Evaluating wood volume estimates derived from Quickbird imagery with GEOBIA for<i>Pinus nigra</i>trees in the Pentalofo forest, northern Greece

Stournara, Panagiota; Πατιάς, Πέτρος; Karamanolis, Dimitrios G.

doi:10.1080/2150704x.2016.1235806

Cited by 4 publications

(1 citation statement)

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“…Dendrometric variables are important forest information and their monitoring is one of the main tasks of the regional forest management plans aimed at preserving the environment and strengthening the local economy (Stournara et al, 2017). The volume is one of these variables and one of the main structural attributes of the forest, as well as the diameter at breast height and basal area, obtained by forest inventory techniques and estimates using general allometric equations (Chinembiri et al, 2013;Gara et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Prediction of dendrometric variables by optical data in Pinus taeda L. in the south of Brazil

et al. 2021

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The objective of this work was to estimate the basal area and volume of a Pinus taeda L. settlement located in Santa Catarina, correlating data from an orbital image of the Landsat-8 / OLI sensor and forest inventory. In this sense, a forest research was carried out, with a random sampling process using the fixed area method. 20 circular parcels of 400 m² were allocated. An orbital image of the Landsat-8 / OLI sensor was used and 10 average vegetation indices per plot were calculated. These were correlated as variables of volume and basal area per plot, decorative by the forest inventory. The index with the best correlation for the volume was GNDVI with 0.47 and for a basal area, the MVI with 0.51. The adjustment of the regression models showed adjusted R² indices of 0.5639 and Syx of 13.31% for volume, and 0.5213 and 11.93% for the basal area. It was possible to estimate the volume and basal area of the stands through the spectral data, however, it is recommended that this same technique be tested in other species of the genus Pinus spp. and with high spatial resolution media.

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Section: Introductionmentioning

confidence: 99%