Multi-angular reflectance spectra of small single trees

Forsström, Petri; Hovi, Aarne; Ghielmetti, Giulia; Schaepman, Michael E.; Rautiainen, Miina

doi:10.1016/j.rse.2021.112302

Cited by 12 publications

(5 citation statements)

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“…Spectra of four tree species could be compared with previous studies and were noted to have similar shape and level of reflectance: silver birch (Juola et al, 2020; Lang et al, 2002), Norway spruce (Forsström et al, 2021; Juola et al, 2020; Lang et al, 2002; Williams, 1991), Scots pine (Forsström et al, 2021; Juola et al, 2020; Lang et al, 2002), and black alder (Clasen et al, 2015; Kuusk et al, 2013). Notably, VIS–NIR stem bark spectra of littleleaf linden, English oak, European ash, gray alder, Norway maple, and European aspen have not been previously published.…”

Section: Discussionmentioning

confidence: 97%

“…However, there are considerable differences in bark spectra depending on tree species (Hadlich et al, 2018), bark moisture content (Elvidge, 1990), or position of the woody material in the canopy (Juola et al, 2020). The majority of previous studies that analyzed the variability of bark spectra were based on measurements of detached pieces of bark, which were conducted in the laboratory with a spectrometer and an integrating sphere (Asner, 1998; Campbell & Borden, 2005; Forsström et al, 2021; Roberts et al, 2004; Williams, 1991), whereas less research has been conducted outdoors for bark on living trees (Girma et al, 2013; Hadlich et al, 2018). Better understanding of bark optical properties in their natural state and environment would provide valuable and complementary knowledge to studies conducted in the laboratory with destructive sampling that require the cutting of branches or felling of trees.…”

Section: Introductionmentioning

confidence: 99%

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

Juola

Hovi

Rautiainen

2022

Ecology and Evolution

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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.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

Juola

Hovi

Rautiainen

2022

Ecology and Evolution

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“…In addition, the reflectance of vegetatio in leaf-off seasons was considerably lower than that in leaf-on seasons, and the reflectan of the green band (band 3) was approximately 10% in leaf-on seasons and <10% in leaf-o seasons. Our results demonstrate that the synergetic use of leaf-on and -off imageries ma provide more spectral information for LCZ mapping and can help in the identification LCZ se ings [111,112]. Figure 16 compares LCZ classification results using leaf-on imageries, leaf-off imageries, and integrating leaf-on and leaf-off imageries.…”

Section: Synergetic Use Of Leaf-on and -Off Imageriesmentioning

confidence: 89%

Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data

Wang

Cao

et al. 2023

Remote Sensing

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Accurate, rapid, and automatic local climate zone (LCZ) mapping is essential for urban climatology and studies in terms of urban heat islands. Remotely sensed imageries incorporated with machine learning algorithms are widely utilized in LCZ labeling. Nevertheless, large-scale LCZ mapping is still challenging due to the complex vertical structure of underlying urban surfaces. This study proposed a new method of LCZ labeling that uses a random forest classifier and multi-source remotely sensed data, including Sentinel 1A Synthetic Aperture Radar (SAR), Sentinel 2 Multispectral Instrument, and Luojia1-01 night-time light data. In particular, leaf-on and -off imageries and surface thermal dynamics were utilized to enhance LCZ labeling. Additionally, we systematically evaluated how daytime and night-time features influence the performance of the classification procedure. Upon examination, the results for Beijing, China, were confirmed to be robust and refined; the Overall Accuracy (OA) value of the proposed method was 88.86%. The accuracy of LCZs 1–9 was considerably increased when using the land surface temperature feature. Among these, the Producer Accuracy (PA) value of LCZ 3 (compact low-rise) significantly increased by 16.10%. Notably, it was found that NTL largely contributed to the classification concerning LCZ 3 (compact low-rise) and LCZ A/B (dense trees). The performance of integrating leaf-on and -off imageries for LCZ labeling was better than merely uses of leaf-on or -off imageries (the OA value increased by 4.75% compared with the single use of leaf-on imagery and by 3.62% with that of leaf-off imagery). Future studies that use social media big data and Very-High-Resolution imageries are required for LCZ mapping. This study shows that combining multispectral, SAR, and night-time light data can improve the performance of the random forest classifier in general, as these data sources capture significant information about surface roughness, surface thermal feature, and night-time features. Moreover, it is found that incorporating both leaf-on and leaf-off remotely sensed imageries can improve LCZ mapping.

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“…spatial arrangement of trees) and multiple scattering driven by vegetation structure as well as mutual shadowing between trees and the fraction of soil (Li and Strahler, 1992, Zeng et al, 2009). This may lead to reflectance differences between data from tree crowns and vegetation canopies (Forsström et al, 2021), thus influencing the distribution of hyperspectral reflectance-based physiological traits. The availability of sub-meter resolution imagery and LiDAR data allows a better characterization of target trees, removing shadow and background contributions (Asner et al, 2007), and capturing the spectral variability within and among crowns.…”

Section: Discussionmentioning

confidence: 99%

Individual tree-based vs pixel-based approaches to mapping forest functional traits and diversity by remote sensing

Zheng

Zeng

Schuman

et al. 2022

Preprint

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Trait-based approaches, focusing on the functional characteristics of vascular plants in a community, have been increasingly used in plant ecology and biodiversity research. Compared with traditional field survey (which typically samples individual trees), remote sensing enables quantifying functional traits over large contiguous areas, but assigning trait values to biological units such as species and individuals is difficult with pixel-based approaches. We used a subtropical forest landscape in China to compare an approach based on LiDAR-delineated individual tree crowns (ITCs) with a pixel-based approach for assessing functional traits from remote sensing data. We compared trait distributions, trait–trait relationships and functional diversity metrics obtained by the two approaches at changing grain and extent. We found that morphological traits derived from airborne laser scanning showed more differences between ITC- and pixel-based approaches than physiological traits estimated by imaging spectroscopy data. Pixel sizes approximating average tree crowns yielded similar results as ITCs, but 95th quantile height and foliage height diversity tended to be overestimated and leaf area index underestimated relative to ITC-based values. With increasing pixel size, the differences to ITC-based trait values became larger and less trait variance was captured, indicating information loss. The consistency of ITC- and pixel-based functional richness measures also decreased with increasing pixel grain, and changed with the observed extent for functional diversity monitoring. We conclude that whereas ITC-based approaches in principle allow partitioning of variation between individuals, genotypes and species, at high resolution, pixel-based approaches come close to this and can be suitable for assessing ecosystem-scale trait variation by weighting individuals and species according to coverage.

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Multi-angular reflectance spectra of small single trees

Cited by 12 publications

References 70 publications

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

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

Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data

Individual tree-based vs pixel-based approaches to mapping forest functional traits and diversity by remote sensing

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