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.

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

The article deals with topical issues of forest land protection in the Russian Federation in the context of modern environmental and legal challenges. The main attention is paid to the analysis of the current forest legislation, its shortcomings and potential for improvement in order to increase the effectiveness of protection measures. Data on the current state of forest resources are presented, including statistics on degradation and restoration of forest ecosystems. The article also explores economic and social aspects of forest management, with special attention paid to the role of local communities and the importance of their participation in the processes of forest land protection and restoration forest lands Based on international experience, specific recommendations are offered to strengthen the legal protection of forests and introduce innovative approaches to monitoring and management of forest resources. The significance of the study lies in practical recommendations to improve the efficiency of rational and sustainable use of forest land in the Russian Federation, as well as proposals to improve the mechanisms of protection and conservation of Russian forests. The work is designed to promote a constructive dialogue between government agencies, the scientific community and non-governmental organizations aimed at achieving sustainable management of Russia’s forest fund.

In the current context of climate change, this paper provides a brief overview of climate-vegetation interactions in the Mediterranean basin, where global warming and aridification trends are predicted to intensify. Rising temperatures coupled with extreme drought events have notable impacts on forest ecosystems, triggering dieback and mortality phenomena worldwide. The health status of forest vegetation may be assessed by either ground surveys or remote sensing. Several investigations of declining oak forests in the Apennine region have shown that the drought vulnerability of Mediterranean oaks is dependent on differences in access to deep soil water pools, which can be related to differences in rooting depth, tree size and growth rate. Forest dieback is widely reported in Italy, but the extent and overall distribution of this phenomenon have not yet been well defined. Thus, understanding the future dynamics of forest stands has several key objectives: inventorying declining forest stands to gain insight into the phenomenon and create a national-level database; locating these stands to allow monitoring of their evolution over time; and monitoring to allow planning of mitigation measures, as well as recovery and adaptation strategies. This background led to the implementation of SilvaCuore, the first web application designed in Italy to report forest health status. Silvacuore is a web application developed with the aim of improving the link between citizens and the scientific community by contributing to the development of a valuable scientific database. The direct involvement of citizens in forest preservation offers an interesting perspective because the innovative use of technology can tap into citizens’ growing enthusiasm for environmental topics. Thus, SilvaCuore may be viewed from a dual perspective as a resource for the scientific community and as a unique opportunity for citizens to take an active role in research activities (Citizen Science project).

Monitoring and predicting land surface phenology (LSP) are essential for understanding ecosystem dynamics, climate change impacts, and forest and agricultural productivity. Satellite Earth observation (EO) missions have played a crucial role in the advancement of LSP research, enabling global and continuous monitoring of vegetation cycles. This review provides a brief overview of key EO satellite missions, including the advanced very-high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS), and the Landsat program, which have played an important role in capturing LSP dynamics at various spatial and temporal scales. Recent advancements in machine learning techniques have further enhanced LSP prediction capabilities, offering promising approaches for short-term prediction of vegetation phenology and cropland suitability assessment. Data cubes, which organize multidimensional EO data, provide an innovative framework for enhancing LSP analyses by integrating diverse data sources and simplifying data access and processing. This brief review highlights the potential of satellite-based monitoring, machine learning models, and data cube infrastructure for advancing LSP research and provides insights into current trends, challenges, and future directions.

This study employed an operational monitoring network to measure soil moisture and runoff behaviour continuously in the Alpine catchment Geroldsbach-Götzens, Austria. We hypothesize that afforestation can have a positive impact on soil water buffering. To analyse the impact of soil properties and vegetation cover changes on soil water dynamics, four experimental plots were established on grassland and monitoring stations were installed in the forest. The rainfall test site is equipped with an automatic weather station to obtain meteorological observations, and weirs to measure surface runoff of natural occurring precipitation events and artificial rainfall simulations. In the plots, 200 soil moisture sensors were installed at five different depths, aimed to track and visualize infiltration and subsurface flow processes. Another twenty sensors monitored soil moisture at different afforestation stages in the forested part of the catchment. The measurements show that soils covered with young and old-growth forest have a higher and more stable soil moisture content than grassland and soils with a lack of vegetation throughout the seasons. We observed large spatial differences at plot scale, where the spatial variability of soil moisture increases with depth and is highest during convective precipitation. The initial conditions and rainfall characteristics play an important role in infiltration processes and soil water storage. Our rainfall test site demonstrated the challenges of innovative monitoring techniques and that it offers opportunities for more experiments to gather evidence-based data as input for flood models. Overall findings confirm the sponge effect of forest soils and indicate that afforestation as Nature-Based Solution reduces the temporal soil moisture variability, buffering soil water during precipitation events, which can be beneficial for runoff reduction in Alpine catchments.

Current trends in agriculture highlight the widespread adoption of information technology and Internet of Things (IoT) sensor networks for monitoring agrophysical soil parameters and phenotyping objects. This approach enables precise, real-time data analysis, optimizing agricultural processes and supporting the development of adaptive management systems. The integration of information technology with the monitoring of agrophysical parameters and phenotyping objects underscores the strategic importance of this approach, especially in the context of climate variability and the growing need to enhance production sustainability. (Research purpose) To develop an intelligent field sensor station for precision farming that ensures high-accuracy, real-time monitoring of agrophysical parameters and plant phenotyping using an Internet of Things sensor network. (Materials and methods) Existing methods for monitoring agrophysical parameters and phenotyping objects were analyzed. Based on these methods, a design for an intelligent field sensor station was developed, and suitable sensors were selected. (Results and discussion) The intelligent field sensor station successfully demonstrated its efficiency, confirming both its functionality and reliability in simultaneous data collection. The data collected on soil agrophysical parameters, meteorological conditions and plant phenotyping provide extensive knowledge for precision farming and optimizing agricultural processes. (Conclusions) Light gray forest soil with high porosity and neutral pH level provided favorable conditions for crops. Preliminary chemical analysis of the soil revealed moderate levels of organic matter, mobile phosphorus, and potassium, indicating a potentially fertile site. Meteorological data playeda key role in agrometeorological analysis, significantly impacting agricultural processes. The developed station introduces an innovative approach to monitoring agricultural parameters, offering promising prospects for modern agriculture.

Social forestry is a program designed to address tenure conflicts, improve community welfare, and enhance environmental quality. Achieving the objectives of social forestry is not only limited to obtaining permits but needs more advanced, concrete, innovative business development that anticipates future challenges and prospects. Therefore, this research aimed to analyze West Sumatra Governor Regulation No. 52/2018 on the Implementation of Social Forestry Facilitation and its practical application in Hutan Nagari Salibutan. Data collection in this research included in-depth interviews and a literature review, while data analysis used descriptive qualitative methods, content analysis, and policy implementation. The results showed that Governor Regulation of West Sumatra No. 52/2018 supported national policy by accelerating social forestry, resolving tenure conflicts, and synchronizing the roles of various stakeholders. This Governor Regulation served as the technical reference in the field, and it comprised facilitation, institutionalization, monitoring and evaluation, and financing. Additionally, the implementation of regulations in Salibutan Nagari Forest correlated with these provisions, as evidenced by the activities carried out by the Forestry Service and the social forestry acceleration working group in collaboration with the Nagari Salibutan forest management institution. The consistency between the regulation's content and field implementation reflected this correlation.

Forest restoration is a suitable tool to mitigate land degradation and enhance the supply of vital goods and services. Social participation in forest restoration has gained increasing interest. Yet, the impact of participation on people's perception of ecological restoration and the restoration process has barely been addressed despite its relevance for the long-term success of restoration actions. We assessed mutual learning of different stakeholder groups in a ten-year demonstration project, and its potential to foster continued participatory forest restoration in Beni Boufrah valley, a semiarid area located in North Morocco. We performed face-to-face interviews to assess post-restoration individual learning for a subset of 15 highly engaged stakeholders using five components of social learning: reciprocal determinism, self-reflective capability, expectations, self-regulation and locus of control. Furthermore, we organized a workshop to assess collective learning in the study area. The lessons learnt by the project team was described using monitoring vegetation measurements, field visits and meetings, and stakeholder recommendations. Two thirds of the stakeholders showed an increasing awareness of the reciprocal relationship between people and the environment, while recognizing the inappropriateness of current behaviors and practices. Farmers showed continuous mistrust towards the Forestry Agency which justifies the need for more innovative approaches to resolve persistent conflicts, integrate forest and agricultural interventions and establish new mechanisms for economic motivation. Stakeholders acquired theoretical and practical concepts on forest restoration, but self-initiated activities were scarce and reinforcement of environmental awareness is still needed. There was a high impact of drought on seedling survival and growth along with a decrease in stakeholder engagement over the years. We could identify and implement a series of practical corrective measures, namely participatory re-planting, plot fencing, establishment of a local monitoring committee, and raising awareness activities. Such participatory implementation of corrective measures may enhance the credibility of the restoration process and it can be further tested in similar semiarid areas of North Africa even at a larger scale. | This study was carried out within the framework of the project SREPA funded by the University of Alicante (Proyectos de Cooperación Universitaria para el Desarrollo), “La Caixa” Foundation, the Society for Biological and Ibero-African Studies (SEBI) in Murcia-Spain, and Conselleria d’Innovació, Universitats, Ciència i Societat Digital, Generalitat Valenciana (Project R2D, CIPROM/2021/001).

Abstract The increasing prevalence of tree bark beetle infestations poses a significant threat to forest ecosystems, leading to detrimental impacts on biodiversity, carbon storage, and timber resources. This study addresses the pressing need for innovative solutions to enhance forest management practices through early detection and assessment of tree health. We developed an intelligent system leveraging multispectral images and generative‐adversarial networks (GANs) to accurately determine the extent of bark beetle damage. Recognizing the challenges posed by traditional neural networks, which require vast amounts of labeled training data, we proposed a novel approach that utilizes a GAN architecture. In this system, a discriminator functions as a classifier, effectively trained on both real and synthetically generated data. Our methodology not only reduces the dependency on extensive labeled datasets but also enhances the robustness of the classification process. The results indicate a classification accuracy of 87.5%, demonstrating significant promise for improving detection capabilities even with limited training resources. The implications of this research are profound, offering potential benefits for the forestry industry through optimized management strategies and economic gains. Furthermore, our findings contribute to the preservation of critical ecosystem services by providing a means to monitor forest health effectively. However, the responsible implementation of this technology is paramount. Continuous refinement of the model, integration with traditional ecological knowledge, and ensuring transparency and equitable access to the developed system are essential for maximizing societal benefits. Additionally, addressing risks such as misinterpretation of data, overreliance on technology, and privacy concerns is crucial to minimize unintended consequences. Overall, this research presents a significant advancement in the field of forest health monitoring and establishes a foundation for future developments in intelligent ecological management systems.