towards image-based monitoring of soil erosion risk in southern australia

Towards image-based monitoring of soil erosion risk in southern Australia.

Megan Lewis

Place and Purpose Symposium 2009

Towards image-based monitoringof soil erosion risk in

South Australian agricultural lands

Assoc. Prof. Megan Lewis

Dr Ken Clarke

Dr Bertram Ostendorf

Landscape Futures ProgramThe University of Adelaide


Overview

• Collaborative research program 2002-present

– University of Adelaide & Soil Conservation Council, Dept Water, Land & Biodiversity Conservation

• Approaches and key findings

• Collaborative process

• Ongoing research


Context

• Cropping regions of SA

• Cereal cropping is integral to Australia’s economy

– ¼ of Australia’s cereal crops produced in SA: 5,382 kT

• Soil conservation essential for economically viability & sustainability

• Legislative requirement for soil conservation

– Soil Conservation & Land Care Act Act 1989

– Natural Resources Management Act 2004

• State Strategic Plan target

– 20% increase in land protected from erosion by 2014

• DWLBC monitors soil cover to minimise erosion

– Land Condition Monitoring Project (2000 – present)


DWLBC Land condition monitoring project methodology

• Windscreen surveys along road transects, 5,500 locations

– 4 surveys / year

• Oblique visual estimate of:

– Cover

– Detachment


DWLBC Land condition monitoring project methodology

• Regional and State-wide reports

Full cover

Bare

• Costly

• Limited to spatial and temporal samples, not spatially explicit


What about remote sensing?

• Collaborative research with University of Adelaide & Dept. Water Land & Biodiversity Conservation 2002-present

• Aim: To bring benefits of remote sensing to SA Land Condition Monitoring Program

– Spatially explicit, comprehensive & objective assessment of cover

– Improved assessment of areas at risk/protected

– Increased temporal frequency

– Cost effective

• Funding: Soil Conservation Council, DWLBC, University of Adelaide, ARC Linkage grant


Collaborative approach

Windscreen Survey data

Image indices

• Develop & test image indices

• Calibrate & validate with extensive existing WSS data

• Demonstrate relationships between field & image measures

• Enhance not replace field survey

• Develop organisational understanding & confidence in remote sensing approach

Operational use of remote sensing for land condition monitoring in SA


Remote sensing approaches

• NOAA AVHRR

• AUSSIEGrass products

• MODIS

– Single date images

– Nadir BRDF adjusted reflectance 16 day composites

• Existing Indices

NDVI

PD54

• New index: UA Vegetation Index based on Cellulose Index

• Single dates

• Annual 16-day sequence

• 2001-present 16 day sequence


Key technical findings

1. UA Vegetation Index best predictor of Cover Rating

– NDVI doesn’t discriminate bare soil from dry stubble

– PD54 difficult to calibrate over time, difficult for operational use


University of Adelaide Vegetation Index

• Strongest relationships with Cover Rating

• UAVI = (Band 6 – Band 7) / (Band 6 + Band 7)

Clarke, K. and Lewis, M. (2009) Report 3: Development of a satellite image-based land condition monitoring system for South Australian agricultural regions. Land condition monitoring reports. Adelaide, The University of Adelaide.

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400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400

Wavelength (nm)

Re

fle

cta

nc

e

Blue Green Red Near infra-red Short wave infra-red

MODIS Bands 6 & 7

Soil

Dead

vegetation

Growing

vegetation






2. Relationships between image indices & Cover Rating from field survey

– Adequate correlations across whole Cropping District

– Stronger correlations when stratified by region

– Relationship to Cover Rating changes with season

– Strong relationships UAVI vs Cover rating when soil exposure high


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0 0.2 0.4

UAVI

Co

ver

Rat

ing

2003 March 09

Relationship between Vegetation Index and Cover Rating changes through seasons

Clarke, K., Lewis, M. and Ostendorf, B. (2004, rev. 2009) Report 2: Assessing the potential of MODIS imagery for monitoring landcondition in the crop lands and non pastoral lease rangelands of South Australia. Land condition monitoring reports. Adelaide,

The University of Adelaide.

Bare soil

Full cover


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2

3

4

5

6

7

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0 0.2 0.4

UAVI

Co

ver

Rat

ing

2003 March 09

2003 June 11




Bare soil

Full cover



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0 0.2 0.4 0.6

UAVI

Co

ver

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ing

2003 March 09

2003 June 11

2003 October 12

R2 = 0.65



Bare soil

Full cover



Region/ March May July October All

index 2006 2006 2006 2006 2006

NDVI

Eyre Peninsula -0.038 -0.286 *** -0.353 *** -0.425 *** -0.108

Murraylands +0.031 -0.281 *** -0.371 *** -0.241 *** -0.124 *

Northern and Yorke -0.032 -0.251 *** -0.296 *** -0.374 *** +0.052

South East +0.076 -0.031 -0.065 +0.007 -0.196 **

All regions combined -0.122*** -0.305 *** -0.382 *** -0.234 *** -0.022

UAVI

Eyre Peninsula -0.223 ** -0.304 *** -0.305 *** -0.407 *** -0.187 **

Murraylands -0.098 -0.275 *** -0.381 *** -0.143 *** -0.241 ***

Northern and Yorke -0.358 *** -0.365 *** -0.277 *** -0.372 *** -0.278 ***

South East +0.023 -0.054 -0.094 +0.052 -0.037

All regions combined -0.201 *** -0.339 *** -0.398 *** -0.258 *** -0.211 ***

p * 0.05

** 0.01

*** 0.001


Spearman Rank Correlation Coefficients






2. Relationships between image indices & Cover Rating from field survey

– Adequate correlations across whole Cropping District

– Stronger correlations when stratified by region

– Relationship to Cover Rating changes with season

– Strong relationships UAVI vs Cover rating when soil exposure high

3. Large proportion of variation in cover/soil exposure is explained by climate (intra-& inter-annual variation)

– Seasonal index profiles track land cover changes through year

– Possible to identify land management influences as deviation from regional mean


Annual profiles of image indices



• Summer (no growth, NPV decay)




• Germination (weed & non-cereal crops)





• Ploughing, spraying off, direct seeding






• Cereal crop germination and tillering & growth to full canopy







• Crop ripening (senescence) and harvest







• Crop ripening (senescence) and harvest

• Summer (NPV decay)


Importance of climatic influence

Relationship between AUSSIEGrass model & Cover rating

Seasonal

Cover RatingMarch

May

June

October

Grech, A., Ostendorf, B. and Lewis, M. (2003) Report 1: Land condition monitoring: integrating ground surveys, Aussie GRASS and satellite remote sensing. Land condition monitoring reports. Adelaide, The University of Adelaide.

Rainfall

+ Total Pasture Growth

+Total Standing Dry Matter


Importance of climatic influence

Grech, A., Ostendorf, B. and Lewis, M. (2003) Report 1: Land condition monitoring: integrating ground surveys, Aussie GRASS and satellite remote sensing. Land condition monitoring reports. Adelaide, The University of Adelaide.

Cover Rating 2001 predicted from AussieGRASS model developed with 2000, 2002, 2003 data


Average annual UAVI (2006)



Cumulative difference from regional mean UAVI: Murraylands


Cumulative difference from regional mea UAVI : Northern and Yorke

• Demonstrates local management influence

– Snowtown example


Land management influences on cover detected by UAVI


Methodological findings

• Oblique view

• Ranked classes of Cover Rating – not a direct measure of projected cover

• Surveyor variability

• Vertical view

• Integrated response of projected soil & vegetation cover

• Objective, calibrated repeatable

Windsceen Survey data

Image indices

Advantages

• Extensive field survey data available

• Familiarity of field method

Disadvantages

• Field & image indices measure soil/cover in different ways

• Error, variability in field data not known

• Need independent cover data for calibration & validation

• Calibration relationship Cover Rating/Image Index not possible


Institutional change process

• Considerable shift in organizational understanding & acceptance of remote sensing approach

– Collaboration in projects

– Sharing of data, findings

– Increased investment in research

• Personnel change a significant factor – new staff with remote sensing backgrounds


The future

• Current ARC Linkage project

– Extend MODIS temporal analysis 2001-present

– Relative Spectral Mixture Analysis: time traces of soil, photosynthetic, non-photosynthetic vegetation

– Identify management influences on soil exposure through comparison of actual vs. climatically-predicted cover

• Relationships with Australian Collaborative Land use and Management Program (ACLUMP): development of field and remote sensing methods for monitoring fractional landcover

• Development of reporting products, information delivery with DWLBC


Acknowledgements

Researchers

• Ken Clarke

• Bertram Ostendorf

• Alana Grech

Collaborators – DWLBC

• Anna Dutkiewicz

• Giles Forward

• Glenn Gale

• Andy McCord

• Richard Payne

Funding

• Soil Conservation Council

• Dept. Water Land & Biodiversity Conservation

• Australian Research Council

The Environment Institute

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