Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices
L. M. Ellsworth A B D , A. P. Dale C , C. M. Litton B and T. Miura B
+ Author Affiliations
- Author Affiliations
A Oregon State University, Department of Fisheries and Wildlife, 104 Nash Hall, Corvallis, OR 97331, USA.
B University of Hawaii at Manoa, Department of Natural Resources and Environmental Management, 1910 East–West Road, Honolulu, HI 96822, USA.
C GISPacific, LLC, 575 Ulumalu Street, Kailua, HI 96734, USA.
D Corresponding author. Email: lisa.ellsworth@oregonstate.edu
International Journal of Wildland Fire 26(5) 384-392 https://doi.org/10.1071/WF16131
Submitted: 20 July 2016 Accepted: 8 March 2017 Published: 27 April 2017
Abstract
The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2 = 0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2 = 0.37) and the Keetch–Byram Drought Index (R2 = 0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2 = 0.19). Site-specific models improved model fit for live fuel moisture (R2 = 0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.
Additional keywords: fire model, guinea grass, Hawaii, invasive grass, Keetch–Byram Drought Index, live fuel moisture, National Fire Danger Rating System, remote sensing.
References
Andrews P, Bevins C, Seli R (2005) BehavePlus fire modeling system, version 4.0: user’s guide. USDA Forest Service, Rocky Mountain Research Station General Technical Report RMRS-GTR-106WWW Revised. (Ogden, UT)Beavers A (2001) Creation and validation of a custom fuel model representing mature Panicum maximum (guinea grass) in Hawaii. Center for Environmental Management of Military Lands. Department of Forest Sciences, Colorado State University. (Fort Collins, CO)
Brooks ML, D’Antonio CM, Richardson DM, Grace JB, Keeley JE, DiTomaso JM, Hobbs RJ, Pellant M, Pyke D (2004) Effects of invasive alien plants on fire regimes. Bioscience 54, 677–688.
| Effects of invasive alien plants on fire regimes.Crossref | GoogleScholarGoogle Scholar |
Caccamo G, Chisholm LA, Bradstock RA, Puotinen ML, Pippen BG (2012) Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data. International Journal of Wildland Fire 21, 257–269.
| Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data.Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Riano D, Aguado I, Cocero D (2002) Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. International Journal of Remote Sensing 23, 2145–2162.
| Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment.Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Cocero D, Riaño D, Martin P, Martínez-Vega J, de la Riva J, Pérez F (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment 92, 322–331.
| Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating.Crossref | GoogleScholarGoogle Scholar |
D’Antonio CM, Vitousek PM (1992) Biological invasions by exotic grasses, the grass/fire cycle, and global change. Annual Review of Ecology and Systematics 23, 63–87.
| Biological invasions by exotic grasses, the grass/fire cycle, and global change.Crossref | GoogleScholarGoogle Scholar |
Danson FM, Bowyer P (2004) Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment 92, 309–321.
| Estimating live fuel moisture content from remotely sensed reflectance.Crossref | GoogleScholarGoogle Scholar |
Dimitrakopoulos AP, Bemmerzouk AM (2003) Predicting live herbaceous moisture content from a seasonal drought index. International Journal of Biometeorology 47, 73–79.
Ellsworth LM (2012) Improved wildfire management in Megathyrsus maximus dominated ecosystems in Hawaii. PhD thesis, University of Hawaii at Manoa.
Ellsworth LM, Litton CM, Taylor AD, Kauffman JB (2013) Spatial and temporal variability of guinea grass (Megathyrsus maximus) fuel loads and moisture on Oahu, Hawaii. International Journal of Wildland Fire 22, 1083–1092.
| Spatial and temporal variability of guinea grass (Megathyrsus maximus) fuel loads and moisture on Oahu, Hawaii.Crossref | GoogleScholarGoogle Scholar |
Ellsworth LM, Litton CM, Dale AP, Miura T (2014) Invasive grasses change landscape structure and fire behaviour in Hawaii. Applied Vegetation Science 17, 680–689.
| Invasive grasses change landscape structure and fire behaviour in Hawaii.Crossref | GoogleScholarGoogle Scholar |
Foxcroft LC, Richardson DM, Rejmanek M, Pysek P (2010) Alien plant invasions in tropical and subtropical savannas: patterns, processes and prospects. Biological Invasions 12, 3913–3933.
| Alien plant invasions in tropical and subtropical savannas: patterns, processes and prospects.Crossref | GoogleScholarGoogle Scholar |
Giambelluca TW, Chen Q, Frazier AG, Price JP, Chen Y-L, Chu P-S, Eischeid JK, Delparte DM (2013) Online rainfall atlas of Hawai’i. Bulletin of the American Meteorological Society 94, 313–316.
| Online rainfall atlas of Hawai’i.Crossref | GoogleScholarGoogle Scholar |
Giambelluca T, Shuai X, Barnes M, Alliss R, Longman R, Miura T, Chen Q, Frazier A, Mudd R, Cuo L, Businger A (2014) Evapotranspiration of Hawaii. Final Report submitted to the US Army Corps of Engineers-Honolulu District, and the Commission on Water Resource Management, State of Hawaii. (Honolulu, HI)
Glenn E, Huete A, Nagler P, Nelson S (2008) Relationship between remotely sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8, 2136–2160.
| Relationship between remotely sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape.Crossref | GoogleScholarGoogle Scholar |
Hao X, Qu J (2007) Retrieval of real-time fuel moisture content using MODIS measurements. Remote Sensing of Environment 108, 130–137.
| Retrieval of real-time fuel moisture content using MODIS measurements.Crossref | GoogleScholarGoogle Scholar |
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213.
| Overview of the radiometric and biophysical performance of the MODIS vegetation indices.Crossref | GoogleScholarGoogle Scholar |
Huete A, Didan K, van Leeuwen W, Miura T, Glenn E (2009) MODIS vegetation indices. In ‘Land remote sensing and global environmental change: NASA’s Earth observing system and the science of ASTER and MODIS’. (Eds B. Ramachandran, C. O. Justice, M. J. Abrams) pp. 579–602. (Springer: New York)
Kauffman JB, Cummings DL, Ward DE (1998) Fire in the Brazilian Amazon. 2. Biomass, nutrient pools and losses in cattle pastures. Oecologia 113, 415–427.
| Fire in the Brazilian Amazon. 2. Biomass, nutrient pools and losses in cattle pastures.Crossref | GoogleScholarGoogle Scholar |
Keetch JJ, Byram GM (1968) A drought factor index for forest fire control. USDA Forest Service, Southeastern Forest Experiment Station, Research paper SE-38. (Asheville, NC)
Miller G, Friedel M, Adam P, Chewings V (2010) Ecological impacts of buffel grass (Cenchrus ciliaris L.) invasion in central Australia – does field evidence support a fire-invasion feedback? The Rangeland Journal 32, 353–365.
| Ecological impacts of buffel grass (Cenchrus ciliaris L.) invasion in central Australia – does field evidence support a fire-invasion feedback?Crossref | GoogleScholarGoogle Scholar |
Nieto H, Aguado I, Chuvieco E, Sandholt I (2010) Dead fuel moisture estimation with MSG-SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content. Agricultural and Forest Meteorology 150, 861–870.
| Dead fuel moisture estimation with MSG-SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content.Crossref | GoogleScholarGoogle Scholar |
Pellizzaro G, Cesaraccio C, Duce P, Ventura A, Zara P (2007) Relationships between seasonal patterns of live fuel moisture and meteorological drought indices for Mediterranean shrubland species. International Journal of Wildland Fire 16, 232–241.
| Relationships between seasonal patterns of live fuel moisture and meteorological drought indices for Mediterranean shrubland species.Crossref | GoogleScholarGoogle Scholar |
Pyšek P, Jarosik V, Hulme PE, Pergl J, Hejda M, Schaffner U, Vila M (2012) A global assessment of invasive plant impacts on resident species, communities and ecosystems: the interaction of impact measures, invading species’ traits and environment. Global Change Biology 18, 1725–1737.
| A global assessment of invasive plant impacts on resident species, communities and ecosystems: the interaction of impact measures, invading species’ traits and environment.Crossref | GoogleScholarGoogle Scholar |
Schlobohm P, Brain J (2002) Gaining an understanding of the National Fire Danger Rating System, PMS 932/NFES 2665. National Wildfire Coordinating Group. Available at https://www.nwcg.gov/sites/default/files/products/pms932.pdf [Verified 23 March 2017]
Vitousek PM, Loope LL, Stone CP (1987) Introduced species in Hawaii: biological effects and opportunities for ecological research. Trends in Ecology & Evolution 2, 224–227.
| Introduced species in Hawaii: biological effects and opportunities for ecological research.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3M7gvFGkug%3D%3D&md5=bc05979be56a488bb8ef9d2ed4539e0fCAS |
Yebra M, Chuvieco E, Riaño D (2008) Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology 148, 523–536.
| Estimation of live fuel moisture content from MODIS images for fire risk assessment.Crossref | GoogleScholarGoogle Scholar |
