Remote exploration and monitoring of geothermal sources: A novel method for foliar element mapping using hyperspectral (VNIR-SWIR) remote sensing

Simple item page
dc.citation.volume111
dc.contributor.authorRodriguez-Gomez C
dc.contributor.authorKereszturi G
dc.contributor.authorJeyakumar P
dc.contributor.authorPullanagari R
dc.contributor.authorReeves R
dc.contributor.authorRae A
dc.contributor.authorProcter JN
dc.date.accessioned2024-06-21T01:36:02Z
dc.date.available2024-06-21T01:36:02Z
dc.date.issued2023-06
dc.description.abstractHyperspectral remote sensing is an emerging technique to develop new cost- and time-effective geophysical mapping methods. To overcome challenges introduced by plant cover in geothermal systems globally, we hypothesise that foliage can be used as a proxy to map underlying surface geothermal activity and heat-flux due to their capability on elemental uptake from geothermal fluids and host rock/soil. This study shows for the first time that foliar elemental mapping can be used to image geothermal systems using both high-resolution airborne and satellite hyperspectral images. This study has specifically targeted kanuka shrub (kunzea ericoides var. microflora) as a proxy media to develop air- and spaceborne hyperspectral solutions to monitor inaccessible, biologically and culturally sensitive geothermal areas. Using high resolution airborne AisaFENIX and PRISMA hyperspectral data, foliar element maps for Ag, As, Ba and Sb have been developed using Kernel Partial Least Squares Regression and Random Forest classification models to track their foliar distribution and develop a conceptual model for metal and thermal induced changes in plants. Our study shows evidence that the created foliar element maps are in concordance with independent LiDAR-retrieved canopy structure and height as well as temperature effects of the underlying geothermal field. This study has proven air- and spaceborne hyperspectral sensors can indeed capture critical information within the VNIR and SWIR regions (e.g. ∼452, ∼500, ∼670, ∼820, ∼970, ∼1180, ∼1400 and ∼2000 nm) that can be used to identify metal and thermal-induced spectral changes in plants reliably (overall accuracy of 0.41–0.66) with remotely sensed imagery. Our non-invasive method using hyperspectral remote sensing can complement existing practices for exploration and management of renewable geothermal resources through timely monitoring from air- and spaceborne platforms.
dc.description.confidentialfalse
dc.edition.editionJune 2023
dc.identifier.citationRodriguez-Gomez C, Kereszturi G, Jeyakumar P, Pullanagari R, Reeves R, Rae A, Procter JN. (2023). Remote exploration and monitoring of geothermal sources: A novel method for foliar element mapping using hyperspectral (VNIR-SWIR) remote sensing. Geothermics. 111.
dc.identifier.doi10.1016/j.geothermics.2023.102716
dc.identifier.eissn1879-3576
dc.identifier.elements-typejournal-article
dc.identifier.issn0375-6505
dc.identifier.number102716
dc.identifier.piiS0375650523000706
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69958
dc.languageEnglish
dc.publisherElsevier Ltd
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0375650523000706
dc.relation.isPartOfGeothermics
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectGeothermal
dc.subjectHyperspectral
dc.subjectFoliar element mapping
dc.subjectRandom forest
dc.subjectPartial least squares regression
dc.subjectImage classification
dc.subjectLiDAR
dc.titleRemote exploration and monitoring of geothermal sources: A novel method for foliar element mapping using hyperspectral (VNIR-SWIR) remote sensing
dc.typeJournal article
pubs.elements-id460915
pubs.organisational-groupOther
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Published version.pdf
Size:
40.77 MB
Format:
Adobe Portable Document Format
Description:
460915 PDF.pdf
Download
Thumbnail Image
Name:
Evidence.docx
Size:
18.14 MB
Format:
Microsoft Word XML
Description:
1-s2.0-S0375650523000706-mmc1.docx
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
9.22 KB
Format:
Plain Text
Description:
Download
Collections
Journal Articles