Remotely sensed carbon content: The role of tree composition and tree diversity

Wallis, Christine I.B.; Crofts, Anna L.; Inamdar, Deep; Arroyo‐Mora, J. Pablo; Kalácska, Margaret; Laliberté, Étienne; Vellend, Mark

doi:10.1016/j.rse.2022.113333

Cited by 11 publications

(24 citation statements)

References 80 publications

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“…These findings indicate that hyperspectral imaging techniques can accurately predict ANPP and successfully characterize the variations in ANPP across a wide range of spatial regions. This study also demonstrates that hyperspectral remote‐sensing imagery has great potential for estimating plant community carbon and nitrogen productivity, both traits being critical proxies of ecosystem function in situ (Bennett et al, 2009; Homolová et al, 2014; Liu et al, 2023; Wallis et al, 2023). The canopy‐level reflectance indices examined in this study can feasibly be used to track small changes in plant community traits, including changes in carbon and nitrogen concentration (Figures 1–3).…”

Section: Discussionmentioning

confidence: 68%

“…In this study, we accurately estimated plant community traits using hyperspectral imaging data at the canopy level. To some degree, our findings effectively address the mismatching of spatial scales for traditional traits between the leaf level and the community scale using hyperspectral imagery (Pullanagari et al, 2021;Liu et al, 2023;Wallis et al, 2023). Zhang et al (2014) accurately predicted plant cover (with an R 2 value of 0.64) using NDSVI and EVI data derived from a GER3700 spectroradiometer based on 61 wellreplicated field measurements across a large spatial area in Inner Mongolia.…”

Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 98%

“…Compared to traditional multispectral broadband remote sensing, hyperspectral remote sensing greatly improves the spectral resolution and estimation accuracy for key vegetation parameters (e.g., Darvishzadeh et al, 2008; Zhang et al, 2010; Gao et al, 2019; Hank et al, 2019). Imaging spectroscopy data can be used to quantify the photosynthetic and carbon sequestration capacities of vegetation, providing effective estimates of many plant traits that are challenging and time‐consuming to measure (Doughty et al, 2017; Wallis et al, 2023). Satellite remote‐sensing techniques have been widely used to estimate and quantify vegetation properties at the regional to global scales (e.g., Yue et al, 2021; Chlus and Townsend, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Predicting community traits along an alpine grassland transect using field imaging spectroscopy

Zhang,

Wu,

et al. 2023

JIPB

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Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change. Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems, although it remains to be tested in areas with dwarf and sparse vegetation, such as the Tibetan Plateau. We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect. We estimated community structural and functional traits, as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices (VIs) derived from hyperspectral images. We quantified the contributions of environmental drivers, VIs, and community traits to community function by structural equation modelling (SEM). Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index, enhanced vegetation index, and simple ratio. SEM showed that VIs and community traits positively affected community function, whereas environmental drivers and specific leaf area had the opposite effect. Additionally, VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits. This study demonstrates that community‐level spectral reflectance will help scale plant trait information measured at the leaf level to larger‐scale ecological processes. Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change.This article is protected by copyright. All rights reserved.

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Section: Discussionmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Predicting community traits along an alpine grassland transect using field imaging spectroscopy

Zhang,

Wu,

et al. 2023

JIPB

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“…As a result of these needs, measuring individual trees using airborne sensors is becoming a key method for forest analysis and carbon calculations (Wallis et al 2023, Tucker et al 2023). Recent open data collection efforts by the National Ecological Observatory Network (NEON) provides an opportunity to advance our regional scale understanding of forests by providing open-access, high resolution remote sensing data over 10,000s of hectares.…”

Section: Introductionmentioning

confidence: 99%

Individual canopy tree species maps for the National Ecological Observatory Network

Weinstein,

Marconi,

Zare

et al. 2023

Preprint

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The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales allows an unprecedented view of forest ecosystems, forest restoration and responses to disturbance. To create detailed maps of tree species, airborne remote sensing can cover areas containing millions of trees at high spatial resolution. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual tree species using ground truthed data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees for 24 sites in the National Ecological Observatory Network. Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1km^2 shapefiles with individual tree species prediction, as well as crown location, crown area and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of six species per site, ranging from 3 to 15 species. All predictions were uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. These data can be used to study forest macro-ecology, functional ecology, and responses to anthropogenic change.

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Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis

Crofts,

Wallis,

St‐Jean

et al. 2024

Ecological Monographs

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Imaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.

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Remotely sensed carbon content: The role of tree composition and tree diversity

Cited by 11 publications

References 80 publications

Predicting community traits along an alpine grassland transect using field imaging spectroscopy

Predicting community traits along an alpine grassland transect using field imaging spectroscopy

Individual canopy tree species maps for the National Ecological Observatory Network

Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis

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