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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, 400â1000 nm), and can measure stem bark spectra in a controlled laboratory setting. We measured multiangular reflectance spectra of silver birch ( Roth), Scots pine ( L.) and Norway spruce ( (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 (400â700 nm), and the highest in near-infrared (700â1000 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.Betula pendulaPinus sylvestrisPicea abies

Spectral libraries have a fundamental role in the development of interpretation methods for airborne and satellite-borne remote sensing data. This paper presents to-date the largest spectral measurement campaign of boreal tree species. Reflectance and transmittance spectra of over 600 leaf and needle samples from 25 species were measured in the Helsinki area (Finland) using integrating sphere systems attached to an ASD FieldSpec 4 spectroradiometer. Factors influencing the spectra and red edge inflection point (REIP) were quantified using one-way analysis of variance. Tree species differed most in the shortwave-infrared (1500â2500 nm) and least in the visible (400â700 nm) wavelength region. Species belonging to same genera showed similar spectral characteristics. Upper (adaxial) and lower (abaxial) leaf sides differed most in the visible region. Canopy position (sunlit/shaded) had a minor role in explaining spectral variation. For evergreen conifers, current and previous year needles differed in their spectra, current-year needles resembling those of broadleaved and deciduous conifers. Two broadleaved species were monitored throughout the growing season (MayâOctober), and two conifers were measured twice during summer (June, September). Rapid changes were observed in the spectra in early spring and late autumn, whereas seasonal variations during summer months were relatively small for both broadleaved and coniferous species. Based on our results, shortwave-infrared seems promising in separating tree species, although it is to-date least studied. The spectral library reported here (Version 1.0) is publicly available through the SPECCHIO Spectral Information System.

This paper addresses tree crown envelope shape modeling from the perspective of optical passive remote sensing. The aims are 1) to review the specific requirements of crown shape models and ground measurement techniques in optical remote sensing, and 2) to present preliminary results from empirical, parametric crown shape and volume modeling of Scots pine and Norway spruce applicable in Finland. Results indicated that the basic dimensions (maximum radius, its height and crown length) of tree crowns were better predicted for pines, but the profile shape of the upper part of the crowns varied more than in spruce. Pine crowns were also slightly less concave than spruce crowns. No regularities were observed concerning the lower part of the crowns. The asymmetry of crowns increased as a function of tree age for both species, spruce crowns being more asymmetric than pine crowns. A comparison of measured crown volume with several simple geometrical crown shape envelopes showed that using a cone as a crown shape model for Scots pine and Norway spruce underestimates crown volume most severely. Other crown envelope shape models (e.g. ellipsoids) rendered crown volumes closer to the measured volume and did not differ considerably from each other.

Accurate field measurement of the forest canopy cover is too laborious to be used in extensive forest inventories. A possible alternative to the separate canopy cover measurements is to utilize the correlations between the percent canopy cover and easier-to-measure forest variables, especially the basal area. A fairly new analysis technique, the beta regression, is specially designed for modelling percentages. As an extension to the generalized linear models, the beta regression takes into account the distribution of the model residuals, and uses a logistic link function to ensure logical predictions. In this study, the beta regression method was found to perform well in conifer dominated study area located in central Finland. The same model shape, with basal area, tree height and an additional predictor (Scots pine: site fertility, Norway spruce: percentage of hardwoods) as independent variables, produced good results for both pine and spruce dominated sites. The models had reasonably high pseudo R-squared values (pine: 0.91, spruce: 0.87) and low standard errors (pine: 6.3%, spruce: 5.9%) for the fitting data, and also performed well in a cross validation test. The models were also tested on separate test plots located in a different geographical area, where the prediction errors were slightly larger (pine: 8.8%, spruce: 7.4%). In pine plots, the model fit was further improved by introducing additional predictors such as stand age and density. This improved also the performance of the models in the cross validation test, but weakened the results for the external data set. Our results indicated that the beta regression method offers a noteworthy alternative to separate canopy cover measurements, especially if time is limited and the models can be applied in the same region where the modelling data were collected.

Estimation of forest canopy cover has recently been included in many forest inventory programmes. In this study, after discussing how canopy cover is defined, different ground-based canopy cover estimation techniques are compared to determine which would be the most feasible for a large scale forest inventory. Canopy cover was estimated in 19 Scots pine or Norway spruce dominated plots using the Cajanus tube, line intersect sampling, modified spherical densiometer, digital photographs, and ocular estimation. The comparisons were based on the differences in values acquired with selected techniques and control values acquired with the Cajanus tube. The statistical significance of the differences between the techniques was tested with the nonparametric Kruskall-Wallis analysis of variance and multiple comparisons. The results indicate that different techniques yield considerably different canopy cover estimates. In general, labour intensive techniques (the Cajanus tube, line intersect sampling) provide unbiased and more precise estimates, whereas the estimates provided by fast techniques (digital photographs, ocular estimation) have larger variances and may also be seriously biased.

The way canopy cover is defined and measured influences the obtained canopy cover percentage. Estimates of canopy cover are needed, for example, in canopy radiation modelling and remote sensing applications and as a tool for political decision-making. In this paper, we demonstrated the use of two methods, the LAI-2000 Plant Canopy Analyzer instrument and the Cajanus tube, in Scots pine stands for canopy cover estimation, and also assessed the number of measurement points required for reliable estimates. The Cajanus tube yielded slightly larger canopy closure values than the LAI-2000 instrument, but the values were nevertheless in good agreement. Both of the methods required approximately 250 measurement points for canopy closure estimates of a stand to become relatively stable. We also present the first measured effective canopy transmittance values for Scots pine stands in Finland and an example of tree pattern mapping with the Cajanus tube.

Estimation of leaf area index (LAI) using spectral vegetation indices (SVIs) was studied based on data from 683 plots on two Scots pine and Norway spruce dominated sites in Finland. The SVIs studied included the normalised difference vegetation index (NDVI), the simple ratio (SR), and the reduced simple ratio (RSR), and were calculated from Landsat ETM images of the two sites. Regular grids of size 1 km2 with gridpoints placed at 50 m intervals were established at the sites and measurements of LAI using the LAI-2000 instrument were taken at the gridpoints. SVI-LAI relationships were examined at plot scale, where the plots were defined as circular areas of radius 70 m around each gridpoint. Plotwise mean LAI was computed as a weighted average of LAI readings taken around the gridpoints belonging to the plot. Mean LAI for the plots ranged from 0.36 to 3.72 (hemisurface area). All of the studied SVIs showed fair positive correlation with LAI but RSR responded more dynamically to LAI than did SR or NDVI. Especially NDVI showed poor sensitivity to changes in LAI. RSR explained 63% of the variation in LAI when all plots were included (n = 683) and the coefficient of determination rose to 75% when data was restricted to homogeneous plots (n = 381). Maps of estimated LAI using RSR showed good agreement with maps of measured LAI for the two sites.

Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), forest floor (based on in situ measurements) and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at: Hovi et al. 2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9 (for laboratory and field data) and Hovi et al. 2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f (for airborne data). [ABSTRACT FROM AUTHOR]

Leaf scattering spectrum is the key optical variable that conveys information about leaf absorbing constituents from remote sensing. It cannot be directly measured from space because the radiation scattered from leaves is affected by the 3D canopy structure. In addition, some radiation is specularly reflected at the surface of leaves. This portion of reflected radiation is partly polarized, does not interact with pigments inside the leaf and therefore contains no information about its interior. Very little empirical data are available on the spectral and angular scattering properties of leaf surfaces. Whereas canopy-structure effects are well understood, the impact of the leaf surface reflectance on estimation of leaf absorption spectra remains uncertain. This paper presents empirical and theoretical analyses of angular, spectral, and polarimetric measurements of light reflected by needles and shoots of Pinus koraiensis and Picea koraiensis species. Our results suggest that ignoring the leaf surface reflected radiation can result in an inaccurate estimation of the leaf absorption spectrum. Polarization measurements may be useful to account for leaf surface effects because radiation reflected from the leaf surface is partly polarized, whereas that from the leaf interior is not.

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. [ABSTRACT FROM AUTHOR]