Transcript PowerPoint presentation (PPT file)

Topic C5. Remotely sensed assessment of
tropical wetlands
Erik Lilleskov, Belinda Margono, and Laura Bourgeau-Chavez
Topic C5. Slide 2 of 26
Learning outcomes
In this presentation you will be introduced to
approaches for using remote sensing to map
wetland extent and change
Topic C5. Slide 3 of 26
Outline

Rationale

Background

Choice of sensors and resolutions

Airborne/spaceborne or ground-based
sensors

Generating maps from sensor data
•
Wetlands
•
Special case: Peatlands

Ground truthing

Validation

Change detection
Topic C5. Slide 4 of 26
Rationale

Deforestation and forest degradation have
been reported to be the 2nd leading cause
of anthropogenic greenhouse gas
emissions

Wetlands, especially peatlands, represent
one of the largest terrestrial, biological
carbon pools and are important wildlife
habitats

Tropical peatlands and mangroves are
being lost at high rates

Quantifying wetland type, extent,
distribution and condition is vital for
mitigation efforts, MRV for REDD+, IPCC
and related efforts

Remote sensing is a major tool in wetland
mapping
Topic C5. Slide 5 of 26
Background: Wetland mapping and remote sensing

Remote sensing data is the main data source for
monitoring and mapping wide areas, including
•
wetland extent and distribution
•
wetland type
– Including extent of mangrove, freshwater peat
swamps and non-forested peatlands
•

Top: National wetlands map of Indonesia. Bottom:
Peatland map for Central Kalimantan Province,
Indonesia. (Margono et al. 2014)
Land-use/land-cover change
Remote sensing provides activity data, a critical
component of estimating human impacts on
wetlands
•
Field studies provide emissions factor (impact of
human activity on greenhouse gas emissions)
•
Both activity data and emission factors are vital
for estimating change in wetland carbon content
•
Baseline wetland extent maps (right) can be used
to assess impacts of land use
Topic C5. Slide 6 of 26
Approaches to wetland mapping

Selected remote sensing tools should detect
some or all of the following:
•
water presence;
•
water temporal dynamics;
•
landforms likely to retain water;
•
vegetation type and floristic differences.

Fusion of multiple data sources often provides
improved maps

Digital mapping suggests that water presence
and dynamics, landform and vegetation type
can be observed using multisource data sets
Topic C5. Slide 7of 26
Overall schematic of map development
Topic C5. Slide 8 of 26
Possible data sources

http://science.nasa.goe/missions/l
andsat -7

http://gliht.gsfc.nasa.gov

http://en.wikipedia.org/wiki/Lidar
#mediaviewer/File:Lidar_P127090
1.jpg
Spaceborne are most important for mapping large regions
•
Multispectral, e.g. Landsat TM, SPOT, MODIS
•
Hyperspectral – Hyperspectral Imager (HSI) on the Lewis
satellite
•
Radar e.g. ALOS PALSAR, SRTM
•
LiDAR e.g. ICESat/GLAS
Airborne can provide higher resolution data for smaller
regions
•
Hyperspectral, e.g., AVIRIS, AHS, HYDICE, AISA
•
LiDAR
•
Multispectral
•
Multiplatform, e.g. G-LiHT (LiDAR, hyperspectral, thermal)
Ground-based sensors are used primarily at the site level
or to validate remote methods
•
Tripod-mounted LiDAR
Topic C5. Slide 9 of 26
Landsat

Landsat is a passive data source, i.e. it relies on incoming solar
radiation. It does not see through clouds.

Series of Landsat TM 5, Landsat 7 ETM+ and Landsat 8

Band 3, 4, 5 and 7 are commonly used and are:
•
suitable for soil-vegetation discrimination (B, G, R)
•
good for mapping biomass content (NIR)
•
very good at detecting and analyzing vegetation (NIR)
•
provides good contrast between different types of vegetation
(SWIR)
•
useful for measuring the moisture content of soil and
vegetation (SWIR)

Landsat imagery captures floristic differences that can be
associated with wetland status, as well as water extent and leaf
moisture content

Available with 30 m spatial resolution, sufficient for mapping at
scale 1 : 100,000 or even 1 : 50,000

Timely data acquisitions are limited by cloud cover

The image to the right shows a false color composite of bands
3, 4 and 5 from Landsat 7 of a region of the Peruvian Amazon
basin near the Marañón River (lower right) that has previously
been shown to contain a peat dome (black star) (BourgeauChavez et al. 2009).
Topic C5. Slide 10 of 26
PALSAR

Phased Array type L-band Synthetic Aperture (PALSAR) is an
active source because it sends out a microwave energy pulse
and collects the returns.

Uses L-band to achieve cloud-free and day-and-night land
observation

10–20 m data are available, but for most national-level
applications, 50 m spatial resolution is suitable

Data available in polarization mode, which enhances landcover information

The different interactions of microwave data (PALSAR) with
surface water compared to vegetation enable improved
discrimination of wetlands

Comparing images from multiple dates (multi-temporal)
improves understanding of hydrology and helps to distinguish
wetlands and wetland types

The image to the right shows a false color composite of three
different dates from ALOS PALSAR of a region of the Peruvian
Amazon Basin near the Marañón River (lower right) that has
previously been shown to contain a peat dome (black star).
Color variation is mostly driven by differences in hydrologic
condition. The areas in brighter colors are sloping portions of
the peat dome (Bourgeau-Chavez et al. 2009).
Topic C5. Slide 11 of 26
PALSAR
Principal Component Analysis

Principal Component Analysis (PCA) is a
multivariate statistical technique that is used to
identify the dominant spatial and temporal
backscatter signatures of a landscape

PCA generates a set of new images, reducing
most of the information to the first few new PC
images

Several advantages including the ability to filter
out temporal autocorrelation and reduce speckle

Helpful in understanding moisture patterns

The image to the right is a single PCA derived
image that extracts the major axes of variation in
the previous PALSAR image.
Topic C5. Slide 12 of 26
DEM from SRTM or LiDAR

Global DEM (topography map) derived from single-pass
interferometric synthetic aperture radar (InSAR) of SRTM

Available globally at 90 m spatial resolution, and 30 m
resolution for some places

Spaceborne LiDAR coverage e.g. ICESat/GLAS is limited to
long transects

Airborne LiDAR coverage varies by country

Using DEMs, a set of topographical indices capture
landforms more likely to retain water.

Example to right: Topographic indices derived from SRTM
for peatlands in Central Kalimantan, Indonesia. The top
figure depicts a flatness index which has clear hydrologic
predictive value; whereas the bottom index depicts
relative elevation of catchments of 121.5 km2 and is
indicative of slope (Margono et al. 2014). Both have been
found to be useful predictors in wetland mapping.
Topic C5. Slide 13 of 26
Data integration/fusion

Data integration (data fusion): Combining data
from different sources

Geospatial data integration e.g.
•
vegetation type, generated from Landsat
•
landform derived from DEM
•
water presence, using topographical indices
generated from DEM
– First derivatives of elevation (e.g. slope)
– Second-order derivatives of elevation (e.g. various
curvatures)
• vegetation and soil wetness, generated
from ALOS-PALSAR
Topic C5. Slide 14 of 26
Example of data integration using Landsat, ALOSPALSAR and SRTM
(a) Landsat image with 5–4–3 spectral combination; (b) terrain flatness; (c) relative elevation of
121.5 km2 (medium) catchments; (d) Landsat band 5 represent soil/vegetation moisture; (e)
false-color r-g-b of (b), (c), and (d); and (f) the initial resulting wetland map as a probability layer
where blue is high wetland cover probability and white low wetland cover probability. Single
date PALSAR (data not shown) contributed a small percentage to the final wetland model.
Topic C5. Slide 15 of 26
Peatlands as a special case
Peatlands are wetlands that
accumulate peat (partially
decomposed organic matter) and so
contain large reserves of carbon
vulnerable to anthropogenic
disturbance, e.g. decomposition or
fire triggered by drainage or climate
change
Topic C5. Slide 16 of 26
Mapping tropical peatlands

Unique vegetation
•
Known peat-forming plant associations
– Peat swamp forests
– Mountain fens
•

Unique hydrology
•
Seasonal hydrologic dynamics of peatlands differ from
other wetland classes
•
Multi-temporal PALSAR can be used to characterize
hydrologic dynamics
http://onlinelibrary.wiley.com/10.1002/agc834/pdf

http://www.fao.org/docrep/003/y1899e/y1899e04
.htm
Landsat can detect unique vegetation signals
Unique geomorphology
•
Many peatlands have convex geomorphology (dome
formation)
•
SRTM or LiDAR-derived DEMs can be used to characterize
and identify domes
Topic C5. Slide 17 of 26
Peatland hydrology & SAR
Hoekman (2007)

Peatland hydrology is driven by
exogenous and endogenous factors.
Doming, which is common in
Indonesian peat swamp forests (and
is being quantified elsewhere)
regulates water flux patterns.

This SAR multi-temporal image
reveals divergent hydrology across
the width of a peat dome, with the
flat top of this peat dome (light blue
areas, A) showing a different time
course of flooding than the edges and
stream channels (redder areas, B)
Topic C5. Slide 18 of 26
Peatland doming
Ballhorn et al. 2011

Peat accumulates over thousands of years where
production outpaces decomposition

In some places, peat rises above the local water table,
creating domes

Doming can be observed as regular, rounded topographic
features sometimes many km across.

These features can be recognized when analyzing
topographic relief, especially in conjunction with wetland
mapping

Quantifying dome morphology can improve estimation of
peatland carbon storage

The example at the right (Ballhorn et al. 2011) illustrates
use of satellite-based LiDAR (ICESat/GLAS) to determine
dome morphology and forest structure on a peatland in
Indonesia. In B the blue points delineate the dome height
in meters over a horizontal distance of about 100 km. The
green points represent canopy height. The method was
validated using airborne LiDAR and ground sampling.
Topic C5. Slide 19 of 26
Ground truthing
Plot
selection
Plot-level
field data
Image
interpretation

Field surveys and image interpretation
• Plot selection: Sampling should be statistically
valid, stratified over putative wetland classes
from initial unsupervised classification
• Logistical constraints on plot selection should
be included in sampling design
• Plot characteristics: Plots should be sized and
oriented to stay within a single map class.
• Image interpretation can derive data from
aerial imagery, e.g. urban areas, lakes, other
distinct features
Topic C5. Slide 20 of 26
Supervised classification

Supervised classification
(e.g. Random Forests)

Supervised classification
•
Based on field or other independent data, a supervised
classification can be run using a portion of the data
•
This divides the data into specific classes of similar
properties that can be more or less resolved
depending on goals of classification.
Validation
•
Using plots not included in supervised classification,
the quality of the classification can be evaluated.
•
Results can be presented as an accuracy assessment
matrix – example below.
Accuracy assessment matrix
Water
Wetland
Upland
Total
Producer's Accuracy
Ommission Error
Water
72005
0
0
72005
100%
0%
Wetland
207
6740
85
7032
96%
4%
Upland
204
211
9873
10288
96%
4%
Total
72416
6951
9958
89325
User's Accuracy Comission Error
99%
1%
97%
3%
99%
1%
99%
Topic C5. Slide 21 of 26
Change detection
Pre-analysis
steps
•Image registration
•Calibration or normalization
•Selection for same spatial/spectral resolution
•Mosaicking

Remote sensing can be used to quantify
change in land use/land cover of
wetlands

This can be accomplished by performing
a change detection analysis using
remote sensing data (e.g. Landsat)
collected over time, known as a multitemporal data set

Involves change from one class to
another (e.g. conversion to agriculture)
or change within a class (e.g. thinning of
forest)

There are many possible change
detection approaches
•Algebra-based
•Transformation-based
•Classification-based
Choose change •Advanced models
detection
•GIS-based, Other
method
•Steps specific to method, involving direct comparison
of spectral data, or some sort of image processing
(transformation, classification, etc.) followed by
Perform change comparison.
analysis
Perform
accuracty
assessment
•Requires field-based reference data, e.g., forest
inventory
•Accuracy assessment matrix/ error matrix, as for other
RS data.
Topic C5. Slide 22 of 26
Example of change
detection work flow
using probability filters
Klemas (2011).
Topic C5. Slide 23 of 26
The future of change detection using
remote sensing
Hansen and Loveland (2012).
 The Landsat archive is available with free
access to terrain-corrected data for many
regions.
 Automated image preprocessing and
land-cover characterization methods will
soon be standard practice.
 The images on the right show change
detection results for the expansion of
bare ground on a national scale from the
US (top) and a close-up of a localized
region, from the Web-Enabled Landsat
Data (WELD) project. Blue areas are
newly bare ground (Hansen and Loveland
2012).
 These large-scale automated methods
should greatly accelerate change analysis
in wetlands.
Topic C5. Slide 24 of 26
References
Adam E, Mutanga O and Rugege D. 2010. Multispectral and hyperspectral remote sensing for
identification and mapping of wetland vegetation: A review. Wetlands Ecology and
Management 18(3):281–96.
Ballhorn U, Jubanski J and Siegert F. 2011. ICESat/GLAS data as a measurement tool for peatland
topography and peat swamp forest biomass in Kalimantan, Indonesia. Remote Sensing 3(9):1957–
82.
Bourgeau-Chavez LL, Riordan K, Powell RB, Miller N and Nowels M. 2009. Improving wetland
characterization with multi-sensor, multi-temporal SAR and optical/infrared data fusion. In
Jedlovec G (ed). Advances in Geoscience and Remote Sensing. Vukovar, Croatia: InTech. 679–708.
Bwangoy JRB, Hansen MC, Roy DP, Grandi GD and Justice CO. 2010. Wetland mapping in the Congo
Basin using optical and radar remotely sensed data and derived topographical indices. Remote
Sensing of Environment 114(1):73–86.
Hansen MC and Loveland TR. 2012. A review of large area monitoring of land cover change using
Landsat data. Remote Sensing of Environment 122:66–74.
Topic C5. Slide 25 of 26
References
Hoekman DH. 2007. Satellite radar observation of tropical peat swamp forest as a tool for
hydrological modelling and environmental protection. Aquatic Conservation: Marine and
Freshwater Ecosystems 17(3):265–75.
Klemas V. 2011. Remote sensing of wetlands: Case studies comparing practical techniques. Journal
of Coastal Research 27(3):418–27.
Margono BA, Bwangoy JRB, Potapov PV and Hansen MC. 2014. Mapping wetlands in Indonesia using
Landsat and PALSAR data-sets and derived topographical indices. Geo-spatial Information
Science 17(1):60–71.
Ozesmi SL and Bauer ME. 2002. Satellite remote sensing of wetlands. Wetlands Ecology and
Management 10(5):381–402.
Thank you
The Sustainable Wetlands Adaptation and Mitigation Program (SWAMP) is a collaborative effort by CIFOR, the USDA Forest Service, and the
Oregon State University with support from USAID.
How to cite this file
Liilleskov E, Margono B and Bourgeau-Chavez L. 2015. Remotely sensed assessment of tropical wetlands [PowerPoint presentation]. In:
SWAMP toolbox: Theme C section C5 Retrieved from <www.cifor.org/swamp-toolbox>
Photo credit
Adam Gynch, Belinda Margono/Ministry of Environment and Forestry, Daniel Murdiyarso/CIFOR, Erik Lilleskov/USFS, Laura Bourgeau-Chavez,
Michelle Cisz, Yayan Indriatmoko/CIFOR.