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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
Assessment of potential bioenergy from coppice forests trough the integration of remote sensing and field surveys
Lasserre , Bruno , Chirici , Gherardo , Chiavetta , U. , Garfì , Vittorio , Tognetti , Roberto , Drigo , Rudi , Di Martino , P. , Marchetti , Marco
remote sensing forest inventory sustainable forest management coppice firewood biomass k-nearest neighbours
Mostra abstract
A spatially explicit knowledge of forest resources is essential to support the sustainable use of wood as a fuel for producing energy (firewood).This paper describes the integrated use of remotely sensed data and sample based forest inventories to derive a biomass map for coppice forest, resulted estimated potential biomass available is contrasted with local domestic consumptions at the municipality level. The test was carried out in an environmentally and socially homogeneous district of Apennine Mountains (Alto Molise, south-central Italy) coupling multispectral high resolution Landsat 7 ETM+ satellite imagery and a local forest inventory trough the application of the non-parametric estimation procedure k-Nearest Neighbours (k-NN). Several forest management scenarios were applied in order to evaluate their impact on the potential availability of firewood from coppice forests.The paper introduces data and methods used and presents the achieved results both in terms of the accuracy of the biomass map produced by k-NN and of the relationship between the potential availability and demand for firewood.These results demonstrated that k-NN is able to estimate the biomass of coppice forest in the test area with an accuracy level comparable with recent similar application of k-NN carried out in Boreal regions (RMSE of 25.6%).The application of different forest management scenarios have a significant impact on local estimated firewood balance between potential supply from coppice forests and demand for domestic consumption, depending of the scenarios the net balance changed up to 84%. © 2010 Elsevier Ltd.
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2011
Rivista
Biomass and Bioenergy
Dettagli
Vol. 35 (1) pp. 716 - 724
DOI