This article presents the concept of an innovative digital system for monitoring and controlling deforestation, aimed at improving the efficiency of forest management and combating illegal logging. The system is based on the use of modern high-resolution satellite data and machine learning methods to analyze changes in forest cover. The paper describes the architecture of the proposed system, which includes modules for data collection, change analysis, classification and visualization. Special attention is paid to the potential advantages of the system, such as improving the accuracy and efficiency of detecting changes in forests. Possible problems and limitations in the implementation of the system are considered, including dependence on the quality of satellite images, requirements for computing resources and legal aspects. The proposed system has the potential to significantly improve the monitoring of forest resources, contributing to their conservation and sustainable use in the context of global climate change.

This final jobs goal- to study facilities on developing processes, using innovative technologies in forest management projects preparation, coordination, approval and implementation monitoring. Final job tasks: • To analyze and summarize the private forest management project preparations and implementations standards and the current situation; • Perform semiotic study of conventional signs used in forest management projects; • To prepare private forest management project using newly created database and technology. Final job analyzes and summarizes private forest management project preparations and implementations standards and the current situation. More than 92% forest management projects in Anykščiai district were approved from 2010 to 2016 30 April. Semiotic study of conventional signs used in forest management projects was accomplished. A review of devices used in the forest management project preparation was made. Private forest management project was prepared using newly created database and technology. Forest domain was measured by static method with GNNS Trimble R10. Plan is drawn with program QGIS. Inventory cards filled in a newly created Forest Information System www.eforest.lt.

As technological advancements in forest monitoring – such as remote sensing and commodity supply chain tracking – allow for the generation and analysis of increasingly large datasets, forest policy makers and practitioners are looking for innovative yet practical ways for information transparency to transform forest governance. Especially in tropical forest countries looking to address the continuing deforestation and forest degradation through climate finance commitments and timber trade agreements, the access to information agenda has been placed at the fore of both the Reducing Emissions from Deforestation and forest Degradation (REDD+) process and the Forest Law Enforcement, Governance and Trade (FLEGT) Action Plan. This paper explores whether and how the proposed transparency agenda is having an impact (or not) in the Southwest Region of Cameroon. Using semi-structured interviews with civil society organizations, this paper examines how information is currently disclosed in the forest sector and the status of REDD+ and FLEGT transparency agendas at the local level.

The reliability of aircraft control surfaces, constructed from thermoplastic materials, can be affected by impacts from airborne particles. Recognizing the exact position of such impacts is essential for correctly estimating the resulting damage. This research intended to address the issue by introducing an innovative structural health monitoring solution capable of autonomously detecting and localizing impacts using acoustic emission monitoring. The objective of this research is to investigate the application of AE for the localization of impacts on aircraft elevators using machine learning techniques, specifically regression algorithms. To achieve this goal, two algorithms, linear regression, and random forest, were employed for predicting the impact locations based on AE signals. The performance of each algorithm was validated on a thermoplastic composite aircraft elevator. Results indicated that both linear regression and random forest models show high accuracy in predicting the impact locations. The random forest model, with an R² value of 0.98616 and an RMSE of 0.6778, outperformed the linear regression model, which exhibited an R² value of 0.9361 and an RMSE of 1.4614.

The monitoring of individuals and forest plots in Central Africa is a complex task. Establishing experimental monitoring sites and conducting inventories requires a significant amount of time, effort, and resources. One solution to reduce the effort is to summarize a forest plot by focusing on its largest trees. These trees play a crucial role in the structure, dynamics, and carbon cycle of forests. The development of remote sensing methods and deep learning enables the automatic detection and segmentation of tree crowns. We also propose an innovative method using Detectree2 for tree detection and Segment Anything Model from Meta for crown contour segmentation. The objective of this research is to compare the results obtained from commonly used detection and segmentation methods, as well as this new algorithm, in the case study of the largest trees in the Luki landscape (DRC). Validation is carried out by comparing the results with a dataset of manually segmented 500 individuals, based on on-site observations, and compared to very high-resolution ortho-images. The results aim to demonstrate an improvement in the quality of tree crown segmentation based on RGB sensors compared to LiDAR, while also considering variations in acquisition conditions. This provides new perspectives for forest monitoring. | Canopi | 13. Climate action

IntroductionRubber trees are an important cash crop in Hainan Province; thus, monitoring sample plots of these trees provides important data for determining growth conditions. However, existing monitoring technology and rubber forest sample plot analysis methods are relatively simple and present widespread issues, such as limited monitoring equipment, transportation difficulties, and relatively poor three-dimensional visualization effects in complex environments. These limitations have complicated the development of rubber forest sample plot monitoring.MethodThis study developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with multi-view stereo method and sample plot survey data. Deviation analyses and accuracy evaluations of sample plot information were performed in the study area for trees to explore the practical significance of this method for monitoring rubber forest sample plots. Furthermore, the relationship between the height of the first branch, diameter at breast height (DBH), and rubber tree volume was explored, and a rubber tree standard volume model was established.ResultsThe Bias, relative Bias, RMSE, and RRMSE of the height of the first branch measured by this method were −0.018 m, −0.371%, 0.562 m, and 11.573%, respectively. The Bias, relative Bias, RMSE, and RRMSE of DBH were −0.484 cm, −1.943%, −2.454 cm, and 9.859%, respectively, which proved that the method had high monitoring accuracy and met the monitoring requirements of rubber forest sample plots. The fitting results of rubber tree standard volume model had an R2 value of 0.541, and the estimated values of each parameter were 1.745, 0.115, and 0.714. The standard volume model accurately estimated the volume of rubber trees and forests using the first branch height and DBH.DiscussionThis study proposed an innovative planning scheme for a terrestrial photogrammetry system for 3D visual monitoring of rubber tree forests, thus providing a novel solution to issues observed in current sample plot monitoring practices. In the future, the application of terrestrial photogrammetry systems to monitor other types of forests will be explored.