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Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

ABSTRACT: Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

SUBMITTER: Pottier J

PROVIDER: S-EPMC4126626 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

Pottier Julien J Malenovský Zbyněk Z Psomas Achilleas A Homolová Lucie L Schaepman Michael E ME Choler Philippe P Thuiller Wilfried W Guisan Antoine A Zimmermann Niklaus E NE

Biology letters 20140701 7

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive t ...[more]

PMID: 25079495

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Project description:BackgroundBiogeographers assess how species distributions and abundances affect the structure, function, and composition of ecosystems. Yet we face a major challenge: it is difficult to precisely map species across landscapes. Novel Earth observations could overcome this challenge for vegetation mapping. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. In this paper, I describe a trait-based approach to species identification with imaging spectroscopy, the Center for Conservation Biology species identification (CCB-ID) method, which was developed as part of an ecological data science evaluation competition.MethodsThese methods were developed using airborne imaging spectroscopy data from the National Ecological Observatory Network (NEON). CCB-ID classified tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method inspired by botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis (PCA) and resampled to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale.ResultsCCB-ID received a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the competition test data. Accuracy and specificity scores were high for all species, but precision and recall scores varied for rare species. PCA transformation improved accuracy scores compared to models trained using reflectance data, but outlier removal and data resampling exacerbated class imbalance problems.DiscussionCCB-ID accurately classified tree species using NEON data, reporting the best scores among participants. However, it failed to overcome several species mapping challenges like precisely identifying rare species. Key takeaways include (1) selecting models using metrics beyond accuracy (e.g., recall) could improve rare species predictions, (2) within-genus trait variation may drive spectral separability, precluding efforts to distinguish between functionally convergent species, (3) outlier removal and data resampling can exacerbate class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) targeted feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support species mapping efforts.

Project description:Evidence of plant root biomass and production in peatlands at the level of species or plant functional type (PFT) is needed for defining ecosystem functioning and predicting its future development. However, such data are limited due to methodological difficulties and the toilsomeness of separating roots from peat. We developed Fourier transform infrared (FTIR) spectroscopy based calibration models for quantifying the mass proportions of several common peatland species, and alternatively, the PFTs that these species represented, in composite root samples. We further tested whether woody roots could be classified into diameter classes, and whether dead and living roots could be separated. We aimed to solve whether general models applicable in different studies can be developed, and what would be the best way to build such models. FTIR spectra were measured from dried and powdered roots: both "pure roots", original samples of 25 species collected in the field, and "root mixtures", artificial composite samples prepared by mixing known amounts of pure roots of different species. Partial least squares regression was used to build the calibration models. The general applicability of the models was tested using roots collected in different sites or times. Our main finding is that pure roots can replace complex mixtures as calibration data. Using pure roots, we constructed generally applicable models for quantification of roots of the main PFTs of northern peatlands. The models provided accurate estimates even for far distant sites, with root mean square error (RMSE) 1.4-6.6% for graminoids, forbs and ferns. For shrubs and trees the estimates were less accurate due to higher within-species heterogeneity, partly related to variation in root diameter. Still, we obtained RMSE 3.9-10.8% for total woody roots, but up to 20.1% for different woody-root types. Species-level and dead-root models performed well within the calibration dataset but provided unacceptable estimates for independent samples, limiting their routine application in field conditions. Our PFT-level models can be applied on roots separated from soil for biomass determination or from ingrowth cores for estimating root production. We present possibilities for further development of species-level or dead-root models using the pure-root approach.

Dataset Information

Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

Publications

Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

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