Webinar sobre Experiencias y Lecciones Aprendidas en el Uso de Datos de Sensores Remotos de Alta Resolución

1. Degradation Monitoring
Methods in the Brazilian Amazon
Carlos Souza Jr., Ph.D.
souzajr@imazon.org.br
Webinars sobre Experiencias y Lecciones Aprendidas en
el Uso ee Datos ee Sensores Remotos ee Alta Resolución

2. 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).

3. Sources of Human Pressure that Cause Forest DegradationSources of Human Pressure that Cause Forest Degradation
Highly DetectableHighly Detectable Marginally DetectableMarginally Detectable Almost UndetectableAlmost Undetectable
► DeforestationDeforestation
► Forest fragmentationForest fragmentation
► Recent slash-and-burn agricultureRecent slash-and-burn agriculture
► Major canopy firesMajor canopy fires
► Major roadsMajor roads
► Conversion to three monoculturesConversion to three monocultures
► Hydroelectric dams and other formsHydroelectric dams and other forms
of flood disturbancesof flood disturbances
► Large-scale miningLarge-scale mining
► Selective loggingSelective logging
► Forest surface firesForest surface fires
► A range of edge-effectsA range of edge-effects
► ‘‘Old-slash-and-burn agricultureOld-slash-and-burn agriculture
► Small scale gold-miningSmall scale gold-mining
► Unpaved secondary roads (6-20-mUnpaved secondary roads (6-20-m
wide)wide)
► Selective thinning of canopy treesSelective thinning of canopy trees
► Hunting and exploitation of animalHunting and exploitation of animal
productsproducts
► Harvesting of most non-timber plantsHarvesting of most non-timber plants
productsproducts
► Old-mechanized selective loggingOld-mechanized selective logging
► Narrow sub-canopy roads (<6-mNarrow sub-canopy roads (<6-m
wide)wide)
► Understorey thinning and and clearUnderstorey thinning and and clear
cuttingcutting
► Invasion of exotic speciesInvasion of exotic species
► Spread of pathogensSpread of pathogens
► Changes in net primary productivityChanges in net primary productivity
► Community wide shifts in plantCommunity wide shifts in plant
species compositionspecies composition
► Other cryptic effects of climateOther cryptic effects of climate
changeschanges
Selective loggingSelective logging
Burned forestsBurned forests
Forest fragmentationForest fragmentation
RoadsRoads
Gold miningGold mining
Peres et al., (2006), TREE
Remote Sensing Detection

4. Selective LoggingSelective Logging
Photo: Carlos Souza Jr.Photo: Carlos Souza Jr.
► Predominantly unplannedPredominantly unplanned
► Harvesting intensity varies from 5 toHarvesting intensity varies from 5 to
40 m40 m33
of logs / haof logs / ha
► Builds extensive road networkBuilds extensive road network
► Creates favor conditions for forestCreates favor conditions for forest
firesfires
Selective Logging in Sinop – MT, BrazilSelective Logging in Sinop – MT, Brazil
Deforestation, Selective Logging and Fires
Souza Jr. and Roberts (2005)
Photo: P. Barreto, Paragominas, PA. 1993Photo: P. Barreto, Paragominas, PA. 1993

5. Available Methods to Detect and Map SelectiveAvailable Methods to Detect and Map Selective
LoggingLoggingMapping Approach Studies Sensor Spatial Extent Objective Advantages Disadvantages
Visual Interpretation
Watrin e Rocha (1992) Landsat TM5 Local Map total logging area Does not require sophisticated
image processing techniques
Labor intensive for large areas
and may be user biased to
define the boundaries.
Stone and Lefebvre (1998) Landsat TM5 Local
Matricardi et al. (2001) Landsat TM5 Brazilian Amazon
Santos et al. (2002) Landsat TM5 Brazilian Amazon
Detection of Logging
Landings + Buffer
Souza Jr. e Barreto (2000)
Matricardi et al. (2001)
Monteiro et al. (2003)
Silva et al. (2003)
Landsat TM5 e
ETM+
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.
Decision Tree
Souza Jr. et al. (2003) 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.
Change Detection
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.
Image Segmentation
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.
CLAS
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+
NDFI+CCA
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.
Gregory P. Asner, Michael Keller, Marco Lentini, Frank Merry, and Carlos Souza Jr., 2009. LAB Chapter 3

6. Logging and Fires in Landsat Images: visual interpretation
Selective
logging
1998
1999
Old Selective
logging
Selective
logging and burning
2000
2001 Old selective
logging and
burning
R5, G4, B3 Souza Jr. et al., (2003)

7. Mapping Infrastructure: GIS mapping
Souza Jr. and Barreto (2002), IJRS 7

8. Texture Enhancement and Visual Interpretation
Matricard et al., 2007. URL: http://dx.doi.org/10.1080/01431160600763014

9. Spatial Distribution of Logging
Matricard et al., 2007. URL: http://dx.doi.org/10.1080/01431160600763014

10. http://www.obt.inpe.br/degrad/

11. CLASlite Forest Monitoring
http://claslite.carnegiescience.edu/en/

12. DEGRAD 2007, INPE

13. ImgTools Software
Souza Jr., et al., (2005); Souza Jr. et al., (2013); Souza Jr. e Siqueira (2013)

14. Spectral Mixture Analysis and NDFI
Souza Jr. et. Al (2005), RSE
Soil
NPV
GV
NDFI =
Vegs −(NPV +Solo)
Vegs +NPV +Sombra
Vegs =
Veg
100 −Sombra
-1 ≤ NDFI ≤1
NDFI baixo a moderado
NDFI = ˜1
Veg - alto
NPV e Solo - Baixo
Veg - baixo a
moderado
NPV e Solo - moderado
a alto

15. Fraction Images and NDFI
a) Paragominas,Pará State - 223/62
Solo
Veg NDFI
NPV

16. NPV Fraction
Roads
Logged
Forest
Mapping Selective Logging with Landsat Image (Souza Jr. et al., 2005)

17. GV Fraction
Logged
Forest
Roads
Mapping Selective Logging with Landsat Image (Souza Jr. et al., 2005)

18. NDFI (Normalized Difference
Fraction Index) Roads
Logged
Forest
Mapping Selective Logging with Landsat Image (Souza Jr. et al., 2005)

19. SMA Application:
Forest Change Detection
Deforestation 1999
GV 1999
GV 2000
∆GV Classified Image
Regeneration
Degradation
New deforestation
IOld deforestation
Forest

20. SMA Application:
Forest and Land Cover Dynamics

21. 1997 1998 1999
2000 2001 2002
NPV Fraction (%)
Small
damage
Burned
forest
Regeneration
Profile location
1997 1998 1999
2000 2001 2002
NPV Fraction (%)
Small
damage
Burned
forest
Regeneration
Profile location
SMA Application:
Forest and Land Cover Dynamics

22. Knowledge based Decision Tree
Souza Jr. et al., (2013), Remote Sensing

23. DEFORESTATION AND FOREST DEGRADATION DYNAMICS
Souza Jr., 2013

24. Classification 2002
R: NDFI02, G: NDFI03
B: NDFI03 Classificaiton 2003
Forest Change Detection
Old Deforestation
New Deforestation
Non-forest
Forest Degradation
Deforestation
Logging
Old Logging
Logging
Deforestation
Logging
Forest loss
Regrowth
Non Change

25. Tracking Forest Cover Dynamics

26. Dynamic of Forest
Degradation
1998
Logged and
Burned
a
Logged
Logged
Old
Logged
Old Logged and
Burned
Old Logged
and Burned
Logged and
Burned
c d
e f
b
• Degrataion signal changes
fast.
• There is a synergism of
forest degradation processes
that can reduces more C
stocks of degraded forests.
• Reccurrent forest degratation
is expected and creates
even more loss of C stocks.
• Annual monitoring is
required to keep track of
forest degrataion process.

27. Deforestation and Forest Degradation in the Brazilian Amazon: 2000-2010
27
Souza Jr. et , 2013, Remote Sensing.

28. Accuracy Assessment

29. Accuracy Assessment
Souza Jr. et al., (2013), Remote Sensing

30. Accuracy Assessment

31. Accuracy Assessment
Souza Jr. et al., (2013), Remote Sensing

32. Mapping Results
Souza Jr. et al., (2013), Remote Sensing

33. Monitoring of forest degradation: a review of methods in the Amazon basin
C Souza Jr - Global forest monitoring from earth observation, 2012

34. Equipe SAD :
Carlos Souza Jr.
Antônio Victor Fonseca
Marcelo Justino
João Victor Siqueira
Dalton Cardoso
Colaboradores:
Beto Veríssimo
Heron Martins
Júlia Ribeiro
Kátia Pereira
Rodney Salomão
Bruno Oliveira