Anton Kuzmin

Bio: Anton Kuzmin is an academic researcher from University of Eastern Finland. The author has contributed to research in topics: Materials science & Keystone species. The author has an hindex of 3, co-authored 5 publications receiving 37 citations.

Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

Abstract: Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery The forest floor was covered by snow so that the contrast betwe

Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

Abstract: European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.

Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands

Abstract: The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas. The objectives are: (1) to test the applicability of UAV visible an near-infrared (VNIR) and geometrical data based on Z values of point dense cloud (PDC) raster to separate forest species and dead trees in the study area; (2) to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling; and (3) to explore the possibility of the qualitative classification of spruce health indicators. Analysis based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir, and for identification of dead tree category. Separation between common beech and fir was distinguished by the object-oriented image analysis. NDVI was able to identify the presence of key indicators of spruce health, such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation, whil...

Fast Detection of Olive Trees Affected by Xylella Fastidiosa from UAVs Using Multispectral Imaging.

Abstract: Xylella fastidiosa (Xf) is a well-known bacterial plant pathogen mainly transmitted by vector insects and is associated with serious diseases affecting a wide variety of plants, both wild and cultivated; it is known that over 350 plant species are prone to Xf attack. In olive trees, it causes olive quick decline syndrome (OQDS), which is currently a serious threat to the survival of hundreds of thousands of olive trees in the south of Italy and in other countries in the European Union. Controls and countermeasures are in place to limit the further spreading of the bacterium, but it is a tough war to fight mainly due to the invasiveness of the actions that can be taken against it. The most effective weapons against the spread of Xf infection in olive trees are the detection of its presence as early as possible and attacks to the development of its vector insects. In this paper, image processing of high-resolution visible and multispectral images acquired by a purposely equipped multirotor unmanned aerial vehicle (UAV) is proposed for fast detection of Xf symptoms in olive trees. Acquired images were processed using a new segmentation algorithm to recognize trees which were subsequently classified using linear discriminant analysis. Preliminary experimental results obtained by flying over olive groves in selected sites in the south of Italy are presented, demonstrating a mean Sorensen–Dice similarity coefficient of about 70% for segmentation, and 98% sensitivity and 93% precision for the classification of affected trees. The high similarity coefficient indicated that the segmentation algorithm was successful at isolating the regions of interest containing trees, while the high sensitivity and precision showed that OQDS can be detected with a low relative number of both false positives and false negatives.

Influence of fire refugia spatial pattern on post-fire forest recovery in Oregon’s Blue Mountains

Abstract: Fire regimes in many dry forests of western North America are substantially different from historical conditions, and there is concern about the ability of these forests to recover following severe wildfire. Fire refugia, unburned or low-severity burned patches where trees survived fire, may serve as essential propagule sources that enable forest regeneration. To quantify the influence of fire refugia spatial pattern and other biophysical factors on the process of post-fire tree regeneration; in particular examining both the proximity and density of surrounding refugia to characterize the landscape of refugial seed sources. We surveyed regeneration at 135 sites in stand-replacement patches across a gradient of fire refugia density in eastern Oregon, USA. We characterized the influence of refugial seed source pattern and other environmental factors on the abundance of regenerating seedlings, and examined the relationship between post-fire climate and the temporal pattern of ponderosa pine seedling establishment. Tree seedlings were present in 83% of plots 12–17 years post-fire, and densities varied substantially (0–67800 stems ha−1, median = 1100). Variation in seedling abundance was driven by the spatial patterns of refugial seed sources. Despite widespread post-fire shrub cover, high-severity burned forests have not undergone a persistent type conversion to shrublands. Ponderosa pine seedling establishment peaked 5–11 years after fire and was not closely associated with post-fire climate. Fire refugia and the seed sources they contain fostered tree regeneration in severely burned patches. Management practices that reduce refugia within post-fire landscapes may negatively influence essential forest recovery processes.

Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles

Abstract: Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned ae...