Comparing the accuracy of three non-destructive methods in estimating aboveground plant biomass

Abstract

Abstract:Aboveground plant biomass is one of the most important features of ecosystems, and it is widely used in ecosystem research. Non-destructive biomass estimation methods provide an important toolkit, because the destructive harvesting method is in many cases not feasible. However, only few studies have compared the accuracy of these methods in grassland communities to date. We studied the accuracy of three widely used methods for estimation of aboveground biomass: the visual cover estimation method, the point intercept method, and field spectroscopy. We applied them in three independent series of field samplings in semi-arid sand grasslands in Central Hungary. For each sampling method, we applied linear regression to assess the strength of the relationship between biomass proxies and actual aboveground biomass, and used coefficient of determination to evaluate accuracy. We found no evidence that the visual cover estimation, which is generally considered as a subjective method, was less accurate than point intercept method or field spectroscopy in estimating biomass. Based on our three datasets, we found that accuracy was lower for the point intercept method compared to the other two methods, while field spectroscopy and visual cover estimation were similar to each other in the semi-arid sand grassland community. We conclude that visual cover estimation can be as accurate for estimating aboveground biomass as other approaches, thus the choice amongst the methods should be based on additional pros and cons associated with each of the method and related to the specific research objective. Nomenclature:The Plant List (2010).

Enhancement of Ecological Field Experimental Research by Means of UAV Multispectral Sensing

“…On the other hand, it also points out the need to assess the homogeneity of the measured effect per plot and per treatment in order to increase the consistency of the results. An analogous effect has been found for the temporal scale in the VULCAN/INCREASE experiment where results show that the effect of drought treatment can be overestimated with only one measurement at the time of the peak biomass, while multiple within-year measurements better describe the response of biomass [27].…”

“…From red (660 nm) and near infrared (810 nm) Cropscan reflectance values bands, we calculated NDVI . The NDVI provides an accurate proxy for plant aboveground green biomass estimation for pioneering plant communities [27,33]. [34] plus the three incorporated in 2010.…”

“…This is the case of the ExDRain experiment set out at Kiskun LTER station where rain manipulation is applied since 2014 for several plots after the application of extreme drought event for some of them [25]. Ecosystem recovery is assessed by periodically measuring plant cover and biomass using NDVI (Normalized Difference Vegetation Index, [26]) collected using a portable CropScan MSR87 multispectral radiometer (Cropscan, Inc., Rochester, MN, USA) as a non-destructive method [27]. Cropscan has 8 narrow (10 nm bandwidth) spectral bands centred at 460, 510, 560, 610, 660, 710, 760 and 810 nm (bands 660 and 810 are usually used for NDVI calculation).…”

Reduction in primary production followed by rapid recovery of plant biomass in response to repeated mid-season droughts in a semiarid shrubland

“…When effects on primary production is in focus, field spectroscopy is one of the feasible solutions for estimating aboveground biomass, or leaf area index (Goodin and Henebry 1997;Pontailler et al 2003;Mulla 2013;Nestola et al 2016;Ónodi et al 2017a). The Normalized Differential Vegetation Index (NDVI) obtained by field spectroscopy is an accurate proxy for aboveground biomass estimations (Gamon et al 1995;Ónodi et al 2017b).…”

Changing methodology results in operational drift in the meaning of leaf area index, necessitating implementation of foliage layer index

“…While in a review of the many ways in which cover values can be obtained, Wilson () thoroughly criticizes such subjective methods, claiming these generate “nonsense measure[s],” Damgaard () considers them unbiased at least, and Ónodi et al. () found them reliable. Regardless, the subjective methods are so rapid and convenient that error in using them is accepted as the price of “doing business.”…”