In situ detection of tree root distribution and biomass by multi-electrode resistivity imaging.
Traditional methods for studying tree roots are destructive and labor intensive, but available nondestructive techniques are applicable only to small scale studies or are strongly limited by soil conditions and root size. Soil electrical resistivity measured by geoelectrical methods has the potential to detect belowground plant structures, but quantitative relationships of these measurements with root traits have not been assessed. We tested the ability of two-dimensional (2-D) DC resistivity tomography to detect the spatial variability of roots and to quantify their biomass in a tree stand. A high-resolution resistivity tomogram was generated along a 11.75 m transect under an Alnus glutinosa (L.) Gaertn. stand based on an alpha-Wenner configuration with 48 electrodes spaced 0.25 m apart. Data were processed by a 2-D finite-element inversion algorithm, and corrected for soil temperature. Data acquisition, inversion and imaging were completed in the field within 60 min. Root dry mass per unit soil volume (root mass density, RMD) was measured destructively on soil samples collected to a depth of 1.05 m. Soil sand, silt, clay and organic matter contents, electrical conductivity, water content and pH were measured on a subset of samples. The spatial pattern of soil resistivity closely matched the spatial distribution of RMD. Multiple linear regression showed that only RMD and soil water content were related to soil resistivity along the transect. Regression analysis of RMD against soil resistivity revealed a highly significant logistic relationship (n = 97), which was confirmed on a separate dataset (n = 67), showing that soil resistivity was quantitatively related to belowground tree root biomass. This relationship provides a basis for developing quick nondestructive methods for detecting root distribution and quantifying root biomass, as well as for optimizing sampling strategies for studying root-driven phenomena.
Amato
Mariana
M
Department of Crop, Forest and Environmental Sciences, University of Basilicata, Via Ateneo Lucano, 10, Potenza 85100, Italy. mariana.amato@unibas.it
Basso
Bruno
B
Celano
Giuseppe
G
Bitella
Giovanni
G
Morelli
Gianfranco
G
Rossi
Roberta
R
D000465:Algorithms; D029661:Alnus / Q000033:anatomy & histology / Q000254:growth & development*; D018533:Biomass; D017097:Electric Impedance; D004566:Electrodes; D008954:Models, Biological; D018517:Plant Roots / Q000033:anatomy & histology / Q000254:growth & development*; D012044:Regression Analysis; D012987:Soil; D014197:Trees / Q000033:anatomy & histology / Q000254:growth & development
D012987:Soil