Topic C5. Remotely sensed assessment of tropical wetlands Erik Lilleskov, Belinda Margono, and Laura Bourgeau-Chavez

Topic C5. Remotely sensed assessment of tropical wetlands

Erik Lilleskov, Belinda Margono, and Laura Bourgeau-Chavez

Topic C5. Slide 2 of 26

Learning outcomesIn this presentation you will be introduced

to approaches for using remote sensing to

map wetland extent and change


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


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


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

• 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

Top: National wetlands map of Indonesia. Bottom: Peatland map for Central Kalimantan Province, Indonesia. (Margono et al. 2014)


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


Possible data sources 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

http://science.nasa.goe/missions/landsat -7

http://en.wikipedia.org/wiki/Lidar#mediaviewer/File:Lidar_P1270901.jpg

http://gliht.gsfc.nasa.gov


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) (Bourgeau-Chavez et al. 2009).


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 land-cover 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).


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.


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.


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


(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.

Example of data integration using Landsat, ALOS-PALSAR and SRTM


Peatlands as a special casePeatlands 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


Mapping tropical peatlands

Unique vegetation• Known peat-forming plant associations

– Peat swamp forests

– Mountain fens

• Landsat can detect unique vegetation signals

Unique hydrology• Seasonal hydrologic dynamics of peatlands differ from

other wetland classes

• Multi-temporal PALSAR can be used to characterize hydrologic dynamics

Unique geomorphology• Many peatlands have convex geomorphology (dome

formation)

• SRTM or LiDAR-derived DEMs can be used to characterize and identify domes

http://onlinelibrary.wiley.com/10.1002/agc834/pdf

http://www.fao.org/docrep/003/y1899e/y1899e04.htm


Peatland hydrology & SAR 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)

Hoekman (2007)


Peatland doming 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.

Ballhorn et al. 2011


Ground truthing 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

Plot selection

Plot-level field data

Image interpretation


Supervised classification 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.

Supervised classification (e.g. Random Forests)

Accuracy assessment matrixWater Wetland Upland Total User's Accuracy Comission Error

Water 72005 207 204 72416 99% 1%Wetland 0 6740 211 6951 97% 3%Upland 0 85 9873 9958 99% 1%Total 72005 7032 10288 89325

Producer's Accuracy 100% 96% 96%Ommission Error 0% 4% 4%

99%


Change detection 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 multi-temporal 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


Klemas (2011).

Example of change detection work flow

using probability filters


The future of change detection using remote sensing

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.

Hansen and Loveland (2012).


ReferencesAdam 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.


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.

References

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 fileLiilleskov 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 creditAdam Gynch, Belinda Margono/Ministry of Environment and Forestry, Daniel Murdiyarso/CIFOR, Erik Lilleskov/USFS, Laura Bourgeau-Chavez, Michelle Cisz, Yayan Indriatmoko/CIFOR.

Thank you

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Topic C5. Remotely sensed assessment of tropical wetlands Erik Lilleskov, Belinda Margono, and Laura Bourgeau-Chavez