ANALYSIS OF DEFORESTATION HOT-SPOTS IN MEXICO OVER
2000-2010 USING TIME-SERIES MODIS VEGETATION
CONTINUOUS FIELDS (VCF) DATA
Yan GAOa, J-F MASa, Jaime PANEQUE-GÁLVEZa, Margaret SKUTSCHa, Antonio
NAVARRETE-PACHECOa, Adrián GHILARDIa , Gabriela CUEVASa, Beth BEEa,
a
CIGA-UNAM, Morelia, Michoacán, email: ygao@ciga.unam.mx , jfmas@ciga.unam.mx ,
jpanequegalvez@gmail.com , mskutsch@ciga.unam.mx , janp@ciga.unam.mx , aghilardi@ciga.unam.mx,
gcuevas@ciga.unam.mx, beth@ciga.unam.mx
ABSTRACT
This paper analyzes deforestation hotspots in Mexico using MODIS Vegetation Continuous Fields (VCF)
data, which contains four science data sets. The first science data set is percent tree cover, which gives an
estimate of the percentage of crown cover in each pixel; the remaining three science data sets can be used
to assess the reliability of the percent tree cover values. VCF data were produced with a regression tree
algorithm based on a 16-day surface reflectance composite including MODIS bands 1-7 and the brightness
temperature band, with training data gathered from high spatial resolution satellite imagery. For this study,
VCF time-series data from 2000 to 2010, as presented in seventy-seven tiles, were downloaded; for each
year, seven image tiles were mosaicked to cover the entire Mexican territory. We first obtained 467
individual deforestation patches greater than 500 ha, including 155 in temperate forest, 246 in tropical
forest and 66 patches each greater than 100 ha from tropical dry forest, from national land cover maps
(2003 and 2007), and regional land cover maps (2005 and 2010). We then used VCF data to obtain the
annual trend of forest cover change from 2000 to 2010 within the deforestation patches, by considering
those three types of forest separately. We found some trends in percent tree cover showing decreasing
during the study period, with evident fluctuations in most of the areas analyzed.
Using deforestation patches generated from national land cover maps, we assessed PTC values at 2003
and 2007, including 1) calculate percentage of pixels which PTC 2003 > PTC 2007 , 2) calculate percentage of
pixels indicating deforestation according to the FAO definition, PTC 2003 > 10 and PTC 2007 < 10, 3) calculate
percentage of pixels indicating deforestation according to the UNFCCC definition, PTC 2003 > 30 and PTC
2007 < 30, 4) calculate percentage of pixels whose PTC 2003 > 10, PTC 2007 > 10, and PTC 2003 > PTC 2007, 5)
calculate percentage of pixels whose PTC 2003 > 30, PTC 2007 > 30, and PTC 2003 > PTC 2007. Results show that
PTC values and known land cover data did not show good correlation. More analysis needs to be carried
out before drawing the conclusion about if MOD44B PTC data can be used for deforestation/forest
degradation hotspots detection.
Key words: Deforestation, MODIS, MOD44B
1 INTRODUCTION
The
objective
is
to
assess
whether
deforestation/forest degradation hotspots in
Mexico can be identified using percent tree cover
(PTC) information from MODIS MOD44B, which
gives the percentage of crown cover per pixel. If
the PTC data accurately identifies areas known
from other sources to be deforested, it would
provide a useful tool in the future to observe the
change in the coverage of forests and identify
hotspots for deforestation. A main advantage of
data from MODIS over other sources is its high
frequency of coverage, which would allow rapid
identification of new deforestation areas.
MODIS Vegetation Continuous Fields (VCF) data
contain four scientific data sets (sds). The first sds
is PTC while the remaining three sds are quality,
PTC standard deviation, and clouds, all of which
indicate the reliability of PTC values (Townshend
et al. 2011). MODIS VCF temporal coverage
begins in 2000 (LPDAA 2012a) and currently 11
years of data are available. VCF data were
delivered as a set of HDF-EOS files divided into
the standard tiles in the Sinusoidal equal area
projection. Each tile has 4800 samples *4800 rows
(250 m spatial resolution). PTC is estimated using
a supervised regression tree algorithm and data
derived from MODIS visible bands contribute to
discriminating tree cover. Hansen et al. (2003)
showed that MODIS data yield greater spatial
detail in the characterization of tree cover
compared to AVHRR data (Hansen et al. 2003).
The product should allow for using successive tree
cover maps in change detection studies at the
global scale (Defries et al. 1995). Initial validation
efforts show a high correlation (R2 = 0.89) between
the MODIS estimated PTC and that from
validation sites (Hansen et al. 2003). Two data sets
derived using field data and multi-resolution
satellite imagery for Colorado (USA), and West
Province (Zambia) have been used to test VCF
data. The validation was for VCF values smaller
than 10, between 11 to 40, from 40 to 60, and
above 60, and the overall standard error of estimate
values was 11.6% for Colorado, and 11.5% for
Zambia (Hansen et al. 2003).
However, some studies have found higher
discrepancies between this MODIS product and
others derived from conventional data sets. For
instance, Liu et al. (2006) found low agreement
between visually interpreted Landsat imagery and
VCF data for estimating continuous tree
distribution in China, with estimates of forest
pixels from Landsat being up to four times higher
for densely forested areas and four times lower for
areas of sparse forest. This coincides with Griffin
(2012), who observed a positive correlation
between Landsat and VCF within each year;
however, the tendency was weak with high
variability across the range of forest cover. Harris
et al. (2005) found that MODIS (MOD12Q1,
global land cover imagery) identified 20% less
forested area than Landsat ETM+ imagery for
forest patches larger than 10 km2 (1000 ha), and
had even lower agreement for smaller patches.
2 DATA AND METHODS
2.1 DATA
The data used in this study are presented in table 1.
Seven MODIS VCF tiles were needed to cover the
entire Mexican territory and seventy-seven tiles
were downloaded to construct annual time-series
data over 2000 – 2010. These images were
imported, re-projected individually using Marine
geospatial
ecology
tools
(http://mgel.env.duke.edu/mget), and, for each
year, seven tiles were mosaicked; thus, an 11-year
time-series of VCF images covering Mexico from
2000 to 2010 was produced for the analysis of
deforestation.
Table 1. Data sets used in the study.
Data
MODIS VCF
Land cover
maps
GIS data
Details
MODIS Vegetation Continuous
Fields v. 5, downloaded from
EarthExplorer
(http://earthexplorer.usgs.gov/).
1.from INEGI, cover Mexican
territory
2. Ayuquila area, Jalisco, derived by
visual interpretation of aerial
photographs (1995) and SPOT
images (2004, 2010).
Mexican country boundaries, state,
and municipality maps from INEGI
2.2 METHODS
Deforestation sites were generated by comparing
two national land cover maps (2003 and 2007),
which had been produced by interpretation of
Landsat images. Deforestation areas in temperate
forest and in tropical forest was considered
separately and used to estimate trends of forest
cover change.
2.2.1 Deforestation patches in temperate
forest and tropical forest based on national
land cover maps from 2003 and 2007
Figure 2, distribution of deforestation patches in
tropical forest, based on national land cover maps
at 2003 and 2007, indicated by red colored patches.
To derive deforestation patches of temperate
forest, as the first step, the two land cover maps
were reclassified into “temperate forest” and “nontemperate forest”. These two reclassified maps
were compared and deforestation patches of
temperate forest were obtained by GIS operations.
False changes were eliminated including the
changes from temperate forest to tropical forest.
Since there were shifts in the boundaries of the two
land cover maps, which caused many small false
change patches and so we kept only patches of
deforestation with areas larger than 500 ha, and we
obtained 155 such patches. In a similar way, we
obtained deforestation patches for tropical forest,
which includes tropical dry forests of various kinds
as well as moist tropical forest. We kept only
patches with areas more than 500 ha and we
obtained 246 such polygons.
2.2.2 Deforestation patches of tropical dry
forest based on regional land cover maps
from 2004 and 2010
Figure 1, distribution of deforestation patches in
temperate forest, based on national land cover
maps at 2003 and 2007, indicated by red colored
patches.
Deforestation patches were also derived from land
cover maps for the Ayuquila basin (Jalisco), where
tropical dry forest predominates. These were
created by visual interpretation of aerial
photographs (1995) and SPOT images (2004,
2010) at the scale of 40,000. The three land cover
maps were compared and deforestation areas over
two time intervals were identified (1995 – 2010,
2004 – 2010) and used to verify the VCF data.
Polygons of tropical dry forest deforestation with
areas more than 100 ha were selected and 66 such
patches were obtained.
2.2.3 Trends of time-series VCF data in
deforestation patches
The VCF time-series data at those 467
deforestation patches of temperate forest, tropical
forest, and tropical dry forest were exported as
ASCII files. An average PTC value in each
deforestation patch was calculated in Excel for the
time-series VCF data, and for each deforestation
patch a trend of PTC value was obtained from
2000 to 2010.
2.2.4 Assessment of PTC values at known
deforestation patches
This part of the analysis is for PTC values from
VCF data. Using deforestation patches generated
by known data source, we analyzed PTC values at
two dates, 2003 and 2007, the same with the dates
of national land cover maps, including 1)
comparison of PTC values at 2003 and 2007, at
those pixels within the detected deforestation
patches; 2) calculate percentage of pixels whose
PTC value in 2003 is above 10, and in 2007 is
below 10. Here the threshold 10 is selected
according to the FAO definition for forest, which
is above 10% of canopy cover; 3) calculate
percentage of pixels whose PTC value in 2003 is
above 30, and PTC value in 2007 is below 30.
Here the threshold 30 is selected according to the
UNFCCC definition for forest, which is above
30% of canopy cover. 4) Calculate percentage of
pixels whose PTC values in 2003 and 2007 are
both higher than 10, and the values in 2003 higher
than 2007. 5) Calculate percentage of pixels whose
PTC values in 2003 and 2007 are both higher than
30, and the values in 2003 higher than 2007.
3.1.1 Time series PTC values at patches of
forest
Figure 3 shows the trends of PTC changes at sites with
temperate forest loss (a), tropical forest loss (b), and
tropical dry forest loss (c).
a.Trends at patches
temperate forest loss
of
c.Trends at patches
tropical dry forest loss
of
b.Trends at patches
tropical forest loss
of
3 RESULTS
3.1 EVALUATION OF MOD44 DATA
USING KNOWN DATA INCLUDING
INEGI LAND COVER MAPS FROM 2003
AND 2007 AND REGIONAL LAND
COVER MAPS
From national land cover maps, we chose 155 sites
with areas more than 500 ha for deforestation in
temperate forest, 246 sites with areas more than
500 ha for deforestation in tropical forest; from
regional land cover maps, we selected 66 sites for
deforestation in tropical forest in regional maps.
The choice for the number and size of the patches
is in accordance with the scales of the maps and
the scales of the patches of deforestation. There is
more deforestation in tropical forest than in
temperate forest. Based on the number and size of
deforestation patches identified, deforestation took
place mainly in the states of Yucatán, Chiapas,
Oaxaca, Guerrero, Michoacán, Jalisco, Nayarit,
Chihuahua, and Durango (figure1 and figure 2).
We obtained trends of VCF time-series data
indicated by PTC values for deforestation in
temperate forest, tropical forest, and tropical dry
forest, represented by curves of PTC values in
function of time. Since we have 155 such curves
for temperate forest, 246 in tropical forest, and 66
in tropical dry forest, we presented only some
examples with clear trends (figure 3).
Figure 3. The Y axis refers to PTC, and the X axis to
time. The thirteen curves refer to thirteen specific
polygons with temperate forest loss (four curves),
tropical forest loss (five curves), during the period 2003
– 2007, and tropical dry forest loss (four curves) during
the period of 2005 – 2010. In the legend, ave52 means
the average PTC values for patch #52 and this applies to
the legend of the rest of the curves.
3.2 RESULTS OF PTC VALUE
ASSESSMENT AT PIXELS OF KNOW
DEFORESTATION PATCHES AT 2003
AND 2007
The analysis result is summarized in table 2.
1) at pixels of temperate forest deforestation, only
39.7% of the pixels have PTC values at 2003
higher than 2007, only 4.8% pixels have PTC
values in 2003 above 10, and in 2007 below 10,
indicating deforestation according to FAO
standard; and only 6.2% pixels have PTC values in
2003 above 30 and in 2007 below 30, indicating
deforestation according to UNFCCC standard.
2) at pixels of tropical forest deforestation, 52.3%
of the pixels have PTC values at 2003 higher than
2007, showing a decrease in PTC values; only
5.3% of the pixels have PTC values in 2003 above
10, and in 2007 below 10, indicating deforestation
according to FAO standard; only 8.2% of the
pixels whose PTC values in 2003 is above 30 and
in 2007 below 30, indicating deforestation
according to UNFCCC standard.
Table 2. percentage of pixels showing the
coincidence with the known deforestation data.
Criteria
No of PTC
pixels at sites
of temperate
forest loss
No of PTC
pixels at sites
of tropical
forest loss
PTC 2003 > PTC 2007
39.7 %
52.3%
PTC 2003 > 10 and
PTC 2007 < 10
4.8 %
5.3 %
PTC 2003 > 30 and
PTC 2007 < 30
6.2 %
8.2%
PTC 2003 > 10, PTC
2007 > 10, and PTC
2003 > PTC 2007
25.9 %
38.7 %
PTC 2003 > 30, PTC
2007 > 30, and PTC
2003 > PTC 2007
9.4%
the known data. PTC values seem to have better
coincidence for sites with degradation than
deforestation by showing higher percentage in
pixels, indicated by rows 4 and 5 comparing to
rows 2 and 3 in table 2. This might indicate
MOD44B PTC values maybe more suitable for
measuring degradation than
deforestation.
However, further verification is needed before we
can conclude if MOD44B VCF data are suitable
for deforestation or degradation hotspots detection.
ACKNOWLEDGEMENTS
This work forms part of the project “Reinforcing
REDD+ readiness in Mexico and enabling SouthSouth cooperation”, in which CIGA/CIECOUNAM is collaborating with CONAFOR.
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4 DISCUSSIONS AND CONCLUSIONS
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