Assessing Forest Loss in Protected Areas: A Philippines Case Study

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University of Southern Queensland
University of Southern Queensland
Toowoomba, Queensland 4350 AUSTRALIA
Toowoomba, Queensland 4350 AUSTRALIA
apana@usq.edu.au
apana@usq.edu.au
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Introduction
Introduction
Methods
Methods
Study Area
Study Area
Data Acquisition
Data Acquisition
Analysis of forest loss
Analysis of forest loss
Logistic regression analysis
Logistic regression analysis
Results and Discussion
Results and Discussion
Rate and extent of forest loss
Rate and extent of forest loss
Logistic regression models
Logistic regression models
Conclusions
Conclusions
p. 2/24
Forests 4 Climate
JLR, 2010
 
 
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Deforestation
Deforestation
 in the Philippines has been
 in the Philippines has been
rampant and rapid.
rampant and rapid.
Forest cover has declined from 
Forest cover has declined from 
17.1 M ha
17.1 M ha
(1937) to
(1937) to
 8.0 M ha 
 8.0 M ha 
(2015)
(2015)
Protected Areas 
Protected Areas 
are effective in reducing
are effective in reducing
deforestation; some are not.
deforestation; some are not.
Need to understand the 
Need to understand the 
drivers
drivers
 of deforestation
 of deforestation
in protected areas.
in protected areas.
Sharif Mukul, 2016
p. 3/24
 
 
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:
:
forest cover loss in all 
forest cover loss in all 
terrestrial
terrestrial
 protected areas (PAs) of the
 protected areas (PAs) of the
entire
entire
 Philippines
 Philippines
covering 
covering 
198 PAs 
198 PAs 
with a total area of 
with a total area of 
4.68 million ha
4.68 million ha
AFP/File, 2013
p. 4/24
 
 
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:
:
1.
to compare the 
to compare the 
rate
rate
 and 
 and 
extent of
extent of
forest loss
forest loss
:
:
entire country 
entire country 
vs.
vs.
 terrestrial
 terrestrial
protected areas 
protected areas 
vs.
vs.
 buffer areas
 buffer areas
2.
to determine the 
to determine the 
significance
significance
 and 
 and 
magnitude
magnitude
 of the
 of the
relationships between 
relationships between 
forest cover 
forest cover 
and selected
and selected
spatially explicit variables
spatially explicit variables
.
.
Philippine EnviroNews 
p. 5/24
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covers 298,170 km
covers 298,170 km
2
2
tropical climate
tropical climate
101 million people (2016)
101 million people (2016)
one of world’s top
one of world’s top
biodiversity-rich countries
biodiversity-rich countries
p. 6/24
 
 
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derived from 
derived from 
Landsat 
Landsat 
imagery (30m)
imagery (30m)
analysis performed using 
analysis performed using 
Google Earth Engine 
Google Earth Engine 
(cloud platform)
(cloud platform)
Trees are defined as “
Trees are defined as “
all vegetation taller than 
all vegetation taller than 
5m
5m
 in height
 in height
forest loss: “
forest loss: “
a stand-replacement 
a stand-replacement 
disturbance
disturbance
 or the 
 or the 
complete
complete
removal 
removal 
of tree cover canopy
of tree cover canopy
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.”
p. 7/24
 
 
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used “
used “
time-series spectral metrics
time-series spectral metrics
” as key algorithm
” as key algorithm
output layers: 
output layers: 
tree cover 
tree cover 
(2000); forest 
(2000); forest 
loss
loss
 and 
 and 
gain
gain
 (2000-2012)
 (2000-2012)
reported accuracy of 
reported accuracy of 
99.6%
99.6%
free 
free 
download
download
p. 8/24
 
 
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Yearly Forest Cover Loss (2001-2012)
Yearly Forest Cover Loss (2001-2012)
p. 9/24
 
 
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p. 10/24
 
 
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p. 11/24
 
 
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p. 12/24
 
 
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p. 13/24
 
 
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Assess 
Assess 
accuracy
accuracy
 of forest
 of forest
cover map (2012)
cover map (2012)
Extract forest areas with
Extract forest areas with
>10% canopy cover
>10% canopy cover
Intersect with “
Intersect with “
Forest
Forest
Cover Loss
Cover Loss
” maps
” maps
Intersect with “
Intersect with “
Protected
Protected
Areas
Areas
” map
” map
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p. 14/24
 
 
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estimated the 
estimated the 
probability 
probability 
of deforestation occurrence
of deforestation occurrence
modelled the relationship between:
modelled the relationship between:
independent variables 
independent variables 
(11 maps)
(11 maps)
dependent variable 
dependent variable 
(“
(“
no forest loss
no forest loss
”, “
”, “
forest loss
forest loss
”)
”)
used 
used 
Spearman's rho 
Spearman's rho 
to assess any multi-collinearity issues
to assess any multi-collinearity issues
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p. 15/24
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Rate of forest loss in 
Rate of forest loss in 
protected areas 
protected areas 
(vs.
(vs.
 
 
entire Philippines
entire Philippines
)
)
 
 
is
is
marginally lower
marginally lower
But it is equivalent to a total of 
But it is equivalent to a total of 
3,738
3,738
 ha over 12 years
 ha over 12 years
p. 16/24
 
 
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Annual and cumulative forest loss in the Philippines
Annual and cumulative forest loss in the Philippines
p. 17/24
 
 
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Inside PAs
Inside PAs
 forest loss rate was lower (
 forest loss rate was lower (
1.87%
1.87%
) vs. 
) vs. 
2-km buffer (
2-km buffer (
2.63%
2.63%
).
).
Forest loss in 
Forest loss in 
buffer zones 
buffer zones 
is 
is 
1.4 times (40.6%)
1.4 times (40.6%)
 higher than the PAs.
 higher than the PAs.
p. 18/24
 
 
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But some PAs have phenomenal 
But some PAs have phenomenal 
forest loss rates 
forest loss rates 
(e.g. 21%)
(e.g. 21%)
p. 19/24
 
 
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Some areas with vast areas of forest loss (e.g. 
Some areas with vast areas of forest loss (e.g. 
48,583 ha
48,583 ha
)
)
p. 20/24
 
 
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p. 21/24
 
 
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p. 23/24
THANK YOU!
THANK YOU!
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The study assesses forest cover loss in terrestrial protected areas of the Philippines, analyzing the extent and rate of deforestation using Hansen's Global Forest Cover Change datasets. The research aims to understand the drivers of deforestation in protected areas, comparing forest loss in the entire country, terrestrial protected areas, and buffer zones. Methods included data acquisition through Landsat imagery analysis, focusing on tree cover and forest loss metrics. The findings provide insights into the significance of relationships between forest cover and spatial variables.


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  1. Using Hansen's Global Forest Cover Change Datasets to Assess Forest Loss in Terrestrial Protected Areas A Case Study of the Philippines Armando Apan (Prof.), L.A. Suarez, Tek Maraseni & Allan Castillo University of Southern Queensland Toowoomba, Queensland 4350 AUSTRALIA apana@usq.edu.au

  2. Outline of Presentation Introduction Methods Study Area Data Acquisition Analysis of forest loss Logistic regression analysis Results and Discussion Rate and extent of forest loss Logistic regression models Conclusions p. 2/24

  3. Introduction Deforestation in the Philippines has been rampant and rapid. Forests 4 Climate Forest cover has declined from 17.1 M ha (1937) to 8.0 M ha (2015) Protected Areas are effective in reducing deforestation; some are not. JLR, 2010 Need to understand the drivers of deforestation in protected areas. Sharif Mukul, 2016 p. 3/24

  4. Introduction This study assessed: forest cover loss in all terrestrial protected areas (PAs) of the entire Philippines covering 198 PAs with a total area of 4.68 million ha AFP/File, 2013 p. 4/24

  5. Introduction Objectives: 1. to compare the rate and extent of forest loss: entire country vs. terrestrial protected areas vs. buffer areas Philippine EnviroNews 2. to determine the significance and magnitude of the relationships between forest cover and selected spatially explicit variables. p. 5/24

  6. Methods Study Area covers 298,170 km2 tropical climate 101 million people (2016) one of world s top biodiversity-rich countries p. 6/24

  7. Methods Data Acquisition 1. Global Forest Change map (Hansen et al., 2013) derived from Landsat imagery (30m) analysis performed using Google Earth Engine (cloud platform) Trees are defined as all vegetation taller than 5m in height forest loss: a stand-replacement disturbance or the complete removal of tree cover canopy. p. 7/24

  8. Methods Data Acquisition used time-series spectral metrics as key algorithm output layers: tree cover (2000); forest loss and gain (2000-2012) reported accuracy of 99.6% free download p. 8/24

  9. Methods Yearly Forest Cover Loss (2001-2012) p. 9/24

  10. Methods Data Acquisition 2. World Database on Protected Areas (UNEP-WCMC, 2015) p. 10/24

  11. Methods Data Acquisition Land use (ISCGM, 2011) Population Density (WorldPop, 2015) Digital Elevation Model (SRTM) Land Cover (NAMRIA, 2013) Road(OpenStreetMap, 2015) River(Lehner et al., 2006) p. 11/24

  12. Methods p. 12/24

  13. Methods p. 13/24

  14. Methods Analysis of Forest Extent and Rate of Forest Loss Extent of Forest Loss in TPA >10% Tree Canopy Cover Tree Canopy Cover (2000) Data Processing & Analysis Rate of Forest Loss in TPA Forest Cover Loss (2001-2012) Terrestrial Protected Areas (TPA) Sample Points (300,000) Protected Areas Assess accuracy of forest cover map (2012) Logistic Regression Analysis Land Use Extract forest areas with >10% canopy cover Elevation Digital Elevation Model Slope Aspect Distance from River Intersect with Forest Cover Loss maps River Logistic Regression Model of Forest Loss in TPA Distance from Road Road Dist. Closed Canopy Forest Intersect with Protected Areas map Dist. Open Canopy Forest Land Cover Dist. Cropping Areas Population Density p. 14/24

  15. Methods Data Processing & Analysis Logistic Regression estimated the probability of deforestation occurrence modelled the relationship between: independent variables (11 maps) dependent variable ( no forest loss , forest loss ) used Spearman's rho to assess any multi-collinearity issues p. 15/24

  16. Results and Discussion Overall Accuracy of Hansen dataset (2012) : 93.1% Rate of forest loss in protected areas (vs. entire Philippines) is marginally lower Protected Area Parameter Philippines Total Forest Loss by 2012 (ha) 529,675 97,007 Average Forest Loss (ha/yr) over 12 years 44,140 8,084 2.69% 2.59% Rate of Forest Loss (%) over 12 years But it is equivalent to a total of 3,738 ha over 12 years p. 16/24

  17. Results and Discussion Annual and cumulative forest loss in the Philippines p. 17/24

  18. Results and Discussion Inside PAs forest loss rate was lower (1.87%) vs. 2-km buffer (2.63%). Forest loss in buffer zones is 1.4 times (40.6%) higher than the PAs. p. 18/24

  19. Results and Discussion But some PAs have phenomenal forest loss rates (e.g. 21%) Cumulative Forest Loss Area (ha) Cumulative Forest Loss Rate (%) Forest Area (ha) Protected Area 2,753 6,317 643 3,267 419 578 660 56 277 31 20.98% 10.45% 8.65% 8.47% 7.44% Magapit Angat Fuyot Springs Dinadiawan River Sohoton p. 19/24

  20. Results and Discussion Some areas with vast areas of forest loss (e.g. 48,583 ha) Cumulative Forest Loss Area (ha) Cumulative Forest Loss Rate (%) Forest Area (ha) (2000) Protected Area 48,583 12,340 5,985 3,531 2,880 980,537 442,095 159,160 120,590 274,905 4.95% 2.79% 3.76% 2.93% 1.05% Palawan Samar Quirino Unnamed NP Northern S. Madre p. 20/24

  21. Results and Discussion Spatial predictor variables have no or weak relationships with forest cover loss. Spearman Correlation (vs. Forest Loss) -0.305 -0.220 0.179 -0.170 0.163 -0.137 0.093 -0.055 -0.033 -0.029 -0.023 Variables Elevation Distance from cropping area Population density Distance from road Distance from closed canopy forest Slope Distance from open canopy forest Land cover Land use Distance from river Aspect p. 21/24

  22. Results and Discussion Model fit and classification accuracies were not good, with only 15% of the variance explained. Model % Correct Baseline, intercept-only (no regression model applied) Socio-economic variables only 50.0 58.9 Proximity variables only 61.1 Topographic variables only 62.9 All variables included 64.9 Only 15% improvement p. 22/24

  23. Conclusions Global Forest Cover Change datasets: useful for the country-wide assessment of forest loss. Protected areas are generally effective in reducing deforestation. However, some areas indicate the ineffectiveness of PAs. Selected variables are not reliable for predictive modelling of forest loss. p. 23/24

  24. THANK YOU!

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