Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

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.Russian Foundation for Basic Research (RFBR) 19-01-00215 20-07-00370European Research Council (ERC) European Commission 647038Spanish Government RYC-2015-18136Consejeria de Economia, Conocimiento y Universidad de la Junta de Andalucia P18-RT-1927DETECTOR A-RNM-256-UGR18European Research and Development Funds (ERDF) progra

Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning

We are very grateful to the reviewers for their valuable comments that helped to improve the paper. We appreciate the support of a vice-director of the “Stolby” State Nature Reserve, Anastasia Knorre. We also thank two Ph.D. students Egor Trukhanov and Anton Perunov from Siberian Federal University for their help in data acquisition (aerial photography from UAV) on two research plots in 2016 and raw imagery processing.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).A.S. was supported by the grant of the Russian Science Foundation No. 16-11-00007. S.T. was supported by the Ramón y Cajal Programme (No. RYC-2015-18136). S.T. and F.H. received funding from the Spanish Ministry of Science and Technology under the project TIN2017-89517-P. D.A.-S. received support from project ECOPOTENTIAL, which received funding from the European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, from the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612 and from project 80NSSC18K0446 of the NASA’s Group on Earth Observations Work Programme 2016. A.R. was supported by the grant of the Russian Science Foundation No. 18-74-10048. Y. M. was supported by the grant of Russian Foundation for Basic Research No. 18-47-242002, Government of Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science

The dynamic model of agricultural land structure on the space images in the precision agriculture tasks

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

Методика информационной поддержки решения задач агромониторинга по данным дистанционного зондирования земли

The article is dedicated to end users (decision makers) of information support technique in the process of agricultural lands’ (AL) remote monitoring with the use of Earth remote sensing (ERS) data. The model of spatial object has been developed, which considers its spatial structure and allows to estimate its state in accordance with preset plan of object development with timeПредставлена методика информационной поддержки конечных пользователей (лиц, принимающих решения) в процессе дистанционного мониторинга земель сельскохозяйственного назначения с использованием открытых данных дистанционного зондирования Земли. Разработана модель пространственного объекта, учитывающая его пространственную структуру и позволяющая оценивать его состояние в соответствии с заданным планом его развития во времен

Методика информационной поддержки решения задач агромониторинга по данным дистанционного зондирования земли

The article is dedicated to end users (decision makers) of information support technique in the process of agricultural lands’ (AL) remote monitoring with the use of Earth remote sensing (ERS) data. The model of spatial object has been developed, which considers its spatial structure and allows to estimate its state in accordance with preset plan of object development with timeПредставлена методика информационной поддержки конечных пользователей (лиц, принимающих решения) в процессе дистанционного мониторинга земель сельскохозяйственного назначения с использованием открытых данных дистанционного зондирования Земли. Разработана модель пространственного объекта, учитывающая его пространственную структуру и позволяющая оценивать его состояние в соответствии с заданным планом его развития во времен