An efficient method for estimating dormant season grass biomass in tallgrass prairie from ultra-high spatial resolution aerial imaging produced with small unmanned aircraft systems
Deon van der Merwe A , Carol E. Baldwin B D and Will Boyer CA Royal GD, 7418 EZ, Deventer, The Netherlands.
B Department of Agriculture, Natural Resources and Community Vitality, 103 Umberger, 1612 Claflin, Kansas State University, Manhattan, KS 66506, USA.
C Kansas Center for Agricultural Resources and the Environment, 44 Waters Hall, Kansas State University, Manhattan, KS 66506, USA.
D Corresponding author. Email: carolbaldwin@k-state.edu
International Journal of Wildland Fire 29(8) 696-701 https://doi.org/10.1071/WF19026
Submitted: 22 February 2019 Accepted: 17 March 2020 Published: 9 April 2020
Journal Compilation © IAWF 2020 Open Access CC BY-NC-ND
Abstract
Fire is used extensively in prairie grassland management in the Flint Hills region of the midwestern United States, particularly at the end of the dormant season (March–April). A model is used to manage grassland fires in the region to avoid deterioration of air quality beyond acceptable standards. Dormant season dry biomass is an important parameter in the model. The commonly used method for producing high-quality biomass estimates relies on clipping, drying and weighing small biomass samples, which is tedious, expensive and does not scale efficiently to larger areas to provide regional estimates. Small unmanned aircraft systems (sUAS) were used to develop a reliable and more efficient method of biomass estimation based on the correlation between biomass and vegetation canopy height derived from digital surface models (DSMs). A linear regression model was developed from data collected at 11 representative sites in the Kansas Flint Hills region, and the model was validated at two sites. Biomass and canopy heights derived from DSMs were correlated, with a Pearson product moment correlation value of 0.881 (P-value <0.001). Biomass estimated from clipped vegetation at two validation sites positively correlated with model-derived biomass estimates, resulting in linear regression R2-values of 0.90 and 0.74 and Pearson moment correlation coefficients of 0.99 (P < 0.001) and 0.86 (P = 0.003). The described sUAS method has the potential to increase the efficiency and reliability of dormant season grassland biomass estimates.
Additional keywords: air quality, fire, Flint Hills, grassland, sUAS, smoke.
References
Axelrod DI (1985) Rise of the grassland biome, Central North-America. Botanical Review 51, 163–201.| Rise of the grassland biome, Central North-America.Crossref | GoogleScholarGoogle Scholar |
Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp ML, Bareth G (2015) Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation 39, 79–87.
| Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley.Crossref | GoogleScholarGoogle Scholar |
Briggs JM, Knapp AK, Brock BL (2002) Expansion of woody plants in tallgrass prairie: a fifteen-year study of fire and fire-grazing interactions. American Midland Naturalist 147, 287–294.
| Expansion of woody plants in tallgrass prairie: a fifteen-year study of fire and fire-grazing interactions.Crossref | GoogleScholarGoogle Scholar |
Brooks K (2012) Exceptional event requests regarding the exceedances of the 8-hour ozone NAAQS at multiple monitors in Kansas during April of 2011. Letter from United States Environmental Protection Agency (EPA) to John Mitchell, Director, Division of the Environment, Kansas Department of Health and Environment December 28, 2012. Available at http://www.kdheks.gov/bar/air-monitor/exceptevent/Flint_Hills_Letter_12-28-12.pdf [verified 23 March 2020]
Cao J, Leng W, Liu K, Liu L, He Z, Zhu Y (2018) Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sensing 10, 89
| Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models.Crossref | GoogleScholarGoogle Scholar |
Catchpole WR, Wheeler CJ (1992) Estimating plant biomass: a review of techniques. Australian Journal of Ecology 17, 121–131.
| Estimating plant biomass: a review of techniques.Crossref | GoogleScholarGoogle Scholar |
Harmoney KR, Moore KJ, George JR, Brummer EC, Russell JR (1997) Determination of pasture biomass using four indirect methods. Agronomy Journal 89, 665–672.
| Determination of pasture biomass using four indirect methods.Crossref | GoogleScholarGoogle Scholar |
Küchler AW (1974) A new vegetation map of Kansas. Ecology 55, 586–604.
| A new vegetation map of Kansas.Crossref | GoogleScholarGoogle Scholar |
Li W, Niu Z, Chen H, Li D, Wu M, Zhao W (2016) Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological Indicators 67, 637–648.
| Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system.Crossref | GoogleScholarGoogle Scholar |
Singh KK, Frazier AE (2018) A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications. International Journal of Remote Sensing 39, 5078–5098.
| A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications.Crossref | GoogleScholarGoogle Scholar |
Towne G, Owensby C (1984) Long-term effects of annual burning at different dates in ungrazed Kansas tallgrass prairie. Journal of Range Management 37, 392–397.
| Long-term effects of annual burning at different dates in ungrazed Kansas tallgrass prairie.Crossref | GoogleScholarGoogle Scholar |
Tucker CJ (1980) A critical review of remote sensing and other methods for non-destructive estimation of standing crop biomass. Grass and Forage Science 35, 177–182.
| A critical review of remote sensing and other methods for non-destructive estimation of standing crop biomass.Crossref | GoogleScholarGoogle Scholar |
Vermeire TL, Gillen R (2001) Estimating herbage standing crop with visual obstruction in tallgrass prairie. Journal of Range Management 54, 57–60.
Wang H, Wang C, Price KP, van der Merwe D, An N (2014) Modeling above-ground biomass in tallgrass prairie using ultra-high spatial resolution sUAS imagery. Photogrammetric Engineering and Remote Sensing 80, 1151–1159.
| Modeling above-ground biomass in tallgrass prairie using ultra-high spatial resolution sUAS imagery.Crossref | GoogleScholarGoogle Scholar |
Winter SL, Allred BW, Hickman KR, Fuhlendorf SD (2015) Tallgrass prairie vegetation response to spring fires and bison grazing. The Southwestern Naturalist 60, 30–35.
| Tallgrass prairie vegetation response to spring fires and bison grazing.Crossref | GoogleScholarGoogle Scholar |
