Institute of Forest Science

Human-driven land-use change has significantly altered forest ecosystems and carbon dynamics in mountainous regions. This study aims to quantify land cover transitions and associated forest carbon stocks changes in Bhutan. It also seeks to support the development of a national measurement, reporting, and verification system. Using Landsat-based satellite imagery and object-based image classification techniques, we assessed forest cover transitions, stand structure variations, and forest type changes across temporal intervals. The analysis revealed a consistent increase in agricultural and built-up areas. It also showed a concomitant decline in coniferous forest cover. In particular, agricultural land increased by approximately 0.77 million ha, while coniferous forest decreased by approximately 0.19 million ha over the study period. These changes were driven by both climatic shifts and socio-economic factors. Approximately 57% of Bhutan’s population depends on agriculture. Correspondingly, forest carbon stocks declined from approximately 570 million tC in 1995 to 405 million tC in 2017. This decline was largely attributed to coniferous forest loss and climate-induced mortality. Bhutan has made significant preparations for the implementation of the Warsaw REDD+ framework under the United Nations Framework Convention on Climate Change. These preparations include the establishment of a forest reference emission level for submission. However, challenges remain in detecting small-scale land use changes. Additional challenges include addressing spectral misclassification in mountainous regions. Our study provides a scientific baseline to support national forest monitoring and carbon accounting systems. It also offers policy-relevant insights for achieving Bhutan’s nationally determined contributions and enhancing its carbon sink potential.

Accelerated drought stress along with global warming has significantly impacted high-elevation ecosystems, causing a massive decline of conifers worldwide, including Korean fir (Abies koreana E.H.Wilson). However, studies on the climate adaptability and underlying physiological mechanisms of coexisting species remain limited, despite their importance for understanding future species composition. To investigate species-specific responses to climate change, a rainfall reduction and heat experiment was implemented by blocking precipitation by 33 and 67% and increasing temperature by 1.5°C for three coexisting high-elevation tree species: Korean fir, Korean pine (Pinus koraiensis Siebold & Zucc.) and Manchurian ash (Fraxinus mandshurica Rupr.). Korean fir exhibited the most sensitive stomatal control to conserve its hydraulic status, which significantly suppressed photosynthesis, depleted root starch reserves and ultimately reduced growth. In contrast, Manchurian ash showed the highest resistance, with stable stomatal response through active leaf osmoregulation and increased chlorophyll content, which supported the maintenance of photosynthesis and root nonstructural carbohydrate reserves. Korean pine exhibited intermediate responses, with the second-most sensitive stomatal and photosynthetic regulation, along with temporarily tolerant traits such as increased leaf sugar and chlorophyll content, while allocating relatively more carbon to growth than to storage. This resulted in the highest mortality in Korean fir, followed by Korean pine and Manchurian ash. This study enhances our understanding of the early stress responses of high-elevation species and provides insights into predicting future forest dynamics.

Hericium erinaceus is a fungus that, in addition to its health-promoting properties (including regenerative properties for gastrointestinal membranes and support for neuronal regeneration in neurodegenerative diseases such as Parkinson’s disease), has the ability to synthesize valuable metabolites, such as flavonoids (polyphenols) and terpenoids. These compounds possess strong biocidal properties. These substances provide the growing H. erinaceus mycelium with protection against colonization by other species of rot fungi, such as Trametes versicolor. For these reasons, the biological compounds produced by H. erinaceus can be used to produce ecological fungicides, which will find innovative applications in protecting forest tree seedlings. It should also be emphasized that valuable fungal substances are synthesized primarily by the mycelium of H. erinaceus during the initial stages of its development. Therefore, we undertook to develop an updated and modernized methodology for cultivating H. erinaceus mycelium in the laboratory, with the goal of commercializing the production of this mycelium, which will be used to isolate fungicidal substances metabolized by the fungus cultures. The biocidal substances obtained will be used to produce innovative fungicides in order to protect forest tree seedlings. The studies were conducted using various types of nutrient media, including Potato Dextrose Agar (PDA), Malt Extract Agar (MEA), and wort medium, at various temperatures ranging from 15 °C to 25 °C. Simultaneously, experiments were conducted using solidified media with a pH ranging from 4.0 to 7.0. The research was also expanded to include the growth and execution of experiments using a processed wood substrate, namely, sawdust made from individual structural wood elements. The sawdust was prepared from the bark, sapwood, and heartwood of sessile oak. The PDA medium was more favourable to the mycelium growth of H. erinaceus at 25 °C. It was also found that an acidic pH in the range of 4.0–5.0 significantly influenced the changes in the growth rate of the mycelium species and their phenotype. It was observed that mycelial growth on a substrate of oak sawdust made from sapwood resulted in intensive mycelial growth and a significant reduction in the wood substrate compared to sawdust made from bark, heartwood, and a mixture of all types of sawdust. The reason for the low mycelial growth, low mass reduction and slight reduction in the mass of sawdust made from bark, heartwood, and a mixture of all types of sawdust was the presence of high levels of tannins, which inhibited the fungal growth.

Understanding how urbanisation shapes species distributions and ecological interactions requires long-term, spatially structured data. Using an exceptionally rare 40-year dataset (1986-2025) from 150 habitat patches and 102 downtown grid cells in St. Petersburg, Russia, we examined patterns in birch (Betula pendula and B. pubescens) persistence, ground conditions, woody vegetation, and the occurrence of Eriocrania leaf-mining moths. Birch presence, birch abundance, and ground quality declined both toward the city centre and over time, whereas woody plant cover showed no clear spatial or temporal pattern. Eriocrania occurrence within birch-containing patches was influenced primarily by habitat type, artificial ground, and birch abundance, while distance to the city centre, year, and woody cover exerted no consistent effects. Habitat characteristics offered only moderate predictive power for local extinction risk in both birches and Eriocrania, indicating that multiple drivers interact to shape patch dynamics. Contrary to the widespread declines observed in many insect taxa, Eriocrania populations exhibited no directional density trend across four decades. This long-term stability highlights the resilience of specialised herbivores in heterogeneous urban landscapes and underscores the value of extended temporal datasets for detecting subtle or unexpected ecological responses to urbanisation.

Under ongoing global warming and recent crop pest invasions, there is an urgent need to fill the gap between experimental research on crop pests’ thermal biology and applied forecasts informing policies for pest management. The R package mappestRisk f

Accurate cloud detection is a crucial step in analyzing optical satellite imagery.The increasing application of small satellites equipped only with Visible and Near-Infrared (VNIR) sensors necessitates the evaluation of cloud detection under limited spectral bands.This study focuses on the intercomparison of three cloud detection methodologies-a custom threshold-based method, a machine learning model (Extreme Gradient Boosting; XGBoost), and a deep learning model (U-Net)-using only five VNIR bands from the CloudSEN12+ dataset.As a result, the U-Net model demonstrated higher performance than other approaches, achieving an Overall Accuracy (OA) of 85.6% and an Intersection over Union (IoU) of 65.3%.In particular, the U-Net significantly outperformed others in detecting cloud shadows, achieving an accuracy of 77.4%, nearly double that of XGBoost (46.0%).In spatial analysis, the threshold method misclassified bright surfaces as clouds, and XGBoost generated salt-and-pepper noise at the pixel level.The U-Net accurately detected the complex boundaries of clouds and shadows.Our results indicate that deep learning approaches that leverage spatial information are highly effective for cloud detection with limited spectral bands.This study provides a quantitative baseline to inform the development of operational cloud-detection algorithms for VNIR-only satellite missions.