A5000940026- Remote Sensing and LiDAR Applications
- Forest ecology and management
- Remote Sensing in Agriculture
- Forest Ecology and Biodiversity Studies
- 3D Surveying and Cultural Heritage
- Species Distribution and Climate Change
- Diverse Scientific Research in Ukraine
- Botany and Plant Ecology Studies
- Biomedical and Chemical Research
- Fire effects on ecosystems
- Computer Graphics and Visualization Techniques
- Satellite Image Processing and Photogrammetry
- Forest Biomass Utilization and Management
- Land Use and Ecosystem Services
- Medical Practices and Rehabilitation
- Legal, Health, Environmental and COVID-19 Challenges
- Wood and Agarwood Research
- Advanced Vision and Imaging
Swiss Federal Institute for Forest, Snow and Landscape Research
2016-2025
Ukrainian National Forestry University
2016
Automatic identification and mapping of tree species is an essential task in forestry conservation. However, applications that can geolocate individual trees identify their heterogeneous forests on a large scale are lacking. Here, we assessed the potential Convolutional Neural Network algorithm, Faster R-CNN, which efficient end-to-end object detection approach, combined with open-source aerial RGB imagery for geolocation upper canopy layer temperate forests. We studied four species, i.e.,...
Countrywide winter and summer Sentinel-1 (S1) backscatter data, cloud-free Sentinel-2 (S2) images, an Airborne Laser Scanning (ALS)-based Digital Terrain Model (DTM) a forest mask were used to model subsequently map Dominant Leaf Type (DLT) with the thematic classes broadleaved coniferous trees for whole of Switzerland. A novel workflow was developed that is robust, cost-efficient highly automated using reference data from aerial image interpretation. Two machine learning approaches based on...
Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local management planning, countrywide mapping approaches on tree type remain rare. This paper presents validates an innovative remote sensing based approach of broadleaved coniferous trees in Switzerland with spatial resolution 3 m. The classification incorporates random classifier, explanatory variables...
Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed non-destructive volume estimation individual trees, which can be converted to with wood basic density. Existing TLS-based approaches range from simple geometrical features virtual 3D reconstruction entire trees. Validating such weight measurements is a key step before the...
Monitoring biodiversity in forests is crucial for their management and preservation, especially light of increasing climatic disturbances. However, traditional methods surveying forest biodiversity, such as the inventory tree-related microhabitats (TreMs), are costly time-consuming. For many years, terrestrial laser scanning (TLS) was main method producing highly accurate 3D models forests. with recent advancements technologies, there now numerous alternatives available on market. The aim...
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...
The increasing frequency and intensity of droughts heat waves driven by climate change have led to a significant increase in tree mortality worldwide. However, the lack accurate consistent data on location, timing, species, structure dead trees across vast geographical areas limits our understanding climate-induced mortality. Furthermore, standing dying are crucial indicators forest health biodiversity but often overlooked existing resource mapping systems.To address this, we present novel...
In the context of COST Action 3DForEcoTech and an ISPRS scientific initiative, a benchmarking activity was conducted to evaluate performance 13 software solutions designed for automated forest inventory using ground-based point clouds. These tools, which serve as digital analogs traditional inventories, were tested on 12 datasets from four distinct plots featuring diverse types acquisition methods, including two different Terrestrial Laser Scanners (TLS) handheld laser scanner. The...
Accurate and scalable tree species identification remains a critical challenge for global forest monitoring management. Despite the increasing availability of remotely sensed data, lack standardized, high-quality ground truth datasets limits potential supervised machine learning models in capturing diversity ecosystems across different environmental geographic contexts. Prior studies have highlighted need global-scale, high-resolution to develop robust algorithms capable ecosystems.Towards...
Tree-related microhabitats (TreMs) play an important role in maintaining forest biodiversity and have recently received more attention ecosystem conservation, management research. However, TreMs until now only been assessed by experts during field surveys, which are time-consuming difficult to reproduce. In this study, we evaluate the potential of close-range terrestrial laser scanning (TLS) for semi-automated identification different (bark, bark pockets, cavities, fungi, ivy mosses) dense...
Historical black-and-white (B&W) aerial images have been recognized as an important source of information for assessing vegetation dynamics. However, the use these is limited by lack multispectral information, well their varying quality. It therefore to study and develop methods that are capable automatic accurate classification B&W while reducing need tedious manual work. The goal this was assess changes over 30 years in woody cover along alpine treeline ecotones using from two time...
Proximally-sensed laser scanning offers significant potential for automated forest data capture, but challenges remain in automatically identifying tree species without additional ground data. Deep learning (DL) shows promise automation, yet progress is slowed by the lack of large, diverse, openly available labeled datasets single point clouds. This has impacted robustness DL models and ability to establish best practices classification. To overcome these challenges, FOR-species20K benchmark...
Abstract In close‐range remote sensing data collected in a forest, occlusion often causes incomplete or sparse point cloud representations of individual trees, impeding accurate 3D reconstruction tree architecture and estimation height volume. Recent developments deep learning (DL) for have produced approaches completion, which could potentially be applied to trees. We explored the potential DL approach fill gaps dense clouds representing main structures deciduous trees by applying an...
Forest structure reflects the forest disturbance regime and can provide important information about rate of human impact. A better understanding structural variability large-scale dynamics natural forests is crucial for “close to nature” management planning. In this study, we developed a partly automated approach assess potential primeval managed beech in Ukrainian Carpathians using WorldView-2 imagery. We analyzed local (50 m × 50 scale) canopy closure these by extracting gaps determined...
Abstract. This paper is an attempt to respond the growing need and demand of 3D data in forestry, especially for mapping. The use terrestrial laser scanners (TLS) dominates contemporary literature under-storey vegetation mapping as this technique provides precise easy-to-use solutions users. However, TLS requires substantial investments terms device acquisition user training. search development low-cost alternatives therefore interesting field inquiry. Here, we 360° cameras combined with...
Abstract. Depth estimation from a single image is challenging task, especially inside the highly structured forest environment. In this paper, we propose supervised deep learning model for monocular depth based on imagery. We train our new data set of RGB-D images that collected using terrestrial laser scanner. Alongside input RGB image, uses sparse channel as to recover dense information. The prediction accuracy significantly higher than state-of-the-art methods when applied in context...
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Accurate estimates of individual tree volume or biomass within forest inventories are essential for calibration and validation mapping products based on Earth observation data.Terrestrial laser scanning (TLS) enables detailed non-destructive estimation trees, with existing approaches ranging from simple geometrical attributes to virtual 3D reconstruction entire trees. Validating such weight measurements is a key step before the integration TLS other close-range technologies into operational...