A new method for non-destructive measurement of biomass, growth rates, vertical biomass distribution and dry matter content based on digital image analysis.

BACKGROUND AND AIMS Biomass is an important trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive. Thus, they do not allow the development of individual plants to be followed and they require many individuals to be cultivated for repeated measurements. Non-destructive methods do not have these limitations. Here, a non-destructive method based on digital image analysis is presented, addressing not only above-ground fresh biomass (FBM) and oven-dried biomass (DBM), but also vertical biomass distribution as well as dry matter content (DMC) and growth rates. METHODS Scaled digital images of the plants silhouettes were taken for 582 individuals of 27 grass species (Poaceae). Above-ground biomass and DMC were measured using destructive methods. With image analysis software Zeiss KS 300, the projected area and the proportion of greenish pixels were calculated, and generalized linear models (GLMs) were developed with destructively measured parameters as dependent variables and parameters derived from image analysis as independent variables. A bootstrap analysis was performed to assess the number of individuals required for re-calibration of the models. KEY RESULTS The results of the developed models showed no systematic errors compared with traditionally measured values and explained most of their variance (R(2) > or = 0.85 for all models). The presented models can be directly applied to herbaceous grasses without further calibration. Applying the models to other growth forms might require a re-calibration which can be based on only 10-20 individuals for FBM or DMC and on 40-50 individuals for DBM. CONCLUSIONS The methods presented are time and cost effective compared with traditional methods, especially if development or growth rates are to be measured repeatedly. Hence, they offer an alternative way of determining biomass, especially as they are non-destructive and address not only FBM and DBM, but also vertical biomass distribution and DMC.

[1]  L. Vermeire,et al.  Estimating herbage standing crop with visual obstruction in tallgrass prairie. , 2001 .

[2]  P. Poschlod,et al.  Databases on life history traits as a tool for risk assessment in plant species. , 2000 .

[3]  Yasuo Takeda,et al.  Measurement of forest canopy structure with a laser plane range-finding method – development of a measurement system and applications to real forests , 1998 .

[4]  Ruprecht Düll,et al.  Zeigerwerte von Pflanzen in Mitteleuropa , 1992 .

[5]  Jacob Weiner,et al.  Mechanisms determining the degree of size asymmetry in competition among plants , 1998, Oecologia.

[6]  M. Chintala,et al.  A rapid, non-destructive method for estimating aboveground biomass of salt marsh grasses , 2002, Wetlands.

[7]  P. Reich,et al.  A handbook of protocols for standardised and easy measurement of plant functional traits worldwide , 2003 .

[8]  Karl J Niklas,et al.  On the Vegetative Biomass Partitioning of Seed Plant Leaves, Stems, and Roots , 2002, The American Naturalist.

[9]  T. Verwijst,et al.  The influence of allometric variation, vertical biomass distribution and sampling procedure on biomass estimates in commercial short-rotation forests , 1995 .

[10]  J. Weiner Allocation, plasticity and allometry in plants , 2004 .

[11]  W. Dulaney,et al.  Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer , 1991 .

[12]  William M. Schaffer,et al.  Plant strategies and the dynamics and structure of plant communities , 1989 .

[13]  D. R. Vann,et al.  Allometric equations for two South American conifers: Test of a non-destructive method , 1998 .

[14]  James H. Brown,et al.  A general model for the structure and allometry of plant vascular systems , 1999, Nature.

[15]  E. Moler,et al.  A chromaticity-based technique for estimation of above-ground plant biomass , 2001 .

[16]  Peter Poschlod,et al.  BIOPOP — A database of plant traits and internet application for nature conservation , 2003, Folia Geobotanica.

[17]  P. Vivin,et al.  Allometric relationships to estimate seasonal above-ground vegetative and reproductive biomass of Vitis vinifera L. , 2002, Annals of botany.

[18]  N. Montes,et al.  A non-destructive method for estimating above-ground forest biomass in threatened woodlands , 2000 .

[19]  A. Zehm,et al.  Multiparameter analysis of vertical vegetation structure based on digital image processing , 2003 .

[20]  Assessing above-ground phytomass in an alpine region using a hand-held radiometer , 2001 .