Synergies of optical, thermal and laser scanning remotely sensed data provide valuable information to study the structure and functioning of terrestrial ecosystems. One of the few fully operational airborne multi-sensor platforms for ecosystem research in Europe is the Flying Laboratory of Imaging Systems (FLIS), operated by the Global Change Research Institute of the Czech Academy of Sciences. The system consists of three commercial imaging spectroradiometers. One spectroradiometer covers the visible and near-infrared, and the other covers the shortwave infrared part of the electromagnetic spectrum. These two provide full spectral data between 380–2450 nm, mainly for the assessment of biochemical properties of vegetation, soil and water. The third spectroradiometer covers the thermal long-wave infrared part of the electromagnetic spectrum and allows for mapping of surface emissivity and temperature properties. The fourth instrument onboard is the full waveform laser scanning system, which provides data on landscape orography and 3D structure. Here, we describe the FLIS design, data acquisition plan and primary data pre-processing. The synchronous acquisition of multiple data sources provides a complex analytical and data framework for the assessment of vegetation ecosystems (such as plant species composition, plant functional traits, biomass and carbon stocks), as well as for studying the role of greenery or blue-green infrastructure on the thermal behaviour of urban systems. In addition, the FLIS airborne infrastructure supports calibration and validation activities for existing and upcoming satellite missions (e.g., FLEX, PRISMA).

High-resolution 3D forest representations are essential for remote sensing applications such as above-ground biomass estimation using radiative transfer modelling. However, existing reconstruction approaches are often time-consuming and rely heavily on manual input. A comprehensive and largely automated end-to-end workflow is presented for reconstructing realistic 3D forest representations from terrestrial laser scanning (TLS) data. The workflow includes five main steps: segmentation of individual trees, semantic classification into wood and foliage using a custom-trained PointNet++ model, reconstruction of woody structures via Quantitative Structure Models, biologically realistic foliage placement, and spatial distribution of trees. Reconstructed forest plots from Central Europe were used to simulate airborne laser scanning (ALS) data in Helios++. The results were validated against real ALS acquisitions. The simulated data showed strong agreement with real ALS data across key forest structure metrics, with correlations ranging from R2 = 0.46 for height standard deviation to R2 = 0.96 for mean canopy height, with corresponding nRMSE values ranging between 23.2% and 12.6%. The largest discrepancies occurred in upper canopy regions due to TLS occlusion effects, where dense vegetation blocked the scanner's line of sight, resulting in these areas being underrepresented in the reconstructed 3D scenes. These results demonstrate that detailed 3D forest reconstructions can be achieved with minimal manual inputs, providing a robust basis for radiative transfer modelling and the generation of synthetic remote sensing datasets, which are critical for improving forest monitoring and carbon stock assessments.

The agricultural landscape of the Czech Republic is facing climate change, and drought is among the most severe stress factors. Thousands of small ponds and naturally wet areas have been drained and transformed into agricultural parcels. Their restoration could increase the landscape’s resilience to climate change. Therefore, we describe the possibility of using hyperspectral aerial surveying for the identification of waterlogged areas in the agricultural landscape based on the example of one of the warmest and driest regions of the Czech Republic—the South Moravian region, an area where water retention in the landscape is highly relevant. Within our study, a total of 33 spectral indices related to the waterlogging of soil selected from previous studies were evaluated. The maximum entropy model (MAXENT) was used in the analysis of these indices. The analysis, which was carried out in several locations during different periods of the year (spring and autumn), shows the varying applicability of individual groups of indices. Regardless of the season, chlorophyll-based indices (MCARI—31.8, CARI—26.3, TCARI2—24.3 average percentage contribution) made the most significant contribution to the creation of probability maps of the occurrence of waterlogged areas. However, more accurate results could be achieved in the spring period by using the NVI index (40.5 average percentage contribution). The results show that remote sensing could be used for the identification of waterlogged sites, especially for initial identification, which should then be confirmed by field survey. Furthermore, the research points out the role of the LAI and chlorophyll content. According to the NVI, low LAI contributes the most to the probability of occurrence in the spring season, while chlorophyll-based indices prove to be the best, contributing high values, which is rather contradictory but could be resolved only by subsequent field research.

Conifer shoots possess highly complex geometrical structures at a very fine spatial resolution. Accurately characterizing the full architecture of a conifer shoot, which influences how radiation is scattered, has proven challenging. Previous radiative transfer models for coniferous stands have represented these structures in a relatively simplified or coarse manner. This paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown. The dataset includes 3D structural information as well optical properties of needles and twigs for 27 shoots of two conifer species present in both locations (3 shoots per species and position in the crown) - Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies L. Karst.). The samples were collected on 22nd April 2024 in Rájec, the Czech Republic and 17th September 2024 in Järvselja, Estonia. Subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. Reflectance and transmittance measurements of needles were obtained using a spectroradiometer and an integrating sphere. For each of these samples, the dataset comprises a photo of the sampled shoot, obtained 3D surface reconstruction, and optical properties of conifer needles and twigs (hemispherical-conical reflectance and transmittance factors) in the spectral range of 400-2000 nm. A detailed 3D representation of needle shoots, when combined with radiative transfer modeling, may offer a means to study and compensate for inaccuracies in the measurement of needle optical properties and to enhance the assessment of shoot scattering characteristics.

Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests.

This dataset provides a comprehensive, multi-scale collection of forest structural data for Central European tree species. The data were mainly derived from extensive terrestrial laser scanning campaigns conducted between 2009 and 2025 across 47 forest sites in the Czech Republic, capturing 12 tree species in both leaf-on and leaf-off conditions. The repository is comprised of three components. First, it includes a library of 3618 individual segmented terrestrial laser scanning tree point clouds, accompanied by detailed metadata coupling field-measured traits with structural parameters derived from a quantitative structure model (QSM). Second, the dataset contains 15,718 virtual airborne laser scanning scenes (50 × 50 m plots) simulated using the HELIOS++ radiative transfer model. These synthetic plots were procedurally assembled using the Forest Factory growth model and provide multi-resolution point clouds (8 points/m² and 16 points/m² densities) alongside precise spatial and structural metadata for every tree. Third, the repository features 273 fully reconstructed three-dimensional (3D) tree representations, combining a QSM for woody architecture with biologically realistic foliage distribution based on empirical traits. This dataset serves as a robust ground-truth resource for training, testing, and validating machine learning algorithms aimed at individual tree segmentation, species classification, and structural parameter extraction from airborne laser scanning data. Furthermore, the ready-to-use 3D representations and virtual scenes facilitate advanced 3D radiative transfer modelling and the development of a virtual forest.