•Seasonal trajectories of nine bands and six indices from Sentinel-2 were analysed.•Seasonal change values were better for early detection than absolute reflectance.•Tasselled Cap Wetness and ...SWIR-based NDVI were most successful.•Separation of green-attack trees was best in autumn before next beetle generation.
In the past decade, massive outbreaks of bark beetles (Ips spp.) have caused large-scale decline of coniferous-dominated, prevailingly managed forests of Central Europe. Timely detection of newly infested trees is important for minimizing economic losses and effectively planning forest management activities to stop or at least slow outbreaks. With the advancement of Copernicus services, a pair of Sentinel-2 satellites provides a unique remote sensing data source of multi-temporal observations in high spatial resolution on the scale of individual forest stands (although not allowing for individual tree detection). This study investigates the potential for using seasonal trajectories of Sentinel-2 bands and selected vegetation indices in early detection of bark beetle infestation (so–called green-attack stage detection) in Norway spruce monoculture forests in the Czech Republic. Spectral trajectories of nine bands and six vegetation indices were constructed for the 2018 vegetation season using 14 satellite observations from April to November to distinguish four infestation classes. We used a random forest algorithm to classify healthy (i.e., stands not infested) and infested trees with various trajectories of decay. The seasonal trajectories of vegetation indices separated the infestation classes better than did the individual bands. Among the most promising vegetation indices we identified the tasselled cap wetness (TCW) component and normalized difference index constructed from near and shortwave infrared bands. Analysing the inter-annual change of the indices was more promising for early detection than is single-date classification. It achieved 96% classification accuracy on day of year 291 for the tested data set.The algorithm for early detection of tree infestation based on the assessment of seasonal changes in TCW was applied on the time series of Sentinel-2 observations from 2019 and its outputs were verified using field observations of forest conditions conducted on 80 spruce forest plots (located in spruce monoculture stands). The overall accuracy of 78% was achieved for the separation of healthy and green-attack classes. Our study highlights the great potential of multi-temporal remote sensing and the use of shortwave infrared wavelengths for early detection of spruce forest decline caused by bark beetle infestation.
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•Field survey, phenology model, airborne hyperspectral remote sensing were combined.•Remote sensing method using VNIR identified infested trees later than field survey.•Red-edge based indices (REIP, ...ANCB) were more sensitive during early infestation.
Detection in the early phase of bark beetle infestation is a vital task for proactive management strategies, as practiced in most Central European forests, to minimize economic losses due to bark beetle infestation and to mitigate their further spreading. For this work, remote sensing methods are coming to be in great demand as an objective approach to enable monitoring bark beetle infestation even at individual tree level.
This case study monitored bark beetle (Ips typographus) activity at local level in Norway spruce forest in the Czech Republic. The main aim of this study was to compare the remote sensing methods against classical field survey conducted by forest workers in detecting newly infested trees.
To compare these two methods, an extensive field and aerial campaign was conducted in the southern part of the Czech Republic during 2020. Bark beetle infestation was monitored by traditional methods (i.e. field survey) on a weekly basis from mid-March to mid-September. During the same period, aerial scans were performed once per month (seven in total) using a CASI-1500 hyperspectral sensor (visible and near-infrared, 400–1000 nm) with spatial resolution of 0.5 m. This work mapped transition from healthy up to red attack of 75 Norway spruce trees that were infested during the same week. The same number of healthy trees were added to the data set for hyperspectral data analysis. Both groups were analysed by vegetation indices, with emphasis on effect caused in the canopy by bark beetles.
The success rate for bark beetle detection is always associated with acquisition time. In order to define the optimal time for data acquisition, we employed a phenology model for I. typographus (RITY 2.0) to take into consideration bark beetle development.
The results of the experiment showed that classic field survey detected infested trees earlier than did analysis using remote sensing data from the visible and near-infrared region. The difference was 23 days for the most successful indices (i.e. REIP, PRI, and ANCB650–720) in our test. Nevertheless, both methods detected the infested trees within 6 weeks after infestation, which is the recommended period for taking measures to prevent bark beetles from spreading further, and thus hyperspectral imagery can be used as a valid information source for bark beetle detection.
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•We review up-to-date remote sensing methods to estimate important plant traits.•We introduce basic concepts of remote sensing of vegetation and scaling mechanisms.•Plant height and nitrogen are ...retrieved with high accuracy from remote sensing.•Phosphorus and leaf mass per area are retrieved with lower accuracies.
Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity.
In this review, we first introduce RS concepts of light–vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10% and 45% of measured mean value, i.e. around 10% for plant height of tall canopies, 20% for plant height of short canopies, 15% for plant nitrogen, 25% for plant phosphorus content/concentration, and 45% for leaf mass per area estimates.
The potential of RS to map plant traits is particularly high when traits are related to leaf biochemistry, photosynthetic processes and canopy structure. There are also other plant traits, i.e. leaf chlorophyll content, water content and leaf area index, which can be retrieved from optical RS well and can be of importance for plant scientists.
We underline the need that future assessments of ecosystem functioning using RS should require comprehensive and integrated measurements of various plant traits together with leaf and canopy spectral properties. By doing so, the interplay between plant structural, physiological, biochemical, phenological and spectral properties can be better understood.
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Clumping describes the spatial distribution of foliage elements (leaves or needles) within a vegetation canopy. Clumping information is important for determining the radiation transfer through ...canopies, photosynthesis, and hydrological processes. Clumping of needles in shoots in conifer stands has posed a challenge because optical instruments have generally been incapable of measuring gaps between needles within a shoot. Previous methods for estimating the needle-to-shoot-area ratio have had in common destructive and/or highly labor-intensive aspects. We introduce blue light 3D photogrammetry scanning as a highly efficient technique for estimating shoot-level clumping, which significantly reduces the labor intensity aspect of the previous approaches. We validate the approach by comparing it to the combined photographic/volume displacement method – an established methodology for quantifying shoot-level clumping. We used shoots of two species - Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies L. Karst.) - collected from trees in the Järvselja RAdiation transfer Model Intercomparison (RAMI) pine stand in Estonia. The needle-to-shoot area ratio values were similar to those measured using the traditional combined photographic/volume displacement method. The demonstrated effectiveness and performance of the blue light 3D photogrammetry scanning method shall lead to more frequent actual measurements of 3D shoot structures. Growth in knowledge about this most elementary yet often overlooked level of foliage clumping in canopies shall improve coniferous forest 3D radiative transfer modeling.
•First estimation of shoot-level clumping using a high precision 3D scanning.•Good agreement between estimates done with volume displacement method, 3D scanning.•High detail 3D shoot scans available for conifers found in RAMI Järvselja pine stand.
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Coniferous species are present in almost all major vegetation biomes on Earth, though they are the most abundant in the northern hemisphere, where they form the northern tree and forest lines close ...to the Arctic Circle. Monitoring coniferous forests with satellite and airborne remote sensing is active, due to the forests’ great ecological and economic importance. We review the current understanding of spectral behavior of different components forming coniferous forests. We look at the spatial, directional, and seasonal variations in needle, shoot, woody element, and understory spectra in coniferous forests, based on measurements. Through selected case studies, we also demonstrate how coniferous canopy spectra vary at different spatial scales, and in different viewing angles and seasons. Finally, we provide a synthesis of gaps in the current knowledge on spectra of elements forming coniferous forests that could also serve as a recommendation for planning scientific efforts in the future.
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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.
•First extensive empirical evaluation of a forest reflectance model using p-theory.•New parameterization taking into account woody elements and directional scattering.•Uncertainties of the modeled forest reflectance reduced in the near-infrared region.•Forest spectra and their relationships with plant area index simulated correctly.•Based on field and airborne data in 50 forest plots from boreal to temperate biomes.
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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 ( (L.) H. Karst.) needle albedo, reflectance, and transmittance spectra across three study sites covering latitudes of 49â62°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.
Picea abies
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.
•Retrieval of forest floor reflectance from near-nadir remote sensing data.•Using a forest reflectance model based on photon recollision probability.•Good performance in sparse forests, but less accurate in dense forests.•Airborne LiDAR as good as in situ hemispherical photos in providing model input.•Informative maps of forest floor reflectance from PRISMA and Sentinel-2 MSI data.
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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.
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We investigate combined continuum removal and radiative transfer (RT) modeling to retrieve leaf chlorophyll a & b content (Cab) from the AISA Eagle airborne imaging spectrometer data of sub-meter ...(0.4 m) spatial resolution. Based on coupled PROSPECT-DART RT simulations of a Norway spruce (Picea abies (L.) Karst.) stand, we propose a new Cab sensitive index located between 650 and 720nm and termed ANCB650–720. The performance of ANCB650–720 was validated against ground-measured Cab of ten spruce crowns and compared with Cab estimated by a conventional artificial neural network (ANN) trained with continuum removed RT simulations and also by three previously published chlorophyll optical indices: normalized difference between reflectance at 925 and 710nm (ND925&710), simple reflectance ratio between 750 and 710nm (SR750/710) and the ratio of TCARI/OSAVI indices. Although all retrieval methods produced visually comparable Cab spatial patterns, the ground validation revealed that the ANCB650–720 and ANN retrievals are more accurate than the other three chlorophyll indices (R2=0.72 for both methods). ANCB650–720 estimated Cab with an RMSE=2.27μgcm−2 (relative RRMSE=4.35%) and ANN with an RMSE=2.18μgcm−2 (RRMSE=4.18%), while SR750/710 with an RMSE=4.16μgcm−2 (RRMSE=7.97%), ND925&710 with an RMSE=9.07μgcm−2 (RRMSE=17.38%) and TCARI/OSAVI with an RMSE=12.30μgcm−2 (RRMSE=23.56%). Also the systematic RMSES was lower than the unsystematic one only for the ANCB650–720 and ANN retrievals. Our results indicate that the newly proposed index can provide the same accuracy as ANN except for Cab values below 30μgcm−2, which are slightly overestimated (RMSE=2.42μgcm−2). The computationally efficient ANCB650–720 retrieval provides accurate high spatial resolution airborne Cab maps, considerable as a suitable reference data for validating satellite-based Cab products.
► Accurate retrieval of spruce leaf chlorophyll content from airborne spectral images. ► Combination of PROSPECT-DART radiative transfer models with continuum removal. ► New leaf chlorophyll estimating optical index termed ANCB650–720. ► Performance of ANCB650–720 comparable to neural network and other optical indices. ► Potential to validate chlorophyll satellite maps of coarser spatial resolution.
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