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CrowNet: a trail-camera canopy monitoring system
Chianucci , Francesco , Lenzi , Alice , Minari , Emma , Guasti , Matteo , Gisondi , Silvia , Gonnelli , Marco , Innocenti , Simone , Ferrara , Carlotta , Campanaro , Alessandro , Ciampelli , Paola , Cutini , Andrea , Puletti , Nicola
camera trapping canopy cover digital cover photography leaf area index phenology
Mostra abstract
Continuous monitoring of forest canopy structure and phenology is pivotal for the assessment of ecosystem responses to environmental variability and changes. The present study evaluated the use of repeat digital trail cameras as a low-cost, flexible, and accessible in situ monitoring solution for quantifying daily canopy attributes, including effective leaf area index (Le) and canopy cover. A trial camera monitoring network (CrowNet) was established encompassing 20 forest stands in Italy, under different management and environmental conditions, resulting in over 44,000 daily images collected over three years. We demonstrated that taking the mean daily canopy attribute allowed to obtain smooth time series from trail cameras, from which phenological transition dates can be inferred. Daily canopy attributes were validated against manual digital cover photography measurement. To further explore the applicability of this monitoring solution, we performed a comparison between daily Le time series derived from a subset of trail cameras located in beech forests and data collected by multitemporal UAV LiDAR. Results demonstrated the close agreement between the two methods across the entire phenological period (start and end of season). We also illustrated use of continuous trail camera estimates to calibrate a vegetation index (NDVI) to infer leaf area and canopy cover from optical multi-temporal UAV data. We further investigated use of trail camera to detect species-specific differences in tree phenology from time series acquired in a mixed oak-hornbeam forest. We found different canopy structure and phenological transition dates in three broadleaved species (oak, ash, hornbeam), supporting the effectiveness of trail cameras for species-oriented phenology monitoring. We conclude that trail cameras provide a reliable solution for daily canopy monitoring, offering a significant cost-effective and flexible alternative to traditional field methods and providing potential to calibrate, validate or integrate remotely-sensed information. However, camera failures during adverse weather, and the need for more efficient image data quality checking procedures, still represent open challenges. Future improvements, such as weatherproof housing and automated pre-processing screening procedures, are therefore recommended for making trail camera fully operational in ground canopy and phenology monitoring. © 2025 Elsevier B.V.
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2025
Rivista
Agricultural and Forest Meteorology
Dettagli
Vol. 372
DOI
Benchmarking tree species classification from proximally sensed laser scanning data: Introducing the FOR-species20K dataset
Puliti , Stefano , Lines , Emily R. , Müllerová , Jana , Frey , Julian , Schindler , Zoe , Straker , Adrian , Allen , Matthew J. , Winiwarter , Lukas , Rehush , Nataliia , Hristova , Hristina S. , Murray , Brent A. , Calders , Kim , Coops , Nicholas C. , Höfle , Bernhard , Irwin , Liam A.K. , Junttila , Samuli , Kruček , Martin , Krok , G. , Král , Kamil , Levick , Shaun R. , Lück , Linda , Missarov , Azim , Mokroš , M. , Owen , Harry Jon Foord , Stereńczak , Krzysztof Jan , Pitkänen , Timo P. , Puletti , Nicola , Saarinen , Ninni , Hopkinson , Chris Dennis , Terryn , Louise , Torresan , C. , Tomelleri , Enrico , Weiser , Hannah , Astrup , Rasmus
lidar remote sensing biodiversity deep learning point cloud classification single-tree inventory
Mostra abstract
Proximally sensed laser scanning presents new opportunities for automated forest ecosystem data capture. However, a gap remains in deriving ecologically pertinent information, such as tree species, without additional ground data. Artificial intelligence approaches, particularly deep learning (DL), have shown promise towards automation. Progress has been limited by the lack of large, diverse, and, most importantly, openly available labelled single-tree point cloud datasets. This has hindered both (1) the robustness of the DL models across varying data types (platforms and sensors) and (2) the ability to effectively track progress, thereby slowing the convergence towards best practice for species classification. To address the above limitations, we compiled the FOR-species20K benchmark dataset, consisting of individual tree point clouds captured using proximally sensed laser scanning data from terrestrial (TLS), mobile (MLS) and drone laser scanning (ULS). Compiled collaboratively, the dataset includes data collected in forests mainly across Europe, covering Mediterranean, temperate and boreal biogeographic regions. It includes scattered tree data from other continents, totaling over 20,000 trees of 33 species and covering a wide range of tree sizes and forms. Alongside the release of FOR-species20K, we benchmarked seven leading DL models for individual tree species classification, including both point cloud (PointNet++, MinkNet, MLP-Mixer, DGCNNs) and multi-view 2D-based methods (SimpleView, DetailView, YOLOv5). 2D Image-based models had, on average, higher overall accuracy (0.77) than 3D point cloud-based models (0.72). Notably, the performance was consistently >0.8 across scanning platforms and sensors, offering versatility in deployment. The top-scoring model, DetailView, demonstrated robustness to training data imbalances and effectively generalized across tree sizes. The FOR-species20K dataset represents an important asset for developing and benchmarking DL models for individual tree species classification using proximally sensed laser scanning data. As such, it serves as a crucial foundation for future efforts to classify accurately and map tree species at various scales using laser scanning technology, as it provides the complete code base, dataset, and an initial baseline representative of the current state-of-the-art of point cloud tree species classification methods. © 2025 The Author(s). Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.
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2025
Rivista
Methods in Ecology and Evolution
Dettagli
Vol. 16 (4) pp. 801 - 818
DOI
Mapped tree dataset of public green areas in the Municipality of Arezzo, Tuscany (Italy)
Chianucci , Francesco , Sansone , Dalila , Lazzerini , Giada , Tiberi , Gioele , Cristina Monteverdi , Maria , Chiavetta , U.
arezzo public green areas tree dataset urban tree inventory
Mostra abstract
The dataset reports data from more than 9,000 trees, which were sampled in 2024-2025 to create a first urban tree inventory of public green areas in the Municipality of Arezzo. For each tree, spatial position, species, diameter were sampled in different public green space types. Data are available as table and spatial vector layer. Data can support urban planners and managers for assessing the state-of-the-art of urban greening, supporting tree management practices and monitoring, feeding urban tree models and calibrating remotely-sensed information. Non-spatial and spatial metrics can be derived to assess the diversity of urban tree spaces to implement sustainable urban greening practices. © 2025 Istituto Sperimentale per la Selvicoltura. All rights reserved.
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2025
Rivista
Annals of Silvicultural Research
Dettagli
Vol. 50 (2) pp. 46 - 51
DOI
Sustainable forest planning: Assessing biodiversity effects of Triad zoning based on empirical data and virtual landscapes
Duflot , Rémi , Heinrichs , Steffi , Balducci , Lorenzo , Chianucci , Francesco , Hofmeister , Jeňýk , Paillet , Yoan , Trentanovi , Giovanni , Archaux , Frédéric , Boch , Steffen , Bouget , Christophe , Dvořák , Daniel , Fischer , Markus , Gosselin , Frédéric , Gosselin , Marion , Goßner , Martin M. , Holá , Eva , Hošek , Jan , Jung , Kirsten G. , Palice , Zdeněk , Renner , Swen C. , Weisser , Wolfgang W. , Nagel , Thomas A. , Burrascano , Sabina , Schall , Peter
forest management forestry biodiversity conservation gamma diversity landscape planning
Mostra abstract
The Triad framework seeks to balance the economic and ecological functions in forested landscapes by combining intensively, extensively, and unmanaged areas, assuming a higher support to biodiversity in extensively rather than in intensively managed forests. We quantified the effects of Triad zoning on biodiversity in (sub)montane eutrophic European beech forests. Using a European-wide multitaxon database and a “virtual” landscape approach (i.e., by resampling empirical data), we evaluated how the proportion of Triad management categories affected the landscape-level species diversity of birds, saproxylic beetles, vascular plants, epiphytic bryophytes, lichens, and wood-inhabiting fungi, as well as multitaxonomic diversity. The results varied greatly among taxonomic groups. Multitaxonomic diversity peaked in landscapes composed of 60% unmanaged and 40% intensively managed forests. While intensive management can benefit some taxa through the creation of open habitats, unmanaged forests are the backbone of biodiversity conservation, underlining the need to safeguard the remaining old-growth forests under natural dynamics, and to extend the current area of unmanaged forests in Europe. Extensive forest management, however, did not contribute to biodiversity conservation as expected. As withdrawing such a high proportion of European forest landscapes from management is unfeasible given the increasing demand for timber, efforts are needed to increase the presence of structural features supporting biodiversity into extensively managed forests. © © 2025 the Author(s).
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2025
Rivista
Proceedings of the National Academy of Sciences of the United States of America
Dettagli
Vol. 122 (39)
DOI
Behaviour of Brown Bears Under Fluctuating Resource Availability
Tattoni , Clara , Corradini , Andrea , Chianucci , Francesco , Ciolli , Marco , Giusti , Roberta , Bragalanti , Natalia , Cagnacci , Francesca , Martinoli , Adriano , Preatoni , Damiano G. , Bisi , Francesco
alps beech mast damage home range maxent modelling ursus arctos
Mostra abstract
Mast seeding, the variable and intermittent production of seeds, has cascading effects on ecosystem functioning. This study explores its influence on the brown bear populations in the Italian Alps, focusing on beechnuts (Fagus sylvatica L.), the primary food source for bears in the region. Using historical data and field sampling, we estimated and mapped the annual seed biomass from 2007 to 2021 for the province of Trento. The energy content of beechnuts was assessed through high heating values, providing the caloric resources available. Data on beechnuts production, records of damages and GPS data from 16 Eurasian brown bears were integrated to perform a temporal and spatial analysis at home range and at landscape level. Standardised damages to beehives and livestock decreased during mast years, suggesting that bears met their trophic needs through natural food sources. In fact, bears used more agricultural areas and less beech forest during years of beech crop failure. At landscape level, agriculture and pasture areas close to beech forests and distant from cities showed a higher risk of damage, providing a tool to anticipate management actions. This work provides insights on the ecological dynamics and conservation implications of brown bears in the study area by mapping the spatial and temporal aspects of mast seeding and bear-related damages. © 2025 The Author(s). Ecology and Evolution published by British Ecological Society and John Wiley & Sons Ltd.
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2025
Rivista
Ecology and Evolution
Dettagli
Vol. 15 (7)
DOI