Seto, K.C., Woodcock, C.E., Song, C. Huang, X., Lu, J. and Kaufmann, R.K. (2002).

Monitoring Land-Use change in the Pearl River Delta

using Landsat TM.

International Journal of Remote Sensing, 23(10): 1985-2004

David Black10/02/2006

OUTLINE

• Economy of the area

• Why monitor land use change

• Land-use and Land cover

• Change detection techniques available

• Study area and pre-processing

• Methodology

• Accuracy assessment

• Main findings

• Conclusions

Why the need to monitor land-use change in he area

• Better understand socio-economic driving forces behind land-use change

• As socio economic activity rises, land available for agricultural practices is limited

Constraints surrounding the Pearl River Delta

• Increased demand for: • Transportation networks

• Residential, industrial and commercial construction

Economy of the area

• Fastest developing regions

• 1985 – 1997 15.3% increase in GDP

• Establishment of Special Economic Zones (SEZ’s)

• Foreign investment

Land-use and Land cover

• Land-use: corresponds to land-cover type i.e. how the land is used

• Land-cover: Measures physical attributes, conditions and characteristics of earth surface

Remote sensing and social sciences

• Recent use of satellite remote sensing in social sciences

• Integrate remote sensing with socio economic data

• Satellites observe land-cover not land-use

• Core requirement is that of how land is used

Change detection techniques available

• Image differencing, image regression, image ratioing etc

• No consensus to a “best” change detection technique

• Technique used depends on availability of data, study area, time and computing constraints and type of application

• Numerous studies carried out with difficulty in differentiating specific features

Advantages and disadvantages of Landsat Thematic Mapper

• Spatial resolution provides better discrimination of urban features

• Limited success from previous urban studies

• Spatial variance for urban environment is high

• Difficult to classify due to heterogeneous nature

STUDY AREA

(Source: Terraserver, 2006)

Shenzhen

• Co Registration

• Nearest neighbour resampling

• First order polynomial

• Landsat TM

• 10/12/1988 – 03/03/1996

• Root means squared (RMSE = 0.30 pixels)

• Radiometric correction

Pre - Processing

Methodology

• Accuracy of change map will at best be product of individual classification accuracy of each date

• These sources of error overcome using multi-temporal principal component technique

• Multidate Tasseled Cap transformation is scene dependent and proved successful in monitoring change

• Above method rotates TM data creating three planes (Brightness, Greenness and Wetness)

• Classification process involves five steps:

1. Define map classes

2. Associate land-covers with land-uses

3. Multi-step classification (supervised classification and spectral disaggregation)

4. Image segmentation

5. Map editing

Spectral locations

Land cover classes and number of training

Site selection based on:

• Visual inspection (lab and field)

• Training sites selected from field in China in February 1998

• Sites Georeferenced

• Limited access to some areas

Multi-step classification

Image segmentation

• Visual inspection indicated speckle

• Distributed without a clear pattern

• Removed using multi-pass, region based image segmentation algorithm

• Segmentation process enlarges regions by merging neighbouring pixels into polygons

Map editing

• Areas of the Delta exclusively agriculture, classified as urban

• Areas at centre of Guangzhou were classified as crops

• Possibly due to atmospheric effects not uniform throughout the image

• Pollution also had an impact

• Single radiometric calibration was applied

• Overall resulted in relabelling 5% of entire image

Land-use change 1988 - 1996

Accuracy assessment

• Conducted using validation data independent of the training data

• 496 2x2 pixel sites

• Stratified random sampling

• 151 sites analysed in the field

• 345 analysed in the lab (at least two analysts)

• Analysis made without knowledge of each analysts choice of class

Users accuracy

Producers accuracy

Area estimates

Accuracy Assessment

Discussion

• Agricultural class main source of confusion, due to diversity and different crop types

• Multi-crop fields, terracing and small field sizes produce different textures and tones

• Generate heterogeneous surfaces

• Harvested rice fields essentially are bare soil areas, thus confusion with construction sites

• 1988 – 1996 experienced unprecedented scale of land conversion

• 1905km2 converted to urban. Increase of 364%

• 1988 2.67% of study area was urban. 1996 almost 10% of study area changed to urban

• ¼ of new urban areas were natural vegetation or water

• 151km2 of water converted to agriculture.

Conclusions

• Conceptual model composed of spectrally diverse land-covers provides good frame work to infer land-use change from land-cover

• Accuracy assessment was essential

• Unprecedented rates of change

• Urbanization rates of 300%

• Economic growth which has directly improved living standards

• Most conversion is from agriculture to urban areas thus has potential serious implications on regional food supply and biogeochemistry

References

• Seto, K.C., Woodcock, C.E., Song, C. Huang, X., Lu, J. and Kaufmann, R.K. (2002). Monitoring Land-Use change in the Pearl River Delta using Landsat TM. International Journal of Remote Sensing, 23(10): 1985-2004

• TERRASERVER (2006) [Online]. http://www.terraserver.com/

Questions ?