AVHRR-based forest proportion map of the Pan-European area
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AVHRR-based forest proportion map of the Pan-European area
Tuomas Hamea,*Pauline StenbergbKaj Anderssona
Yrjo RausteaPamela KennedycSten FolvingcJanne Sarkealad
aVTT Automation, Box 1304, FIN-02044 VTT, FinlandbUniversity of Helsinki, Helsinki, Finland
cSpace Applications Institute, Joint Research Centre, ItalydStora Enso Forest Consulting Ltd., Finland
Received 30 December 1999; received in revised form 23 January 2001; accepted 27 January 2001
Abstract
A methodology was developed and applied to estimating forest area and producing forest maps. The method utilizes satellite data and
ground reference data. It takes into consideration the fact that a pixel rarely represents any single ground cover class. This is particularly true
for low-spatial-resolution data. It also takes into consideration that the spectral classes overlap. The image was first classified using an
unsupervised clustering method. A (multinormal) spectral density function was estimated for each class based on the spectral vectors
(reflectance values) of the cluster members. Values of the target variable — the proportion of forested area — were determined for the
spectral classes using sampling from CORINE (Coordination of Information on the Environment) Land Cover database. Each pixel was
assigned class membership probabilities, which were proportional to the value of the density function of the respective class evaluated at the
spectral value of the pixel. The estimate of forest area for the pixel was finally computed by multiplying the class membership probabilities
by the class forest area and summing over all the classes. The method was applied over a mosaic of 49 Advanced Very High Resolution
Radiometer (AVHRR) images acquired from the National Oceanic and Atmospheric Administration (NOAA)-14 satellite. The estimated
forest areas were compared with those extracted from the full-coverage CORINE data and with official forest statistics reported to the
European Commission’s Statistical Office (EUROSTAT). The forest percentage (proportion of forest area of the total land area) of 12
countries of the European Union was underestimated by 1.8% compared to the CORINE data. It was underestimated by 4.2% when
compared with EUROSTAT’s statistics and 6.0% when compared to United Nations Economic Commission for Europe/Food and
Agricultural Organization (UN-ECE/FAO) statistics. The largest underestimation of forest percentage within a country (compared to
CORINE) was in France (5.9%). The largest overestimation was found in Ireland, 15.6%. D 2001 Elsevier Science Inc. All rights reserved.
1. Introduction and objective
The forestry land in Europe is around 195 million ha.
This comprises 77% of forestland (sites capable of growing
closed stands) and 23% other wooded land (sites capable of
growing only scattered and stunted trees and bushes).
Forestland amounts to approximately 149 million ha, of
which 133 million ha are classified as being exploitable (i.e.,
where wood production is the major function). Unexploit-
able forest amounts to about 16 million ha and consists
largely of national parks, other nature conservation areas,
and areas in the mountains where wood production is not
profitable. The area of other wooded land is 46 million ha,
of which 36 million ha are located in the Mediterranean
region (Kuusela, 1994). It is predicted that Europe’s forest
area could increase by a further 5 million ha in the coming
20 to 30 years due to afforestation (e.g., on abandoned
agricultural land) (Kuusela, 1994).
Long-term economic utilization of forests requires that
they should be sustainably managed. Apart from being a
primary source of raw material, there is an increasing need
for geo-referenced information to monitor the condition and
function of forests and their role in carbon dynamics
(Kauppi, Mielikainen, & Kuusela, 1992). Information on
forest habitats should be improved, in order to identify
priority actions for protecting biodiversity at regional levels.
The forestry statistics archived by the European Com-
mission’s Statistical Office (EUROSTAT, 1998) and by
United Nations Economic Commission for Europe/Food
and Agricultural Organization (UN-ECE/FAO) are derived
0034-4257/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved.
PII: S0034 -4257 (01 )00195 -X
* Corresponding author.
E-mail address: [email protected] (T. Hame).
www.elsevier.com/locate/rse
Remote Sensing of Environment 77 (2001) 76–91
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from national enquiries but using independent procedures.
Data are collected from national correspondents by means
of a questionnaire and adjusted to approximate interna-
tional definitions.
A methodology to produce satellite image-based maps on
forests at a European level was developed in the Coordina-
tion of Information on the Environment (CORINE) Land
Cover Project that started in 1985 (EEA Task Force, 1992).
The CORINE Land Cover, obtained from satellite data
[mainly Landsat Thematic Mapper (TM)], is a digital geo-
graphical database describing vegetation and land use in 44
classes in vector format at an equivalent map scale of
1:100,000. Today, the CORINE Land Cover database covers
31 countries across Europe and North Africa. The next
CORINE mapping will be made during the period 2000–
2003. The CORINE database includes three classes of forest:
coniferous, deciduous, and mixed forest. The database has
been generalized so that areas smaller than 25 ha are not
considered but merged to the dominant land cover class.
The utility of low spatial resolution satellite data, partic-
ularly those acquired by the Advanced Very High Resolu-
tion Radiometer (AVHRR), for land cover mapping at
continental to global scales has been widely demonstrated
(e.g., Cihlar, Ly, & Quinghan, 1996; Eidenshink, 1992;
Lambin & Erlich, 1996; 1997; Malingreau & Belward,
1994; Townshend, 1994).
The most commonly used spectral feature in AVHRR-
based vegetation and forest mapping has been the Normal-
ized Difference Vegetation Index (NDVI) or the simple ratio
of near infrared (NIR) to red. The seasonal trajectories of
NDVI have also been applied in forest and land cover
classification (e.g., DeFries & Townshend, 1994; Gaston
et al., 1994; Lambin & Ehrlich, 1996; Loveland, Merchant,
Ohlen, & Brown, 1991; Myneni, Keeling, Tucker, Asrar, &
Nemani, 1997; Nemani & Running, 1996; Roy, 1997; Roy,
Kennedy, & Folving, 1997; Running, Peterson, Spanner, &
Teuber, 1986).
In the United States, forest vegetation has been mapped
into 25 forest type groups using Landsat TM and AVHRR
data (Zhu & Evans, 1994). The forest area was mapped by
computing regression models in which the AVHRR spectral
bands were the predictor variables and Landsat-derived
forest/nonforest maps were the predicted variables (Iverson,
Cook, & Graham, 1989). The mapping was made in a
stratified manner, using 15 physiographic regions covering
the Unites States.
Awall-to-wall map and digital database of forest cover of
the tropical belt are key products from the Tropical Ecosys-
tem Environment observation by Satellite (TREES) Project
(D’Souza & Malingreau, 1994; Malingreau et al., 1995). In
other studies of tropical forests (e.g., Malingreau & Tucker,
1988; Skole & Tucker, 1993), emphasis has been more on
monitoring deforestation, whether caused by logging or
forest fires (e.g., Belward, Kennedy, & Gregoire, 1994;
Kaufman, Setzer, Justice, Tucker, & Fung, 1990), rather
than on forest mapping.
A map from the Pan-European forests was prepared to
promote the International Space Year in 1992. This product,
derived from the National Oceanic and Atmospheric
Administration-AVHRR (NOAA-AVHRR) data, had a
reported classification accuracy of 82.5% (Hausler, Sara-
deth, & Amitai, 1993). Another map of Europe’s forests
forms part of the International Geosphere–Biosphere Pro-
gramme’s (IGBP) database, an exercise carried out to map
the land cover of the globe (Townshend, 1992).
More recently, methodologies for forest mapping at
continental scales have been within the framework of the
Forest Monitoring in Europe with Remote Sensing Project
(FMERS). Imagery from several instruments including
Landsat TM, SPOT, Indian Remote Sensing Satellite–Wide
Angle Field Sensor (IRS-WiFS), and European Remote
Sensing Satellite-Synthetic Aperture Radar (ERS-SAR)
were tested (Hame, Andersson, et al., 1998). A forest map
covering most of the European Union was produced using
stratified unsupervised clustering of a calibrated WiFS
image mosaic. In the Finnish national forest inventory,
satellite imagery from Landsat TM and Systeme pour
l’Observation de la Terre (SPOT) are now used operation-
ally. The method is based on a combination of a systematic
ground sample grid and satellite images, using the k-nearest
neighbor approach (Kilkki & Paivinen, 1987; Tomppo,
1991). The procedure resembles the method of Poso,
Paananen, and Simila (1987).
The most serious drawbacks of the existing satellite
image-based forest maps over Europe are firstly, that the
information is often binary, i.e., forest/nonforest. Secondly,
due to the small and fragmented forest areas common
throughout temperate and Mediterranean Europe, simple
detection and discrimination of the forest cover with low
spatial resolution satellite data becomes problematic. The
general tendency is that forest area is underestimated if the
forest is fragmented and overestimated if the forest cover is
homogeneous and uniform over large areas (Kuusela &
Paivinen, 1995). Finally, the accuracy and reliability of
existing European forest data sets have not been estimated
using statistically sound procedures.
Studies investigating alternative techniques to the con-
ventional image classification have included regression
analysis and mixture modeling. Regression analysis has
been applied to forest area estimation using AVHRR data
(e.g., DeFries et al., 1997; Iverson, Cook, & Graham, 1994;
Zhu & Evans, 1994). It has also been proven effective for
biomass estimation of coniferous forests, but less successful
where the target area includes a mixture of conifers and
broad-leaved trees. This is because the reflectance of the
NIR of broad-leaved trees is 1.5 times to twice as high as
that of conifers (Hame, Salli, Andersson, & Lohi, 1997).
Classification techniques applied to coarse resolution
satellite data such as the AVHRR have the inherent problem
that pixels seldom belong exclusively to any distinct ground
class (Foody, Campbell, Trodd, & Wood, 1992). Spectral
mixture modeling that allows a pixel having components
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from several classes has proven to be effective in vegetation
mapping. The benefit of the mixture modeling has been
particularly apparent with coarse resolution data, where
several different surface components may occur within an
image pixel (e.g., Cross, Settle, Drake, & Paivinen, 1991).
Most often, the mixing of the classes is assumed be linear,
but also nonlinear approaches have been tested (Foody,
Lucas, Curran, & Honzak, 1997). A major difficulty in
applying a mixture model is the accurate selection of
spectral end-members, which is critical for the model
performance. Global mapping of percent woody cover,
herbaceous vegetation, and bare soil has been performed
using linear mixture analysis to the 8-km AVHRR database.
The mapping resulted in fair agreement with other land
cover data sets (DeFries, Hansen, & Townshend., 2000).
The objective of this study was to develop a method for
producing a Pan-European raster database where forest
proportion (FP) is estimated for each pixel. The database
could also be referred to as a forest probability database,
because the value of the pixel gives the probability that a
randomly selected point within the pixel falls in forest.
In the presented method, the estimate of FP for a pixel is
obtained as a probability-weighted average of the propor-
tions of forest area within the different spectral classes.
Unlike the traditional classification approach, where pixels
would be assigned the value of the ‘most probable’ class,
pixels are not classified but represent different classes with
different probabilities. The method resembles spectral mix-
ture modeling in-so-far-as it takes into account that a pixel
may have components from several spectrally distinct
classes. However, it does not require pure ‘end members’
(e.g., forest/nonforest classes). Instead, the classes represent
different values (ranges) of the proportion of forest area. The
maximum number of variables to be estimated is not related
to the number of the spectral channels, either.
2. Materials
2.1. Satellite data
Imagery from theAVHRR instrument onboard theNOAA-
14 satellite was selected as the reference satellite data. Such
data remain the most economically viable for use over the
Pan-European area. NOAA-14 was chosen as its overpass
time is around noon at local time. NOAA-12 data were
rejected on the basis of the early morning or late afternoon
overpass, meaning that reliable atmospheric corrections can-
not been applied due to the low sun elevation angles.
The year 1996 was chosen as the target year. It was
deemed recent enough to reflect the current situation in
European forests considering the scale of the study. The
reason for the selection of 1996 was also practical, since a
great number of images was already available from an
earlier study. One scene from 1997 was selected to reduce
the cloud-covered area. Some mountainous areas, such as
the Carpathians remained partially cloud-covered. The red
and NIR channels of the AVHRR instrument of the NOAA-
14 satellite were chosen as the input spectral data. The
original channels were used instead of the commonly used
NDVI, because the NDVI has not been proven effective in
estimating biomass, nor discriminating forest from other
land cover types (Hame et al., 1997; Hame, Salli, & Lahti,
1992). The surface temperature was considered to vary too
much from day to day to make the thermal channel an
applicable spectral feature for this study. Thermal data were
utilized for cloud masking.
The images were selected from the most stable part of the
growing season when the leaves and new needles are fully
developed, but before the initiation of the discoloration and
loss of foliage in the autumn. Utilization of seasonal data
would also have been possible but unrealistic because the
applied image interpretation methodology required that
spectral data would have been available for every pheno-
logical stage across the whole Pan-European area. Seasonal
and cloud-free image data would have been very difficult to
collect (Kasischke & French, 1997). Furthermore, the pos-
sible seasonal range would have varied from 9 months in the
southernmost areas to some 3 to 4 months in the northern-
most borders (DeFries et al., 2000). This is because the
presence of snow complicates optical image interpretation
(Hame, 1991; Oke, 1987).
An approach was taken in which calibrated images are
compiled to a single image mosaic, covering the whole
study area. Making the forest area estimation on an image
mosaic (instead of using individual images) was consid-
ered a more practical procedure than the traditional image
by image approach. The estimation of forest area on an
image by image basis would have been very time con-
suming and problematic as large proportion of images
were cloud-covered and thus unusable. Independent image
estimates would further require that each image be inde-
pendently validated.
The image mosaic was compiled using the raw AVHRR
data. This was carried out in preference to using an existing
mosaic (e.g., the 1-km data set from the EROS Data Center),
in order to better follow and understand each step of the
processing chain.
2.2. Ancillary data
For geocoding, the images, aeronautical and tactical
pilotage charts, and tourist maps at a scale of 1:400,000 to
1:1,000,000 were purchased. The maps covered the Euro-
pean continent. In addition, topographic map sheets at a
scale of 1:50,000 were available for the Finnish territory.
Map sheets at a scale of 1:1,000,000 were used only in those
areas where appropriate maps of larger scales were not
available (i.e., Norway, Spain).
The CORINE Land Cover data represented the ground
data and thus the definition of forest in this study follows the
CORINE nomenclature. Unfortunately, the definition of
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forest in CORINE is rather vague (area dominated by trees)
(CORINE Land Cover, 1993). This, plus the fact that the
map has been generalized and its reliability is not com-
pletely validated decrease the value of the CORINE as a
reference data set. Despite its known weaknesses, the
CORINE was found the best available database covering a
large part of the target area. CORINE data were taken as the
ground reference data without any modification.
3. Methodology
3.1. Image mosaic compilation
The image mosaic was compiled using 49 images that
covered the entire target area. The images were acquired
from four different sources: (1) Dundee Satellite Receiving
Station, (2) Tromso Satellite Station, (3) Finnish Meteoro-
logical Institute, and (4) Satellite Active Archive of NOAA.
3.1.1. Preprocessing
Image preprocessing consisted of the following tasks:
� image extraction from the distribution media;� computing or extracting a tie point grid with sun and
satellite angles;� radiometric corrections;� cloud masking;� geometric corrections for individual images.
3.1.2. Radiometric correction
Raw digital counts were first converted to TOA (top of
atmosphere) reflectance using time dependent calibration
coefficients that are published monthly by NOAA. These
reflectance values were then converted to atmospherically
corrected top-of-canopy reflectance by using the SMAC
(simplified method for atmospheric correction) algorithm
(Rahman & Dedieu, 1994). The aerosol optical depth of the
atmosphere was found to be the most significant variable
affecting this algorithm. Reflectance values were computed
for several images using different aerosol optical depth
values. The reflectance over mature coniferous forests was
evaluated. A value of 0.1 at 550 nm (unit-less) was found to
be the most appropriate. It produced reflectance values of
2% to 4% for mature forests in the red and 15 to 25% in the
NIR. Usually a large AVHRR pixel includes several vege-
tation types whereas one homogeneous forest stand has
many Landsat TM pixels. Thus, the reflectance in areas,
dominated by mature forests, can be one percentage unit
higher than the reflectance of individual mature forest stands
in a Landsat image. Finally, a BRDF (bidirectional reflec-
tion distribution function) correction was applied. The
BRDF method was based on the Roujean model with forest
surface parameters presented by Wu, Li, and Cihlar (1995).
The images were normalized to a nadir view with a solar
zenith of 45�. The atmospheric correction was applied to
every pixel.
A threshold using red and thermal channels was defined
interactively for each image to mask out the clouds. A pixel
was masked if the intensity in the red channel was higher
than the selected reflectance limit and the intensity in the
thermal channel 4 was lower than the temperature limit.
Cloud shadows were removed from the rectified images by
masking out approximately 3.5 km north from a detected
cloud. Threshold values were strict to remove also thinner
clouds. The maximum NDVI criterion was not used in cloud
screening since several overlapping images were used to
define the reflectance of the image mosaic.
3.1.3. Geocoding
A procedure developed by Andersson (1999) was applied
for image geocoding. First, an image mosaic covering the
research area was compiled to a rectangular latitude/longi-
tude coordinate system using eight manually selected
AVHRR scenes. This image mosaic served later as a
reference map for other images. Second, all the raw AVHRR
images were rectified to this coordinate system by using the
orbital parameters of the NOAA satellite. Using the refer-
ence mosaic, control points were selected automatically for
the images that had been rectified using the orbital param-
eters. In the final stage, all the images were rectified again
starting from the raw data. Thus, each image had only one
resampling phase. The pixel size was set to 0.006�N and
0.01�E. This was equivalent to about 700 m in the N–S
direction and 400–1000 m in the E–W direction, depending
on the latitude. An affine transformation with the nearest
neighbor resampling was applied in the computations. The
maximum satellite scan angle was limited to 45� by masking
300 pixels from the image edges. The reason for eliminating
the edges was that the atmospheric corrections do not
perform well at high scanning angles. Furthermore, the
pixel size on the ground is over 4 km at the image edge,
whereas it is around 2 km when the 300 pixels furthest from
nadir are excluded.
The final mosaic was compiled from radiometrically and
geometrically corrected images, by patching them together
using their geo-coordinates (Fig. 1). Clouds and cloud
shadows had been masked earlier from individual images
and, in cases of overlapping images, the mean reflectance
value was inserted. Computation of the mean reflectance of
several overlapping spectral measurements (pixels) was
considered to reduce the random noise in the images
compared to an alternative of using the reflectance of a
pixel from a single image. When the maximum NDVI
criterion was tested in image mosaic compilation, it was
noticed that two rather different combinations of red and
NIR could give the same NDVI. This resulted in large
radiometric variation spatially in the mosaic of original
spectral bands, since the neighboring pixels could be from
different images. Furthermore, the maximum NDVI crite-
rion would favor pixels from early summer images on
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agricultural areas, whereas in the forested regions selection
of the images would likely vary more.
The maximum number of overlapping images was 21. In
most locations, there were three or four overlapping images
but in Scandinavia their average number was around 10. No
geometrical shifts or radiometric differences between
images could be seen in the mosaic. Despite the relatively
high number of images, some mountainous and arctic areas
remained cloud-covered.
3.2. Image interpretation
The image interpretation algorithm used the reflectance
mosaic as input. It produced a value of the target variable FP
for each pixel of the study area (Fig. 2).
3.2.1. Image clustering
Image clustering was carried out using an unsupervised
clustering method that was originally developed for change
detection purposes. In the method, an automatically selected
training sample consisting of the spectral means of 2� 2
pixels is clustered in a predetermined number (N) of
clusters, using the k-means algorithm. These 2� 2 pixel
groups are assumed to represent homogeneous ground
targets. The criterion for the homogeneity was the length
of the standard deviation vector of the reflectance values of
a 2� 2 pixel group compared to the standard deviation
vector of the whole image. The applied standard deviation
of a group varied from 2% to 6% of the deviation of the
whole image. This resulted in 2% to 10% samples from the
image pixels to be included in the clustering. The clusters
Fig. 1. Red band of the image mosaic in Lambert equal area projection. The size of the area is 4800� 4000 km2.
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were sorted using their red reflectance. The cluster with the
lowest red reflectance was assigned cluster code 1, whereas
the cluster with the highest red reflectance was assigned the
highest cluster code, i.e., the predefined total number of
clusters. The details of the clustering method are given in
Hame, Heiler, and San-Miguel Ayanz (1998). The spectral
mean vectors of the 2� 2 pixel groups, selected for the
clustering are from here on referred to as ‘observations’.
The clustering was performed on the image mosaic with
and without a geographic stratification. This meant that it
was performed on the whole Pan-European mosaic, and
separately for the Mediterranean and non-Mediterranean
regions (preclustering stratification). In all three cases, the
number of clusters was set to N = 50, which was found to
give a good coverage of the spectral space defined by the red
and NIR reflectance values over the study area. In the case
without a geographic preclustering stratification, postcluster-
ing stratification was implemented. The class-wise value of
FP was computed independently for the geographic strata.
The pixels representing water were masked out in the
clustering. If the NIR reflectance of a pixel was 12.0% or
lower, the pixel was assigned as water. This threshold was
defined interactively.
3.2.2. Class statistics and selection of observations for
ground sampling
The measured reflectance values in red and NIR defined
the spectral vector (x) of an observation. Using the spectral
vectors of the cluster members defining the spectral classes
(c), the mean (mc) and the covariance matrix (Sc) between
the two spectral channels were computed. A bivariate
normal density function ( fc) for each class (c) was estimated
based on these statistics:
fcðxÞ ¼1
2p j Sc j1=2
e�1=2ðx�mcÞTS�1c ðx�mcÞ: ð1Þ
The quadratic product appearing in the exponential of Eq.
(1) represents the squared distance from a spectral vector (x)
to the class mean (mc) as scaled and corrected for the
variance and covariance of the class (Strahler, 1980). Under
the normality assumption, it is c2-distributed with two (the
number of channels) degrees of freedom. The condition:
ðx� mcÞTS�1c ðx� mcÞ < c2
1�a ð2Þ
then defines the set of observations (x) for which the area
under the normal density curve is 1�a. It may be
interpreted that an observation not fulfilling Eq. (2) has a
probability smaller than a of belonging to the class (c).
A threshold value of 3.219 (a=.2) was used in selecting
observations for the CORINE sampling, i.e., observations
(x) for which the quadratic product (Eq. (2)) exceeded this
value for all classes (c) were not selected as representatives
for the sampling. The chosen threshold extracted approx-
imately 20% of the observations involved in the clustering
(the training data). The rationale behind this procedure was
to reduce the influence of members on the borders or
overlapping areas of the class contents. Namely, an inher-
ent problem of the probability method, where observations
are not assigned to a specific class but may have compo-
nents from several classes, is that, to determine the class
contents, the training data initially need to be classified.
By confining the ground sampling to the central part of a
class, only observations with high a posteriori probability
of belonging to a specific class were allowed to define the
content of this class.
The map coordinates of the accepted observations were
used to select ground-sampling units from the CORINE data.
Fig. 2. The main phases of the image interpretation process.
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3.2.3. Computation of forest proportion
For each ground sample unit ( j) of a given class (c), the
forest proportion (probability) FPc( j) was estimated as the
area proportion of the unit classified as forest in the
CORINE data. The final forest probability for each class
(c), FPc, was computed as the mean of FPc( j) over the
sample units ( j). In the case where clustering had been
performed on the whole area (no preclustering stratifica-
tion), values of FPc were also determined for the Mediter-
ranean and non-Mediterranean regions separately. By this
‘postclustering geographical stratification’, the same spec-
tral class corresponded to a different ground content in the
two regions.
The sampling unit applied to the CORINE was 1�1
km2. It was assumed that this unit size unit fits within the
2� 2 pixel observation of the clustering even if some
rectification errors were involved. The 1-km2 squares were
located in the CORINE database using their map coordi-
nates. The areas of the different CORINE Land Cover
classes falling within each square were calculated. Then,
the areas were summed up into two classes: forest and
nonforest. These were then converted into the percentage
forest coverage. The CORINE classes, summed up to the
forest area were: broad-leaved forest (CORINE code 311),
coniferous forest (312), and mixed forest (313).
The output of the sampling phase was the mean propor-
tion of forest area, and its standard deviation for each
AVHRR class (Table 1). Five geographic stratification
approaches were tested. Thus, the CORINE sampling was
also performed five times.
The phases of the estimation procedure thus far involved
only those observations that were selected for the clustering.
In the final stage, a value of FP was assigned to each pixel
on the study area. It was computed as the sum of the product
of class membership probabilities for the pixel and the class
content. The probability P(cjx) of a pixel with spectral
vector (x) to belong to a specific class (c) was set propor-
tional to the value of the density function of this class at x,
fc(x) (Eq. (1)); i.e., equal a priori class probabilities were
assumed (Gorte & Stein, 1998). The class membership
probabilities for a pixel consequently were defined as:
Pðc j xÞ ¼ fcðxÞPNc¼1
fcðxÞð3Þ
fulfilling the condition that the sum over all the classes
should be equal to 1.
The forest proportion FP(x) assigned to the pixel (x) was
obtained by multiplying the class membership probabilities
P(cjx) by the class values (FPc), and summing over all the
classes (Eq. (4)):
FPðxÞ ¼XNc¼1
Pðc j xÞFPc: ð4Þ
FP represents a weighted average of the proportions of
forest area estimated (by ground sampling) for the different
spectral classes. The weights, here termed class membership
probabilities, are measures of the likelihood by which a
pixel ‘belongs to’ a specific class. In a traditional
classification approach, the pixel would be assigned to the
‘most probable’ class and the target variable be given the
value (often binary) estimated for that class. For pixels with
a spectral signal close to a class mean, the estimate of FP by
Table 1
An example of the result of the CORINE sampling from the Mediterra-
nean area
Class Sample size
Forest
percentage
Standard deviation of
forest percentage (%)
14 2570 31.667 30.89
15 3271 31.193 34.692
16 1808 9.558 22.944
17 3183 25.388 31.831
18 1276 5.055 17.139
19 2549 26.271 33.887
20 3014 22.056 29.16
21 1756 10.504 21.183
22 3164 18.069 29.731
23 2220 11.969 21.634
24 3475 16.141 27.677
25 3146 15.904 27.694
26 1305 3.203 10.445
27 1705 9.644 22.584
28 626 3.406 14.454
29 2874 9.794 21.938
The total number of classes was 50.
Table 2
Statistics of the three clustering classifications
Clustering characteristics
Clustering
Number
of classes
Standard
deviation
limit (%)
Number of
observations
in clustering
Proportion
of whole
image (%)
Number of
observations
in largest class
Number of
observations in
smallest class
Size of CORINE
sample
Whole 50 2.0 608,048 2.2 53,944 77 39,637 (28,865TrB)
(10,744M)
Mediterranean 50 6.0 137,968 9.9 4725 572 99,455
Temperate and
boreal
50 4.0 366,926 2.9 22,458 36 48,968
Numbers in the parenthesis show the sample size in the postclustering stratification.
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the present method will be very similar to the class mean
because the value of P(cjx) (Eq. (3)) for this specific class ishigh (close to one). In the case of ‘uncertain’ pixels, whose
spectral signal is not close to any class mean, the fact that
they are not ‘forced’ to a specific class eliminates the bias
from misclassification.
4. Results
4.1. Characteristics of spectral classes
A user-given clustering parameter gave the highest
allowed standard deviation (length of the deviation vector)
within a candidate observation (2� 2 pixel group) com-
pared to the standard deviation of the whole image (Hame,
Heiler, et al., 1998). This parameter determines the number
of the sampled observations and thus regulates the compu-
tations in the clustering procedure, ensuring that they do not
to become too heavy. A lower value had to be given in the
clustering of the whole mosaic because the area was large
and thus the number of candidate observations higher (Table
2). The CORINE data were not available in the vast
majority of the target area. This is illustrated by the
relatively low CORINE sample size in the classification
of the whole image.
Those 2� 2 pixel groups that passed the deviation test
and were thus included in the clustering process were not
evenly distributed between the land cover classes but con-
centrated in the forestry classes. This is because the forests,
with their low reflectance values, showed a higher spatial
homogeneity. It was not assumed to harm the result, because
the number of observations within a cluster was in any case
rather high. However, there is a low risk that ground cover
Fig. 3. Spectral mean values of the original classes in the clustering of the whole mosaic. The drawn lines divide the spectral space to classes whose forest
percentage is above and below 10% according to the CORINE sample. The classes have been sorted by their increasing red reflectance. Solid line: All the
CORINE data had been used in the sampling. Dash and dot line: Only Mediterranean CORINE data had been used in the sampling (poststratification). Dash
line: Only the temperate and boreal CORINE data had been used in the sampling (poststratification). Note that the 10% limit is not referring to within-forest
canopy closure. Forest density was not considered within the forest class in the CORINE data.
Fig. 4. Spectral mean values of the original classes in the clustering to the Mediterranean mosaic. The line divides the spectral space to classes whose forest
percentage is above and below 10% according to the CORINE sample from the Mediterranean zone.
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types whose inherent characteristics is one of heterogeneity,
were not represented in the clustering process at all. Such
cover types could be cities, for instance.
Figs. 3–5 show that the 10% forest border (practically
nonforested areas) in the spectral space runs consistently
between red reflectance 5% to 10%. When the red reflec-
tance is close to 10%, the NIR reflectance of the forested
areas is low. The border is at somewhat higher reflectance
levels in the Mediterranean area than elsewhere, but the
differences are small. Fig. 5 shows also that the highest
reflectance values (above 20% red reflectance) are practi-
cally missing in the temperate and boreal regions.
In the Mediterranean zone (preclustering stratification),
the area proportions of the original classes match very well
Fig. 5. Spectral mean values of the original classes in the clustering to the temperate and boreal mosaic. The line divides the spectral space to classes whose
forest percentage is above and below 10% according to the CORINE sample from the temperate and boreal areas.
Fig. 6. Area proportion of the original classes in the image mosaic (including all image pixels) and distribution of the CORINE sample into the classes.
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Fig. 7. Forest percentage of the original classes (solid line) ± standard deviation of the percentage (dashed line) computed from the CORINE sample.
Fig. 8. The final FP map (combined from the Mediterranean and the temperate and boreal strata). Prob_final means an estimate of FP within a 1�1-km2 pixel.
White is water and gray is cloud or snow. The maximum estimated FP was 93%. Original scale: 1:6,000,000.
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with the class proportions in the CORINE sample. This
indicates that the CORINE data were representative for the
Mediterranean zone. For the temperate and boreal zones, the
matching is much poorer (Fig. 6). In the temperate and
boreal areas, the CORINE sample was concentrated in the
higher numbered classes, i.e., classes representing a lower
forest percentage. This reflects the fact that the boreal region
was included in the clustering but excluded in the CORINE
data. However, the sample size was in all geographic strata
several hundreds of 1-km2 units for clusters that represented
any significant FP.
The standard deviations of forest percentages within each
class were high (Fig. 7). In the forested classes, they were
typically over 30%. The stratification did not clearly
decrease the deviation — with one exception. When the
clustering and CORINE sampling were made to the temper-
ate and boreal areas only, the standard deviations of the
classes, representing highest forest cover percentage (low
Fig. 9. Original clustering result from central Spain and Portugal (above) and final FP map (below). For the upper figure, the classes have been sorted by their
decreasing red reflectance (class with lowest red reflectance has the lightest gray tone value). The FP estimates below have been scaled with a linear scaling.
The minimum estimated forest percentage is 1% and the maximum 66%. The area size is approximately 620� 500 km2.
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class numbers), were less than 30%. This was lower than in
the other sampling/clustering combinations. In this prestra-
tification approach, only nine classes (from 42 to 50) had
forest percentage less than 10%.
It was concluded that the best alternative for image
interpretation was the stratified approach in which the image
interpretation is done separately for each stratum, i.e., the
preclustering approach. The justification to this conclusion
was the good matching of the CORINE and ground data in
the Mediterranean area and the decreased standard devia-
tions of the classes representing the highest FP in the
temperate and boreal areas. Furthermore, a visual evaluation
showed that the tundra areas were better discriminated from
the forested areas in the stratified approach.
4.2. Evaluation
All the interpretation phases were done to the image
mosaics in the latitude–longitude coordinate system. The
final map was transformed to one specific version of the
Lambert azimuthal equal area projection (Fig. 8). This
projection is the one used in the CORINE Land Cover
database. The spatial pattern of the classes in the original
unsupervised classification and in the final forest proportion
maps was very similar (Fig. 9). The forest area at high
latitudes may be overestimated. There are some obvious
errors, such as the estimated 47% forest cover for Iceland.
Iceland may be a special case in which the reflectance of the
dark volcanic rocks resembles that of forests.
The AVHRR forest area estimates show rather good
agreement with the CORINE data and the official forest
statistics. In the 12 countries shown in Fig. 10, the forest
area in the FP map was 4.2% lower than that in the official
EUROSTAT statistics. The underestimation compared to the
UN-ECE/FAO statistics was 6.0%. The underestimation was
1.8% compared to the CORINE data, which represented the
ground data in this study. It was considered appropriate to
evaluate the performance using CORINE data because the
sampling rate from CORINE was low, particularly in the
temperate and boreal region (Table 2). In fact, the compar-
ison with the official forest statistics is somewhat irrelevant
since the CORINE Land Cover Map and the official
statistics have completely different origins. However, the
comparison also indicates that the CORINE map, with few
exceptions, harmonizes well with the official statistics
although the forest area is smaller.
The most serious underestimation of forest area may
have occurred in France (Fig. 11). The reason for this is
still somewhat unclear. The possible reasons include the
mosaicking procedure, a seasonal effect, atmospheric cor-
rection, and the impact of the geographical stratification
on the image interpretation. The forest statistics show that
Fig. 10. Forest area in 12 countries of the European Union. Clouds have been taken into consideration in the AVHRR-based forest area by assuming the cloudy
areas to have the same forest cover proportion as the country in general.
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Fig. 12. Forest area in Germany by regions.
Fig. 11. Forest area in France by regions.
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the coniferous percentage in France is only 38% (Kuusela,
1994). The percentage is approximately 70% in Germany
where the forest area estimation was more successful (Fig.
12). France and Germany belonged to the same geo-
graphic stratum.
The area-weighted root mean square errors (RMSE)
for country-wise forest percentage and regional forest
percentage for France and Germany were computed using
Eq. (5).
RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXi
ai
Aðpi � piÞ2
sð5Þ
where ai are the areas of the different countries or regions
considered, A is their total area (sum of ai:s), and pi and pidenote the estimated and reference (CORINE) forest
percentages, respectively, for the different countries/regions.
The country-wise RMSE for 12 European Union coun-
tries was 4.6%, for regions in France 8.8%, and in Germa-
ny 3.9%.
An exception to the general underestimation was Ireland,
where forest area was seriously overestimated (Fig. 10). The
reason for this is thought to be the moorlands, which possess
a signature similar to that of coniferous forests, even though
the main vegetation consists of dwarf shrubs. A similar
overestimation could be observed in Scotland (Fig. 8). The
area of moorlands was too small at the European scale to be
adequately represented in the CORINE sample. For Finland,
the forest area was overestimated by 3.2% and in Sweden by
5.6% compared to the official statistics from the national
forest inventories.
5. Discussion
The estimated forest areas were close to the forest areas
of the forest statistics although in general the forest area
was somewhat underestimated. The underestimation was
most severe in France and in the Mediterranean region. In
the Mediterranean region, the underestimation was some-
what surprising since the area selected in the CORINE
sampling was almost 10% of the total area. In the evalua-
tion, it was assumed that the cloudy areas had the same FP
as the country or region in general. This may be a
conservative estimate to the forest distribution because
both forests and clouds occur more frequently in mountain-
ous areas. However, in Austria, the correction may have
been too strong, because the snow-covered Alps was also
considered as ‘clouds’ in the interpretation. The proportion
of clouds varied from less than 1% of Spain and Portugal
to 12% and 10% in Austria and Germany, respectively.
When the cloudy areas were considered to have the same
FP as that of the forest statistics, the forest area increased
in the comparison procedure by 5% in Austria and 3% in
Germany. Elsewhere, the effect was smaller. The role of
the missing CORINE data in the overestimation of the
forest area in Finland and Sweden and in the apparent
overestimation in the tree line zone in Northern Russia
remains unclear.
If the forest area in the CORINE is systematically over-
estimated or underestimated, it would cause a corresponding
overestimation and underestimation of the forested area in
the maps that are derived from the AVHRR images. How-
ever, this relationship is not straightforward for the proce-
dure applied in this study because the approach is not based
on a multiphase or multistage sampling strategy. The forest
percentage for the spectral classes was derived using only
such observations whose reflectance values were rather
close to the mean reflectance of a class. Moreover, the
requirement of spectral homogeneity in selecting the train-
ing data also implied that the ground sampling was
restricted to homogeneous areas. Due to the area general-
ization procedure of the CORINE, it can be assumed that in
areas where the true forest percentage is low, the forest area
in CORINE is underestimated and vice versa.
The possible geographic mismatch between the CORINE
data and the AVHRR mosaic and the effect of overlapping
AVHRR pixels should lead to the averaging of forest cover
percentages for the classes. The forest cover percentages of
classes with low FP should increase and percentages of
classes with high FP should decrease. This should introduce
no bias to the general procedure. Possible inconsistency in
forest definition between countries in CORINE would have
a similar effect as the possible geographic mismatch
between the data sets. The inconsistency averages the forest
percentages of the classes within a stratum. In general, the
forest area in CORINE was lower than the forest area in the
official statistics, but possible intercountry inconsistency in
forest definition could not be confirmed.
Despite checking almost all relevant AVHRR scenes
over one summer, significant parts of mountainous and
northernmost areas were still cloud-covered. A partial
solution to the cloud problem is to combine images from
different summers. In an earlier study, images from three
years were successfully combined (Hame et al., 1997).
Another possibility is to use any possible image and
automatic selection of likely cloud-free pixels (Roy et al.,
1997; Running et al., 1994). However, this approach may be
difficult to combine with the idea of using averages of
several pixels in the mosaic.
A conclusion of the study was that the number of
geographic strata should be increased. In addition to select-
ing the boreal zone to a specific stratum (if ground data are
available), it seems to be important to separate a specific
Atlantic stratum from more continental temperate forests. A
successful estimation of Irish and Scottish moorlands would
require a very detailed stratification.
In the future research, a more physical-based approach in
the class labeling will be tested to reduce the problems with
the reference data. Furthermore, a more detailed geographic
stratification will be applied and variables other than forest
area will be considered.
ˆ
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Acknowledgments
We would like to acknowledge the support of the
European Environment Agency’s Topic Centre for Land
Cover, for making the CORINE Land Cover database
available for this work. We also wish to express our
gratitude to the anonymous reviewers for their good and
constructive comments and suggestions.
References
Andersson, K. (1999). NOAA AVHRR workstation software. In: Proceed-
ings of the IGARSS ’99 Symposium, Hamburg, 28 June–2 July 1999
( pp. 1229–1231). Piscataway: Institute of Electrical and Electronics
Engineers (IEEE Catalog Number 99CH36293).
Belward, A. S., Kennedy, P. J., & Gregoire, J.-M. (1994). The limitations
and potential of AVHRR-GAC data for continental scale fire studies.
International Journal of Remote Sensing, 15, 2215–2234.
Cihlar, J., Ly, H., & Quinghan, X. (1996). Land cover classification with
AVHRR multichannel composites in northern environments. Remote
Sensing of Environment, 58, 36–51.
Commission des Communautes Europpeennes (1993). CORINE Land Cov-
er, guide technique (148 pp.). Commission des Communautes Euro-
ppeennes EUR 12585 FR. ISBN 92-826-2579-6.
Cross, A. M., Settle, J. J., Drake, N. A., & Paivinen, R. T. M. (1991).
Subpixel measurement of tropical forest cover using AVHRR data.
International Journal of Remote Sensing, 12 (5), 1119–1129.
DeFries, R., Hansen, M., Steininger, M., Dubayah, R., Sohlberg, R., &
Townshend, J. (1997). Subpixel forest cover in Central Africa from
multisensor, multitemporal data. Remote Sensing of Environment, 60,
228–246.
DeFries, R., Hansen, M., & Townshend, J. (2000). Global continuous fields
of vegetation characteristics: a linear mixture model applied to multi-
year 8 km AVHRR data. International Journal of Remote Sensing, 21
(6/7), 1389–1414.
DeFries, R., & Townshend, J. R. G. (1994). NDVI-derived land classifica-
tions at a global scale. International Journal of Remote Sensing, 15,
3567–3586.
D’Souza, G., & Malingreau, J.-P. (1994). The use of NOAA-AVHRR for
vegetation mapping and monitoring in the Amazon Basin. Remote Sens-
ing Reviews, 10, 5–34.
EEATask Force (1992). CORINE Land Cover (22 pp.). Brochure prepared
as contribution to European Conference of the International Space Year,
Munich, 30 March–4 April 1992.
Eidenshink, J. C. (1992). The 1990 conterminous US AVHRR data set.
Photogrammetric Engineering and Remote Sensing, 57, 809–815.
EUROSTAT. (1998). Forststatistik, forestry statistics, statistiques forest-
ieres, 1992–1996. Luxembourg: European Communities (ISBN 92-
828-3684-3).
Foody, G. M., Campbell, N. A., Trodd, N. M., & Wood, T. F. (1992).
Derivation and applications of probabilistic measures of class member-
ship from the maximum-likelihood classification. Photogrammetric En-
gineering and Remote Sensing, 58 (9), 1335–1341.
Foody, G. M., Lucas, R. M., Curran, P. J., & Honzak, M. (1997). Non-
linear mixture modelling without end-members using an artificial
neural network. International Journal of Remote Sensing, 18 (4),
937–953.
Gaston, G. G., Jackson, P. L., Vinson, T. S., Kolchugina, T. P., Botch, M., &
Kobak, K. (1994). Identification of carbon quantifiable regions in the
former Soviet Union using unsupervised classification of AVHRR glob-
al vegetation index images. International Journal of Remote Sensing,
15 (16), 3199–3221.
Gorte, B., & Stein, A. (1998). Bayesian classification and class area esti-
mation of satellite images using stratification. IEEE Transactions on
Geoscience and Remote Sensing, 36 (3), 803–812.
Hame, T. (1991). Spectral interpretation of changes in forest using satellite
scanner images. Helsinki. Acta Forestalia Fennica, 222 (p. 111) (ISBN
951-651-092-2).
Hame, T., Andersson, K., Lohi, A., Kohl, M., Paivinen, R., JeanJean, H.,
Spence, I., Letoan, T., Quegan, S., Estreguil, C., Folving, S., & Ken-
nedy, P. (1998). Validated forest variable maps and estimates across
Europe using multi-resolution satellite data: results of the first phase
of the FMERS study. In: 27th International Symposium on Remote
Sensing of Environment, Tromsø, Norway (pp. 705–708). Norwegian
Space Centre (NSC), Tromsø, Norway: International Symposia on Re-
mote Sensing of Environment (ISRSE).
Hame, T., Heiler, I., & San-Miguel Ayanz, J. (1998). An unsupervised
change detection and recognition system for forestry. International
Journal of Remote Sensing, 19 (6), 1079–1099.
Hame, T., Salli, A., Andersson, K., & Lohi, A. (1997). A new methodology
for the estimation of biomass of conifer-dominated boreal forest using
NOAA-AHRR data. International Journal of Remote Sensing, 18 (15),
3211–3243.
Hame, T., Salli, A., & Lahti, K. (1992). Estimation of carbon storage and
organic matter in boreal forests using optical remote sensing data. In:
Proceedings of the Central Symposium of the International Space Year
Conference, Munich, March 30–April 4, 1992. European Space Agency,
Special Publication (vol. 341, pp. 75–78). Noordwijk: ESA, ESTEC.
Hausler, T., Saradeth, S., & Amitai, Y. (1993). NOAA-AVHRR forest map
of Europe. In: Proceedings of the International Symposium Operation-
alization of Remote Sensing, 19–23 April (pp. 37–48). Enschede, The
Netherlands: ITC.
Iverson, L., Cook, E., & Graham, R. (1989). A technique for extrapolating
and validating forest cover across large regions. International Journal
of Remote Sensing, 10, 1805–1812.
Iverson, L. R., Cook, E. A., & Graham, R. L. (1994). Regional forest cover
estimation via remote sensing: the calibration center concept. Land-
scape Ecology, 9 (3), 159–174.
Kasischke, E. S., & French, N. H. F. (1997). Constraints on using AVHRR
composite index imagery to study patterns of vegetation cover in boreal
forests. International Journal of Remote Sensing, 18 (11), 2403–2426.
Kaufman, Y. J., Setzer, A., Justice, C., Tucker, C. J., & Fung, I. (1990).
Remote sensing of biomass burning in the tropics. In: J. G. Goldammer
(Ed.), Fires in tropical biotaEcological Studies (vol. 82, pp. 371–399).
Berlin: Springer.
Kauppi, P. E., Mielikainen, K., & Kuusela, K. (1992). Biomass and carbon
budget of European forests, 1971 to 1990. Science, 256, 70–74.
Kilkki, P., & Paivinen, R. (1987). Reference sample plots to combine field
measurements and satellite data in forest inventory. Proceedings of the
Remote Sensing-Aided Forest Inventory (pp. 209–215). Seminars or-
ganized by SNS (Samarbetsnamnden for Nordisk Skogsforskning) and
Taksaattoriklubi (Forest Mensurationist Club), Hyytiala, Finland, De-
cember 10–12, 1986.
Kuusela, K. (1994). Forest resources in Europe 1950–1990 (154 pp.).
European Forest Institute Research Report 1. Cambridge Univ. Press.
Kuusela, K., & Paivinen, R. (1995). On the classification of ecosystems in
boreal and temperate forests. In: Proceedings of an international work-
shop designing a system of nomenclature for European forest mapping,
Joensuu, Finland, June 13–15, 1994 (pp. 387–393). Luxemburg: Joint
Research Centre, European Forest Institute (Report EUR 16113 EN).
Lambin, E. F., & Ehrlich, D. (1996). The surface temperature–vegetation
index space for land cover and land-cover change analysis. Interna-
tional Journal of Remote Sensing, 17, 463–487.
Lambin, E. F., & Ehrlich, D. (1997). Land cover changes in sub-Saharan
Africa (1982–1991): application of a change index based on remotely
sensed surface temperature and vegetation indices at a continental scale.
Remote Sensing of Environment, 61, 181–200.
Loveland, T., Merchant, J., Ohlen, D., & Brown, J. (1991). Development of
a land-cover characteristics database for the conterminous US. Photo-
grammetric Engineering and Remote Sensing, 57, 1453–1463.
T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–9190
![Page 16: AVHRR-based forest proportion map of the Pan-European area](https://reader036.fdocuments.net/reader036/viewer/2022080312/5750217c1a28ab877ea0010e/html5/thumbnails/16.jpg)
Malingreau, J.-P., Achard, F., D’Souza, G., Stibig, H., D’Souza, J.,
Estreguil, C., & Eva, H. (1995). AVHRR for global tropical forest
monitoring: the lessons of the TREES project. Remote Sensing Re-
views, 12, 29–40.
Malingreau, J.-P., & Belward, A. S. (1994). Recent activities in the Euro-
pean Community for the creation and analysis of global AVHRR data
sets. International Journal of Remote Sensing, 15, 3397–3416.
Malingreau, J.-P., & Tucker, C. J. (1988). Large-scale deforestation in the
Southeastern Amazon basin of Brazil. Ambio, 17, 49–55.
Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G., & Nemani, R. R.
(1997). Increased plant growth in the northern high latitudes from 1981
to 1991. Nature, 386, 698–702.
Nemani, R., & Running, S. (1996). Land cover characterisation using mul-
titemporal red, near-ir and thermal-ir data from NOAA/AVHRR. Eco-
logical Applications, 7, 79–90.
Oke, T. R. (1987). Boundary layer climates (2nd ed.). London and New
York: Methuen and Co (435 pp.).
Poso, S., Paananen, R., & Simila, M. (1987). Forest inventory by compart-
ments. Silva Fennica, 21 (1), 69–94.
Rahman, H., & Dedieu, G. (1994). SMAC: a simplified method for the
atmospheric correction of satellite measurements in the solar spectrum.
International Journal of Remote Sensing, 15, 123–143.
Roy, D. P. (1997). Investigation of the maximum normalised difference
vegetation index (NDVI) and the maximum surface temperature
(Ts) AVHRR compositing procedures for the extraction of NDVI
and Ts over forest. International Journal of Remote Sensing, 18,
2383–2401.
Roy, D. P., Kennedy, P., & Folving, S. (1997). Combination of the normal-
ised difference vegetation index and surface temperature for regional
scale European forest cover mapping using AVHRR data. International
Journal of Remote Sensing, 18, 1189–1195.
Running, S. W., Peterson, D. L., Spanner, M. A., & Teuber, K. B. (1986).
Remote sensing of coniferous forest leaf area. Ecology, 67, 273–276.
Running, S. W., Justice, C. O., Salomonson, V., Hall, D., Barker, J., Kauf-
mann, Y. J., Strahler, A. H., Huete, A. R., Muller, J.-P., Vanderbilt, V.,
Wan, Z. M., Teillet P., & Carneggie, D. (1994). Terrestrial remote
sensing science and algorithms planned for EOS/MODIS. International
Journal of Remote Sensing, 15, 3587–3620.
Skole, D., & Tucker, C. (1993). Tropical deforestation and habitat fragmen-
tation in the amazon: satellite data from 1978 to 1988. Science, 260,
1905–1910.
Strahler, A. H. (1980). The use of prior probabilities in maximum like-
lihood classification of remotely sensed data. Remote Sensing of Envi-
ronment, 10, 135–163.
Tomppo, E. (1991). Satellite image based national forest inventory of Fin-
land. Proceedings of the Symposium on Global and Environmental
Monitoring, Techniques and Impacts, September 17–21, 1990, Victo-
ria, British Columbia, Canada. International Archives of Photogramme-
try and Remote Sensing, 28 (Part 7-1), 419–424.
Townshend, J. (Ed.). (1992). Improved global data for land applications.
Stockholm: IGBP/ICSU (International Geosphere –Biosphere Pro-
grammme, Report No. 20).
Townshend, J. R. G. (1994). Global data sets for land applications from the
advanced very high resolution radiometer: an introduction. Internation-
al Journal of Remote Sensing, 15, 3319–3332.
Wu, A., Li, Z., & Cihlar, J. (1995). Effects of land cover type and greenness
on AVHRR bidirectional reflectances. Journal of Geophysical Re-
search, 100, 9179–9192.
Zhu, Z., & Evans, D. L. (1994). US forest types and predicted percent forest
cover from AVHRR data. Photogrammetric Engineering and Remote
Sensing, 60, 525–531.
T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–91 91