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Article << Previous     |     Next >>   Contents Vol 24(3)

Integrating ground and satellite-based observations to determine the degree of grassland curing

Danielle Martin A B, Tao Chen A, David Nichols A, Rachel Bessell A, Susan Kidnie A and Jude Alexander A

A Country Fire Authority, PO Box 701, Mount Waverley, Vic. 3149, Australia.
B Corresponding author. Email: danielle.martin@cfa.vic.gov.au

International Journal of Wildland Fire 24(3) 329-339 http://dx.doi.org/10.1071/WF14029
Submitted: 12 March 2014  Accepted: 16 September 2014   Published: 26 March 2015


 
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Abstract

In Australia, the Grassland Fire Danger Index is determined by several inputs including an essential component, the degree of grassland curing, defined as the proportion of senescent material. In the state of Victoria (south-eastern Australia), techniques used for curing assessment have included the use of ground-based observations and the use of satellite imagery. Both techniques alone have inherent limitations. An improved technique has been developed for estimating the degree of curing that entails the use of satellite observations adjusted by observations from the ground. First, a satellite model was developed, named MapVictoria, based on historical satellite and ground-based observations. Second, with use of the new (MapVictoria) satellite model, an integrated model was developed, named the Victorian Improved Satellite Curing Algorithm, combining near-real-time satellite data with weekly observations of curing from the ground. This integrated model was deployed in operations supporting accurate fire danger calculations for grasslands in Victoria in 2013.

Additional keywords: grassland fire danger, MODIS, Victoria.


References

Anderson SAJ, Anderson WR, Hines F, Fountain A (2005) Determination of field sampling methods for the assessment of curing levels in grasslands. Bushfire Cooperative Research Centre, Project A1.4 Report. (Melbourne, Vic.)

Anderson SAJ, Anderson WR, Hollis JJ, Botha EJ (2011) A simple method for field-based grassland curing assessment. International Journal of Wildland Fire 20, 804–814.
CrossRef |

Barber J (1990) ‘Monitoring the Curing of Grassland Fire Fuels in Victoria, Australia, with Sensors in Satellites and Aircraft.’ (Country Fire Authority: Melbourne, Vic.)

Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76, 156–172.
CrossRef |

Campbell JB (2002) ‘Introduction to Remote Sensing, 3rd Edn.’ (The Guilford Press: New York, NY)

Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S (2002) Designing a spectral index to estimate vegetation water content from remote sensing data. Part 1: theoretical approach. Remote Sensing of Environment 82, 188–197.
CrossRef |

CFA (2013) ‘Ground-Based Curing Data for Victoria, October 2005–January 2013.’ (Country Fire Authority: Melbourne, Vic.)

CFA (2014a) ‘Grassland Curing Guide’ (Country Fire Authority: Melbourne, Vic.)

CFA (2014b) CFA Online Administration – Grassland Curing. (Country Fire Authority: Melbourne, Vic.) Available at www.cfa.vic.gov.au/grass [Accessed August 2014]

Chen D, Huang J, Jackson TJ (2005) Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sensing of Environment 98, 225–236.
CrossRef |

Cheney P, Sullivan A (2008) ‘Grassfires – Fuel, Weather and Fire Behaviour, 2nd Edn.’ (CSIRO Publishing: Melbourne, Vic.)

Dilley A, Millie S, O’Brien D, Edwards M (2004) The relation between Normalized Difference Vegetation Index and vegetation moisture content at three grassland locations in Victoria, Australia. International Journal of Remote Sensing 25, 3913–3928.
CrossRef |

DSE (2011) ‘State-wide 20-m Digital Elevation Model Dataset.’ (Department of Sustainability and Environment: Melbourne, Vic.)

DSE (2012) ‘Tree Density – VicMap Vegetation Dataset.’ (Department of Sustainability and Environment: Melbourne, Vic.)

DSE (2013) ‘Water-bodies – VicMap Hydro Dataset.’ (Department of Sustainability and Environment: Melbourne, Vic.)

Gallant JC, Dowling TI (2003) A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research 39, 1347–1360.
CrossRef |

Guerschman JP, Hill MJ, Renzullo LJ, Barrett DJ, Marks AS, Botha EJ (2009a) 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, 928–945.
CrossRef |

Guerschman JP, Van Dijk AIJM, Mattersdorf G, Beringer J, Hutley LB, Leuning R, Pipunic RC, Sherman BS (2009b) Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia. Journal of Hydrology 369, 107–119.
CrossRef |

Guerschman JP, Warren G, Byrne G, Lymburner L, Mueller N, Van-Dijk A (2011) MODIS-based standing water detection for flood and large reservoir mapping: algorithm development and applications for the Australian continent. CSIRO: Water for a Healthy Country National Research Flagship Report. (Canberra, ACT)

Hosking R (1990) Grassland Curing Index – a district model that allows forecasting of curing. Mathematical and Computer Modelling 13, 73–82.
CrossRef |

Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309.
CrossRef |

Huete AR, Justice C, Van Leeuwen W (1999) ‘MODIS Vegetation Index (MOD 13) – Algorithm Theoretical Basis Document.’ (NASA Goddard Space Flight Center, Greenbelt, Washington, DC)

Islam AS, Bala SK, Haque A (2010) Flood inundation map of Bangladesh using MODIS time-series images. Journal of Flood Risk Management 3, 210–222.

Jordan CF (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology 50, 663–666.
CrossRef |

Levy EB, Madden EA (1933) The point method of pasture analysis. New Zealand Journal of Agriculture 46, 267–279.

Martin DN (2009) Development of satellite vegetation indices to assess grassland curing across Australia and New Zealand. PhD dissertation, RMIT University, Melbourne, Vic.

NASA (2013) Goddard Space Flight Center MODIS Specifications. Available at http://modisgsfcnasagov/about/specificationsphp [Accessed December 2013]

Newnham GJ, Grant IF, Martin DN, Anderson SAJ (2010) Improved methods for assessment and prediction of grassland curing – satellite-based curing methods and mapping. Bushfire Cooperative Research Centre Project A1.4 Report No. A.11.10 (Melbourne, Vic.)

Paget MJ, King EA (2008) MODIS land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. (Canberra)

Paltridge GW, Barber J (1988) Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sensing of Environment 25, 381–394.
CrossRef |

Rouse JW, Haas RW, Schell JA, Deering DH (1973) Monitoring vegetation systems in the Great Plains with ERTS. ‘Proceedings of the Third ERTS Symposium, 10–14 December 1973, Washington, DC’, pp. 309–317 (NASA, Washington, DC)

Sakamoto T, Nguyen NV, Kotera A, Ohno H, Ishitsuka N, Yokozawa M (2007) Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery. Remote Sensing of Environment 109, 295–313.
CrossRef |

Sheffield K, Morse-McNabb E (2013) Creating an historical land-cover data set for the Wimmera region, Victoria, Australia, from the USGS Landsat archive. In ‘Proceedings Geoscience and Remote Sensing Symposium (IGARSS) 2013 IEEE International, 21–26 July 2013, Melbourne’ (IEEE, Melbourne, Vic.)

Sun D, Yu Y, Goldberg M (2011) Deriving water fraction and flood maps from MODIS images using a decision tree approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4, 814–825.
CrossRef |

Willmott CJ (1982) Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63, 1309–1313.
CrossRef |

Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research 30, 79–82.
CrossRef |

Xiao X, Boles S, Liu J, Zhuang D, Frolking S, Li C, Salas W, Moore B (2005) Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment 95, 480–492.
CrossRef |

Xu D, Guo X, Li Z, Yang X, Yin H (2014) Measuring the dead component of mixed grassland with Landsat imagery. Remote Sensing of Environment 142, 33–43.
CrossRef |


   
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