The Impact of Mānuka-Dominated Riparian Vegetation on Lake Water Quality: A Multisource Remote Sensing Approach

Simna Rassak; Alvaro Orsi; Albert Bifet; María Jesús Gutiérrez Ginés; Kristin Bohm; Kevin I.Kai Wang; Akshat Bisht

doi:10.1109/LGRS.2025.3584674

The Impact of Mānuka-Dominated Riparian Vegetation on Lake Water Quality: A Multisource Remote Sensing Approach

Simna Rassak
, Alvaro Orsi
, Albert Bifet
, María Jesús Gutiérrez Ginés
, Kristin Bohm
, Kevin I.Kai Wang
, Akshat Bisht

Institute of Environmental Science And Research(ESR)
University of Waikato
University of Auckland

Research output: Contribution to journal › Article › peer-review

Abstract

Mānuka trees in riparian plantings along lake banks can enhance water quality and ecosystem resilience. This study uses multisource remote sensing data from an experimental Mānuka plot in the Lake Waikare catchment to assess their role in mitigating pollution and climate change effects. Soil moisture sensors were deployed in both Mānuka and non-Mānuka areas to compare soil moisture, water retention, and soil loss (SL). The proposed soil moisture prediction model achieved high accuracy R² value of 0.88. Results showed that Mānuka riparian areas had 53% lower soil moisture than nonriparian areas, and vegetation indices (VIs) exhibited significant differences between plots. Furthermore, the riparian Mānuka plot reduced SL by 65% compared with nonriparian areas. These findings highlight the potential of Mānuka trees in riparian zones to enhance soil stability, reduce erosion, and support ecosystem resilience.

Original language	English
Article number	5506905
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	22
DOIs	https://doi.org/10.1109/LGRS.2025.3584674
Publication status	Published - 1 Jan 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

Machine learning
remote sensing
soil loss (SL)
soil moisture prediction
soil water retention
vegetation indices (VIs)

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10.1109/LGRS.2025.3584674

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title = "The Impact of Mānuka-Dominated Riparian Vegetation on Lake Water Quality: A Multisource Remote Sensing Approach",

abstract = "Mānuka trees in riparian plantings along lake banks can enhance water quality and ecosystem resilience. This study uses multisource remote sensing data from an experimental Mānuka plot in the Lake Waikare catchment to assess their role in mitigating pollution and climate change effects. Soil moisture sensors were deployed in both Mānuka and non-Mānuka areas to compare soil moisture, water retention, and soil loss (SL). The proposed soil moisture prediction model achieved high accuracy R2 value of 0.88. Results showed that Mānuka riparian areas had 53\% lower soil moisture than nonriparian areas, and vegetation indices (VIs) exhibited significant differences between plots. Furthermore, the riparian Mānuka plot reduced SL by 65\% compared with nonriparian areas. These findings highlight the potential of Mānuka trees in riparian zones to enhance soil stability, reduce erosion, and support ecosystem resilience.",

keywords = "Machine learning, remote sensing, soil loss (SL), soil moisture prediction, soil water retention, vegetation indices (VIs)",

author = "Simna Rassak and Alvaro Orsi and Albert Bifet and Gin{\'e}s, \{Mar{\'i}a Jes{\'u}s Guti{\'e}rrez\} and Kristin Bohm and Wang, \{Kevin I.Kai\} and Akshat Bisht",

note = "Publisher Copyright: {\textcopyright} 2004-2012 IEEE.",

year = "2025",

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day = "1",

doi = "10.1109/LGRS.2025.3584674",

language = "English",

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T1 - The Impact of Mānuka-Dominated Riparian Vegetation on Lake Water Quality

T2 - A Multisource Remote Sensing Approach

AU - Rassak, Simna

AU - Orsi, Alvaro

AU - Bifet, Albert

AU - Ginés, María Jesús Gutiérrez

AU - Bohm, Kristin

AU - Wang, Kevin I.Kai

AU - Bisht, Akshat

PY - 2025/1/1

Y1 - 2025/1/1

N2 - Mānuka trees in riparian plantings along lake banks can enhance water quality and ecosystem resilience. This study uses multisource remote sensing data from an experimental Mānuka plot in the Lake Waikare catchment to assess their role in mitigating pollution and climate change effects. Soil moisture sensors were deployed in both Mānuka and non-Mānuka areas to compare soil moisture, water retention, and soil loss (SL). The proposed soil moisture prediction model achieved high accuracy R2 value of 0.88. Results showed that Mānuka riparian areas had 53% lower soil moisture than nonriparian areas, and vegetation indices (VIs) exhibited significant differences between plots. Furthermore, the riparian Mānuka plot reduced SL by 65% compared with nonriparian areas. These findings highlight the potential of Mānuka trees in riparian zones to enhance soil stability, reduce erosion, and support ecosystem resilience.

AB - Mānuka trees in riparian plantings along lake banks can enhance water quality and ecosystem resilience. This study uses multisource remote sensing data from an experimental Mānuka plot in the Lake Waikare catchment to assess their role in mitigating pollution and climate change effects. Soil moisture sensors were deployed in both Mānuka and non-Mānuka areas to compare soil moisture, water retention, and soil loss (SL). The proposed soil moisture prediction model achieved high accuracy R2 value of 0.88. Results showed that Mānuka riparian areas had 53% lower soil moisture than nonriparian areas, and vegetation indices (VIs) exhibited significant differences between plots. Furthermore, the riparian Mānuka plot reduced SL by 65% compared with nonriparian areas. These findings highlight the potential of Mānuka trees in riparian zones to enhance soil stability, reduce erosion, and support ecosystem resilience.

KW - Machine learning

KW - remote sensing

KW - soil loss (SL)

KW - soil moisture prediction

KW - soil water retention

KW - vegetation indices (VIs)

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DO - 10.1109/LGRS.2025.3584674

M3 - Article

AN - SCOPUS:105009973336

SN - 1545-598X

VL - 22

JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

M1 - 5506905

ER -

The Impact of Mānuka-Dominated Riparian Vegetation on Lake Water Quality: A Multisource Remote Sensing Approach

Abstract

UN SDGs

Keywords

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