Degradation monitoring - GOFC-GOLD Land Cover Project Office

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Degradation monitoring - GOFC-GOLD Land Cover Project Office
Degradation Monitoring
Methods
Carlos Souza Jr.
[email protected]
Imazon
www.imazon.org.br
Forest Degradation
Selectively logged forest, Sinop-MT
Deforested area for plantation, Sinop-MT
Forest degradation has been defined as a type of land modification,
which means that the original land cover structure and composition is
temporarily or permanently changed, but it is not replaced by other type
of land cover type (Lambin, 1999).
Sources of Human Pressure that Cause Forest
Degradation
Remote Sensing Detection
Highly Detectable
Marginally
Detectable
Almost Undetectable
Deforestation
► Forest fragmentation
► Recent slash-and-burn
agriculture
► Major canopy fires
► Major roads
► Conversion to three
monocultures
► Hydroelectric dams and other
forms of flood disturbances
► Large-scale mining
Selective logging
► Forest surface fires
► A range of edge-effects
► ‘Old-slash-and-burn agriculture
► Small scale gold-mining
Unpaved secondary
roads (6► Selective
logging
20-m wide)
Burned forests
► Selective thinning of canopy
Forest fragmentation
trees
Hunting and exploitation of
animal products
► Harvesting of most non-timber
plants products
► Old-mechanized selective
logging
► Narrow sub-canopy roads (<6m wide)
► Understorey thinning and and
clear cutting
► Invasion of exotic species
► Spread of pathogens
► Changes in net primary
productivity
► Community wide shifts in plant
species composition
► Other cryptic effects of climate
changes
► Most higher-order effects
►
►
Roads
Gold mining
Peres et al., (2006), TREE
►
Selective Logging
Selective Logging in Sinop – MT, Brazil
Photo: Carlos Souza Jr.
Deforestation, Selective Logging and Fires
Photo: P. Barreto, Paragominas,
Paragominas, PA. 1993
►
Predominantly unplanned
►
Harvesting intensity varies from 5
to 40 m3 of logs / ha
►
Builds extensive road network
►
Creates favor conditions for forest
fires
►
Catalyzes deforestation
Souza Jr. and Roberts (2005)
Available Methods to Detect and Map Selective Logging
Mapping
Approach
Visual
Interpretation
Detection of
Logging Landings
+ Buffer
Studies
Sensor
Spatial
Extent
Disadvantages
Does not require
sophisticated image
processing techniques
Labor intensive for large
areas and may be user
biased to define the
boundaries.
Local
Map total logging
area (canopy
damage, clearings
and undamaged
forest)
Relatively simple to
implement and
satisfactorily estimate
the total logging area
Logging buffers varies
across the landscape
and does not reproduce
the actual shape of the
logged area.
SPOT 4
Local
Map forest canopy
damage associated
with logging and
burning
Simple and intuitive
classification rules.
It has not been tested in
very large areas and
classification rules may
vary across the
landscape.
Souza Jr. et al. (2002)
Landsat
TM5 e
ETM+
Local
Map forest canopy
damage associated
with logging and
burning
Enhances forest canopy
damaged areas.
Requires two pairs of
images and does not
separate natural and
anthropogenic forest
changes.
Alencastro Graça et al.
(2005)
Landsat
TM5
Local
Map total logging
area (canopy
damage, clearings
and undamaged
forest)
Relatively simple to
implement and
satisfactorily estimate
the total logging area.
Free software available.
It has not been tested in
very large areas and
segmentation rules may
vary across the
landscape.
Asner et al., 2005
Landsat
TM5 e
ETM+
Three states of
the Brazilian
Amazon (PA,
MT and AC)
Map total logging
area (canopy
damage, clearings
and undamaged
forest)
Fully automated and
standardized to very
large areas.
Requires very high
computation power, and
pairs of images to forest
change detection. Tested
only with Landsat ETM+
Souza Jr., 2005b
Landsat
TM5 e
ETM+
Local
Map forest canopy
damage associated
with logging and
burning
Enhances forest canopy
damaged areas.
It has not been tested in
very large areas and
does not separate
logging from burning
damages.
Landsat
TM5
Local
Stone and Lefebvre
(1998)
Landsat
TM5
Local
Matricardi et al. (2001)
Landsat
TM5
Brazilian
Amazon
Santos et al. (2002)
Landsat
TM5
Brazilian
Amazon
Souza Jr. e Barreto
(2000)
Matricardi et al. (2001)
Monteiro et al. (2003)
Silva et al. (2003)
Landsat
TM5 e
ETM+
Souza Jr. et al. (2003)
Change Detection
CLAS
NDFI+CCA
Advantages
Map total logging
area
Watrin e Rocha (1992)
Decision Tree
Image
Segmentation
Objective
LAB - human dimension book, Chapter 3 (in prep.)
Selective Logged and Forests Forest
in Landsat Images
2000
1998
Selective
logging and burning
Selective
logging
2001
1999
Old selective
logging and
burning
Old Selective
logging
R5, G4, B3
Souza Jr. et al., (2003)
Image Processing Steps
(1) PRE-PROCESSING
Image Registration
Radiance Conversion
Estimate Visibility
and water vapor
Landsat
Correct
Haze?
Yes
No
Atmospheric
Correction
(ACORN)
CCA
NDFI ≤ 0.75
(3) SMA
Canopy
Damage
Soil ≥ 10%
1 pixel ≤ Area ≤ 4 pixels
Souza Jr. et al. (2005), RSE
Landsat
NPV
Pixel Purity
Index - (PPI)
40 million
GV
SVDC
Soil
Extract
Patios
Reflectance
Space
Apply Carlotto’s
Technique
(4) Enhance and Detect
Canopy Damage
NDFI
(2) Build
Spectral Library
Shade
GV + NPV + Soil + Shade = 1
pixels
Visualization
Scatter matrix
Spectral curves
Generic Image
Endmembers
Haze Correction
Contaminated Image
Corrected Image
Ji-Parana, 231/67 – R3, G2, B1
Generic Image Endmembers
Souza Jr. et al. (2005), RSE
Mapping Selective Logging with Landsat Image
Soil Fraction
Roads
Logged
Forest
226/68 - 2001 (Sinop - MT)
(Souza Jr. et al., 2005)
Mapping Selective Logging with Landsat Image
NPV Fraction
Roads
Logged
Forest
226/68 - 2001 (Sinop - MT)
(Souza Jr. et al., 2005)
Mapping Selective Logging with Landsat Image
GV Fraction
Roads
Logged
Forest
226/68 - 2001 (Sinop - MT)
(Souza Jr. et al., 2005)
Mapping Selective Logging with Landsat Image
NDFI (Normalized Difference
Fraction Index)
Logged
Forest
226/68 - 2001 (Sinop - MT)
Roads
(Souza Jr. et al., 2005)
Mapping Burned Forests with Landsat Image
NDFI
226/68 - 2000 (Sinop - MT)
(Souza Jr. et al., 2005)
Mapping Burned Forests with Landsat Image
NDFI
226/68 - 2001 (Sinop - MT)
(Souza Jr. et al., 2005)
Mapping Burned Forests with Landsat Image
NDFI
226/68 - 2003 (Sinop - MT)
(Souza Jr. et al., 2005)
Standardized
NDFI (2001)
Rondônia
Contextual Classification Algorithm - CCA
Step 1:
•Find log landings
Soil > 10%
•Find regions and calculate area
1 ≤ Area ≤ 4 pixels
Step 2:
•Grow a canopy damage region around log
landings
•Search for NDFI neighboring cell NDFI values
If NDFI > 0.75 then Intact Forest
if 0 ≤ NDFI ≤ 0.75 then Canopy Damage
Soil
Fraction
Log
Landings
Canopy
Damage
CCA Results
Conventional Logging
Logged and Burned
Souza Jr. et al. (2005), RSE
Temporal Mapping Frequency
Canopy Damage – Sinop, 226/68 2004
Canopy Damage 20-year Time Series – Sinop, 226/68 2004
Logging Damage Intensity
1
1
0.8
0.8
1
ML
CL
0.8
Forest
0.4
0.6
CDF
0.6
CDF
CDF
0.6
0.4
0.4
ML
0.2
0.2
CL
ML
0.2
CL
Forest
Forest
0
0
20
40
GV
60
0
60
80
70
80
90
0
100
0
5
GVshd
GVshd
GV
1
10
NPV
15
20
NPV
1
1
0.8
0.6
0.6
0.4
CL
Forest
0.8
0.6
CDF
0.8
CDF
CDF
ML
0.4
0.4
ML
0.2
0
ML
0.2
CL
Forest
0
5
10
15
Soil
20
25
Soil
Monteiro et al., (2007), SBSR
0
0.2
0.7
0.8
NDFI
NDFI
0.9
1
0
CL
Forest
0.8
1
NDVI
NDVI
1.2
1.4
Class Separability
Nonmechanized
Logging
Intact
Class
Mean
Stdev.
Mean
Stdev.
Managed
Logging
Mean
Stdev.
Conventional
Logging
Mean
Stdev.
Logged and
Burned
Mean
Stdev.
GV
40a
4
41a
5
41a
5
38b
9
25c
7
NPV
6a
2
5a
2
6a
2
10bc
4
11bd
3
Soil
2a
1
1a
1
3ab
1
4bc
3
7d
3
Shade
51a
3
53a
5
51ab
4
49bc
3
56d
3
NDFI
0.84a
0.08 0.87a 0.07 0.79b 0.07
Tukey test at P<0.01
0.58c
0.24 0.49d 0.22
Forest Fragments, Ji-Paraná - RO
Forest Fragments
Characterization
Characterizing Forest
Fragments
114
Alta Floresta – MT
227/67 - 2000
61
Fragments
Inventoried
62
29
R=Soil, G=GV, B=NPV
Characterizing Forest
Fragments
114
Alta Floresta – MT
227/67 - 2000
61
Fragments
Inventoried
62
29
INPE vs. Imazon
INPE
Total Gross Deforestation 2001 (16,186 km²)
Deforestation
Forest
INPE vs. Imazon
IMAZON
Total Gross Deforestation 2001 (13.915 km²)
Deforestation
Forest
Land Cover Classification
NDFI
NDFI
NDFI
NDFI
NDFI
Shade Fraction (%)
Genetic Decision Tree Classifier
NDFI
NDFI
Land Cover Mapping
Legend
Forest
Green pasture/regeneration
Pasture/agriculture
Rock
Savanna
Soil
Urban
Clouds
Shade
Water
Mapping Unofficial Roads
Landsat TM - B3
Brandão Jr. and Souza Jr. (2006)
Mapping Unofficial Roads
Human Pressure - Aggregated
Thanks!

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