Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia).

Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images.

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This article provides research for the models describing the spatial object of Agricultural Land (AL) as observed at medium-and high-spatial resolution satellite images. This object is characterized by variable reflectance features during the vegetation season. These variations are caused both by natural and man-induced environmental effects and by soil cover properties. The vegetation cover growth rates may vary at different areas within the agricultural contour which leads to the creation and development of heterogeneities. The research focuses on the spatial modeling of the agricultural object with heterogeneous dynamically changing spatial structure. A model which allows matching the values of parameters measured at space images with reference and abnormal object behavior has been developed. This model describes the object of research on the level of geometric and relational structures, thereby making it possible to determine spectral and metrical features as part of image heterogeneities, find interrelations between these features within the time limits and map these heterogeneities. The results obtained were tested when monitoring the state and development trends for grain crops at the tested objects in the Sukhobuzimsky district of the Krasnoyarsk Territory.

The study is devoted to the analysis of specific features of spacial objects referred to “agricultural land” class in Central Siberia according to the results of the Earth remote sensing from the space for information support in the precision agriculture tasks. The subject of the study is temporal variability of spectral, textural and geometrical features of a land area with homogenous vegetation (hereinafter agricultural contour). During the vegetation period the agricultural contour is subject to changes caused by a combination of natural and antropogenic factors. These factors are the result of the natural course of vegetation (change of phenological phases), weather conditions and agricultural engineering measures implemented. They typically cause the change of the spacial structure of the agricultural contour resulting in non-homogenous vegetation of an agricultural crop within the agricultural contour.