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7/27/2019 Hansen Et Al 2002b
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Towards an operational MODIS continuous field of percent tree cover
algorithm: examples using AVHRR and MODIS data
M.C. Hansen a,*, R.S. DeFries a,b, J.R.G. Townshend a,c,R. Sohlberg a, C. Dimiceli a, M. Carroll a
aDepartment of Geography, University of Maryland, 2181 LeFrak Hall, College Park, MD 20742, USAbEarth System Science Interdisciplinary Center, Univ ersity of Maryland, College Park, MD 20742, USA
cInstitute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA
Received 1 May 2001; received in revised form 21 February 2002; accepted 12 March 2002
Abstract
The continuous fields Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products are 500-m sub-pixel representations
of basic vegetation characteristics including tree, herbaceous and bare ground cover. Our previous approach to deriving continuous fields
used a linear mixture model based on spectral endmembers of forest, grassland and bare ground training. We present here a new approach for
estimating percent tree cover employing continuous training data over the whole range of tree cover. The continuous training data set is
derived by aggregating high-resolution tree cover to coarse scales and is used with multi-temporal metrics based on a full year of coarse
resolution satellite data. A regression tree algorithm is used to predict the dependent variable of tree cover based on signatures from the multi-
temporal metrics. The automated algorithm was tested globally using Advanced Very High Resolution Radiometer (AVHRR) data, as a full
year of MODIS data has not yet been collected. A root mean square error (rmse) of 9.06% tree cover was found from the global training data
set. Preliminary MODIS products are also presented, including a 250-m map of the lower 48 United States and 500-m maps of tree cover and
leaf type for North America. Results show that the new approach used with MODIS data offers an improved characterization of land cover.
D
2002 Elsevier Science Inc. All rights reserved.
1. Introduction
Tree cover mapping has grown in importance as the need
to quantify global tree stocks has increased. Tree cover is an
important variable for modeling of global biogeochemical
cycles and climate (Sellers et al., 1997; Townshend et al.,
1994). Additionally, tree cover mapping has taken on
increased importance in the policy arena. Quantifying carbon
stocks has been deemed a necessity in global treaties regard-
ing release and sequestration of carbon to and from the
atmosphere (IGBP, 1998). The use of tree cover mapping
in assessing the condition of global ecosystems is also
important (Ayensu, Claasen, Collins, et al., 1999). In order
to meet the needs of the users of such data, the remote sensing
community has begun to promote the benefits of the synoptic,
standardized view provided by satellite data (DeFries, Han-
sen, Townshend, Janetos, & Loveland, 2000). One of the
annual Moderate Resolution Imaging Spectroradiometer
(MODIS) land cover products is the vegetation continuous
fields layers. The layers include percent bare ground, herba-
ceous and tree cover and, for tree cover, percent evergreen,
deciduous, needleleaf and broadleaf. These maps have the
potential to meet many of the needs of both the scientific and
policy communities. This paper describes an improved
methodology for deriving percent tree cover estimates over
previous methodologies. The procedure is presented along
with a global Advanced Very High Resolution Radiometer
(AVHRR) application and two examples using MODIS data.
Continuous fields of vegetation properties offer advan-
tages over traditional discrete classifications. By depicting
each pixel as a percent coverage, areas of heterogeneity are
better represented. Discrete classes do not allow for the
depiction of variability for spatially complex areas (DeFries,
Field, Fung, et al., 1995). Many spatially complex areas
occur because of anthropogenic land cover change. By
using proportional estimates, sub-pixel cover can be mapped
with the prospect of measuring change over time. Since the
0034-4257/02/$ - see front matterD 2002 Elsevier Science Inc. All rights reserved.
PII: S 0 0 3 4 - 4 2 5 7 ( 0 2 ) 0 0 0 7 9 - 2
* Corresponding author. Tel.: +1-301-314-2585.
E-mail address: [email protected] (M.C. Hansen).
www.elsevier.com/locate/rse
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scale of human-induced land cover change is typically finer
than 250-m (Townshend & Justice, 1988), continuous fields
from MODIS data may yield a usable land cover change
product.
2. Procedure
The approach presented in this paper for mapping con-
tinuous fields of tree cover differs from that of the initial
prototype (DeFries et al., 2000). Fig. 1 outlines the proto-
type methodology and the improved technique presented
here. The two approaches share one feature: the use of
annual phenological metrics as the independent variables to
predict tree cover. They differ in the following ways:
n the new technique is fully automated
n the new training data set is a continuous variable, not
discrete class labels
n the new algorithm is a regression tree as opposed to a
linear mixture model modified by a land cover
classification
n the new approach operates globally, without per continent
adjustments of the mixture model.
The most important advancement is the automation of
the algorithm. The prototype approach relied on a classi-
fication methodology which was partially dependent on an
expert interpreters input (Hansen, DeFries, Townshend, &
Sohlberg, 2000). This step has been eliminated in the newtechnique. The main parts integral to the methodology are
described in the following sections.
2.1. Annual metrics
Global multi-temporal metrics capture the salient points
of phenological variation by calculating annual means,
maxima, minima and amplitudes of spectral information.
The value of metric generation versus using a series of
monthly values is that the metrics are not sensitive to time of
year or the seasonal cycle and can limit the inclusion of
atmospheric contamination. Fig. 2 shows monthly values for
red reflectance from AVHRR data for February 1995 to
January 1996 for the Amazon basin. Use of any individual
month would include cloud contamination whereas the
annual minimum provides a cleaner metric for viewing land
cover.
Fig. 3 shows another example of the utility of metrics
from Central Africa. Here, the maximum annual Normalized
Fig. 1. Flow chart of major steps in generation of global continuous field of tree cover products for (a) prototype methodology of DeFries et al. (2000) and (b)
MODIS implementation.
Fig. 2. Derived minimum annual red reflectance from monthly composites of red reflectance associated with maximum monthly NDVI for (a) January 1996, (b)
February 1995, (c) March 1995, (d) April 1995, (e) May 1995, (f) June 1995, (g) July 1995, (h) August 1995, (i) September 1995, (j) October 1995, (k)
November 1995, (l) December 1995. (m) is derived metric. All 13 subsets have the same image enhancement applied.
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Fig. 3. (a) AVHRR metrics for area in central Africa: red= maximum annual NDVI, cyan= minimum annual red reflectance; (b) AVHRR metric of mean
temperature of the four warmest months from band 5; (c) continuous tree cover result; (d) high-resolution imagery, false color composite for an area in the
Democratic Republic of the Congo; (e) classified high-resolution imagery: green = forest (80% canopy cover), dark maroon = woodland (50% canopy cover),
light maroon = parkland (25% canopy cover), yellow= no trees (0% canopy cover); (f) derived training data by aggregating classified image to 500-m pixels.
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Difference Vegetation Index (NDVI) is shown with the
minimum annual red reflectance metric. Minimum annual
red reflectance is negatively correlated with tree cover as the
combined effects of chlorophyll absorption and canopy
shadowing make denser tree cover darker. Maximum annual
NDVI, on the other hand, has a positive correlation with tree
cover as increasing leaf area of canopies makes forestsappear greener. However, for this area, woodlands of
approximately 60% cover are indistinguishable from denser
forests for these metrics. Another metric based on surface
temperature allows for the stratification of these two areas
using the regression tree. The four warmest months of the
year based on surface temperature correlate with the dry
season as the seasonal woodlands have senesced and evap-
otranspiration is lower: this allows for a clean delineation of
the forest/woodland boundary. These metrics also discrim-
inate the northern edge of the Central African rainforest as
they are insensitive to the specific time of year. Metric
generation will continue to develop using MODIS data as a
full year of consistent data becomes available and the full
global suite of metrics can be derived.
The metrics to be tested will mimic those for this work
shown in Table 1. Each band is ranked individually and also
ordered by corresponding greenness and temperature rank-
ings. The individual bands, NDVI and surface temperature
are ranked; lowest to highest for visible and infrared bands,
highest to lowest for NDVI and surface temperature. From
these rankings a set of metrics is derived. The bands are also
ordered according to highest and lowest corresponding
NDVI and surface temperature values, and metrics are
derived based on these orderings. Metrics results such as
near-infrared reflectance at maximum annual NDVI, ormean NDVI of the four warmest surface temperature
months are used. Table 1 shows metrics for an example
using a red reflectance band.
2.2. Continuous training data
Past training data were created by classifying and inter-
preting high-resolution imagery to identify homogeneous
areas. These areas were then aggregated to develop a coarse
resolution training data set for a discrete classification
system, the modified International Geosphere Biosphere
Programmes (IGBP) University of Maryland land cover
legend (DeFries, Hansen, Townshend, & Sohlberg, 1998;
Hansen et al., 2000). The 12 classes in this legend can be
aggregated to four tree cover strata. These strata are 0 10%,
11 40%, 41 60% and 61 100% tree canopy cover. In the
new approach, the high resolution classifications are aggre-gated to coarser scales by labeling each stratum with a mean
cover value (0%, 25%, 50% and 80% for the aforemen-
tioned classes) and then averaging over the coarser output
cells. In this way a continuous tree cover training data set is
created. Fig. 3 shows the approach for deriving the current
global training data set for an example from the Democratic
Republic of the Congo.
Thus, the new approach includes the use of training
pixels of intermediate cover, whether they are homogeneous
open woodlands or fragmented forest. This is an improve-
ment over spectral end members, which employ only
signatures characteristic of pure class types. As prior work
was based on identifying core, homogeneous areas for all
cover classes, a new training data set had to be assembled.
The archival data sets were re-interpreted wall-to-wall,
where possible, to acquire training in mixed areas. This
allows for a more consistent depiction of transition areas and
ecotones which are of interest to many researchers of land
cover change. An important effect of the continuous training
is the increased ability to automate the procedure. By having
the full range of tree cover heterogeneity for training, the
algorithm produces more stable results.
2.3. Regression tree algorithm
Regression trees have previously been used with remote
sensing data (DeFries et al., 1997; Michaelson, Schimel,
Friedl, Davis, & Dubayah, 1994; Prince & Steininger,
1999). They offer a robust tool for handling nonlinear
relationships within remotely sensed data sets. The algo-
rithm uses a set of independent variables, in this case annual
multi-temporal metrics, to recursively split a dependent
variable, in this case tree cover, into subsets which max-
imize the reduction in the residual sum of squares. The
algorithm uses only those metrics which best separate the
Table 1
This table shows examples of metrics derived for the red reflectance band
Ranking criteria: Each band is individually ranked and also ordered based on NDVI and surface temperature rankings
Ranking of individual bands Greenest based on NDVI Warmest based on surface temperature
Metric
types
Individual
monthly values
minimum, median and maximum
annual red reflectance
red reflectance associated with peak,
median, minimum greenness
red reflectance associated with peak,
median and minimum surface temperature
Means mean of four, six and eight darkest
red reflectance monthly values
mean red reflectance of four,
six and eight greenest months
mean red reflectance of four, six
and eight warmest months
Amplitudes amplitude of red reflectance for
minimum, median and maximum
red values
amplitude of red reflectance
associated with peak, median,
minimum greenness
amplitude of red reflectance associated
with peak, median, minimum surface
temperature
The same metrics are calculated for other bands and NDVI. For AVHRR, bands 1 5 were used; for MODIS, bands 1 7 and surface temperature will be used.
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tree strata. In this way, unlike unsupervised classifiers,
metrics that provide no discriminatory information areignored. For example, the individual months of Fig. 2
may not be used at all, since the derived index of minimum
red reflectance best depicts tree cover information.
All input metrics are analyzed across digital number
values and right and left splits are examined. The split that
produces the greatest reduction in the residual sum of
squares, or deviance, is used to divide the data and the
process begins again for the two newly created subsets. The
regression tree algorithm takes the following form:
D Ds Dt Du
where s represents the parent node, and tand u are the splits
from s. The deviance for nodes is calculated from theequation:
Di X
casesj
yi uj2
for all j cases of y and the mean value of those cases, u.
Our implementation of the regression tree algorithm is
performed as follows. Two samples of training pixels are
taken from the training data set. One is used to grow the
regression tree and one to prune it. Pruning is required
because tree algorithms are very robust and delineate even
Fig. 4. Example of tree cover mapping methodology. (a) Scatter of 1999 8-km global tree cover training data where the feature space is minimum annual red
reflectance on the y-axis and minimum annual near-infrared reflectance on the x-axis with derived NDVI from these two values also used; (b) node partitions
and node numbers derived from the pruned regression tree; (c) mean node estimates resulting from the regression tree; (d) per node stepwise regression
estimates; (e) per node median adjustment results. In addition to slightly improving the root mean s quare error estimates, the last two steps in (d) and (e) create
a more continuous result and improve depictions in extreme low and high cover nodes. Refer to Fig. 5 to see the actual tree structure.
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individual pixels isolated in spectral space. By having a set-
aside of training data, a more generalized tree can be
generated. This generalization is achieved by passing the
second sample of data down the initial tree. As the datacascade down the tree, the overall sum of squares begins to
level out and eventually begins to increase. This indicates
an overfitting of the initial tree. For this work, pruning is
performed not where the sum of squares begins to increase,
but where additional nodes represent a reduction of less
than 0.01% of the overall sum of squares for the data. The
end result is an easily interpreted hierarchy of splits, which,
when followed, allow for a ready biophysical interpretation
of the relationship between vegetation cover and satellite
signal.
An additional step is the fitting of a linear regression
model to the data in each node. The regression tree output
yields a mean cover value based on training pixels present
in each node. However, the predicted values can be
improved by running a linear model using the independent
variables to predict tree cover for each node. This is done
by using a stepwise regression procedure per node in order
to use the combination of image data which best explains
tree cover variation. This step represents a fine-tuning of the
result to produce a more continuous product and does not
greatly change the regression tree results. For example,
from Fig. 3, the regression tree might use the temperature
metric to separate the forest from the woodlands. Then
metrics such as maximum annual NDVI would be used in
the stepwise regression phase to improve the mean node
estimates.
Many nodes at the extremes of tree cover extent have
skewed data distributions. While the regression tree yieldssuitable splits in these instances, the use of the mean value
in assigning a cover value may reduce values at the high
cover end and increase values for extremely low cover
Fig. 5. Tree structure from Fig. 4, which employs 1999 minimum red and near-infrared reflectances and derived NDVI for 8-km Pathfinder AVHRR data.
Training data are resampled from the high-resolution classifications to the 8-km grid. Ellipses represent nonterminal nodes; rectangles, terminal nodes. Inside
nodes are mean tree cover estimates based on 50% sample used to grow tree. Splitting rules are shown under nonterminal nodes. Terminal node numbers match
those in Fig. 4b.
Table 2
Node statistics for example tree in Figs. 4 and 5
Node Training
mean
Standard
deviation
Median Number of
pixels
1 42.0 20.5 43 32
2 58.1 15.1 62 282
3 63.5 16.1 65 46
4 68.5 8.9 70 1543
5 11.1 7.6 10 15
6 37.1 15.9 27 190
7 26.7 11.5 27 337
8 45.2 13.7 42 110
9 55.5 11.1 53 337
10 42.7 10.4 42 228
11 37.1 8.8 39 269
12 17.8 9.5 14 256
13 30.0 8.9 34 218
14 21.6 9.2 26 649
15 13.1 7.3 10 889
16 0.4 2.0 0 9001
17 8.7 6.1 9 1604
18 5.4 5.5 2 1095
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Fig. 6. (a) Percent tree cover map automatically generated using global 1-km AVHRR data from 1995 96 data and (b) subset of preliminary linear endmember
mixture model approach for an area of New York state; (c) same area for new approach; (d) preliminary approach for an area in Mato Grosso state, Brazil; (e)
same area for new approach.
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nodes. A simple solution to this is to adjust the final node
values by adding the median minus the mean for each node.
Again, this represents a subtle adjustment to the final
product, but experiments with the procedure show that it
slightly improves overall root mean square errors and high
and low end cover estimates.
Fig. 4 shows a graphic representation of the procedure.This example uses actual inputs, but is a simplified illus-
tration to aid understanding of the procedure. Three input
metrics, minimum annual red and near infrared reflectances
and derived NDVI from 1999 AVHRR data, are used as the
independent variables. The training data are from the global
training set aggregated to 8-km resolution. The 50% sample
used to grow the tree creat ed a 2954 node tree when
perfectly fit to the scatter in Fig. 4a. Using the other 50%
of data to prune and find the 0.01% cutoff threshold, an 18-
node tree is derived as shown in Figs. 4b and 5. The overall
mean of the training data is 14.2% tree cover as can be seen
in the root node in Fig. 5. Using this estimate for all pixels
yields a root mean square error (rmse) of 17.73%. The mean
estimates from the 18 nodes reduce the rmse to 3.43%. The
next steps of stepwise regression and median adjustment
lower this value to 3.35% and 3.31%, respectively. Thus, the
most significant predictor is the original pruned tree itself,
while the subsequent steps create a more continuous and
slightly improved result.
The tree structure and associated node statistics are
informative since trees allow for meaningful interpretation
from a biophysical perspective. The first three splits in the
tree use red reflectance, indicating the importance of this
metric in tree mapping. The combined effects of chlor-
ophyll absorption and canopy shadowing in the visible redwavelengths are most significant among these variables in
discriminating dense tree cover. Node 5 is an example of a
low tree cover node which could be associated with burns
as it has both very low red reflectance and NDVI. Table 2
shows statistics for each node. Note that the mean node
values are slightly different than those of the tree in Fig. 5,
because the tree is originally defined using a 50% sample
whereas the Table 2 statistics include all pixels. In this
table, nodes with great variability represent inseparable
signatures. Increasing the feature space by adding metrics
might be required in this instance to enhance separability.
An arc of increased inseparability is seen across the feature
space for nodes 1, 2, 3, 6, 7, 8, 9 and 10. This type of
information is useful, especially for change detection
studies because it allows for an assignment of confidence
which can be employed to measure change. For instance,
given two successive time periods and similar tree struc-
tures, only pixels which started and ended in the high
confidence zones above and below this low confidence arc
would be labeled as changed pixels. Only node 6 exhibits
a significant degree of skewing. The mean and median are
fully 10% apart. This node represents a bimodal distribu-
tion which is inseparable and best estimated by adjusting
node values using the median.
3. Results
3.1. AVHRR global prototype using MODIS algorithm
The initial attempt to use the regression tree was per-
formed using the AVHRR 1-km data set processed at the
EROS Data Center under the guidance of the IGBP (Eiden-shink & Faudeen, 1994). Metrics describing the phenolog-
ical variation of vegetation were derived for the year dating
February 1995 to January 1996. This test employed 144
metrics, many derivative of those used in the land cover
classification of Hansen et al. (2000). Table 1 shows an
outline of the metrics used. At 1-km resolution, the training
data consists of nearly 6 million pixels, and a systematic
sampling of roughly every fifth training pixel was taken to
drive the analysis. The final product and improved informa-
tion content in the algorithm can be seen in Fig. 6. A much
more detailed, sharper depiction is shown for subsets
centered on the Hudson River valley, United States and
the upper Xingu River valley, Brazil as compared to the
initial methodology. The previous methodology using end-
members in a linear model tends to overestimate forest
cover at the high end. This is due to the small dynamic range
of dense tree cover (f>40%) for many metrics, such as the
red reflectance metric shown in Fig. 4. The linear model
tends to flatten tree cover variability, which is captured in
the regression tree approach.
The initial regression tree mean cover values for 189,092
pixels yielded an rmse of 9.28 compared to the training data.
After applying the regression models to each node, the rmse
was reduced to 9.06% tree cover. The final scaling using the
median adjustment also resulted in an rmse of 9.06%.Comparison of the training values to results for both
methodologies are listed in Table 3. The average rmse
values indicate a more robust result across all strata with
the new algorithm.
3.2. Conterminous United States 250-m tree cover map from
2000 summer and fall maximum NDVI composites
To test the procedure further and to examine the robust-
ness of the MODIS data, a preliminary United States tree
Table 3Comparison of global continuous training pixel values with results from
two approaches depicting tree cover, the linear mixture approach ofDeFries
et al. (2000) and the regression tree approach planned for use with MODIS
data
Tree cover
strata
Linear mixture model
+ classification (%)
Regression tree
algorithm (%)
0 10 5.5 4.37
11 25 16.9 11.9
26 40 18.3 13.4
41 60 15.8 13.8
61 100 9.4 10.3
average rmse 13.8 10.8
overall rmse 10.6 9.1
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Fig. 7. (a) Continuous tree cover training at 250-m resolution used to create test map. (b) Test product of tree cover for the conterminous United States from two
maximum NDVI composites from data between June 10 and July 27, 2000 and between October 7 and October 31, 2000.
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cover map was made using two maximum NDVI compo-
sites from available summer and fall data for the year 2000.
The high-resolution training data resampled to the 250-m
MODIS cell size resulted in over 20 million training pixels
for the contiguous United States alone. The 250-m training
data are shown in Fig. 7. A 1% sample of these sites was
randomly taken.The 250-m bands were chosen to be included in the
MODIS sensor as Townshend and Justice (1988) found this
to be the resolution necessary to depict human-induced land
cover change. It is clear from much of the MODIS 250-m
raw imagery that this was a useful choice. When viewing
raw swaths, many forest clearings and other features asso-
ciated with human activity are plainly visible. However,
when comparing the raw inputs to a maximum NDVI
composite, it is clear that a lot of this information is lost.
Fig. 8 shows NDVI data from the MODIS 250-m bands.
The raw swath has a great amount of detail present, which is
lost or blurred in the autumn composited image used to
make the country-wide product. Small clearings and water
courses in the Congaree bottomland hardwood forest, which
appears as the bright fork shape in the center of the images,
are plainly visible in the L1B data, but not in the composite.
This composite is not an official MODIS product (Huete
et al., 2002, this issue), but a simple test to observe the
quality of a traditional procedure. It is possible that the
blurring is related to geolocation errors or the inclusion of
extreme view angle values, which may be easily corrected.
However, it is apparent that compositing issues are critical
to maximizing the usefulness of MODIS data. In past work,
the AVHRR sensors resolution of 1.1 km did not allow for
the depiction of such detail and the effects of compositing,
while well-characterized by many, (Cihlar, Manak, &
DIorio, 1994; Holben, 1986; Moody & Strahler, 1994),
did not appear to result in such a potential dramatic loss ofinformation. That is because the original resolution and
sensor characteristics of the AVHRR captured an image
which was too coarse to view many of the features which
are visible with MODIS. Compositing is now of increased
importance, as blurring of the data can preclude the useful-
ness of the data in change detection studies.
3.3. North America 500-m tree cover and leaf type products
The operational MODIS algorithm was implemented on
4 months of 500-m data (Julian days 305337 for 2000 and
81153 of 2001) for North America. This is the resolution
of the official MODIS continuous cover products. The time
periods used capture some seasonality, but are not sufficient
temporally to derive useful metrics. A consistently pro-
cessed year of data for metric generation was not available
at the time of this study. However, the results of this
preliminary product reveal the robustness of the MODIS
data. The data were compiled into 40-day composites and
the training data binned to the 500-m MODIS Integerized
Sinusoidal grid. The 500-m data were sampled in a similar
Fig. 8. (a) Maximum NDVI composite from October 2000 composite of tiled MODIS 250-m data for an area in South Carolina. Columbia is at left, center of
the image. (b) NDVI derived from raw level 1B data for October 12, 2000 level 1B 250.
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Fig. 9. Preliminary 500-m MODIS percent tree cover map for North America.
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Fig. 10. Preliminary 500-m MODIS percent tree leaf type for North America.
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Fig. 11. (a) Per state thresholds at which the area estimate of the 500-m tree cover map matches United States Forest Service estimates. This value is found per
state by starting at the highest percent tree cover values in the 500-m map and calculating area totals as the tree cover threshold is lowered. For the 500-m map,
the area of tree cover greater than or equal to the threshold value shown yields the same area as estimated by the USFS. (b) Application of weighted mean
threshold (35% tree cover) which yields an areal match with the Forest Service data for the lower 48 United States. Gray is tree cover greater than or equal to
35%; black is less than 35%.
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Fig. 12. Regional comparisons of threshold matches between 500-m continuous tree cover map and United States Forest Service estimates.
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fashion to the 1-km AVHRR by taking every tenth pixel to
reduce data volumes. A final tree of 90 nodes was created
from the 24 input channels (bands 17 and NDVI for three
40-day composites). The initial node estimates yielded an
rmse for the 82,082 training pixels of 11.07% tree cover
which was reduced to 10.32% and 9.93% after regression
and median refinements. The result is shown in Fig. 9.The same procedure was followed for tree leaf type,
resulting in a map of percent needleleaf and broadleaf tree
cover. For training sites with greater than 10% tree cover,
the percent contribution of broadleaf tree cover was used as
training. This yielded 48,105 training pixels. The procedure
was followed as before and the percent needleleaf calculated
by taking the difference of the percent total tree cover less
the product of the percent broadleaf and percent tree cover.
The result is shown in Fig. 10. The subsets in both Figs. 9
and 10 show the increased detail available with MODIS
compared to AVHRR.
4. Evaluation of preliminary 500-m tree cover for lower
48 United States
The 500-m tree cover map was compared to United States
Forest Service (USFS) statistics for the lower 48 United
States (Powell, Faulkner, Darr, Zhu, & MacCleery, 1992).
Beginning with the densest forest stratum and lowering the
continuous field threshold, a cutoff can be found for which
the forest area estimate of the USFS can be matched. Fig. 11
shows for each state which continuous field threshold yields
an equivalent areal estimate. A mean weighted by USFS state
area estimates was derived, which results in a match for totalforest area for the lower 48 states. A threshold of 35% results
in a total of 2.35 million km2 compared to the USFS estimate
of 2.42 million km2. The Forest Service definition of forest is
land at least 10% stocked by trees of any size (Powell et al.,
1992), but also includes areas formerly with tree cover with
plans to be afforested. Fig. 11 also shows the resulting forest/
nonforest map after applying this threshold to the continuous
field map. States in Fig. 11a with thresholds below and above
this cutoff will, respectively, under- and overestimate the
USFS figures.
There are many regional differences in terms of which
threshold best matches the USFS state areas totals. Fig. 12
shows these findings. For example, the intermountain west,
centered on desert southwest states, has the lowest matching
thresholds of any region. A clear reason for this is the
inclusion of shorter stands of woody cover as forest in the
USFS forest definition. Pygmy pinyon forests, chaparral and
shorter oak scrub are labeled forest in the USFS definition
(Powell et al., 1992). The continuous field implementation
uses a definition of tree as any woody plant in excess of 5 m
in height. Much of the moisture limited woody cover found
in the western United States does not meet this definition. A
continuous training data set for short woody vegetation is
being developed to augment the tree cover layer.
The corn belt is not a traditional regional subset like the
other regions, but is included here due to the consistently
low threshold found for the dominant corn producing
states. This could be the result of an increased fragmenta-
tion of forest in this area and a confusion in spectral space
between crops and sub-pixel forest which is biased toward
crops. The rest of the Midwest and Great Plains states havegreat consistency in a threshold of at or near 36%. As one
trends east the thresholds increase with the highest match-
ing thresholds being the heavily forested south and north-
east.
These results show that the algorithm is producing
consistent results which compare well with the USFS
statistical database. Such results should be repeatable and
allow for developing thresholds of change detection for
monitoring purposes. This would help augment the labor-
intensive approach to forest area estimation employed by
the USFS. However, calculating area totals can be compli-
cated by fragmentation, as a pixel with half of its area in
100% tree cover will yield the same cover area estimate as a
uniform, homogeneous 50% woodland pixel. Fragmentation
could be developed as an ancillary layer in improving area
estimates at the sub-pixel level.
5. Conclusion
The new procedure for depicting a continuous field of
tree cover is an improvement over the prototype approach.
The main advance is that the algorithm is fully automated.
All of the products here were generated using the new
technique and do not include an interpreters input. Thecontinuous field training data have been critical to this
advance by containing signatures across a wide range of
spatial and spectral mixtures. The algorithm is made more
stable in this way as signatures are not derived from only
core cover exemplar sites. The regression tree algorithm is
an advance as well, in that it can handle the nonlinear
relationships present in a global sample of tree cover.
Present work for the 500-m MODIS continuous field layers
includes creating the annual metrics and producing global
tree cover, leaf type and leaf longevity layers. The examples
shown here indicate that MODIS data will be a substantial
improvement over AVHRR in mapping tree cover. The
spatial detail present in MODIS imagery is unprecedented
for satellites of this kind. However, preserving the finest
spatial detail within the compositing process might require
new approaches.
Acknowledgements
This research was funded by the National Aeronautics
and Space Administration under contract NAS596060, grant
NAG59339, and the Earth Science Information Partnership
(ESIP) program under grant NCC5300.
M.C. Hansen et al. / Remote Sensing of Environment 83 (2002) 303319318
7/27/2019 Hansen Et Al 2002b
17/17
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