research product . 2015

Mean forest volume estimation by high-resolution aerial RGB imagery and digital surface model with Trestima as validation technique

Rybakov, Georgy;
Open Access English
  • Published: 01 Jan 2015
  • Publisher: Yrkeshögskolan Novia
  • Country: Finland
Forests typically cover large areas and are hard to access, which is why it is critical to have reliable and cheap means for their surveying. The more accurate the data is and the cheaper its acquisition is, the higher the efficiency of the surveys (i.e. forest inventories) performed is. Potentially also the efficiency of future forest management operations could be higher. This study considers a remote sensing technique for the mean volume estimation based on a very high-resolution (VHR) aerial RGB imagery obtained using a small-sized unmanned aerial vehicle (sUAV) and a high-resolution photogrammetric digital surface model (DSM) as well as an innovative technology for field measurements Trestima as a validation tool. The study area covers approx. 220 ha of forest land in the municipality of Raseborg, Finland. The work concerns the entire process from remote sensing and field data acquisition to statistical analysis and the forest volume wall-to-wall mapping. The study showed that the VHR aerial imagery and the high-resolution DSM produced based on the application of the sUAV have good prospects for forest inventory. At the estimation of forest variables such as Height, Basal Area and mean Volume (V, m3/ha), Root Mean Square Error constituted 6.61%, 22.63% and 28.48%, respectively. The application of Trestima showed stunning performance at all the selected forest compartments with very little difference over existing Forest Management Plan. Simultaneously, the results of the study confirmed that the technologies and the tools applied at this work could be the reliable and cheap means of forest data acquisition with high potential of operational use.
free text keywords: forestry, remote sensing, unmanned aerial vehicle, Trestima, GIS, inventory, forest management, digital surface model, mean forest volume, RGB, Forestry, Remote sensing, fi=Luonnonvara- ja ympäristöala|sv=Bioekonomi och Miljöbranschen|en=Natural Resources and Environment|, Integrated coastal zone management
  • Rural Digital Europe
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