UNESCO Workshop

Methods of Detection and Analysis of Change and FeedbackIn the Earth Sciences

Geography and Landscape Ecology

James (Jim) WickhamU.S. Environmental Protection AgencyNational Exposure Research Laboratory

Research Triangle Park, North Carolina, USA

UNESCO.ORG

Geography and Landscape Ecology

The Need for Routine Land-cover Monitoring

The Need for Routine Land-cover Monitoring

Why Land cover? (case studies)

Review

• Satellite systems (AVHRR Landsat TM)

• Change detection methods

• Accuracy Assessment

Challenges

• Impact of land-cover accuracy on modeling

• Modeling versus monitoring

• Need for institutionalized land-cover modeling (multi-scale)

Summary

Why Land Cover?

Keystone Species (Paine 1969) = define ecosystem example: ironwood (nurse plant)

Ironwood (Olneya tesota)

Reference:Foley et al. 2005. Science 309:570Paine TR. 1969. Amer. Nat. 103:91

Land cover from remotely sensed datanow defines, to a great degree, how weview and study our environment (Foley et al. 2005).

Case studies

• global change in greenness

• forest fragmentation (US)

• “desertification” in Florida

Land-cover Case Studies

Legend: < -8% -8% < X < -4% -4% < X < +4% +4% < X < +4% > +8%

AVHRR Data – Global Climate Change

Methods: Difference in NDVI (1999 – 1983); calibrated with persistence index

Main Result: Net increase in NDVI (>+4%) for ~20% of earth’s surface

Drivers of NDVI increase: warming (high latitudes), ENSO, human activity

NDVI = ([NIR - red ] / [NIR + red])

Source: Young SS, Harris R. 2005. Int. J. of Remote Sensing 26:4537

Fragmentation of Continental US Forests

0

20

40

60

80

100

1 10 100 1000 10000

Window Size (ha)

Per

cen

t o

f al

l Fo

rest

Core forest, east

Core forest, west

Interior forest, east

Interior forest, west

Dominant forest, eastDominant forest, west

Source: Riitters et al. 2002. Ecosystems 5:815

Label Forest density

Core 100%

Interior 90%

Dominant

60%

Land-cover Case Studies

Main Results – Fragmentation of Continental US Forests

If forests were not fragmented, then the area density of forest (Pf) would not decrease with increasing landscape size.

(a) Forest fragmentation potentially influences ecological processes over at least half of all forest land, and while local impacts will depend on circumstances, most forested locations are probably not immune to impacts of one kind or another.

(b) How does that compare to estimates of tropical deforestation?

Fragmentation in the US is as at least as severe as in the tropics?

Closed Open Fragmented Regrowth Sum

Closed 82.67 1.02 0.38 0.10

Open 0.16 11.05 0.22 0.00

Fragmented 0.16 0.09 3.46 0.00 0.27

Regrowth 0.00 0.00 0.00 0.66

Sum 0.60

1997 Percentages

19

90

Perc

enta

ges

Adapted from Achard et al. 2002. Science 297:999

Land-cover Case Studies

Landsat TM Data – Regional Climate Change

Davis (1943) potential vegetation map Extant land cover (ca. 1990)

Problem statement: observational data and anecdotal information suggest increased drought over the Florida peninsula. Land-cover change is regarded as one of the main drivers of the observed climate shift.

Main result: paired climate model (one member for each land-cover scenario [above]) was consistent with observational data: land-cover change increased daytime maxima and weakened the sea breeze circulation, resulting in hotter and drier conditions during the 20th century.

Source: Marshall et al. 2004. Monthly Weather Review 132:28

Land-cover Case Studies

Review - Satellite Systems

Advanced Very High Resolution Radiometer (AVHRR)

Review - Satellite Systems

First launched on TIROS N satellite, 1978 (4-band AVHRR); 14 subsequent launches; 5-band instrument (1988);6-band instrument (1998); 1.1km resolution at nadir; 6+km at edge; ~2400km swath width; daily collection; common applications include snow and ice mapping, cloud distribution, vegetation phenology and disturbance.

Band Description Band width (μm)

1 Visible green 0.85 – 0.68

2 Reflected infrared 0.725 – 1.05

3a Middle infrared 1.58 – 1.64

3b Hybrid reflected/thermal infrared 3.55 – 3.92

4 Thermal infrared 10.3 – 11.3

5 Thermal infrared 11.3 – 12.5

Source: http://www.ngdc.noaa.gov/seg/cdroms/AVHRR97_d1/avhrr4.htm; PE&RS. 1992. 58:1183

Landsat Thematic Mapper (TM)

Review - Satellite Systems

First launched in 1972 (MSS), MSS upgraded to TM with launch of Landsat 4 (1982). TM spatial resolution = 30m (0.09 ha); 16-day repeat cycle; wide array of environmental applications

Band Description Band width (μm)

1 Visible blue 0.45 – 0.52

2 Visible green 0.52 – 0.60

3 Visible Red 0.63 – 0.69

4 Near infrared (NIR) 0.76 – 0.90

5 Middle infrared 1.55 – 1.75

6 Thermal 10.4 – 12.5

7 Middle infrared 2.08 – 2.35

9 Panchromatic 0.52 – 0.90

Source: http://landsat.usgs.gov/

Landsat Thematic Mapper (TM)

Review - Satellite Systems

An objective behind the upgrade from MSS to TM was better detection of earth surface features, particularly vegetative (red edge) [Landsat D report].

Red edge

Band Description Band width (μm)

1 Visible green 0.5 – 0.6

2 Visible red 0.6 – 0.7

3 NIR 0.7– 0.8

4 Middle infrared 0.8 – 1.1

Common Applications - AVHRR

Temporal Indices (for Change)

PAL – NOAA/NASA (Pathfinder AVHRR Land) program. Ten-day and monthly composites of NDVI (and additional bands) at 8-km spatial resolution from 1982 to present (2003).

GIMMS – (Global Inventory Modeling and Modeling Studies) NDVI data set at 8-km resolution covering the period from 1981 to 2003; corrected for view geometry, volcanic aerosols and other factors not related to vegetation change. NDVI ([NIR –red]/NIR+red])

SST – (Sea Surface Temperature) at multiple resolutions from bands 4 and 5 covering the periods 1985 to present (2003).

Review - Satellite Systems

SourcesPAL: http://daac.gsfc.nasa.gov/interdisc/readmes/pal_ftp.shtmlGIMMS: Tucker et al. 2005. Int. J. of Remote Sensing 26:4485; http://www.landcover.org/gimmsSST: http://podaac-www.jpl.nasa.gov/sst

GIMMS

Common Applications Landsat TM

Review - Satellite Systems

1: land-cover classification and mapping

Classification – use of an array of techniques to convert satellite reflective to nominal categories (forest, water, agriculture, urban).

MRLC (MultiResolution Land Characteristics) Consortium: US multi-agency group using Landsat TM to create national maps of land cover (1992 - land cover; 2001 - land cover, impervious surface canopy density)

NOAA C-CAP (Coastal Change Analysis Program). US federal agency using Landsat TM to map land-cover change over five-year periods for coastal US counties

GAP (GAP Analysis Program): A US federal-state partnership using Landsat TM to make detailed vegetation maps that are then combined with models to determine optimal locations to preserve.

CORINE (Coordination of Information on the Environment)– A European Environment Agency (EEA) project to map land cover for the European Union (EU) using Landsat TM.

India – Department of Space (India) effort to use IRS (Indian Resource Satellite) to map the numerous seasonal (complex) cropping practices in India.

Common Applications Landsat TM

Review - Satellite Systems

2: land-cover change detection

NOAA C-CAP (Coastal Change Analysis Program). US federal agency using Landsat TM to map land-cover change over five-year periods for coastal US counties.

Source: http://www.csc.noaa.gov/crs/lca/ccap.html

Web search of “Landat TM change detection” (all words) produced nearly 200,000 returns.

NALC (N. Am. Landscape Characterization). Project to collect a three-point times series of Landsat MSS data over the US, Mexico, and Canada, and process the data for change detection.

Review – Change Detection

There is a wide array of change detection methods that fall into two major groups: spectral and classification

Spectral Classification

Forest Agriculture

Urban

Forest 0.92 0.03 0.05

Agriculture

0.02 0.95 0.03

Urban 0.00 0.00 1.00

T2 land cover

T1 lan

d c

over

Reflectance Difference

Freq

uen

cy

0-1 +1

chang

e

chang

e

Technical issues: number of bands, threshold of change, accuracies of T1 & T2 classifications.

Perception: spectral methods are more precise, classification methods have desired output.

Review – Change Detection

There is not a strong consensus toward a preferred approach, but an emerging trend seems to be a combination of spectral and classification methods

The combination combines the strengths of both

Spectral = precision and control

Classification = desired output

NOAA C-CAP program is a good example of combining both change detection methods

A single-date classification for T2

spectral change applied to T1 and T2 imagery to distinguish change from no change

classify just T1 pixels that are identified as change

Review – Accuracy Assessment

Accuracy assessment is considered a routine and necessary element of land-cover mapping.

The pixels in the map are treated as a population. The population is sampled. Independent land-cover classifications are obtained for the samples. The map and sample (reference) labels are compared for agreement. Reference labels are obtained from independent (higher resolution) imagery or field visits. Protocols for accuracy are sometimes established prior to mapping.

Forest Agriculture

Urban

Forest 0.40 0.03 0.00

Agriculture

0.02 0.50 0.00

Urban 0.00 0.00 0.05

Reference

Map

References

Foody GM. 2002. Status of land-cover classification accuracy assessment. Remote Sensing of Environment 80:185.

Stehman SV. 2001. Statistical rigor and practical utility in thematic map accuracy assessment. Photogrammetric Engineering & Remote Sensing 67:727.

Com

mis

sion

Omission Overall accuracy

Issues – Impact of land-cover accuracy on modeling

Accuracy assessment is considered a routine and necessary element of land-cover mapping, but it is not given the same status as map-making itself.

Accuracy assessment only documents product quality. It does not improve the product, but it does add to the cost of product development.

The “less-than-full” status of accuracy assessment is compounded when land-cover mapping moves to change detection, at least partly because of the geometric increase in the number of classes (i.e., 6 classes at T1 & T2 = 36 possible combinations of change).

A geometric increase in the number of classes translates to a substantial increase in the sampling effort for accuracy assessment. Most classes are rare. There are other ways to tackle the problem, but it is not clear that they are simplifications.

Outcome: land-cover change accuracy is not likely to become routine. It will be the responsibility of the modelers to understand land-cover change detection and incorporate error in land-cover change into their models. (runoff: QT1 = 0.4in; QT2 = 0.55in ± 0.08in)

References

Biging GS et al. 1998. In Lunetta R & Elvidge C (Eds.) Remote Sensing Change Detection. pp. 281-308. Ann Arbor Press, MI.Stehman SV and Wickham JD. 2006. Photogrammetric Engineering & Remote Sensing 72:175.

Issues – Land Use Land Cover Change Modeling

LULCC modeling is a well-established area of academic research.

There are (probably) more the 25 different LULCC modeling in the literature (GEM, PLM, CLUE, LUCAS, NELUP, SLEUTH, DELTA, GEOMOD).

CLUE model output (Philippines)

Several LULCC review papers are in the literature.

LULCC presents several technical issues

Model output: just land-use and ecological change? * only 4 of 19 models included ecological change

Scale and drivers * cellular automata versus Kyoto Protocol

Portability * do I have the data necessary to transfer model X from place A to B? * can I scale model X from Italy to the EU?

References

Agarwal et al. 2002. General Technical Report NE-297, Newton Square, PA, U.S. Department of Agriculture, Forest Service. Parker et al. 2003. Annals of the Association of American Geographers 93:314.

Issues – Land Use Land Cover Change Modeling

Why? They are:

• too narrowly focused on anthropogenic drivers of change; still they never are all-inclusive;• in general, not scalable (watershed to state/country);• often not portable (too many input parameters, and scale).• validation with existing land cover?

Mountaintop Mining

Wickham et al. 2007. Landscape Ecology 22:179.

What has caused more land-cover change from2000 to 2005, urbanization or fire?

http://www.mrlc.gov

My opinion …. NO.

Can LULCC replicate land-cover monitoring?

Summary

Remotely sensed data have demonstrated their value for environmental studies over the last 25 years.

References

Cracknell AP. 2001. Advances in Space Research 28:233Ojima DS et al. 1994. Bioscience 44:300.

But the demonstrated value comes from ecologists discovering the data, rather than remote sensors finding ecologists (Ojima et al. 1994).

Use of broad-scale remotely sensed data (i.e., AVHRR) for environmental study has matured and has contributed much to global change (Cracknell 2001). There is a long time series of indices (SST, NDVI) that were developed specifically monitoring, and a few global land-cover maps.

Regional-scale remotely sensed data (.i.e., Landsat TM) are used routinely for (classified) land-cover mapping (MRLC, CORINE), but classified land-cover change detection is not yet routine.

Accuracy assessment is a not a fully formalized element of regional-scale land-cover mapping, and considerable research is needed regarding change detection. Modelers should be familiar with how remotely-sensed change detection is accomplished, and incorporate sensitivity into their analyses.

LULCC modeling is a vigorous area of research, but it is doubtful (my opinion) that it is a suitable replicate for land-cover monitoring.

Summary – What is the State Land-cover Monitoring?

Landsat at a crossroads (Goward 1989).

References

Goward SN. Remote Sensing of Environment 28:3

Are we at a crossroads in land-cover monitoring?

The routine use of remote sensing for land-cover monitoring has not been downscaled from AVHRR to Landsat TM.

There are several examples of land-cover mapping programs (MRLC, CORINE), but none are land-cover monitoring programs.

Lack of finer scale land-cover monitoring programs is despite the 25-year old upgrade of Landsat from MSS to TM.

Summary – Consequences of the Current State of Land-cover Monitoring

We cannot integrate monitoring results across scale.

• (AVHRR) greenness increase – are temperate deciduous forests less deciduous?• Would land-cover monitoring integration foster advances in other areas of environmental

science (e.g., climate models)?• Nonlinear interactions between land-cover and climate?

We cannot inventory and quantify the relative importance of drivers of change.

• Fire, grazing, drought, logging, urban sprawl.

We cannot systematically assess the impact of land-cover change on extant resources.

• Are land-cover maps a sampling frame for measuring and assessing environmental impacts?• Edges (sinks), scale (forest), change in variance (water quality).

Conclusions

Are we at a crossroads in land-cover monitoring? In 15 years will we have fine resolution, temporal land-cover maps for entire continents?

I honestly do not know. Programs like CORINE and MRLC seem to offer that potential, but they are only mapping programs at this point.

I do believe though that multi-scale land-cover monitoring, if it were to happen, would eventually change our view of earth’s ecosystems, how they operate, how we measure them, how we study and model them.