Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy

Seeley, Megan; Vaughn, Nicholas R.; Shanks, B.; Martin, Robert E.; König, Marcel; Asner, Gregory P.

doi:10.20944/preprints202307.1570.v1

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…After individual tree crowns have been delineated, the next step towards airborne forest inventories is to assign each crown a taxonomic label [ 17 ]. Dozens of models have been proposed using classical image processing [ 18 ], feature-based machine learning [ 19 , 20 ], and deep learning [ 21 – 23 ] but it is unclear if they are successful when applied to a variety of ecosystems with differences in tree density, abundance distributions, and spectral backgrounds. Given the very low sample sizes of training data in most studies, it is difficult to capture the range of species present and the spectral representations for each species.…”

Section: Introductionmentioning

confidence: 99%

Individual canopy tree species maps for the National Ecological Observatory Network

Weinstein,

Marconi,

Zare

et al. 2024

PLoS Biol

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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 provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. 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 canopy tree species through labeled 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 across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 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 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling.

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Section: Introductionmentioning

confidence: 99%

Individual canopy tree species maps for the National Ecological Observatory Network

Weinstein,

Marconi,

Zare

et al. 2024

PLoS Biol

View full text Add to dashboard Cite

show abstract

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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Large-Scale Controls on the Leaf Economic Spectrum of the Overstory Tree Species Metrosideros polymorpha

Seeley,

Asner

2023

Remote Sensing

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The role of intraspecific trait variation in functional ecology has gained traction in recent years as many papers have observed its importance in driving community diversity and ecology. Yet much of the work in this field relies on field-based trait surveys. Here, we used continuous canopy trait information derived from remote sensing data of a highly polymorphic tree species, Metrosideros polymorpha, to quantify environmental controls on intraspecific trait variation. M. polymorpha, an endemic, keystone tree species in Hawai’i, varies morphologically, chemically, and genetically across broad elevation and soil substrate age gradients, making it an ideal model organism to explore large-scale environmental drivers of intraspecific trait variation. M. polymorpha canopy reflectance (visible to shortwave infrared; 380–2510 nm) and light detection and ranging (LiDAR) data collected by the Global Airborne Observatory were modeled to canopy trait estimates of leaf mass per area, chlorophyll a and b, carotenoids, total carbon, nitrogen, phosphorus, phenols, cellulose, and top of canopy height using previously developed leaf chemometric equations. We explored how these derived traits varied across environmental gradients by extracting elevation, slope, aspect, precipitation, and soil substrate age data at canopy locations. We then obtained the feature importance values of the environmental factors in predicting each leaf trait by training random forest models to predict leaf traits individually. Of these environmental factors, elevation was the most important predictor for all canopy traits. Elevation not only affected canopy traits directly but also indirectly by influencing the relationships between soil substrate age and canopy traits as well as between nitrogen and other traits, as indicated by the change in slope between the variables at different elevation ranges. In conclusion, intraspecific variation in M. polymorpha traits derived from remote sensing adheres to known leaf economic spectrum (LES) patterns as well as interspecific LES traits previously mapped using imaging spectroscopy.

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Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy

Cited by 4 publications

References 45 publications

Individual canopy tree species maps for the National Ecological Observatory Network

Individual canopy tree species maps for the National Ecological Observatory Network

Individual canopy tree species maps for the National Ecological Observatory Network

Large-Scale Controls on the Leaf Economic Spectrum of the Overstory Tree Species Metrosideros polymorpha

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