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Using Leaf Spectra to Elucidate the ...

Quinteros Casaverde, Natalia Leonor,

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Using Leaf Spectra to Elucidate the Taxonomy and Ecology of Living and Dried Plants /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using Leaf Spectra to Elucidate the Taxonomy and Ecology of Living and Dried Plants // Natalia Leonor Quinteros Casaverde.
Author:	Quinteros Casaverde, Natalia Leonor,
Description:	1 electronic resource (120 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
Subject:	Ecology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30818499
ISBN:	9798381380309

Using Leaf Spectra to Elucidate the Taxonomy and Ecology of Living and Dried Plants /
Quinteros Casaverde, Natalia Leonor,

Using Leaf Spectra to Elucidate the Taxonomy and Ecology of Living and Dried Plants /Natalia Leonor Quinteros Casaverde. - 1 electronic resource (120 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

Identifying plants and understanding of changes in plant communities are crucial to the conservation and management of nature. The shortwave spectral reflectance of leaves is a promising tool for rapidly identifying species at different taxonomic ranks and predicting important plant functional traits. However, the spectral reflectance of leaves changes in response to biotic and abiotic conditions.The aim of this dissertation is to investigate the potential of shortwave spectral reflectance to investigate (Chapter 1) how its variation affects the accuracy of methods used to predict plant taxonomies and what environmental factors most influence biophysically predicted traits, (Chapter 2) what are the changes within the spectral signature of leaves after controlled leaf desiccation, and what are the consequences for the prediction of leaf traits through radiative transfer models and inference of leaf-soil relationships, and finally (Chapter 3) how I can predict the past water content using only desiccated leaves, and what can I do to improve the prediction.In Chapter One, I found that the leaf reflectance classification accuracy improved when I used its natural variance in the classification model. I also found that species' relatedness does not influence biophysically predicted traits, and environmental factors affect the biophysically predicted leaf traits in both magnitude and direction. In Chapter Two, I found that the reflectance of leaves changes the most in the NIR wavelengths after leaf desiccation but not that much in known absorption features. I also found that after leaf desiccation, the uncertainties around the prediction of PROSPECT leaf traits such as photosynthetic pigments increased while in phenolics and structural leaf traits decreased, and that PROSPECT traits predicted from dried leaves are better at inferring relationships with the nutritional composition of the rhizosphere. Finally, in Chapter Three, I predicted past water content using the empirically derived traits leaf dry mass per area, nitrogen, and phenolic content, from the spectral reflectance of dry leaves. I observed that some samples had a lower prediction error than others, and I attribute these outcomes to the tightly shared trait space and environmental spaces of these samples compared to the sparsity of the others.In conclusion, I have shown the potential of using the spectral reflectance of leaves in plant taxonomy, ecology, and trait reconstruction by using the spectral reflectance of leaves in fresh and dry states. I believe that further investment in the field of spectral biology will be of great use to aid the quick identification of plants, generate a deeper understanding of their ecologies, and increase the application of herbarium samples in the study of plant life.

English

ISBN: 9798381380309Subjects--Topical Terms:

516476
Ecology.
Subjects--Index Terms:

Functional traits

Using Leaf Spectra to Elucidate the Taxonomy and Ecology of Living and Dried Plants /
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502 $b Ph.D. $c City University of New York $d 2024.
520 $a Identifying plants and understanding of changes in plant communities are crucial to the conservation and management of nature. The shortwave spectral reflectance of leaves is a promising tool for rapidly identifying species at different taxonomic ranks and predicting important plant functional traits. However, the spectral reflectance of leaves changes in response to biotic and abiotic conditions.The aim of this dissertation is to investigate the potential of shortwave spectral reflectance to investigate (Chapter 1) how its variation affects the accuracy of methods used to predict plant taxonomies and what environmental factors most influence biophysically predicted traits, (Chapter 2) what are the changes within the spectral signature of leaves after controlled leaf desiccation, and what are the consequences for the prediction of leaf traits through radiative transfer models and inference of leaf-soil relationships, and finally (Chapter 3) how I can predict the past water content using only desiccated leaves, and what can I do to improve the prediction.In Chapter One, I found that the leaf reflectance classification accuracy improved when I used its natural variance in the classification model. I also found that species' relatedness does not influence biophysically predicted traits, and environmental factors affect the biophysically predicted leaf traits in both magnitude and direction. In Chapter Two, I found that the reflectance of leaves changes the most in the NIR wavelengths after leaf desiccation but not that much in known absorption features. I also found that after leaf desiccation, the uncertainties around the prediction of PROSPECT leaf traits such as photosynthetic pigments increased while in phenolics and structural leaf traits decreased, and that PROSPECT traits predicted from dried leaves are better at inferring relationships with the nutritional composition of the rhizosphere. Finally, in Chapter Three, I predicted past water content using the empirically derived traits leaf dry mass per area, nitrogen, and phenolic content, from the spectral reflectance of dry leaves. I observed that some samples had a lower prediction error than others, and I attribute these outcomes to the tightly shared trait space and environmental spaces of these samples compared to the sparsity of the others.In conclusion, I have shown the potential of using the spectral reflectance of leaves in plant taxonomy, ecology, and trait reconstruction by using the spectral reflectance of leaves in fresh and dry states. I believe that further investment in the field of spectral biology will be of great use to aid the quick identification of plants, generate a deeper understanding of their ecologies, and increase the application of herbarium samples in the study of plant life.
546 $a English
590 $a School code: 0046
650 4 $a Ecology. $3 516476
650 4 $a Plant sciences. $3 3173832
650 4 $a Remote sensing. $3 535394
653 $a Functional traits
653 $a Hyperspectral reflectance
653 $a Leaf spectroscopy
653 $a Plant-soil relationships
653 $a Taxonomy
690 $a 0329
690 $a 0479
690 $a 0799
710 2 $a City University of New York. $b Biology. $e degree granting institution. $3 3770566
720 1 $a Daly, Douglas C. $e degree supervisor.
720 1 $a Serbin, Shawn P. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-07B.
790 $a 0046
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30818499