東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Remote Sensing of Aboveground and Be...

Hamer, Alexandra H.

Linked to FindBook

Google Book

Amazon

博客來

Remote Sensing of Aboveground and Belowground Function in Grassland Ecosystems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Remote Sensing of Aboveground and Belowground Function in Grassland Ecosystems./
Author:	Hamer, Alexandra H.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	201 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Fungi. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31040119
ISBN:	9798382636542

Remote Sensing of Aboveground and Belowground Function in Grassland Ecosystems.
Hamer, Alexandra H.

Remote Sensing of Aboveground and Belowground Function in Grassland Ecosystems. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 201 p.

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2023.

Grasslands are one of the most extensive biomes on Earth, are integral to global biogeochemical cycling, and are directly depended upon by human societies for food and fuel. Important functions of grassland ecosystems include carbon storage, biomass production and nutrient cycling, which are driven by closely interlinked grassland plant and soil communities. Measuring and monitoring these communities across large spatial extents is vital in order to understand and predict the impacts of climate and land-use changes which affect grasslands currently and are forecast into the future. However, current field-based methods are intensive and time-consuming to carry out, and therefore limited in their spatial and temporal coverage. Remote sensing technologies including imagery from airborne and satellite platforms have transformed capabilities to measure the biosphere at large scales. However, grassland ecosystems are under-represented in remote sensing research, and the application of remote sensing to belowground ecosystem functions remains relatively unexplored. The aim of this thesis is to investigate how remote sensing data can be used to retrieve important information about the above- and belowground portions of grassland ecosystems, across large spatial extents. This aim is achieved by pairing in-situ soil and plant community data from large-scale ecological monitoring networks with hyperspectral and multispectral imagery from airborne and satellite platforms, to answer three specific research objectives. 1) to predict variation in soil microbial communities from airborne hyperspectral imagery, 2) to retrieve community-level plant functional traits from multispectral satellite data and 3) to predict ecosystem multifunctionality using long-term (decadal) timeseries of satellite-derived vegetation indices. All objectives are addressed using observations from varied natural grassland communities distributed at continental and global scales. The results of the three research chapters demonstrate that soil microbial communities, in particular microbial community structure, can be retrieved with high accuracy (up to R2 0.68, NRMSE 9%) from hyperspectral sensing. Satellite multispectral data was able to retrieve variation in 13 out of 20 foliar traits, in some cases with comparable accuracy to recent hyperspectral mapping (up to R 20.76, NRMSE 12%). The ability of satellite systems to facilitate a multitemporal perspective on the ecosystem was valuable for predicting ecosystem multifunctionality, where past vegetation dynamics and stability have important legacy effects on contemporary functioning. Overall, the thesis shows that remote sensing technologies have the potential to contribute to understanding of above- and belowground grassland functions by capturing ecological properties of the vegetation surface which are not able to be measured in the field, and by expanding the spatial and temporal scales across which relationships can be found. Remote sensing should be incorporated into the design of future large-scale ecological monitoring efforts, with a view to understanding and preserving grassland ecosystem functions for the future.

ISBN: 9798382636542Subjects--Topical Terms:

571472
Fungi.

Remote Sensing of Aboveground and Belowground Function in Grassland Ecosystems.
LDR:04416nmm a2200397 4500 001 2401443
005 20241022112617.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798382636542
035 $a (MiAaPQ)AAI31040119
035 $a (MiAaPQ)Manchester_UKd5934d59-026f-4539-9e3f-9b6c37065ce4
035 $a AAI31040119
035 $a 2401443
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hamer, Alexandra H. $3 3771537
245 1 0 $a Remote Sensing of Aboveground and Belowground Function in Grassland Ecosystems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 201 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Bardgett, Richard;Allott, Timothy;Harris, Angela.
502 $a Thesis (Ph.D.)--The University of Manchester (United Kingdom), 2023.
520 $a Grasslands are one of the most extensive biomes on Earth, are integral to global biogeochemical cycling, and are directly depended upon by human societies for food and fuel. Important functions of grassland ecosystems include carbon storage, biomass production and nutrient cycling, which are driven by closely interlinked grassland plant and soil communities. Measuring and monitoring these communities across large spatial extents is vital in order to understand and predict the impacts of climate and land-use changes which affect grasslands currently and are forecast into the future. However, current field-based methods are intensive and time-consuming to carry out, and therefore limited in their spatial and temporal coverage. Remote sensing technologies including imagery from airborne and satellite platforms have transformed capabilities to measure the biosphere at large scales. However, grassland ecosystems are under-represented in remote sensing research, and the application of remote sensing to belowground ecosystem functions remains relatively unexplored. The aim of this thesis is to investigate how remote sensing data can be used to retrieve important information about the above- and belowground portions of grassland ecosystems, across large spatial extents. This aim is achieved by pairing in-situ soil and plant community data from large-scale ecological monitoring networks with hyperspectral and multispectral imagery from airborne and satellite platforms, to answer three specific research objectives. 1) to predict variation in soil microbial communities from airborne hyperspectral imagery, 2) to retrieve community-level plant functional traits from multispectral satellite data and 3) to predict ecosystem multifunctionality using long-term (decadal) timeseries of satellite-derived vegetation indices. All objectives are addressed using observations from varied natural grassland communities distributed at continental and global scales. The results of the three research chapters demonstrate that soil microbial communities, in particular microbial community structure, can be retrieved with high accuracy (up to R2 0.68, NRMSE 9%) from hyperspectral sensing. Satellite multispectral data was able to retrieve variation in 13 out of 20 foliar traits, in some cases with comparable accuracy to recent hyperspectral mapping (up to R 20.76, NRMSE 12%). The ability of satellite systems to facilitate a multitemporal perspective on the ecosystem was valuable for predicting ecosystem multifunctionality, where past vegetation dynamics and stability have important legacy effects on contemporary functioning. Overall, the thesis shows that remote sensing technologies have the potential to contribute to understanding of above- and belowground grassland functions by capturing ecological properties of the vegetation surface which are not able to be measured in the field, and by expanding the spatial and temporal scales across which relationships can be found. Remote sensing should be incorporated into the design of future large-scale ecological monitoring efforts, with a view to understanding and preserving grassland ecosystem functions for the future.
590 $a School code: 1543.
650 4 $a Fungi. $3 571472
650 4 $a Flowers & plants. $3 3564028
650 4 $a Seasons. $3 523975
650 4 $a Bacteria. $3 550366
650 4 $a Color. $3 533870
650 4 $a Phenology. $3 578250
650 4 $a Biomass. $3 1013462
650 4 $a Ecosystems. $3 595401
650 4 $a Landsat satellites. $3 3695193
650 4 $a Ecology. $3 516476
650 4 $a Vegetation. $3 3560047
650 4 $a Precipitation. $3 3680799
650 4 $a Remote sensing. $3 535394
650 4 $a Spectrum analysis. $3 520440
650 4 $a Carbon. $3 604057
650 4 $a Environmental monitoring. $3 542712
650 4 $a Grasslands. $3 650086
650 4 $a Nutrients. $3 3562665
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Analytical chemistry. $3 3168300
650 4 $a Biology. $3 522710
650 4 $a Botany. $3 516217
650 4 $a Optics. $3 517925
690 $a 0329
690 $a 0799
690 $a 0538
690 $a 0486
690 $a 0306
690 $a 0309
690 $a 0752
710 2 $a The University of Manchester (United Kingdom). $3 3422292
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 1543
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31040119