The Use of AVHRR NDVI in Environmental Applications

Contact: Elizabeth R. McDonaldERIN Remote Sensing Coordinator

Department of the Environment and Heritage

Policy FrameworkCommonwealth Programmes Using RS

EPBC Act– Compliance

State of the Environment Report 2006– Indicator Reporting

National Resource Management – National Matters for Target

CERF (Commonwealth Environment Research Fund)

BRS Climate Group– Drought Exceptional Circumstances– National Agricultural Monitoring System

DEH Remote Sensing Archives

ERIN AVHRR NDVI - continental ‘91 – currentAGO Landsat Archive – continental ’72 - 04– Images– Image Products

Other Data - ad hoc collections– MODIS– SPOT– Quickbird– Ikonos

ERIN AVHRR Processing

Data is supplied by DLI to ERIN as continental 14 day composites of raw satellite counts.

Calibrated using a variant of Roa and Chen (1999) using reference points from the Australian deserts.

As the gain values change over time a regression equation is fitted relating the gain values to the time since launch.

Cloud detection and masking by individual pixels through time and identified using an arbitrary threshold.

http://www.deh.gov.au/erin/ndvi/index.html

State of the Environment Reporting

1. Data Types – Medium spatial resolution data

• Landsat

– High temporal resolution data• AVHRR NDVI

2. Applications– Forest non Forest– Forested stream lengths– Urban expansion including coastal urban expansion– Mangrove mapping

NRM – National Matters for Target

1. Data Types – High to Medium spatial resolution data

• SPOT data• Landsat

– High temporal resolution data• AVHRR NDVI

2. Applications– Forest non Forest and forested stream length– Monitoring and Trend analysis– Water Quality assessment

Forest Loss/Gain Spatial Extent

Forest Extent Through Time

Forest Change by Decade

Forest Loss/Gain Spatial Extent

NDVI Time Traces

Inland Aquatic Ecosystems Integrity

River Condition* Forested Stream Length by NHT region

Wetland ecosystem extent and distribution* Extent and change of regionally significant wetlands using an algorithm supplied by of Tim Danaher, QLD Dept. Natural Resources, Mines and Water (pers. Comm. 2005).

Wetland ecosystem condition* Colour (turbidity index)* Transparency (light penetration)* Vegetation (presence/absence)* Vegetation (NDVI greenness)

Nuga Catchment

Nuga Catchment Surface Water 1989

September 1989 September 1989

Nuga Catchment Surface Water 1995

March 1995 September 1994

Nuga Catchment Surface Water 2002

March 2002 December 2001

Nuga Catchment Surface Water 2004

January 2004 March 2004

BRS Climate Group

1. Data Types – High temporal resolution data

• AVHRR NDVI• MODIS NDVI

2. Applications– NDVI Time traces for drought exceptional circumstances– NDVI Anomaly for the National Agricultural Monitoring

System. The Anomoly algorithm is sourced from: McVicar, T.R., Briggs, P.R., King, E.A. and Raupach, M.R. (2003) A Review of Predictive Modelling from a Natural Resource Management Perspective: The Role of Remote Sensing of the Terrestrial Environment. CSIRO Land and Water Client Report to the Bureau of Rural Sciences (also available as CSIRO Earth Observation Centre Report 2003/03 and CSIRO Atmospheric Research Report 2003/31), Canberra, Australia, pp. 26. http://www.clw.csiro.au/publications/consultancy/2003/Review_Of_Remote_Sensing.pdf)

NDVI Anomaly

(NDVIm – NDVIt)/(NDVIt)

NDVIm = NDVI for a particular month (eg. May 2004)

NDVIt = the archive average of that month (eg May 1991 – 2004)

MODIS/AVHRR NDVI Comparison

Comparison of AVHRR NDVI and MOD13Q1 (CSIRO, 2004) using four major land covers:forests; shrublands, grassland and croplands.In all cases MODIS was positively offset and the seasonal variation is greater

Vegetation Class R2

Tall Forest 0.02Low Shrubland 0.41

Tussock Grassland 0.82 Cereal Crop 0.85

Unexplained variance limits the direct simple translation for all but basic applications.