Use of digital photographs as an alternative to diameter tape and caliper in upper-stem diameter measurements (Research, Society and Development, 2021)
In forest volume estimation, biases do not derive only from data processing and estimates but may be of selection when the sample does not guarantee the representativeness of population, of admission when the sample came from a special population, of wrong sampling, or it can be immeasurable such as systematic errors and caused by the operator or the instrument itself. The volume of a stand can be obtained using mathematical expressions that use the variables height and diameter, or circumference where the diameter is obtained using caliper and the circumference through diameter tape, and both incur errors that depend on the shape and deformity of the trunk cross sections. This paper proposes a new methodology based on the use of digital images of the trunk sections and the use of the Artificial Neural Networks technique to obtain the sectional area in upper-stem diameter measurements whose volume estimation is more precise and accurate than the obtained by caliper and diameter tape when compared to the real volume data obtained by the xylometer. With a database of Eucalyptus sp. clonal stands, the proposed methodology was able to accurately estimate the sectional area of trunk cross sections, resulting in volume estimates with only 0.26% of average variation compared to the xylometer, while caliper and tape presented, respectively, -1.41% and 4.08% of variation. The results obtained from the digital images also proved not to be biased, while the diameter tape overestimated volumes and the caliper underestimated them.