2023 Ecosystem Transformation 6 (2), 33–48

Evaluation of the quality of automatic tree detection using photogrammetric canopy height models and orthomosaic

Ivanova N.V.  , Lebedev A.V., Shashkov M.P.

The work was performed in the old-growth linden-spruce forest of the Kologrivsky Forest Nature Reserve (Kostroma Oblast, Russia) based on aerial photography with a quadcopter. Automatic detection algorithms made it possible to detect most of the trees in the forest canopy. Tree detection by orthomosaic using neural network algorithm ‘DeepForest’ turned out to be of better quality than detection based on the canopy height model using an algorithm based on the sliding window method. As a rule, both methods showed better results for conifers compared to deciduous trees. Comparison of the average heights of trees estimated from remote data and measured by ground survey did not reveal significant differences. Additional ground surveys to assess the quality of undergrowth detection are needed.

N. V. Ivanova
Institute of Mathematical Problems of Biology RAS – the Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences
ul. professora Vitkevicha 1, Pushchino, Moscow Oblast, 142290 Russia

natalya.dryomys@gmail.com

A. V. Lebedev
Russian State Agrarian University - Moscow Timiryazev Agricultural Academy
ul. Timiryazevskaya 49, Moscow, 127434 Russia

Kologrivsky Forest Nature Reserve
ul. Nekrasova 48, Kologriv, Kostroma Oblast, 157440 Russia


M. P. Shashkov
Institute of Mathematical Problems of Biology RAS – the Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences
ul. professora Vitkevicha 1, Pushchino, Moscow Oblast, 142290 Russia

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