Estimating wood volume from canopy area in deciduous woodlands of Zimbabwe

Abstract

In this study we tested the predictive ability of canopy area in estimating wood volume in deciduous woodlands of Zimbabwe. The study was carried out in four sites of different climatic conditions. We used regression analysis to statistically quantify the prediction of wood volume from canopy area at species and stand level using field data. Our results revealed that canopy area significantly (P < 0.05) predicted wood volume at both levels. Furthermore, the results show that at the species-specific level, canopy area explained 54–81% of the variance in wood volume with standard error of estimate (SEE) ranging from 0.056 to 0.71 m3. At stand level, canopy area significantly (P < 0.05) explained 58–84% of the variance in total wood volume with SEE ranging from 0.15 to 3.99 m3 ha−1. Across all study sites, the relationship between canopy area and wood volume at stand level was best described by a logistic regression function, with a R2 value of 0.65 and SEE of 0.7 m3. We concluded that canopy area significantly (P < 0.05) predicted wood volume of dominant tree species in Zimbabwean deciduous woodlands. The relationship between wood volume and canopy area provides an opportunity of estimating wood volume using remote sensing as canopy area can be viewed and measured from aerial, as well as satellite-borne sensors.