1. Pregled in analiza virov podatkov o poseku na nacionalni ravniGal Kušar, Andrej Ficko, Matjaž Guček, Polona Hafner, Luka Krajnc, Anže Martin Pintar, Rok Pisek, Aleš Poljanec, Mitja Skudnik, Špela Ščap, 2025, treatise, preliminary study, study Keywords: posek, gozdna statistika
Published in DiRROS: 10.02.2026; Views: 460; Downloads: 76
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2. Poročilo o izvedbi naloge javnega naročila: »Vzorčenje nadzemne žive in odmrle biomase ter tal v gozdovih 2025-2026« MEJNIK 2Andreja Ferreira, Polona Hafner, Janez Kermavnar, Luka Krajnc, Gal Kušar, Lado Kutnar, Tom Levanič, Boštjan Mali, Aleksander Marinšek, Iza Petek, Anže Martin Pintar, Primož Simončič, Saša Šercer, Mitja Skudnik, Polona Vukovič, Daniel Žlindra, 2025, treatise, preliminary study, study Keywords: gozdna statistika
Published in DiRROS: 28.01.2026; Views: 288; Downloads: 0
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3. Uporaba iPhone lidar tehnologije za izmero dreves na vzorčnih ploskvah gozdne inventureJaka Križaj, Mitja Skudnik, 2025, original scientific article Abstract: Lasersko skeniranje dreves je lahko koristna tehnologija za dopolnitev in overitev informacij, pridobljenih s klasično gozdno inventuro. Profesionalni terestrični in mobilni skenerji so dragi in zahtevajo usposobljene uporabnike. Zato smo v delu preizkusili uporabnost cenovno dostopnejše tehnologije Apple iPhone lidar, in sicer za pridobivanje dveh poglavitnih informacij o drevesih na vzorčnih ploskvah gozdne inventure: to sta premer drevesa na višini 1,3 metra nad tlemi (d1,3) in oddaljenost drevesa od središča ploskve. Na štirih krožnih raziskovalnih ploskvah na Rožniku smo v treh ločenih ekipah gozdarskih strokovnjakov izmerili referenčne podatke za vsa drevesa z d1,3 ≥ 10 cm s tradicionalnimi metodami, nato smo ploskve poskenirali še z napravo Apple iPhone 13 Pro Max. Podatke skeniranj smo obdelali z odprtokodnimi programi. Ugotovili smo, da so vrednosti d1,3, pridobljene iz podatkov laserskega skeniranja, v povprečju precenjene za 0,95 centimetra (RMSE = 2,43 cm), napaka je značilno večja pri tanjših drevesih. Horizontalne razdalje so bile večinoma podcenjene, v povprečju za 6,7 centimetra (RMSE = 24,1 cm). Napaka v horizontalni razdalji se z naraščajočim časom skeniranja povečuje, medtem ko trajanje skeniranja ne vpliva na vrednosti prsnih premerov. Prav tako smo ugotovili, da so lesne zaloge, izračunane iz podatkov skeniranja, v povprečju precenjene za 2,6 %.
Keywords: iPhone, Apple lidar, nizkocenovni lidar, gozdna inventura, prsni premer, lokacije dreves
Published in DiRROS: 21.01.2026; Views: 223; Downloads: 134
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4. Terrestrial and mobile laser scanning for national forest inventories : from theory to implementationJustin Holvoet, Markus P. Eichhorn, Francesca Giannetti, Daniel Kükenbrink, Xinlian Liang, Martin Mokroš, Jan Novotný, Timo P. Pitkänen, Stefano Puliti, Mitja Skudnik, 2025, original scientific article Abstract: Light Detection and Ranging (LiDAR) has emerged as an important data source for monitoring forest resources. Terrestrial laser scanning (TLS) and Mobile laser scanning (MLS) have already shown high potential in further advancing forest inventory development. By enabling the retrieval of new forest attributes in addition to traditional ones, these technologies could drive forest inventories into a new paradigm by introducing innovative approaches to measuring and monitoring forests. The debate on the possible implementation of TLS and MLS in forest inventories, particularly in national forest inventories (NFIs), continues in both the scientific community and the public institutions. To date, few studies have evaluated the application of TLS and MLS technologies in large-scale forest inventories or assessed their practical operational limits. In this practice-oriented paper, we first detail TLS and MLS data acquisition and processing for tree attribute estimation, assessing their maturity and main limitations. We then explore three European case studies—from the French, Finnish, and Swiss National Forest Inventories (NFIs)—where these technologies have been tested. Based on these experiences, we identify the main constraints and challenges for operational implementation. Lastly, we discuss the prospects for TLS and MLS within the NFI context and the requirements for their successful adoption. We conclude that TLS and MLS should be viewed not as a replacement for, but as a complement to and enhancement of, traditional NFI practices. Emphasis should be placed on the new opportunities these technologies offer, rather than on direct comparisons with conventional methods.
Keywords: enhanced NFI, close-range remote sensing, ground-based LiDAR, point cloud, tree attribute accuracy, explorative implementation
Published in DiRROS: 21.01.2026; Views: 291; Downloads: 180
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5. Detecting bark beetle-induced changes in coniferous alpine forests using Sentinel-2 time series and in-situ felling dataAna Potočnik Buhvald, Krištof Oštir, Mitja Skudnik, 2025, published scientific conference contribution Abstract: Mapping forest areas affected by bark beetle infestation using remote sensing imagery is crucial for effective hazard management and risk assessment. This study evaluates the potential of Sentinel-2 satellite image time series (SITS) in combination with in-situ felling data to detect bark beetle infestation in coniferous forests in Pokljuka, Slovenia. The analysis uses the CuSum method, all Sentinel-2 spectral bands and key spectral indices such as NDVI and NBSI to identify changes and areas of forest loss in the period 2017–2021. The resulting geospatial dataset, which integrates these remote sensing results with field data, serves as a basis for further analyses using advanced machine and deep learning methods and various remote sensing data such as hyperspectral datasets. In addition, we found that the most useful bands for detecting the loss of alpine coniferous forests are SWIR (B11, B12), Red (B04) and Red-Edge (B05) as well as the two spectral in dices used, NDVI and NBSI.
Keywords: Norway Spruce, CUSUM, Pokljuka, Slovenia, deep learning dataset
Published in DiRROS: 21.01.2026; Views: 188; Downloads: 144
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6. Raznolikost avtohtonih in neavtohtonih drevesnih in grmovnih vrst na ploskvah Nacionalne gozdne inventure v SlovenijiAnže Martin Pintar, Andreja Ferreira, Luka Krajnc, Gal Kušar, Mitja Skudnik, 2025, published scientific conference contribution abstract Keywords: Nacionalna gozdna inventura, NGI, gozdne inventure, drevesne vrste, grmovne vrste
Published in DiRROS: 27.11.2025; Views: 537; Downloads: 142
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7. High-resolution Pan-European forest structure maps : an integration of earth observation and national forest inventory dataJukka Miettinen, Johannes Breidenbach, Patricia Adame, Radim Adolt, Iciar Alberdi, Oleg Antropov, Ólafur Arnarsson, Rasmus Astrup, Ambros Berger, Jón Bogason, Luka Krajnc, Mitja Skudnik, 2024, complete scientific database of research data Abstract: We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m2 for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data. For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40 European countries, forming a continuous coverage of the western part of the European continent. A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to 73 % the South-Eastern area. The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.
Keywords: European forest monitoring system, remote sensing, in-situ data, forest attribute maps
Published in DiRROS: 17.06.2025; Views: 747; Downloads: 242
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8. Pan-European forest maps produced with a combination of earth observation data and national forest inventory plotsJukka Miettinen, Johannes Breidenbach, Patricia Adame, Radim Adolt, Iciar Alberdi, Oleg Antropov, Ólafur Arnarsson, Rasmus Astrup, Ambros Berger, Jón Bogason, Luka Krajnc, Mitja Skudnik, 2025, other scientific articles Abstract: The dataset includes Pan-European maps of timber volume (Vol), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10×10 m for the reference year 2020. In addition, a measure of prediction uncertainty is provided for each pixel. The maps have been created using a combination of a Sentinel-2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data. The mapping was done with the k-Nearest Neighbour (kNN, k=7) approach with harmonized data of species-specific Vol and AGB from 14 NFIs consisting of approximately 151 000 field plots across Europe. The maps cover 40 European countries, forming a continuous coverage of the western part of the European continent. A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to 73 % in the South-Eastern area. The maps are on average nearly unbiased on European level (1.0 % of the mean AGB), but show significant overestimation for small biomass values (53 % bias for forests with AGB less than 150 t/ha) and underestimation for high biomass values (-55 % bias for forests with AGB higher than 500 t/ha). The created maps are the first of their kind as they are utilizing a large number of harmonized NFI plot observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high Vol and AGB values tend to be underestimated. We therefore discourage from summarizing map values (pixel counting) over areas in interest, as this may inadvertently result in biased estimates.
Keywords: European forest monitoring system, remote sensing, in-situ data, forest attribute maps
Published in DiRROS: 17.06.2025; Views: 767; Downloads: 512
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