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.

In recent years, massive outbreaks of the European spruce bark beetle ( Ips typographus , (L.)) have caused colossal harm to coniferous forests. The main solution for this problem is the timely prevention of the bark beetle spread, for which it is necessary to identify damaged trees in their early stages of infestation. Fortunately, high-resolution unmanned aerial vehicle (UAV) imagery together with modern detection models provide a high potential for addressing such issues. In this work, we evaluate and compare three You Only Look Once (YOLO) deep neural network architectures, namely YOLOv2, YOLOv3, and YOLOv4, in the task of detecting infested trees in UAV images. We built a new dataset for training and testing these models and used a pre-processing balance contrast enhancement technique (BCET) that improves the generalization capacity of the models. Our experiments show that YOLOv4 achieves particularly good results when applying the BCET pre-processing. The best test result when comparing YOLO models was obtained for YOLOv4 with the mean average precision up to 95%. As a result of applying artificial data augmentation, the improvement for models YOLOv2, YOLOv3, and YOLOv4 was 65.0%, 7.22%, and 3.19%, respectively.

Monitoring the structure parameters and damage to trees plays an important role in forest management. Remote-sensing data collected by an unmanned aerial vehicle (UAV) provides valuable resources to improve the efficiency of decision making. In this work, we propose an approach to enhance algorithms for species classification and assessment of the vital status of forest stands by using automated individual tree crowns delineation (ITCD). The approach can be potentially used for inventory and identifying the health status of trees in regional-scale forest areas. The proposed ITCD algorithm goes through three stages: preprocessing (contrast enhancement), crown segmentation based on wavelet transformation and morphological operations, and boundaries detection. The performance of the ITCD algorithm was demonstrated for different test plots containing homogeneous and complex structured forest stands. For typical scenes, the crown contouring accuracy is about 95%. The pixel-by-pixel classification is based on the ensemble supervised classification method error correcting output codes with the Gaussian kernel support vector machine chosen as a binary learner. We demonstrated that pixel-by-pixel species classification of multi-spectral images can be performed with a total error of about 1%, which is significantly less than by processing RGB images. The advantage of the proposed approach lies in the combined processing of multispectral and RGB photo images.

In a changing climate, forest ecosystems have become increasingly vulnerable to continuously exacerbating heat and associated drought conditions. Climate stress resilience is governed by a complex interplay of global, regional, and local factors, with hydrological conditions being among the key players. We studied a Scots pine (Pinus sylvestris L.) forest ecosystem located near the southern edge of the boreal ecotone, which is particularly subjected to frequent and prolonged droughts. By comparing the dendrochronological series of pines growing in apparently contrasting hydrological conditions ranging from the waterlogged peat bog area to the dry soil at the surrounding elevations, we investigated how the soil water regime affects the climate response and drought stress resilience of the forest ecosystem. We found that in the dry land area, a significant fraction of the trees were replaced after two major climate extremes: prolonged drought and extremely low winter temperatures. The latter has also been followed by a three- to ten-fold growth reduction of the trees that survived in the next year, whereas no similar effect has been observed in the peat bog area. Multi-scale detrended partial cross-correlation analysis (DPCCA) indicated that tree-ring width (TRW) was negatively correlated with spring and summer temperatures and positively correlated with the Palmer drought severity index (PDSI) for the same year. For the elevated dry land area, the above effect extends to interannual scales, indicating that prolonged heatwaves and associated droughts are among the factors that limit tree growth. In marked contrast, in the waterlogged peat bog area, a reversed tendency was observed, with prolonged dry periods as well as warmer springs and summers over several consecutive years, leading to increasing tree growth with a one- to three-year time lag. Altogether, our results indicate that the pessimal conditions of a warming climate could become favorable through the preservation of the soil water regime.