Remote mine site rehabilitation monitoring using airborne hyperspectral imaging and landscape function analysis (LFA)

I. C. Lau*, R. D. Hewson, C. C.H. Ong, D. J. Tongway

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

5 Citations (Scopus)


Landscape Function Analysis (LFA) is an environmental monitoring technique which is internationally recognized as a method of measuring and monitoring ecosystem function and rehabilitation progress. Airborne hyperspectral data were used to generate maps of the three Landscape Function Analysis (LFA) Indices over a 5 × 20 km area of a rehabilitated bauxite mine in southwestern Australia. The technique involved measuring field spectra of 35 LFA sites and generating Final Regression Coefficients using partial least squares analysis for the three LFA Indices, Stability, Infiltration and Nutrient Cycling. Both field and airborne spectral data were used in the calibration dataset, as the ground based sampling technique was not able to capture the information on the crowns of the tall eucalypts in rehabilitation older than eight years of age. The Final Regression Coefficients were applied to the airborne hyperspectral imagery to produce LFA Maps with good correlations to the field LFA measurements (Stability r2=0.76, Infiltration r2=0.67 and Nutrient Cycling r2=0.71 for n=15). The results demonstrated that airborne hyperspectral data could be reliably used to derive spatially continuous LFA maps over forest rehabilitated after bauxite mining in the Darling Range of Western Australia.

Original languageEnglish
Pages (from-to)325-330
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Publication statusPublished - 1 Jan 2008
Externally publishedYes
Event21st Congress of the International Society for Photogrammetry and Remote Sensing 2008 - Beijing, China
Duration: 3 Jul 200811 Jul 2008
Conference number: 21


  • Ecosystem
  • Environment
  • Hyperspectral
  • Mapping
  • Vegetation

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