES
project, involved qualitative comparison of the class assignments made on the AVHRR-derived
classification, with the classifications made from high spatial resolution imagery, which in turn was
normally assisted with fieldwork. This qualitative comparison assisted in the improved labelling of
automatically derived AVHRR spectral classes.
Once the AVHRR classification was completed, a more rigorous quantitative comparison and
subsequent correction (called calibration) of forest area measurements was made. This involves the
comparison of AVHRR-based and TM- or SPOT-based forest/non-forest results over a number of 15
x 15km areas evenly distributed over the high spatial resolution image extent (see section 6).
5.4.6 Coverage, Resolution and Availability
TREES results cover most of the pan-tropical area (except for some areas where tropical forest was
not thought to be of major interest, e.g. the Indian sub-continent, China, East Africa). The results are
available at 1km resolution, and because the extents of the assigned classes was made on
26
interactively-processed single-date imagery, the geometric accuracy is likely to be within 1 – 2km.
Continental maps have been published at a scale of 1: 5 million for Latin America (Eva et. al., 1999);
West and Central Africa (Mayaux et. al., 1999) and South-East Asia (Achard et. al., 1999) and these
have been widely disseminated. A CD-Rom outlining the project and some of its results has also been
widely disseminated. More details are available on:
http://www.trees.gvm.sai.jrc.it/
5.4.7 Problems Or Constraints In use
The TREES project has only compiled data for the pan-tropical area, and within that has also
excluded some areas such as India, China and East Africa. Its chosen legend also differs from other
projects. Finally, the data used were mostly from 1992 – 1995, so the maps may need updating.
5.5
FIRS – Forest Information from Remote Sensing: A forest information
system for the entire European continent
5.5.1 Purpose
The FIRS project has a number of objectives and foundation actions (Kennedy and Folving, 1994).
Of most interest here are the objectives:
•
•
To produce a standardised European Forest Map at a scale of 1: 1 million to provide a general
overview for small scale monitoring
To develop methods and models for mapping and monitoring the dynamics of forested areas
using NOAA AVHRR data.
5.5.2 Legend
A simple forest / non-forest legend was used for the initial phases of the work (Roy 1997). In a
subsequent phase, an assignment of the probability of forest cover was also assigned to each 1km
pixel (Hame et. al. 1999).
5.5.3 Data Sources
For the first map, maximum value monthly composite NDVI and surface temperature images were
used for an 8-month period (March – October 1993). The probability map was based on a composite
made from 49 individual images acquired between June and September 1996.
5.5.4 Classification and Mapping Methodology
In the derivation of the first map, AVHRR data in each of 82 forest strata were classified separately.
Where sufficient cloud-free data were available, all 8 NDVI and surface temperature data samples
were used. Otherwise the best 3 months were selected. In cases where cloud cover proved to be very
persistent, the nest NDVI and surface temperature image was used independently. Training and
verification sample pixels were chosen using various reference data (Roy 1997) and maximum
likelihood classification was used.
In the derivation of the probability-based map, an unsupervised clustering program (Hame et. al.
1998) was run to automatically select training samples consisting of 2 x 2 pixels representing
27
homogeneous ground targets in the red /near-infrared mosaic, after water pixels were removed. Then,
a maximum likelihood classification method with equal a priori probabilities was used to purify the 2
x 2 pixel blocks in each cluster. For each remaining 2 x 2 pixel block of each cluster, the probability
of forest was computed by reference to the CORINE land cover database (Kennedy et. al., 1999).
Thus the final value of forest cover assigned to each cluster was obtained by calculating the mean of
the values from its sample components.
5.5.5 Validation and Accuracy
For the first forest/non-forest map accuracy assessment was made by comparison with reported forest
percentages by EUROSTAT (Roy 1997). Small scale comparison of the forest map with
independently collected forest statistics gave a very high level of agreement (R-squared 0.88 to 0.91)
for France and Germany when forest map misclassification biases were taken into account.
The application of the AVHRR-based probability mapping method, and the accuracy of the derived
information is clearly strongly dependent on the quality and reliability of ground data used as well as
the cleanness of the satellite data (Kennedy et. al. 1999). On quantitative evaluation of the results, it
was apparent that the forest area was generally underestimated, especially in France and the
Mediterranean region (Hame et. al. 2000).
5.6
Other Forest Cover Maps
As well as the major projects listed above, there are a few other notable efforts in vegetation, and in
particular, forest cover mapping using coarse spatial resolution data. These include:
•
•
•
Vegetation of South America, by Stone et. al. (1994);
Forest cover mapping for Mexico, by Eggen-McIntosh et. al. (1992);
AVHRR-based forest mapping for West Africa , by UNEP/GRID (1991);
5.7
Summary
A number of projects that sought to utilise coarse spatial resolution imagery (mainly NOAA
AVHRR) for forest mapping and monitoring have been reviewed. Earlier efforts consisted of close
examination and interactive identification of forest boundaries from single-date imagery over quite
small areas. As computing power and the size of datasets increased, increasingly automated methods
over large areas have been implemented.
In most cases, “acceptable” maps of forest extent have been produced as scales of 1: 1 million to 1: 5
million. On qualitative comparison, these seem to depict the extent of the forest biomes quite well,
and in many cases, better than any other existing dataset. They certainly seem useful for stratification
of forest areas, perhaps to identify areas where more detailed investigation is necessary. However,
because the different results were obtained with very different legends and for different purposes, it is
very difficult to make any quantitative comparisons.
Accuracy assessment has mostly consisted of peer review, comparison with other data sources, and
comparison with classification of higher spatial resolution imagery from a sample set of sites. In
general, all show that forest cover estimates derived from AVHRR are underestimated if the forest is
28
fragmented and generally overestimated if the forest cover is homogenous and uniform over large
areas (D’Souza et. al., 1996; Kuusela and Paivinen, 1995).
Repeat classifications using the same methodology have rarely been tried, so it is difficult to assess
the repeatability of each classification. Therefore we cannot say much about the usefulness of the
reviewed methods for forest extent or condition monitoring/change detection. However, given the
difficulties in geometric, spectral, classification methodologies, etc. probably only large and marked
changes (of about 3 – 5km, and of considerable spectral transformation) would be detectable on an
operational basis. Most operational monitoring projects (e.g. TREES-II) have gone to a phase of
“hot-spot” detection, where apparent changes in AVHRR reflectance response are combined with
other evidence such as the occurrence of forest fire (Achard et. al. 1998).
29
6
Combining high and low spatial resolution classifications
As seen from the reviews above, NOAA AVHRR data have been used operationally for providing
global scale land-cover maps. However, the estimation of land-cover proportions from these maps
will be associated with a systematic bias due to spatial aggregation effects, especially in areas where
the amount of spatial fragmentation is high (Nelson 1989; Gervin et. al., 1985). For this reason, a
number of researchers have indicated that NOAA AVHRR data should be used in conjunction with
data from higher spatial resolution data for mapping and monitoring purposes (e.g. Nelson and
Holben 1986; Van Roessel 1990; Jeanjean and Achard 1997)
We have already discussed the use of higher spatial resolution data for calibrating and validating
various NOAA AVHRR sub-pixel classifications (see section 4.5). In this section we review the
methods suggested for the use of image classifications from high spatial resolution data to improve
the results of the classifications obtained at coarse spatial resolution. A few studies have applied a
simple “regression estimator” approach. This involves relating the fine scale forest cover directly to
the coarse scale forest cover (Nelson 1989; Stone and Schlesinger 1990; Amaral 1992; Stibig 1993).
Forest/non-forest proportions are determined over blocks of AVHRR-size pixels, and related to the
forest/non-forest proportions for the corresponding areas on the fine scale classifications. A simple
linear regression is sought and then applied inversely to the whole of the AVHRR classification to
correct for the estimation bias. However, the regression parameters depend heavily on the type of
landscape analysed. When calculating the simple regression over large areas, ignoring the effect of
spatial organisation leads to a large instability of the results (Nelson 1989).
In more recent work, Mayaux and Lambin (1995) showed that different regressions were obtained
between Landsat TM-based and AVHRR-based forest percentages when the spatial pattern of the
AVHRR-based forest/non-forest classification was taken into account. Mayaux and Lambin (1995)
used AVHRR forest/non-forest classifications and Landsat TM classifications from 13 sites within
the TREES project area. They aggregated forest proportion amounts in 13 x 13 km blocks, and used a
simple spatial index (the Matheron Index as defined by Kleinn et. al., 1993) in a two-step correction
function. The various stages of the two-step correction process were to:
− Geometrically co-register fine and coarse resolution data
− Overlay of a grid of contiguous pixel blocks. The block size selected is dependent on the
accuracy of co-registration. Kleinn et. al. (1993) demonstrated in simulation studies that,
assuming a misregistration of Landsat TM and AVHRR data of around two pixels, the
influence of misregistration on the calculation of proportional errors for block sizes of 13 x 13
AVHRR pixels becomes negligible. If better registration can be achieved, smaller blocks may
be used.
− Measure in each pixel block: (i) the forest cover at fine resolution; (ii) the forest cover at
coarse resolution; (iii) the spatial pattern (e.g. Matheron Index) at coarse resolution
− Partition the population of pixel blocks in equal-sized subsets of similar spatial patterns
− Make First step regressions: Linear regressions within each subset between forest cover
proportions at fine and coarse resolution. The observations are the pixel blocks.
− Make Second step regressions: Regressions between the spatial measure and (i) the intercept
and (ii) the slope of the first-step regression. The observations are the subsets of the blocks.
Mayaux and Lambin (1995) were able to conclude that the integration of spatial information in a
correction model significantly improves the retrieval of proportions of cover types from coarse
resolution data compared with a simple correction function relating directly proportions at coarse and
30
fine scale resolutions. This was true even is a simple spatial measure such as the Matheron Index was
applied to the coarse resolution classification.
In a subsequent study, Mayaux and Lambin (1997) improved the two-step correction model by
including textural measures derived from the coarse resolution data (e.g. minimum local variance for
3 x 3 AVHRR channel 2 pixels) instead of the Matheron Index. In a detailed study that tested a
number of hypotheses they were able to show that:
−
−
−
−
−
Area estimates of land-cover categories should not be extracted directly from coarse spatial
resolution classifications because of the biases inherent in assigning a single class to a large
pixel area;
The correction of broad scale maps for the proportional errors introduced by spatial scaling
drastically improves the reliability of the estimation of forest-cover areas;
If only AVHRR data are available, the two-step procedures integrating around pixel texture
with proportions estimated from a coarse resolution classification gave an equivalent
performance to a mixed pixel regression-based estimator. The mixed pixel estimator,
however, did not require the aggregation to larger block sizes (of say 9 x 9) but was
dependent on the application of a suitable bulk correction for atmospheric effects;
The two-step calibration procedure required a very large sample of high spatial resolution
data since it depended on two levels of nested regressions;
A more advanced concept of a land-cover map should consist of a multilayered database
representing, for every layer, the calibrated proportion of a given land-cover class in a coarse
resolution sampling unit. Or, pixel’s attributes should be vectors of within-pixel land-cover
proportions rather than single land-cover categories.
31
7
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41
FRA Working Papers
0.
How to write a FRA Working Paper (10 pp. – E)
1.
FRA 2000 Terms and Definitions (18 pp. - E/F/S/P)
2.
FRA 2000 Guidelines for assessments in tropical and sub-tropical countries (43 pp. - E/F/S/P)
3.
The status of the forest resources assessment in the South-Asian sub-region and the country capacity
building needs. Proceedings of the GCP/RAS/162/JPN regional workshop held in Dehradun, India, 8-12
June 1998. (186 pp. - E)
4.
Volume/Biomass Special Study: georeferenced forest volume data for Latin America (93 pp. - E)
5.
Volume/Biomass Special Study: georeferenced forest volume data for Asia and Tropical Oceania (102
pp. - E)
6.
Country Maps for the Forestry Department website (21 pp. - E)
7.
Forest Resources Information System (FORIS) – Concepts and Status Report (20 pp. E)
8.
Remote Sensing and Forest Monitoring in FRA 2000 and beyond. (22 pp. - E)
9.
Volume/Biomass special Study: Georeferenced Forest Volume Data for Tropical Africa (97 pp. – E)
10. Memorias del Taller sobre el Programa de Evaluación de los Recursos Forestales en once Países
Latinoamericanos (S)
11. Non-wood forest Products study for Mexico, Cuba and South America (draft for comments) (82 pp. – E)
12. Annotated bibliography on Forest cover change – Nepal (59 pp. – E)
13. Annotated bibliography on Forest cover change – Guatemala (66 pp. – E)
14. Forest Resources of Bhutan - Country Report (18 pp. – E)
15. Forest Resources of Bangladesh – Country Report (89 pp. – E)
16. Forest Resources of Nepal – Country Report (under preparation)
17. Forest Resources of Sri Lanka – Country Report (under preparation)
18. Forest plantation resource in developing countries (75 pp. – E)
19. Global forest cover map (14 pp. – E)
20. A concept and strategy for ecological zoning for the global FRA 2000 (23 pp. – E)
21. Planning and information needs assessment for forest fires component (32 pp. – E)
22. Evaluación de los productos forestales no madereros en América Central (102 pp. – S)
23. Forest resources documentation, archiving and research for the Global FRA 2000 (77 pp. – E)
24. Maintenance of Country Texts on the FAO Forestry Department Website (under preparation)
25. Field documentation of forest cover changes for the Global FRA 2000 (under preparation)
26. FRA 2000 Global Ecological Zones Mapping Workshop Report Cambridge, 28-30 July 1999 (53 pp –E)
27. Tropical Deforestation Literature:Geographical and Historical Patterns in the Availability of
Information and the Analysis of Causes (17 pp. – E)
28. World Forest Survey – Concept Paper (under preparation)
Please send a message to [email protected] for electronic copies or download from
http://www.fao.org/FORESTRY/FO/FRA/index.jsp
42