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Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we asses...

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Autores principales: Salehi Hikouei, Iman, Kim, S. Sonny, Mishra, Deepak R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271383/
https://www.ncbi.nlm.nih.gov/pubmed/34199102
http://dx.doi.org/10.3390/s21134408
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author Salehi Hikouei, Iman
Kim, S. Sonny
Mishra, Deepak R.
author_facet Salehi Hikouei, Iman
Kim, S. Sonny
Mishra, Deepak R.
author_sort Salehi Hikouei, Iman
collection PubMed
description Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm(3)) or high (0.752 g/cm(3) to 1.893 g/cm(3)) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.
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spelling pubmed-82713832021-07-11 Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments Salehi Hikouei, Iman Kim, S. Sonny Mishra, Deepak R. Sensors (Basel) Article Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm(3)) or high (0.752 g/cm(3) to 1.893 g/cm(3)) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices. MDPI 2021-06-27 /pmc/articles/PMC8271383/ /pubmed/34199102 http://dx.doi.org/10.3390/s21134408 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Salehi Hikouei, Iman
Kim, S. Sonny
Mishra, Deepak R.
Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments
title Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments
title_full Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments
title_fullStr Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments
title_full_unstemmed Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments
title_short Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments
title_sort machine-learning classification of soil bulk density in salt marsh environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271383/
https://www.ncbi.nlm.nih.gov/pubmed/34199102
http://dx.doi.org/10.3390/s21134408
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