Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal

PREMISE

Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data.

METHODS

Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model.

RESULTS

Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species.

CONCLUSIONS

Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

Impact of potentially toxic elements on pines in a former ore processing mine: Exploitation of hyperspectral response from needle and canopy scales

Anthropic potentially toxic element (PTE) releases can lead to persistent pollution in soil. Monitoring PTEs by their detection and quantification on large scale is of great interest. The vegetation exposed to PTEs can exhibit a reduction of physiological activities, structural damage … Such vegetation trait changes impact the spectral signature in the reflective domain 0.4-2.5 μm. The objective of this study is to characterize the impact of PTEs on the spectral signature of two pine species (Aleppo and Stone pines) in the reflective domain and ensure their assessment. The study focuses on nine PTEs: As, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Zn. The spectra are measured by an in-field spectrometer and an aerial hyperspectral instrument on a former ore processing site. They are completed by measurements related to vegetation traits at needle and tree scales (photosynthetic pigments, dry matter, morphometry …) to define the most sensitive vegetation parameter to each PTE in soil. A result of this study is that chlorophylls and carotenoids are the most correlated to PTE contents. Context-specific spectral indices are specified and used to assess metal contents in soil by regression. These new vegetation indices are compared at needle and canopy scales to literature indices. Most of the PTE contents are predicted at both scales with Pearson correlation scores between 0.6 and 0.9, depending on species and scale.

Imaging particulate matter exposed pine trees by vehicle exhaust experiment and hyperspectral analysis

This study analyzed spectral variations of the particulate matter (PM hereafter)-exposed pine trees using a spectrometer and a hyperspectral imager to derive the most effective spectral indices to detect the pine needle exposure to PM emission. We found that the spectral variation in the near-infrared (NIR hereafter) bands systemically coincided with the variations in PM concentration, showing larger variations for the diesel group whereas larger dust particles showed spectral variations in both visible and NIR bands. It is because the PM adsorption on needles is the main source of NIR band variation, and the combination of visible and NIR spectra can detect PM absorption. Fourteen bands were selected to classify PM-exposed pine trees with an accuracy of 82% and a kappa coefficient of 0.61. Given that this index employed both visible and NIR bands, it would be able to detect PM adsorption. The findings can be transferred to real-world applications for monitoring air pollution in an urban area.

An Integration of Linear Model and ‘Random Forest’ Techniques for Prediction of Norway Spruce Vitality: A Case Study of the Hemiboreal Forest, Latvia

The increasing extreme weather and climate events have a significant impact on the resistance and resilience of Norway spruce trees. The responses and adaptation of individual trees to certain factors can be assessed through the tree breeding programmes. Tree breeding programmes combined with multispectral unmanned aircraft vehicle (UAV) platforms may assist in acquiring regular information of individual traits from large areas of progeny trials. Therefore, the aim of this study was to investigate the vegetation indices (VI) to detect the early stages of tree stress in Norway spruce stands under prolonged drought and summer heatwave. Eight plots within four stands throughout the vegetation season of 2021 were monitored by assessing spectral differences of tree health classes (Healthy, Crown damage, New crown damage, Dead trees, Stem damage, Root rot). From all tested VI, our models showed a moderate marginal R2 and total explanatory power—for Normalized Difference Red-edge Index (NDRE), marginal R2 was 0.26, and conditional R2 was 0.49 (p < 0.001); for Normalized Difference Vegetation Index (NDVI), marginal R2 was 0.34, and conditional R2 was 0.60 (p < 0.001); for Red Green Index (RGI), marginal R2 was 0.36, and conditional R2 was 0.55 (p < 0.001); while for Chlorophyll Index (CI), marginal R2 was 0.27, and conditional R2 was 0.49 (p < 0.001). The reliability of the identification of tree health classes for selected VI was weak to fair (overall classification accuracy ranged from 34.4% to 56.8%, kappa coefficients ranged from 0.09 to 0.34) if six classes were assessed, and moderate to substantial (overall classification accuracy ranged from 71.1% to 89.6% and kappa coefficient from 0.39 to 0.71) if two classes (Crown damage and Healthy trees) were tested.

A Proposal for a Forest Digital Twin Framework and Its Perspectives

The increasing importance of forest ecosystems for human society and planetary health is widely recognized, and the advancement of data collection technologies enables new and integrated ways for forest ecosystems monitoring. Therefore, the target of this paper is to propose a framework to design a forest digital twin (FDT) that, by integrating different state variables at both tree and forest levels, creates a virtual copy of the forest. The integration of these data sets could be used for scientific purposes, for reporting the health status of forests, and ultimately for implementing sustainable forest management practices on the basis of the use cases that a specific implementation of the framework would underpin. Achieving such outcomes requires the twinning of single trees as a core element of the FDT by recording the physical and biotic state variables of the tree and of the near environment via real–virtual digital sockets. Following a nested approach, the twinned trees and the related physical and physiological processes are then part of a broader twinning of the entire forest realized by capturing data at forest scale from sources such as remote sensing technologies and flux towers. Ultimately, to unlock the economic value of forest ecosystem services, the FDT should implement a distributed ledger-based on blockchain and smart contracts to ensure the highest transparency, reliability, and thoroughness of the data and the related transactions and to sharpen forest risk management with the final goal to improve the capital flow towards sustainable practices of forest management.

A spectral analysis of stem bark for boreal and temperate tree species

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.

A Photon Force and Flow for Dissipative Structuring: Application to Pigments, Plants and Ecosystems

Through a modern derivation of Planck’s formula for the entropy of an arbitrary beam of photons, we derive a general expression for entropy production due to the irreversible process of the absorption of an arbitrary incident photon spectrum in material and its dissipation into an infrared-shifted grey-body emitted spectrum, with the rest being reflected or transmitted. Employing the framework of Classical Irreversible Thermodynamic theory, we define the generalized thermodynamic flow as the flow of photons from the incident beam into the material and the generalized thermodynamic force is, then, the entropy production divided by the photon flow, which is the entropy production per unit photon at a given wavelength. We compare the entropy production of different inorganic and organic materials (water, desert, leaves and forests) under sunlight and show that organic materials are the greater entropy-producing materials. Intriguingly, plant and phytoplankton pigments (including chlorophyll) reach peak absorption exactly where entropy production through photon dissipation is maximal for our solar spectrum 430<λ<550 nm, while photosynthetic efficiency is maximal between 600 and 700 nm. These results suggest that the evolution of pigments, plants and ecosystems has been towards optimizing entropy production, rather than photosynthesis. We propose using the wavelength dependence of global entropy production as a biosignature for discovering life on planets of other stars.

Tree Species Classification in a Temperate Mixed Mountain Forest Landscape Using Random Forest and Multiple Datasets

For monitoring protected forest landscapes over time it is essential to follow changes in tree species composition and forest dynamics. Data driven remote sensing methods provide valuable options if terrestrial approaches for forest inventories and monitoring activities cannot be applied efficiently due to restrictions or the size of the study area. We demonstrate how species can be detected at a single tree level utilizing a Random Forest (RF) model using the Black Forest National Park as an example of a Central European forest landscape with complex relief. The classes were European silver fir (Abies alba, AA), Norway spruce (Picea abies, PA), Scots pine (Pinus sylvestris, PS), European larch (Larix decidua including Larix kampferii, LD), Douglas fir (Pseudotsuga menziesii, PM), deciduous broadleaved species (DB) and standing dead trees (snags, WD). Based on a multi-temporal (leaf-on and leaf-off phenophase) and multi-spectral mosaic (R-G-B-NIR) with 10 cm spatial resolution, digital elevation models (DTM, DSM, CHM) with 40 cm spatial resolution and a LiDAR dataset with 25 pulses per m2, 126 variables were derived and used to train the RF algorithm with 1130 individual trees. The main objective was to determine a subset of meaningful variables for the RF model classification on four heterogeneous test sites. Using feature selection techniques, mainly passive optical variables from the leaf-off phenophase were considered due to their ability to differentiate between conifers and the two broader classes. An examination of the two phenological phases (using the difference of the respective NDVIs) is important to clearly distinguish deciduous trees from other classes including snags (WD). We also found that the variables of the first derivation of NIR and the tree metrics play a crucial role in discriminating PA und PS. With this unique set of variables some classes can be differentiated more reliably, especially LD and DB but also AA, PA and WD, whereas difficulties exist in identifying PM and PS. Overall, the non-parametric object-based approach has proved to be highly suitable for accurately detecting (OA: 89.5%) of the analyzed classes. Finally, the successful classification of complex 265 km2 study area substantiates our findings.

Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions

Plantations of fast-growing forest species such as black locust (Robinia Pseudoacacia) can contribute to energy transformation, mitigate industrial pollution, and restore degraded, marginal land. In this study, the synergistic use of Sentinel-2 and Sentinel-1 time series data is explored for modeling aboveground biomass (AGB) in black locust short-rotation plantations in northeastern Greece. Optimal modeling dates and EO sensor data are also identified through the analysis. Random forest (RF) models were originally developed using monthly Sentinel-2 spectral indices, while, progressively, monthly Sentinel-1 bands were incorporated in the statistical analysis. The highest accuracy was observed for the models generated using Sentinel-2 August composites (R2 = 0.52). The inclusion of Sentinel-1 bands in the spectral indices’ models had a negligible effect on modeling accuracy during the leaf-on period. The correlation and comparative performance of the spectral indices in terms of pairwise correlation with AGB varied among the phenophases of the forest plantations. Overall, the field-measured AGB in the forest plantations plots presented a higher correlation with the optical Sentinel-2 images. The synergy of Sentinel-1 and Sentinel-2 data proved to be a non-efficient approach for improving forest biomass RF models throughout the year within the geographical and environmental context of our study.

Evaluating the effect of stand properties and site conditions on the forest reflectance from Sentinel-2 time series

Forest stand reflectance at the canopy level results from various factors, such as vegetation chemical properties, leaf morphology, canopy structure, and tree sizes. These factors are dependent on the species, age, and health statuses of trees, as well as the site conditions. Sentinel-2 imagery with the high spatial, spectral, and temporal resolution, has enabled analysis of the relationships between vegetation properties and their spectral responses at large spatial scales. A comprehensive study of these relationships is needed to understand the drivers of vegetation spectral patterns and is essential from the point of view of remote sensing data interpretation. Our study aimed to quantify the site and forest parameters affecting forest stands reflectance. The analysis was conducted for common beech-, silver fir- and Scots pine-dominated stands in a mountainous area of the Polish Carpathians. The effect of stands and site properties on reflectance in different parts of the growing season was captured using the dense time series provided by Sentinel-2 from 2018-2019. The results indicate that the reflectance of common beech stands is mainly influenced by elevation, particularly during spring and autumn. Other factors influencing beech stand reflectance include the share of the broadleaved understory, aspect, and, during summer, the age of stands. The reflectance of coniferous species, i.e., Scots pine and silver fir, is mainly influenced by the age and stand properties, namely the crown closure and stand density. The age is a primary driver for silver fir stands reflectance changes, while the stand properties have a large impact on Scots pine stands reflectance. Also, the understory influences Scots pine stands reflectance, while there appears to be no impact on silver fir stands. The influence of the abovementioned factors is highly diverse, depending on the used band and time of the season.

Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for the accurate and timely monitoring of forests. In this study, multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top, and height, as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge, and Near infrared (NIR) bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested: (i) to what extent can the linear relationship be established between selected vegetation indexes (Normalized Difference Vegetation Index (NDVI) and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents) and (ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have the potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level; however, the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the second year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data that were acquired by the UAV platform at a very high spatial resolution.

Foliage Biophysical Trait Prediction from Laboratory Spectra in Norway Spruce Is More Affected by Needle Age Than by Site Soil Conditions

Scaling leaf-level optical signals to the canopy level is essential for airborne and satellite-based forest monitoring. In evergreen trees, biophysical and optical traits may change as foliage ages. This study aims to evaluate the effect of age in Norway spruce needle on biophysical trait-prediction based on laboratory leaf-level spectra. Mature Norway spruce trees were sampled at forest stands in ten headwater catchments with different soil properties. Foliage biophysical traits (pigments, phenolics, lignin, cellulose, leaf mass per area, water, and nitrogen content) were assessed for three needle-age classes. Complementary samples for needle reflectance and transmittance were measured using an integrating sphere. Partial least square regression (PLSR) models were constructed for predicting needle biophysical traits from reflectance—separating needle age classes and assessing all age classes together. The ten study sites differed in soil properties rather than in needle biophysical traits. Optical properties consistently varied among age classes; however, variation related to the soil conditions was less pronounced. The predictive power of PLSR models was needle-age dependent for all studied traits. The following traits were predicted with moderate accuracy: needle pigments, phenolics, leaf mass per area and water content. PLSR models always performed better if all needle age classes were included (rather than individual age classes separately). This also applied to needle-age independent traits (water and lignin). Thus, we recommend including not only current but also older needle traits as a ground truth for evergreen conifers with long needle lifespan.

Changes of Norway Spruce Health in the Białowieża Forest (CE Europe) in 2013–2019 during a Bark Beetle Infestation, Studied with Landsat Imagery

Among the largest disturbances affecting the health of spruce forests is the large-scale appearance of bark beetles. Knowledge on the spatial distribution of infected-spruce areas is vital for effective and sustainable forest management. Medium-spatial-resolution (20–30 m) satellite images are well-suited for spruce forest disturbance monitoring at a landscape and regional scale following bark beetle outbreaks. The aim of this study was to evaluate the health of a Norway spruce stand after a bark beetle outbreak based on Landsat 8 images and thematic and vector data, supplemented with selected climate variables. This research was conducted for a spruce stand in the Białowieża Forest District in 2013, 2015, 2017, and 2019. We hypothesised that the changes in spruce health would significantly influence the NDVI distributions during the studied years. Our research revealed that the weather conditions in the period of May–September were beneficial for beetle development and detrimental for the spruce stand, particularly in 2015, 2018, and 2019. SWIR-NIR-G and NDVI images showed a gradual deterioration in spruce health. The quantitative NDVI distributions varied; the minimum, mean, and median decreased; and the distribution shape of the index values changed over the studied years. An analysis of the spatial NDVI distributions revealed that the threshold NDVI value separating spruce stand areas in good and poor health was ca. 0.6. This study confirmed the applicability of NDVI for monitoring alterations in spruce stands, and indicated that spatial NDVI distributions can provide valuable support in forest monitoring at a landscape scale, since medium-resolution, ready-to-use NDVI images are easily available from the Landsat archives, facilitating the routine assessment of stand health.

Simulation-Based Evaluation of the Estimation Methods of Far-Red Solar-Induced Chlorophyll Fluorescence Escape Probability in Discontinuous Forest Canopies

The escape probability of Solar-induced chlorophyll fluorescence (SIF) can be remotely estimated using reflectance measurements based on spectral invariants theory. This can then be used to correct the effects of canopy structure on canopy-leaving SIF. However, the feasibility of these estimation methods is untested in heterogeneous vegetation such as the discontinuous forest canopy layer under evaluation here. In this study, the Discrete Anisotropic Radiative Transfer (DART) model is used to simulate canopy-leaving SIF, canopy total emitted SIF, canopy interceptance, and the fraction of absorbed photosynthetically active radiation (fAPAR) in order to evaluate the estimation methods of SIF escape probability in discontinuous forest canopies. Our simulation results show that the normalized difference vegetation index (NDVI) can be used to partly eliminate the effects of background reflectance on the estimation of SIF escape probability in most cases, but fails to produce accurate estimations if the background is partly or totally covered by vegetation. We also found that SIF escape probabilities estimated at a high solar zenith angle have better estimation accuracy than those estimated at a lower solar zenith angle. Our results show that additional errors will be introduced to the estimation of SIF escape probability with the use of satellite products, especially when the product of leaf area index (LAI) and clumping index (CI) was underestimated. In other results, fAPAR has comparable estimation accuracy of SIF escape probability when compared to canopy interceptance. Additionally, fAPAR for the entire canopy has better estimation accuracy of SIF escape probability than fPAR for leaf only in sparse forest canopies. These results help us to better understand the current estimation results of SIF escape probability based on spectral invariants theory, and to improve its estimation accuracy in discontinuous forest canopies.

Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model

Vegetation top-of-canopy reflectance contains valuable information for estimating vegetation biochemical and structural properties, and canopy photosynthesis (gross primary production (GPP)). Satellite images allow studying temporal variations in vegetation properties and photosynthesis. The National Aeronautics and Space Administration (NASA) has produced a harmonized Landsat-8 and Sentinel-2 (HLS) data set to improve temporal coverage. In this study, we aimed to explore the potential and investigate the information content of the HLS data set using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model to retrieve the temporal variations in vegetation properties, followed by the GPP simulations during the 2016 growing season of an evergreen Norway spruce dominated forest stand. We optimized the optical radiative transfer routine of the SCOPE model to retrieve vegetation properties such as leaf area index and leaf chlorophyll, water, and dry matter contents. The results indicated percentage differences less than 30% between the retrieved and measured vegetation properties. Additionally, we compared the retrievals from HLS data with those from hyperspectral airborne data for the same site, showing that HLS data preserve a considerable amount of information about the vegetation properties. Time series of vegetation properties, retrieved from HLS data, served as the SCOPE inputs for the time series of GPP simulations. The SCOPE model reproduced the temporal cycle of local flux tower measurements of GPP, as indicated by the high Nash–Sutcliffe efficiency value (>0.5). However, GPP simulations did not significantly change when we ran the SCOPE model with constant vegetation properties during the growing season. This might be attributed to the low variability in the vegetation properties of the evergreen forest stand within a vegetation season. We further observed that the temporal variation in maximum carboxylation capacity had a pronounced effect on GPP simulations. We focused on an evergreen forest stand. Further studies should investigate the potential of HLS data across different forest types, such as deciduous stand.

Empirical validation of photon recollision probability in single crowns of tree seedlings

Physically-based methods in remote sensing provide benefits over statistical approaches in monitoring biophysical characteristics of vegetation. However, physically-based models still demand large computational resources and often require rather detailed informative priors on various aspects of vegetation and atmospheric status. Spectral invariants and photon recollision probability theories provide a solid theoretical framework for developing relatively simple models of forest canopy reflectance. Empirical validation of these theories is, however, scarce. Here we present results of a first empirical validation of a model based on photon recollision probability at the level of individual trees. Multiangular spectra of pine, spruce, and oak tree seedlings (height = 0.38-0.7 m) were measured using a goniometer, and tree hemispherical reflectance was derived from those measurements. We evaluated the agreement between modeled and measured tree reflectance. The model predicted the spectral signatures of the tree seedlings in the wavelength range between 400 and 2300 nm well, with wavelength-specific bias between -0.048 and 0.034 in reflectance units. In relative terms, the model errors were the smallest in the near-infrared (relative RMSE up to 4%, 7%, and 4% for pine, spruce, and oak seedlings, respectively) and the largest in the visible wavelength region (relative RMSE up to 34%, 20%, and 60%). The errors in the visible region could be partly attributed to wavelength-dependent directional scattering properties of the leaves. Including woody parts of tree seedlings in the model improved the results by reducing the relative RMSE by up to 10% depending on species and wavelength. Spectrally invariant model parameters, i.e. total and directional escape probabilities, depended on spherically averaged silhouette to total area ratio (STAR) of the tree seedlings. Overall, the modeled and measured tree reflectance mainly agreed within measurement uncertainties, but the results indicate that the assumption of isotropic scattering by the leaves can result in large errors in the visible wavelength region for some tree species. Our results help increasing the confidence when using photon recollision probability and spectral invariants -based models to interpret satellite images, but they also lead to an improved understanding of the assumptions and limitations of these theories.

Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables

The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after the Korean War. In this study, we developed a model to classify the five dominant tree species in North Korea (Korean red pine, Korean pine, Japanese larch, needle fir, and Oak) using satellite data and machine-learning techniques. The model was applied to the Gwangneung Forest area in South Korea; the Mt. Baekdu area of China, which borders North Korea; and to Goseong-gun, at the border of South Korea and North Korea, to evaluate the model’s applicability to North Korea. Eighty-three percent accuracy was achieved in the classification of the Gwangneung Forest area. In classifying forest types in the Mt. Baekdu area and Goseong-gun, even higher accuracies of 91% and 90% were achieved, respectively. These results confirm the model’s regional applicability. To expand the model for application to North Korea, a new model was developed by integrating training data from the three study areas. The integrated model’s classification of forest types in Goseong-gun (South Korea) was relatively accurate (80%); thus, the model was utilized to produce a map of the predicted dominant tree species in Goseong-gun (North Korea).

Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance ( R 2 = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of X i + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.

Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series

Climate change and severe extreme events, i.e., changes in precipitation and higher drought frequency, have a large impact on forests. In Poland, particularly Norway spruce and Scots pine forest stands are exposed to disturbances and have, thus experienced changes in recent years. Considering that Scots pine stands cover approximately 58% of forests in Poland, mapping these areas with an early and timely detection of forest cover changes is important, e.g., for forest management decisions. A cost-efficient way of monitoring forest changes is the use of remote sensing data from the Sentinel-2 satellites. They monitor the Earth’s surface with a high temporal (2–3 days), spatial (10–20 m), and spectral resolution, and thus, enable effective monitoring of vegetation. In this study, we used the dense time series of Sentinel-2 data from the years 2015–2019, (49 images in total), to detect changes in coniferous forest stands dominated by Scots pine. The simple approach was developed to analyze the spectral trajectories of all pixels, which were previously assigned to the probable forest change mask between 2015 and 2019. The spectral trajectories were calculated using the selected Sentinel-2 bands (visible red, red-edge 1–3, near-infrared 1, and short-wave infrared 1–2) and selected vegetation indices (Normalized Difference Moisture Index, Tasseled Cap Wetness, Moisture Stress Index, and Normalized Burn Ratio). Based on these, we calculated the breakpoints to determine when the forest change occurred. Then, a map of forest changes was created, based on the breakpoint dates. An accuracy assessment was performed for each detected date class using 861 points for 46 classes (45 dates and one class representing no changes detected). The results of our study showed that the short-wave infrared 1 band was the most useful for discriminating Scots pine forest stand changes, with the best overall accuracy of 75%. The evaluated vegetation indices underperformed single bands in detecting forest change dates. The presented approach is straightforward and might be useful in operational forest monitoring.

Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data

The endemic New Zealand kauri trees (Agathis australis) are under threat by the deadly kauri dieback disease (Phytophthora agathidicida (PA)). This study aimed to identify spectral index combinations for characterising visible stress symptoms in the kauri canopy. The analysis is based on an aerial AISA hyperspectral image mosaic and 1258 reference crowns in three study sites in the Waitakere Ranges west of Auckland. A field-based assessment scheme for canopy stress symptoms (classes 1–5) was further optimised for use with RGB aerial images. A combination of four indices with six bands in the spectral range 450–1205 nm resulted in a correlation of 0.93 (mean absolute error 0.27, RMSE 0.48) for all crown sizes. Comparable results were achieved with five indices in the 450–970 nm region. A Random Forest (RF) regression gave the most accurate predictions while a M5P regression tree performed nearly as well and a linear regression resulted in slightly lower correlations. Normalised Difference Vegetation Indices (NDVI) in the near-infrared / red spectral range were the most important index combinations, followed by indices with bands in the near-infrared spectral range from 800 to 1205 nm. A test on different crown sizes revealed that stress symptoms in smaller crowns with denser foliage are best described in combination with pigment-sensitive indices that include bands in the green and blue spectral range. A stratified approach with individual models for pre-segmented low and high forest stands improved the overall performance. The regression models were also tested in a pixel-based analysis. A manual interpretation of the resulting raster map with stress symptom patterns observed in aerial imagery indicated a good match. With bandwidths of 10 nm and a maximum number of six bands, the selected index combinations can be used for large-area monitoring on an airborne multispectral sensor. This study establishes the base for a cost-efficient, objective monitoring method for stress symptoms in kauri canopies, suitable to cover large forest areas with an airborne multispectral sensor.

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.

Reflectance Properties of Hemiboreal Mixed Forest Canopies with Focus on Red Edge and Near Infrared Spectral Regions

This study present the results of airborne top-of-canopy measurements of reflectance spectra in the spectral domain of 350–1050 nm over the hemiboreal mixed forest. We investigated spectral transformations that were originally designed for utilization at very different spectral resolutions. We found that the estimates of red edge inflection point by two methods—the linear four-point interpolation approach (S2REP) and searching the maximum of the first derivative spectrum ( D m a x ) according to the mathematical definition of red edge inflection point—were well related to each other but S2REP produced a continuously shifting location of red edge inflection point while D m a x resulted in a discrete variable with peak jumps between fixed locations around 717 nm and 727 nm for forest canopy (the third maximum at 700 nm appeared only in clearcut areas). We found that, with medium high spectral resolution (bandwidth 10 nm, spectral step 3.3 nm), the in-filling of the O 2 -A Fraunhofer line ( F a r e a ) was very strongly related to single band reflectance factor in NIR spectral region ( ρ = 0.91, p < 0.001) and not related to Photochemical Reflectance Index (PRI). Stemwood volume, basal area and tree height of dominant layer were negatively correlated with reflectance factors at both visible and NIR spectral region due to the increase in roughness of canopy surface and the amount of shade. Forest age was best related to single band reflectance at NIR region ( ρ = −0.48, p < 0.001) and the best predictor for allometric LAI was the single band reflectance at red spectral region ( ρ = −0.52, p < 0.001) outperforming all studied vegetation indices. It suggests that Sentinel-2 MSI bands with higher spatial resolution (10 m pixel size) could be more beneficial than increased spectral resolution for monitoring forest LAI and age. The new index R 751 /R 736 originally developed for leaf chlorophyll content estimation, also performed well at the canopy level and was mainly influenced by the location of red edge inflection point ( ρ = 0.99, p < 0.001) providing similar info in a simpler mathematical form and using a narrow spectral region very close to the O 2 -A Fraunhofer line.

Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

In forest management, site index information is essential for planning silvicultural operations and forecasting forest development. Site index is most commonly expressed as the average height of the dominant trees at a certain index age, and can be determined either by photo interpretation, field measurements, or projection of age combined with height estimates from remote sensing. However, recently it has been shown that site index can be accurately predicted from bi-temporal airborne laser scanner (ALS) data. Furthermore, single-time hyperspectral data have also been shown to be correlated to site index. The aim of the current study was to compare the accuracy of modelling site index using (1) data from bi-temporal ALS; (2) single-time hyperspectral data with different types of preprocessing; and (3) combined bi-temporal ALS and single-time hyperspectral data. The period between the ALS acquisitions was 11 years. The preprocessing of the hyperspectral data included an atmospheric correction and/or a normalization of the reflectance. Furthermore, a selection of pixels was carried out based on NDVI and compared to using all pixels. The results showed that bi-temporal ALS data explained about 70% (R2) of the variation in the site index, and the RMSE values from a cross-validation were 3.0 m and 2.2 m for spruce- and pine-dominated plots, respectively. Corresponding values for the different single-time hyperspectral datasets were 54%, 3.9 m, and 2.5 m. With bi-temporal ALS data and hyperspectral data used in combination, the results indicated that the contribution from the hyperspectral data was marginal compared to just using bi-temporal ALS. We also found that models constructed with normalized hyperspectral data produced lower RMSE values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels.

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data

Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen’s kappa (κ) = 0.909). The highest user’s and producer’s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% κ = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns.