Browsing by Author "Juola, Jussi"

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Assessment of a photon recollision probability based forest reflectance model in European boreal and temperate forests
(2022-02) Hovi, Aarne; Schraik, Daniel; Hanuš, Jan; Homolová, Lucie; Juola, Jussi; Lang, Mait; Lukeš, Petr; Pisek, Jan; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
We report a new version and an empirical evaluation of a forest reflectance model based on photon recollision probability (p). For the first time, a p-based approach to modeling forest reflectance was tested in a wide range of differently structured forests from different biomes. To parameterize the model, we measured forest canopy structure and spectral characteristics for 50 forest plots in four study sites spanning from boreal to temperate biomes in Europe (48°–62°N). We compared modeled forest reflectance spectra against airborne hyperspectral data at wavelengths of 450–2200 nm. Large overestimation occurred, especially in the near-infrared region, when the model was parameterized considering only leaves or needles as plant elements and assuming a Lambertian canopy. The model root mean square error (RMSE) was on average 80%, 80%, 54% for coniferous, broadleaved, and mixed forests, respectively. We suggest a new parameterization that takes into account the nadir to hemispherical reflectance ratio of the canopy and contribution of woody elements to the forest reflectance. We evaluated the new parameterization based on inversion of the model, which resulted in average RMSE of 20%, 15%, and 11% for coniferous, broadleaved, and mixed forests. The model requires only few structural parameters and the spectra of foliage, woody elements, and forest floor as input. It can be used in interpretation of multi- and hyperspectral remote sensing data, as well as in land surface and climate modeling. In general, our results also indicate that even though the foliage spectra are not dramatically different between coniferous and broadleaved forests, they can still explain a large part of reflectance differences between these forest types in the near-infrared, where sensitivity of the reflectance of dense forests to changes in the scattering properties of the foliage is high.
Avoimia spektrikirjastoja Suomen metsistä
(2022) Rautiainen, Miina; Hovi, Aarne; Kuusinen, Nea; Juola, Jussi; Forsström, Petri; Salko, Sini-Selina; Schraik, Daniel; Burdun, Iuliia
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Classification of tree species based on hyperspectral reflectance images of stem bark
(2023) Juola, Jussi; Hovi, Aarne; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Automatization of tree species identification in the field is crucial in improving forest-based bioeconomy, supporting forest management, and facilitating in situ data collection for remote sensing applications. However, tree species recognition has never been addressed with hyperspectral reflectance images of stem bark before. We investigated how stem bark texture differs between tree species using a hyperspectral camera set-up and gray level co-occurrence matrices and assessed the potential of using reflectance spectra and texture features of stem bark to identify tree species. The analyses were based on 200 hyperspectral reflectance data cubes (415–925 nm) representing ten tree species. There were subtle interspecific differences in bark texture. Using average spectral features in linear discriminant analysis classifier resulted in classification accuracy of 92–96.5%. Using spectral and texture features together resulted in accuracy of 93–97.5%. With a convolutional neural network, we obtained an accuracy of 94%. Our study showed that the spectral features of stem bark were robust for classifying tree species, but importantly, bark texture is beneficial when combined with spectral data. Our results suggest that in situ imaging spectroscopy is a promising sensor technology for developing accurate tree species identification applications to support remote sensing.
Comparison of contemporaneous Sentinel-2 and EnMAP data for vegetation index-based estimation of leaf area index and canopy closure of a boreal forest
(2024-11-27) Juola, Jussi; Hovi, Aarne; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Data from the new hyperspectral satellite missions such as EnMAP are anticipated to refine leaf area index (LAI) or canopy closure (CC) monitoring in conifer-dominated forest areas. We compared contemporaneous multispectral and hyperspectral satellite images from Sentinel-2 MSI (S2) and EnMAP and assessed whether hyperspectral images offer added value in estimating LAI, effective LAI (LAIeff), and CC in a European boreal forest area. The estimations were performed using univariate and multivariate generalized additive models. The models utilized field measurements of LAI and CC from 38 forest plots and an extensive set of vegetation indices (VIs) derived from the satellite data. The best univariate models for each of the three response variables had small differences between the two sensors, but in general, EnMAP had more well-performing VIs which was reflected in the better multivariate model performances. The best performing multivariate models with the EnMAP data had ~1–6% lower relative RMSEs than S2. Wavelengths near the green, red-edge, and shortwave infrared regions were frequently utilized in estimating LAI, LAIeff, and CC with EnMAP data. Because EnMAP could estimate LAI better, the results suggest that EnMAP may be more useful than multispectral satellite sensors, such as S2, in monitoring biophysical variables of coniferous-dominated forests. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Estimation of boreal forest attributes using hyperspectral remote sensing data
(2025-02-04) Karhuvaara, Henriikka
School of Engineering | Master's thesis
Multispectral and hyperspectral satellite remote sensing can be applied to observe the Earth and detect changes caused by human actions or the climate. Currently there are global multispectral satellite missions that can observe a large variety of different targets on the Earth’s surface and atmosphere by measuring their reflectance spectra with a few selected spectral bands. By 2030, satellite mission providers such as NASA and ESA plan to offer enhanced observation of different targets by measuring their reflectance spectra in hundreds of narrow spectral bands with global hyperspectral satellite missions. In this work, hyperspectral satellite images collected by EnMAP (The Environmental Mapping and Analysis Program) mission were applied to estimate stem volumes and tree species proportions in five different boreal forest locations of Finland. Estimations were performed with two machine learning regression methods, and the estimation accuracies were assessed based on available field data collected by the Finnish Forest Centre. In general, the best estimation accuracies were reached for total stem volumes with RRMSE values between 52–93 %. Estimation accuracies of total stem volumes were also the most consistent between the study locations. Estimation accuracies of stem volumes and proportions related to single species were more dependent on the species distribution of the study locations, and the best accuracies were reached for the most dominant species of each location with RRMSE values between 44–143 %. Higher mean species volumes and proportions in the field data were related to better estimation accuracy also between the study locations. This study gave information on the applicability of hyperspectral satellite data to estimate selected boreal forest attributes in a few targeted locations. More reliable conclusions on the applicability of the estimations, for example, to monitor forest biodiversity can be made once the availability of hyperspectral satellite data increases.
Evaluating the performance of a double integrating sphere in measurement of reflectance, transmittance, and albedo of coniferous needles
(2020-01-01) Hovi, Aarne; Mõttus, Matti; Juola, Jussi; Manoocheri, Farshid; Ikonen, Erkki; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Leaf reflectance and transmittance spectra are essential information in many applications such as developing remote sensing methods, computing shortwave energy balance (albedo) of forest canopies, and monitoring health or stress of trees. Measurement of coniferous needle spectra has usually been carried out with single integrating spheres, which has involved a lot of tedious manual work. A small double integrating sphere would make the measurements considerably faster, because of its ease of operation and small sample sizes required. Here we applied a compact double integrating sphere setup, used previously for measurement of broad leaves, for measurement of coniferous needles. Test measurements with the double integrating sphere showed relative underestimation of needle albedo by 5–39% compared to a well-established single integrating sphere setup. A small part of the bias can be explained by the bias of the single sphere. Yet the observed bias is quite significant if absolute accuracy of measurements is required. For relative measurements, e.g. for monitoring development of needle spectra over time, the double sphere system provides notable improvement. Furthermore, it might be possible to reduce the bias by building an optimized measurement setup that minimizes absorption losses in the sample port. Our study indicates that double spheres, after some technical improvement, may provide a new and fast way to collect extensive spectral libraries of tree species.
Hyperspectral characterization of vegetation in hemiboreal, boreal and Arctic peatlands using a geographically extensive field dataset
(2024-09) Salko, Sini-Selina; Hovi, Aarne; Burdun, Iuliia; Juola, Jussi; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Northern peatlands store up to 25% of global soil organic carbon and function as important hotspots for biodiversity. However, they are facing degradation from climate change driven by human activities as well as anthropogenic land use changes, up to the point of endangering the ecosystems' functioning and the storage of soil organic carbon entailed within them. The surface vegetation of northern peatlands is an important indicator of the ecosystem's functioning and ecohydrology, highlighting the importance of its large-scale, continuous monitoring. Approaches utilizing hyperspectral data for monitoring vegetation health and species composition can also be applied to peatland vegetation. To support the development of methods for interpreting hyperspectral satellite data from peatlands, we conducted a comprehensive in situ study of hemiboreal, boreal, sub-Arctic and Arctic peatland vegetation. We measured the reflectance spectra (350–2500 nm), soil moisture, and various vegetation-related attributes from a total of 446 vegetation plots in Estonia and Finland, from a 1500 km south-north interval. We then investigated (i) the spectral variation in surface vegetation of hemiboreal, boreal, sub-Arctic and Arctic peatlands and (ii) explored its connection to plant functional types (PFTs) and soil moisture, as well as evaluated the potential of hyperspectral data in estimating PFT cover using simple vegetation indices and partial least square (PLS) regression. Our results indicate that (i) the best spectral regions to retrieve information regarding the PFT vary greatly especially between vascular plants and bryophytes, (ii) the reflectance at an individual wavelength as well as simple vegetational index can, to some extent, predict the PFT, and that (iii) the PLS regression can predict the PFT with good accuracy. Overall, our findings demonstrate the potential of using hyperspectral data in monitoring PFTs in northern peatlands. The spectral library and the ancillary data from the peatland sites collected for this study are available as open data.
Hyperspectral imaging of tree stems
(2023) Juola, Jussi
School of Engineering | Doctoral dissertation (article-based)
With the recent development of portable hyperspectral imaging spectrometers, there is a unique opportunity to acquire detailed information on the spectral properties of individual forest canopy elements, such as tree stems. Information on the spectral properties of stem bark is important for remote sensing of forests, biodiversity mapping, and forestry applications. In remote sensing, information on the fine scale physical interactions between shortwave solar radiation and stem bark is needed to interpret remotely sensed signals of forests more accurately. For biodiversity mapping and forestry applications, information on the spectral properties of stem bark could be used to support accurate identification of tree species. The aim of this dissertation was to develop close-range imaging spectroscopy as a method to measure the reflectance of stem bark in laboratory and field conditions, explore the spectral characteristics of stem bark, and assess the use of spectral data on bark in the identification of boreal and temperate tree species. The results demonstrated that there is potential in utilizing close-range imaging spectroscopy as a novel data source for studying the spectral characteristics of stem bark in the laboratory and in the field. The reflectance spectra of the measured boreal and temperate tree species had a similar shape in the visible to near-infrared region, but the overall levels of reflectance varied substantially within-and between-species. In general, stem bark reflectance for the measured boreal and temperate tree species was highly variable and anisotropic. Stem bark reflectance was affected by the spatial location of the sample along the stem and by the angular effects associated with the view-illumination geometry. Greatest interspecific differences in stem bark reflectance were in the near-infrared region and varying absorption features were observed at around 670–700 nm. The spectral features of stem bark were robust for identifying tree species but combining them with the texture features extracted from the hyperspectral reflectance images improved results further. The results also underlined the importance of accounting for meteorological and radiation conditions when measuring the spectra of stem bark in field conditions with pushbroom sensor technology. This dissertation gave a unique perspective into the spectral characteristics of stem bark for boreal and temperate tree species. The results contributed towards understanding the spectral diversity of forests more comprehensively and the portable hyperspectral imaging spectrometer showed promising results as a new technology for developing tree species identification methods for the forest industry and biodiversity mapping applications.
Intra- and interspecific variation in spectral properties of dominant Sphagnum moss species in boreal peatlands
(2023-06) Salko, Sini Selina; Juola, Jussi; Burdun, Iuliia; Vasander, Harri; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Boreal peatlands store ~25 % of global soil organic carbon and host many endangered species; however, they face degradation due to climate change and anthropogenic drainage. In boreal peatlands, vegetation indicates ecohydrological conditions of the ecosystem. Applying remote sensing would enable spatially and temporally continuous monitoring of peatland vegetation. New multi- and hyperspectral satellite data offer promising approaches for understanding the spectral properties of peatland vegetation at high temporal and spectral resolutions. However, using spectral satellite data to their fullest potential requires detailed spectral analyses of dominant species in peatlands. A dominant feature of peatland vegetation is the genus Sphagnum mosses. We investigated how the reflectance spectra of common boreal Sphagnum mosses, collected from waterlogged natural conditions after snowmelt, change when the mosses are desiccated. We conducted a laboratory experiment where the reflectance spectra (350–2500 nm) and the mass of 90 moss samples (representing nine species) were measured repetitively. Furthermore, we examined (i) their inter- and intraspecific spectral differences and (ii) whether the species or their respective habitats could be identified based on their spectral signatures in varying states of drying. Our findings show that the most informative spectral regions to retrieve information about the Sphagnum species and their state of desiccation are in the shortwave infrared region. Furthermore, the visible and near-infrared spectral regions contain less information on species and moisture content. Our results also indicate that hyperspectral data can, to a limited extent, be used to separate mosses belonging to meso- and ombrotrophic habitats. Overall, this study demonstrates the importance of including data especially from the shortwave infrared region (1100–2500 nm) in remote sensing applications of boreal peatlands. The spectral library of Sphagnum mosses collected in this study is available as open data and can be used to develop new methods for remote monitoring of boreal peatlands.
Links between light availability and spectral properties of forest floor in European forests
(2023-06-01) Forsström, Petri R.; Hovi, Aarne; Juola, Jussi; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Remote sensing using spectral data has been commonly applied to retrieve tree layer properties while the monitoring of forest floor remains a less studied topic. We investigated the links between light availability at forest floor, and forest floor's spectral reflectance properties (350–2500 nm) and fractional cover across boreal and temperate Europe. We hypothesized that tree canopy structure (and thus, light availability at forest floor) is linked not only to the vegetation composition of forest floor, as has been shown previously, but also to forest floor's spectral reflectance properties, and that these relationships differ between forest biomes. Data were collected in situ from a total of 67 forest stands in southern boreal, hemiboreal, temperate floodplain, and temperate mountain sites. The variation of light availability at forest floor was linked to both the forest floor's composition and spectral reflectance properties. Each study site exhibited site-specific spectral features and a different mean reflectance spectrum. Openness in tree canopies was related to an increase in the fractional cover of vascular plants and to a decrease of plant litter, consequently enhancing the forest floors’ spectral absorptance features in the red and shortwave-infrared wavelengths, as well as reflectance in the near-infrared region. Also, the variations of normalized difference index values and red edge positions as functions light availability at forest floor and forest floor's composition were different for each site. Our results suggest that incorporating biome-specific relationships between tree canopy structure and forest floor reflectance properties would improve interpretation of optical remote sensing data. The measurement data are openly available.
Multi-angular measurement of woody tree structures with mobile hyperspectral camera
(2019-08-19) Juola, Jussi
Insinööritieteiden korkeakoulu | Master's thesis
Laboratory measurement settings that can acquire spectral and multi-angular information on canopy elements (e.g. leaves and woody tree structures) provide invaluable data for the interpretation and development of forest reflectance models and other optical remote sensing techniques. Previous studies have pointed out that the spectral properties of woody tree structures of boreal tree species have been studied little in comparison to leaves, and that there is a need to fill this gap in knowledge. This thesis presents a custom-built multi-angular measurement system with imaging capabilities that was used to acquire a hyperspectral dataset of boreal woody tree structures of the three most common tree species found in Finland. A total of six trees, two trees per species of Norway spruce (Picea abies (L.) Karst), Scots pine (Pinus sylvestris L.) and silver birch (Betula pendula Roth) stems were sampled at different heights (at every meter of height between 1–10 m) and sides (northward and southward facing sides of the stem), and the stem surface (bark) was measured with a novel mobile hyperspectral camera called Specim IQ. The camera operates in the wavelength range of 400–1000 nm. The acquired dataset contains hyperspectral images of 120 stem samples, each imaged from six different view angles. A designed pixel-by-pixel data processing chain is described. It can calculate and extract accurate pixel specific reflectance information that is invariant to uneven spatial distribution of incident irradiance from the lamp. Finally, the processed data was analyzed to reveal within- and between-species, angular, and spatial variations in stem bark reflectance for the three species. In concordance to previous studies, this thesis found that the species varied highly in their mean spectra and were distinguishable from one another. In addition, the within-species variation and standard deviation between mean spectra of samples was surprisingly low with very similar spectral signatures between samples of the same species. Investigating angular variation revealed that both pine and birch present strong specular reflections in the forward-scattering angles, in comparison to spruce, which presented a hot spot effect in the backward-scattering angles when measured near the lamp. Birch and spruce showed weak trends when looking at the spatial variations occurring in reflectance due to sampling height or side of the stem. However, pine displayed a clear increase in reflectance from 1 m to 4 m height at 663.81 nm (red band) and from 1 m to 5 m height at 865.5 nm (near-infrared band). The data obtained in this study show potential for future tasks such as tree species classification and the further development of forest reflectance models. The methods and materials presented in this study can give ideas for developing imaging goniometer systems that can acquire even more information on various vegetation canopy elements than what were presented in this thesis.
Multiangular spectra of tree bark for common boreal tree species in Europe
(2020) Juola, Jussi; Hovi, Aarne; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Despite the importance of spectral properties of woody tree structures, they are seldom represented in research related to forests, remote sensing, and reflectance modeling. This study presents a novel imaging multiangular measurement set-up that utilizes a mobile handheld hyperspectral camera (Specim IQ, 400–1000 nm), and can measure stem bark spectra in a controlled laboratory setting. We measured multiangular reflectance spectra of silver birch (Betula pendula Roth), Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) stem bark, and demonstrated the potential of using bark spectra in identifying tree species using a Support Vector Machine (SVM) based approach. Intraspecific reflectance variability was the lowest in visible (400–700 nm), and the highest in near-infrared (700–1000 nm) wavelength regions. Interspecific variation was the largest in the red, red-edge and near-infrared spectral bands. Spatial variation of reflectance along the tree height and different sides of the stem (north and south) were found. Both birch and pine had increased reflectance in the forward-scattering directions for visible to near-infrared wavelength regions, whilst spruce displayed the same only for the visible wavelength region. In addition, spruce had increased reflectance in the backward-scattering directions. In spite of the intraspecific variations, SVM could identify tree species with 88.8% overall accuracy when using pixel-specific spectra, and with 97.2% overall accuracy when using mean spectra per image. Based on our results it is possible to identify common boreal tree species based on their stem bark spectra using images from mobile hyperspectral cameras.
Physically based illumination correction for sub-centimeter spatial resolution hyperspectral data
(2023-12-01) Ihalainen, Olli; Juola, Jussi; Mõttus, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Vegetation biophysical- and chemical traits, defined on the basis of leaf area, can be retrieved from their spectral reflectance. Ultra high resolution hyperspectral images, such as ones collected from drones, allows measuring the spectra of individual leaves. The reflectance signal of such data is calibrated with respect to the top-of-canopy (TOC) irradiance, as the local illumination conditions on leaf surfaces are largely unknown and can vary significantly from the TOC conditions. We developed an inversion algorithm that uses the PROSPECT leaf radiative transfer model and the theory of spectral invariants to retrieve the actual leaf reflectance from TOC-calibrated hyperspectral images. Compared with more traditional canopy reflectance models, this approach accounts for the spatial variation in leaf-level irradiance visible in sub-centimeter-resolution images and is computationally more efficient. We used simulated and measured leaf and canopy reflectance data to validate the approach and found the retrieved leaf reflectances to match closely the actual reflectances (relative RMSD was 12% for simulated data on the average and below 10% for measured data). The proposed method provides an efficient approach for illumination correction, enabling reliable, physically based applications for monitoring vegetation biochemical and biophysical properties from ultra-high-resolution spectral imagery.
Practical recommendations and limitations for pushbroom hyperspectral imaging of tree stems
(2023-12-01) Juola, Jussi; Hovi, Aarne; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
In this short communication, we present a pilot study testing a new close-range sensing technology – a portable, pushbroom hyperspectral camera – in varying field conditions in forests. We evaluate how measurement conditions affect the in situ collection of stem bark spectra. In situ spectral libraries of woody elements are needed in, e.g., physically-based remote sensing applications, biodiversity mapping, and 3D vegetation modeling. Recent technological advancements bring portable and close-range capable sensors, such as small pushbroom imaging spectrometers, for consumer and research use. However, it is important to investigate the strengths and limitations of sensors utilizing pushbroom technology. Spectral measurements under forest canopies are challenging due to varying illumination conditions, which can have a significant effect on the quality of the data. We acquired hyperspectral reflectance images of Norway spruce (Picea abies (L.) Karst), Scots pine (Pinus sylvestris L.), and silver birch (Betula pendula Roth) stem bark directly in the forest. For each tree we collected reflectance images at 30-minute intervals throughout a day from a fixed view angle. The most significant change in the measured spectra occurred due to spatially varying irradiance between the white reference panel and the bark surface. The spatial variation of irradiance had the largest effect on data quality in visible and red-edge regions, and the smallest in near-infrared. In non-diffuse conditions, changes in irradiance were often unpredictable as clouds or canopy elements moved in and out of the direct solar beams. Diffuse overcast days with clouds can extend the time window for measurements, making it a practical choice for acquiring hyperspectral images of stem bark. We concluded that with a well-planned measurement set-up it is possible to improve the precision of in situ collected spectra of stem bark.
Relationships between understory spectra and fractional cover in northern European boreal forests
(2021-10-15) Forsström, Petri R.; Juola, Jussi; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Modern satellite and airborne optical images have increasingly higher resolutions and enable the study of all layers of forests, not just the forest canopy. To understand the contribution of different types of understory on the overall spectral reflectance signal, ground reference data are needed from different types of forests. In this paper, we present the analysis of spectral reflectance factors (350-2300 nm) and fractional covers of understory from 36 boreal forest stands. The data were collected during peak growing season in a southern boreal forest area in Finland. The study stands represent four different forest site fertility types. We used a spectrometer to measure understory spectra in nadir and vegetation quadrats to estimate fractional cover. We showed that the understory has specific spectral features related to the site fertility type and fractional cover. Our results suggest that remote sensing can be used to differentiate forest site fertility types and estimate understory green fractional cover in northern European boreal forests. The collected data are openly available in an open data repository.
Retrieval of moisture content of common Sphagnum peat moss species from hyperspectral and multispectral data
(2024-12-15) Karlqvist, Susanna; Burdun, Iuliia; Salko, Sini Selina; Juola, Jussi; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Peatlands store enormous amounts of carbon in a peat layer, the formation and preservation of which can only occur under waterlogged conditions. Monitoring peatland moisture conditions is critically important because a decrease in moisture leads to peat oxidation and the release of accumulated carbon back into the atmosphere as a greenhouse gas. Optical remote sensing enables the indirect monitoring of peatland moisture conditions by identifying moisture-driven changes in vegetation spectral signatures. The vegetation of northern peatlands is dominated by Sphagnum mosses, whose spectral signatures are known to be highly sensitive to changes in moisture content. In this study, we tested methods to estimate Sphagnum moisture content from spectral data using seven spectral moisture indices, the OPtical TRApezoid Model (OPTRAM) and the Continuous Wavelet Transform (CWT). This study was based on data representing nine Sphagnum species sampled from two habitats in southern boreal peatlands in Finland. Our results showed that both multi- and hyperspectral data can be used to estimate the moisture content of Sphagnum mosses. Nevertheless, the optimal retrieval method depended on habitat characteristics. Using hyperspectral data, we found that: (i) the CWT exhibited superior performance for all studied moss species (RMarg2= 0.72, ICC = 0.40), (ii) the exponential OPTRAM performed best for the mesotrophic species (RMarg2= 0.70, ICC = 0.08), and (iii) the Modified Moisture Stress Index (MMSI) yielded the best results (RMarg2= 0.68, ICC = 0.55) for the ombrotrophic species. Furthermore, we demonstrated that using multispectral data instead of hyperspectral data provides comparable results in moisture estimation when used as input with OPTRAM or Moisture Stress Index (MSI). This approach could lead to new insights into the moisture dynamics in Sphagnum-dominated peatlands over the span of the multispectral satellite era.
Small geographical variability observed in Norway spruce needle spectra across Europe
(2022) Hovi, Aarne; Lukeš, Petr; Homolová, Lucie; Juola, Jussi; Rautiainen, Miina
Comment/debate
Foliage spectra form an important input to physically-based forest reflectance models. However, little is known about geographical variability of coniferous needle spectra. In this research note, we present an assessment of the geographical variability of Norway spruce (Picea abies (L.) H. Karst.) needle albedo, reflectance, and transmittance spectra across three study sites covering latitudes of 49–62°N in Europe. All spectra were measured and processed using exactly the same methodology and parameters, which guarantees reliable conclusions about geographical variability. Small geographical variability in Norway spruce needle spectra was observed, when compared to variability observed between previous measurement campaigns (employing slightly varying measurement and processing parameters), or to variability between plant functional types (broadleaved vs. coniferous). Our results suggest that variability of needle spectra is not a major factor introducing geographical variability to forest reflectance. The results also highlight the importance of harmonizing measurement protocols when collecting needle spectral libraries. Furthermore, the data collected for this study can be useful in studies where accurate information on spectral differences between broadleaved and coniferous tree foliage is needed.
A spectral analysis of common boreal ground lichen species
(2020-09-15) Kuusinen, Nea; Juola, Jussi; Karki, Bijay; Stenroos, Soili; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Lichens dominate a significant part of the Earth's land surface, and are valuable bioindicators of various environmental changes. In the northern hemisphere, the largest lichen biomass is in the woodlands and heathlands of the boreal zone and in tundra. Despite the global coverage of lichens, there has been only limited research on their spectral properties in the context of remote sensing of the environment. In this paper, we report spectral properties of 12 common boreal lichen species. Measurements of reflectance spectra were made in laboratory conditions with a standard spectrometer (350–2500 nm) and a novel mobile hyperspectral camera (400–1000 nm) which was used in a multiangular setting. Our results show that interspecific differences in reflectance spectra were the most pronounced in the ultraviolet and visible spectral range, and that dry samples always had higher reflectance than fresh (moist) samples in the shortwave infrared region. All study species had higher reflectance in the backward scattering direction compared to nadir or forward scattering directions. Our results also reveal, for the first time, that there is large intraspecific variation in reflectance of lichen species. This emphasizes the importance of measuring several replicates of each species when analyzing lichen spectra. In addition, we used the data in a spectral clustering analysis to study the spectral similarity between samples and species, and how these similarities could be linked to different physical traits or phylogenetic closeness of the species. Overall, our results suggest that spectra of some lichen species with large ground coverage can be used for species identification from high spatial resolution remote sensing imagery. On the other hand, for lichen species growing as small assemblages, mobile hyperspectral cameras may offer a solution for in-situ species identification. The spectral library collected in this study is available in the SPECCHIO Spectral Information System.
A spectral analysis of stem bark for boreal and temperate tree species
(2022-03) Juola, Jussi; Hovi, Aarne; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The woody material of forest canopies has a significant effect on the total forest reflectance and on the interpretation of remotely sensed data, yet research on the spectral properties of bark has been limited. We developed a novel measurement setup for acquiring stem bark reflectance spectra in field conditions, using a mobile hyperspectral camera. The setup was used for stem bark reflectance measurements of ten boreal and temperate tree species in the visible (VIS) to near-infrared (NIR) (400–1000 nm) wavelength region. Twenty trees of each species were measured, constituting a total of 200 hyperspectral reflectance images. The mean bark spectra of species were similar in the VIS region, and the interspecific variation was largest in the NIR region. The intraspecific variation of bark spectra was high for all studied species from the VIS to the NIR region. The spectral similarity of our study species did not correspond to the general phylogenetic lineages. The hyperspectral reflectance images revealed that the distributions of per-pixel reflectance values within images were species-specific. The spectral library collected in this study contributes toward building a comprehensive understanding of the spectral diversity of forests needed not only in remote sensing applications but also in, for example, biodiversity or land surface modeling studies.
Synergistic use of multi- and hyperspectral remote sensing data and airborne LiDAR to retrieve forest floor reflectance
(2023-08-01) Hovi, Aarne; Schraik, Daniel; Kuusinen, Nea; Fabiánek, Tomáš; Hanuš, Jan; Homolová, Lucie; Juola, Jussi; Lukeš, Petr; Rautiainen, Miina
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Forest floor vegetation can account for a notable fraction of forest productivity and species diversity, and the composition of forest floor vegetation is an important indicator of site type. The signal from the forest floor influences the interpretation of optical remote sensing (RS) data. Retrieval of forest floor reflectance properties has commonly been investigated with multiangular RS data, which often have a coarse spatial resolution. We developed a method that utilizes a forest reflectance model based on photon recollision probability to retrieve forest floor reflectance from near-nadir data. The method was tested in boreal, hemiboreal, and temperate forests in Europe, with hemispherical photos and airborne LiDAR as alternative data sources to provide forest canopy structural information. These two data sources showed comparable performance, thus demonstrating the value of using airborne LiDAR as the structural reflectance model input to derive wall-to-wall maps of forest floor reflectance. We derived such maps from multispectral Sentinel-2 MSI and hyperspectral PRISMA satellite images for a boreal forest site. The validation against in situ measurements showed fairly good performance of the retrievals in sparse forests (that had effective plant area index less than 2). In dense forests, the retrievals were less accurate, due to the small contribution of forest floor to the RS signal. We also demonstrated the use of the method in monitoring the recovery of forest floor vegetation after a thinning disturbance. The reflectance model that we used is computationally efficient, making it well applicable also to data from new and forthcoming hyperspectral satellite missions.