An assessment of the accuracy of satellite-derived woody and grass foliage cover estimates for Australia

Randall J. Donohue; Luigi J. Renzullo

doi:10.1071/BT24060

RESEARCH ARTICLE (Open Access)

Previous Contents Vol 73(2)

An assessment of the accuracy of satellite-derived woody and grass foliage cover estimates for Australia

Randall J. Donohue

^A ^* and Luigi J. Renzullo ^B

+ Author Affiliations

- Author Affiliations

^A CSIRO Environment, GPO Box 1700, Canberra, ACT 2601, Australia.

^B Bureau of Meteorology, Research Program, GPO Box 2334, Canberra, ACT 2600, Australia.

^* Correspondence to: Randall.Donohue@csiro.au

Handling Editor: James Camac

Australian Journal of Botany 73, BT24060 https://doi.org/10.1071/BT24060

Submitted: 23 September 2024 Accepted: 4 February 2025 Published: 6 March 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Understanding the functional role of vegetation across landscapes requires the ability to monitor tree and grass foliage cover dynamics. Several satellite-derived products describe total and woody foliage cover across Australia. Few of these are suitable for monitoring changes in woody foliage cover and only one can currently describe subseasonal dynamics in both woody and grass cover.

Aims

(1) To improve the accuracy of woody and grass foliage cover estimates in Australia’s arid environments, around major disturbances and in perennially green pastures. (2) To gain a detailed understanding of the accuracy of woody and grass foliage cover estimates for Australia.

Methods

Satellite-derived greenness data were converted to total foliage cover fraction (0.0–1.0), accounting for differences in background soil affects. Total cover was split into component woody and grass cover by using a modified persistent–recurrent splitting algorithm. Results were compared with 4214 field measurements of cover.

Key results

Accuracy varied between woody and grassland vegetation types, with total, woody and grass foliage cover having low errors (of ~0.08) and near-zero biases across all woody vegetation types. Across grasslands, errors were higher (up to 0.28), and biases were greater (and negative), with both scaling with foliage density.

Conclusions

Foliage cover was accurately estimated for forested through to sparsely wooded ecosystems. Foliage cover of pure, dense grasslands was systematically underpredicted.

Implications

This is the only Australian cover product that can generate temporally dense woody and grass foliage cover data and is invaluable for monitoring vegetation dynamics, particularly across Australia’s mixed tree–grass landscapes.

Keywords: Australia, dynamics, foliage cover, grass, satellite, time-series, vegetation, woody.

References

ABARES (2021) Catchment scale land use of Australia – update December 2020. Australian Bureau of Agricultural and Resource Economics and Sciences. Available at https://doi.org/10.25814/aqjw-rq15

Armston JD, Denham RJ, Danaher TJ, Scarth PF, Moffiet TN (2009) Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery. Journal of Applied Remote Sensing 3(1), 033540.
| Crossref | Google Scholar |

Asrar G, Fuchs M, Kanemasu ET, Hatfield JL (1984) Estimating absorbed photosynthetic radiation and leaf-area index from spectral reflectance in wheat. Agronomy Journal 76(2), 300-306.
| Crossref | Google Scholar |

Australian National Greenhouse Accounts (2023) National inventory report vol. 2. Department of Climate Change, Energy, the Environment and Water, Canberra, ACT, Australia.

Berry SL, Roderick ML (2002) Estimating mixtures of leaf functional types using continental-scale satellite and climatic data. Global Ecology and Biogeography 11(1), 23-39.
| Crossref | Google Scholar |

Berry SL, Roderick ML (2006) Changing Australian vegetation from 1788 to 1988: effects of CO₂ and land-use change. Australian Journal of Botany 54(4), 325-338.
| Crossref | Google Scholar |

Bowman DMJS, Prior LD (2005) Why do evergreen trees dominate the Australian seasonal tropics? Australian Journal of Botany 53(5), 379-399.
| Crossref | Google Scholar |

Bradford M, Ford A (2022) Robson creek rainforest vegetation structure data, 2014. Version 1.0. Terrestrial Ecosystem Research Network. Available at https://doi.org/10.25901/7brp-f114

Brockerhoff EG, Barbaro L, Castagneyrol B, Forrester DI, Gardiner B, González-Olabarria JR, Lyver P, Meurisse N, Oxbrough A, Taki H, Thompson ID, van der Plas F, Jactel H (2017) Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodiversity and Conservation 26(13), 3005-3035.
| Crossref | Google Scholar |

Budyko MI (1974) ‘Climate and life.’ (Academic Press: New York, NY, USA)

Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 62(3), 241-252.
| Crossref | Google Scholar |

Collett L (2024) Foliage projective cover – Sentinel-2, DES algorithm, QLD coverage. Version 1.0. Department of Environment and Science, Queensland Government. Available at https://portal.tern.org.au/metadata/TERN/c65ad708-e270-431a-bb5b-13f1a4ec13db

Cranko Page J, Abramowitz G, De Kauwe MG, Pitman AJ (2024) Are plant functional types fit for purpose? Geophysical Research Letters 51(1), e2023GL104962.
| Crossref | Google Scholar |

CSIRO and National Resource Information Centre (1991) Atlas of Australian soils (digital), version 3. CSIRO. Available at 10.25919/5f1632a855c17

DAWE (2020) National vegetation information system, version 6.0. Australian Government Department of Agriculture, Water and the Environment.

Department of Environment and Science (2021) Foliage projective cover – Landsat, DES algorithm, QLD coverage. Version 1.0. Department of Environment and Science, Queensland Government. Available at https://portal.tern.org.au/metadata/TERN/508f322a-a9fd-459f-9122-df52e2b6579e

Department of Environment and Science (2022a) Seasonal persistent green – Landsat, JRSRP algorithm version 3.0, Australia coverage. Department of Environment and Science, Queensland Government. Available at https://portal.tern.org.au/metadata/TERN/dd359b61-3ce2-4cd5-bc63-d54d2d0e2509

Department of Environment and Science (2022b) SLATS star transects – Australian field sites. Version 1.0. Department of Environment and Science, Queensland Government. Available at https://portal.tern.org.au/metadata/TERN/24a40c29-0d7c-4fe8-bdde-9c4ea495bfb8

Dobarco RM, Wadoux A, Malone B, Minasny B, McBratney A, Searle R (2024) Soil and landscape grid national soil attribute maps – coarse fragments (3″ resolution) – Release 1. Version 1.0. Terrestrial Ecosystem Research Network. Available at https://doi.org/10.25919/c583-fd02

Donohue RJ (2021) Testing the accuracy of Gramina, a model for monitoring Australia’s pasture and grassland biomass. CSIRO, Canberra, ACT, Australia.

Donohue RJ (2023) Australian C4 grass cover percentage, version 1. CSIRO. Available at https://doi.org/10.25919/ffp4-b663

Donohue RJ, Roderick ML, McVicar TR (2008) Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework. Remote Sensing of Environment 112(6), 2938-2949.
| Crossref | Google Scholar |

Donohue RJ, McVicar TR, Roderick ML (2009) Climate-related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006. Global Change Biology 15(4), 1025-1039.
| Crossref | Google Scholar |

Donohue RJ, McVicar TR, Roderick ML (2013) Australian monthly fPAR derived from advanced very high resolution radiometer reflectances – version 5. CSIRO. Available at https://doi.org/10.4225/08/50FE0CBE0DD06

Donohue RJ, Hume IH, Roderick ML, McVicar TR, Beringer J, Hutley LB, Gallant JC, Austin JM, van Gorsel E, Cleverly JR, Meyer WS, Arndt SK (2014) Evaluation of the remote-sensing-based DIFFUSE model for estimating photosynthesis of vegetation. Remote Sensing of Environment 155, 349-365.
| Crossref | Google Scholar |

Donohue RJ, Lawes RA, Mata G, Gobbett DL, Ouzman J (2018) Towards a national, remote-sensing-based model for predicting field-scale crop yield. Field Crops Research 227, 79-90.
| Crossref | Google Scholar |

Fisher A, Day M, Gill T, Roff A, Danaher T, Flood N (2016) Large-area, high-resolution tree cover mapping with multi-temporal SPOT5 imagery, New South Wales, Australia. Remote Sensing 8(6), 515.
| Crossref | Google Scholar |

Gill TK (2021) Woody vegetation cover – Landsat, JRSRP, Australian coverage, 2000–2010. Version 1.0. Terrestrial Ecosystem Research Network. Available at https://portal.tern.org.au/metadata/TERN/e4de7f56-f1a5-418e-9118-3220f6f365f8

Gill TK, Armston JD, Phinn SR, Pailthorpe BA (2006) A comparison of MODIS time series decomposition methods for estimating evergreen foliage cover. In ‘13th Australasian remote sensing and photogrammetry conference’, 20–24 November 2006, Canberra, ACT, Australia. (Spatial Sciences Institute)

Guerschman JP, Hill MJ (2018) Calibration and validation of the Australian fractional cover product for MODIS collection 6. Remote Sensing Letters 9(7), 696-705.
| Crossref | Google Scholar |

Guerschman JP, Hill MJ, Renzullo LJ, Barrett DJ, Marks AS, Botha EJ (2009) Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sensing of Environment 113(5), 928-945.
| Crossref | Google Scholar |

Guerschman JP, Scarth PF, McVicar TR, Renzullo LJ, Malthus TJ, Stewart JB, Rickards JE, Trevithick R (2015) Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data. Remote Sensing of Environment 161, 12-26.
| Crossref | Google Scholar |

Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342, 6160 850-853.
| Crossref | Google Scholar | PubMed |

Hattersley PW (1983) The distribution of C₃ and C₄ grasses in Australia in relation to climate. Oecologia 57, 113-128.
| Crossref | Google Scholar | PubMed |

Held A, Phinn S, Soto-Berelov M, Jones S (Eds) (2015) ‘AusCover good practice guidelines: a technical handbook supporting calibration and validation activities of remotely sensed data products. Version 1.1.’ (TERN AusCover: Brisbane, Qld, Australia)

Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25(3), 295-309.
| Crossref | Google Scholar |

Jackson RB, Schenk HJ, Jobbágy EG, Canadell J, Colello GD, Dickinson RE, Field CB, Friedlingstein P, Heimann M, Hibbard K, Kicklighter DW, Kleidon A, Neilson RP, Parton WJ, Sala OE, Sykes MT (2000) Belowground consequences of vegetation change and their treatment in models. Ecological Applications 10(2), 470-483.
| Crossref | Google Scholar |

Johnston WH (1996) The place of C4 grasses in temperate pastures in Australia. New Zealand Journal of Agricultural Research 39(4), 527-540.
| Crossref | Google Scholar |

Joint Remote Sensing Research Program and Department of Environment and Science (2021) Monthly blended fractional cover – Landsat and Sentinel-2, JRSRP algorithm, Queensland coverage. Joint Remote Sensing Research Program and Department of Environment and Science, Queensland Government. Available at https://portal.tern.org.au/metadata/TERN/2d52273c-115a-41ca-88f3-d70fb7b8e831

Joint Remote Sensing Research Program and Department of Environment and Science (2022) Seasonal fractional cover – Sentinel-2, JRSRP algorithm version 3.0, eastern and central australia coverage. Joint Remote Sensing Research Program and Department of Environment and Science, Queensland Government. Available at https://portal.tern.org.au/metadata/TERN/13810293-c6b5-442b-bfcd-817700738e0d

Jones D, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal 58, 233-248.
| Crossref | Google Scholar |

Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A, Lucht W, Myneni RB, Knyazikhin Y, Running SW, Nemani RR, Zhengming Wan, Huete AR, van Leeuwen W, Wolfe RE, Giglio L, Muller J, Lewis P, Barnsley MJ (1998) The moderate resolution imaging spectroradiometer (MODIS): land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing 36(4), 1228-1249.
| Crossref | Google Scholar |

Karan M (2015) ‘SuperSites vegetation monitoring protocols v1.12.’ (TERN Australian SuperSite Network)

Keith DA, Simpson CC (2012) Vegetation formations and classes of NSW (version 3.03-200m raster). State Government of NSW and NSW Department of Climate Change, Energy, the Environment and Water. Available at https://datasets.seed.nsw.gov.au/dataset/31986103-db62-4994-9702-054949281f56

Larcher W (2003) ‘Physiological plant ecology: ecophysiology and stress physiology of functional groups.’ (Springer: Berlin, Germany)

Liao Z, Van Dijk AIJM, He B, Larraondo PR, Scarth PF (2020) Woody vegetation cover, height and biomass at 25-m resolution across Australia derived from multiple site, airborne and satellite observations. International Journal of Applied Earth Observation and Geoinformation 93, 102209.
| Crossref | Google Scholar |

Liddell M, Laurance S (2024) Daintree rainforest, cape tribulation leaf area index data. Version 1.0. Terrestrial Ecosystem Research Network. Available at https://doi.org/10.25901/d2s5-de69

Lu H, Raupach MR, McVicar TR, Barrett DJ (2003) Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series. Remote Sensing of Environment 86(1), 1-18.
| Crossref | Google Scholar |

Montandon LM, Small EE (2008) The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sensing of Environment 112(4), 1835-1845.
| Crossref | Google Scholar |

Monteith JL (1972) Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology 9(3), 747-766.
| Crossref | Google Scholar |

Muir J, Schmidt M, Tindall D, Trevithick R, Scarth P, Stewart JB (2011) ‘Field measurement of fractional ground cover: a technical handbook supporting ground cover monitoring for Australia.’ (Australian Bureau of Agricultural and Resource Economics and Sciences: Canberra, ACT, Australia)

Office of Environment and Heritage (2021) Woody extent and foliage projective cover – spot, OEH algorithm, NSW. Version 1.0. NSW Office of Environment and Heritage. Available at https://doi.org/10.25901/dcje-yt71

Pook EW (1986) Canopy dynamics of Eucalyptus maculata Hook. IV. Contrasting responses to two severe droughts. Australian Journal of Botany 34, 1, 1-14.
| Crossref | Google Scholar |

Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sensing of Environment 48(2), 119-126.
| Crossref | Google Scholar |

Roderick ML, Noble IR, Cridland SW (1999) Estimating woody and herbaceous vegetation cover from time series satellite observations. Global Ecology and Biogeography 8(6), 501-508.
| Crossref | Google Scholar |

Ruiz-Vásquez M, Sungmin O, Arduini G, Boussetta S, Brenning A, Bastos A, Koirala S, Balsamo G, Reichstein M, Orth R (2023) Impact of updating vegetation information on land surface model performance. Journal of Geophysical Research: Atmospheres 128, e2023JD039076.
| Crossref | Google Scholar |

Specht RL (1970) Vegetation. In ‘Australian environment’. (Ed. GW Leeper) pp. 44–67. (Melbourne University Press: Melbourne, Vic, Australia)

Specht RL (1972) Water use by perennial evergreen plant communities in Australia and Papua New Guinea. Australian Journal of Botany 20(3), 273-299.
| Crossref | Google Scholar |

Specht RL, Specht A (1999) ‘Australian plant communities: dynamics of structure, growth and biodiversity.’ (Oxford: Melbourne, Vic, Australia)

Stewart S (2021) Annual woody vegetation and canopy cover grids for Tasmania. v6. CSIRO. Available at https://doi.org/10.25919/8580-1m41

Sutton A, Fisher A, Metternicht G (2022) Assessing the accuracy of Landsat vegetation fractional cover for monitoring Australian drylands. Remote Sensing 14(24), 6322.
| Crossref | Google Scholar |

Wardlaw T (2023) Warra tall eucalypt leaf area index data, 2015. Version 1.0. Terrestrial Ecosystem Research Network.
| Crossref | Google Scholar |

Watkinson AR, Ormerod SJ (2001) Grasslands, grazing and biodiversity: editors’ introduction. Journal of Applied Ecology 38(2), 233-237.
| Crossref | Google Scholar |

Yoshioka H, Miura T, Huete AR, Ganapol BD (2000) Analysis of vegetation isolines in red-NIR reflectance space. Remote Sensing of Environment 74(2), 313-326.
| Crossref | Google Scholar |