Large Area Mapping of Forest and Land Cover
from ALOS/PALSAR data
Wayne Walker, Josef Kellndorfer, Alessandro Baccini, Scott Goetz, & Nadine Laporte
Ruhengeri Rwanda  June 21-24, 2011
The Woods Hole Research Center
Science, Education, and Public Policy for a Habitable Earth
• The Woods Hole Research Center
seeks to conserve and sustain the
planet’s forests, soils, water, and
climate by demonstrating their value
to human health and economic
prosperity.
• Approximately 60 staff members,
including scientists, policy experts,
research and administrative staff.
• Funding is provided through
government grants, foundation and
corporate support, and individual
donors.
The Woods Hole Research Center
Science, Education, and Public Policy for a Habitable Earth
•
The Center has initiatives in the
Amazon, the Arctic, Africa, Russia,
Asia, Boreal North America, and
throughout the United States.
•
Center programs focus on the
global carbon cycle, forest
function, land cover/land use, and
water and chemical cycles in the
environment.
•
We work in collaboration with
partners ranging from local NGOs
and research centers to national
governments and the United
Nations.
Woods Hole Pan-tropical Mapping Project
I.
•
•
Pan-tropical Mapping
ll.
•
•
Capacity Building/Training
2007 Forest cover (15 m)
2007 Above-ground biomass (500 m)
Pan-tropical forest scholars program
Technical workshops on forest measurement/monitoring
Woods Hole Pan-tropical Mapping Project
I.
•
Pan-tropical Mapping
2007 Forest cover (15 m)
Pan-tropical forest cover mapping
Product highlights:
• Based on cloud-free RADAR data
• Consistent across space and time
• Unprecedented spatial resolution (15 m)
• National maps to be available online for all countries
RADAR: Contributions to tropical forest monitoring
 Cloud, dust, haze, and smoke penetration
 All-weather and day/night image acquisition
 Sensitivity to 3-D forest structure
RADAR has the potential to provide for wall-to-wall tropical
forest monitoring over very narrow (sub-annual) time frames.
RADAR: Contributions to tropical forest monitoring
Advanced Land Observing Satellite/Phased-Array L-band SAR
• Launched on January 24, 2006 by JAXA
• First polarimetric L-band sensor on a free-flying satellite
• High spatial resolution (10 -20 meters)
• High geolocation accuracy (9.3 meters)
• First-of-its-kind systematic global land observation strategy
• Ceased operation on April 22, 2011 after 5+ years of service
 Annual coverage of all major forest biomes
ALOS/PALSAR: Global observation strategy
HH/HV HH Polarimetric
ALOS/PALSAR: Assembling a pan-tropical mosaic
ALOS/PALSAR: Assembling a pan-tropical mosaic
Locations of 17,000 dual-polarimetric ALOS/PALSAR scenes to be used
for forest cover mapping
ALOS/PALSAR: State-of-the-art processing stream
ALOS/PALSAR: 2007 pan-tropical mosaic
Image Data By JAXA/METI
ALOS/PALSAR: Africa example
5754 Dual-Polarimetric Scenes
– 76% from Jun-Aug 2007
– 94% from Jun-Oct 2007
Forest cover mapping: Pan-tropical results
Mexico: ALOS/PALSAR coverage with 950 scenes
16
Mexico: ALOS/PALSAR mosaic
Mexico: National forest inventory data
Linking imagery with field observations
Mexico: Forest/Non-Forest map
Cloud-free forest/non-forest map
19
Objectives: Forest and land cover pilot research
Evaluate the suitability of ALOS/PALSAR data for the production of high-resolution
(≤ 30 m) wall-to-wall maps of tropical land cover with an emphasis on forest-cover
(i.e., forest/non-forest) mapping at regional to continental scales.
The specific objectives were to classify land cover:
1.At five different levels of class aggregation (15, 10, 7, 6, and 2 classes)
2.Using two different predictor-variable subsets (spectral and spectral/ancillary)
3.For both PALSAR- and Landsat-based data sets resulting in 20 different classifications
Study area: Xingu River Headwaters region
•
387,000-km2 (~15 times larger than
Rwanda)
•
More dense humid forest (ca. 221,000
km2) than 90% of tropical nations
•
An exceptional landscape for exploring
policy scenarios/technological options
to inform the design of REDD+ mechanisms.
Image data
•
2007 ALOS/PALSAR mosaic consisting of 116 Level 1.1 (single-look complex) fine
beam, dual-polarimetric scenes acquired 8-June to 22-July (RGB = HH,HV,HH-HV)
•
2007 Landsat 5 mosaic consisting of 12 Level 1G scenes acquired 16-June to 17August (RGB = Bands 5,4,3)
Image segmentation
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PALSAR segmentation -- 24 image-object attributes (predictor variables) computed.
•
Landsat segmentation -- 49 image-object attributes computed.
Predictor variable subsets
Spectral and ancillary
spatial/topographic
predictor variables
used in the PALSARand Landsat-derived
classifications.
Land cover reference data set
Locations of 535 ground (26-June to
23-August 2006) and 361 supplemental
reference (2007) points used in the
calibration and cross-validation of both
the PALSAR- and Landsat-based
classifications.
Point locations are superimposed on a
PALSAR-based map forest cover.
Land cover classification scheme
Hierarchical,
multi-level land
cover classification
scheme developed
for the Xingu River
headwaters region.
Results: Classification accuracy comparison
A comparison of ALOS/PALSAR- and Landsat-derived overall classification
accuracies for the five levels of class aggregation (15, 10, 7, 6, and 2 classes).
Results: Mapping of forest cover
Walker et al. 2010. Large-area classification and mapping of forest and land cover in the Brazilian
Amazon: A comparative analysis of ALOS/PALSAR and Landsat data sources. IEEE Journal of
Selected Topics in Applied Earth Observation and Remote Sensing.
Results: Spatial map comparison
Spatial similarity (%) based on a two-way fuzzy comparison procedure
between PALSAR, Landsat, and PRODES map products.
†
‡
Analysis conducted within the region of PALSAR/Landsat acquisition overlap.
Analysis conducted within the region of PALSAR/Landsat acquisition overlap following
PRODES-based masking.
Walker et al. 2010. Large-area classification and mapping of forest and land cover in the Brazilian
Amazon: A comparative analysis of ALOS/PALSAR and Landsat data sources. IEEE Journal of
Selected Topics in Applied Earth Observation and Remote Sensing.
Conclusions

ALOS/PALSAR represents a consistent and accurate source for
estimates of forest cover across large areas with accuracies nearly
equaling those produced by Landsat, the most widely used
monitoring sensor, and PRODES, the most highly regarded national
monitoring system.

The potential exists for PALSAR imagery to be used either by itself as
the primary data source or to potentially fill voids in optical
acquisitions associated with the presence of haze, smoke or clouds.
Future work

Conduct case studies in regions like Central and East Africa where forest disturbance
occurs at a much finer scale (≤ 1 hectare).

Complete 2007 baseline map of pan-tropical forest cover.

Following recent JAXA release of the 2010 m PALSAR mosaic, begin work on a threeyear (2007-2010) pan-tropical map of forest change.

Investigate possible fusion of PALSAR data with MODIS, GLAS LiDAR, and field
observations as part of our ongoing pan-tropical forest cover and carbon stock
mapping effort.

Partner with countries to build capacity and assist in developing MRV strategies.
Future work
Deforestation Monitoring
2007
2008
Fire Degradation Monitoring
2007
2008
Future work
2007 15-50m PALSAR Mosaic
© JAXA, METI Analyzed by WHRC
2009 50m PALSAR Mosaic
Thank you!
www.whrc.org/pantropical