The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape

The transformation of the natural landscape into an impervious surface due to urbanization has often been considered an important driver of environmental change, affecting essential urban ecological processes and ecosystem services. Continuous forest degradation and deforestation due to urbanization...

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Autores principales:	Mthembeni Mngadi, John Odindi, Onisimo Mutanga
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	reforestation ecosystem services carbon stock random forest Science Q
Acceso en línea:	https://doaj.org/article/52e3560096a64243a59ff029873442ab
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spelling	oai:doaj.org-article:52e3560096a64243a59ff029873442ab2021-11-11T18:52:48ZThe Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape10.3390/rs132142812072-4292https://doaj.org/article/52e3560096a64243a59ff029873442ab2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4281https://doaj.org/toc/2072-4292The transformation of the natural landscape into an impervious surface due to urbanization has often been considered an important driver of environmental change, affecting essential urban ecological processes and ecosystem services. Continuous forest degradation and deforestation due to urbanization have led to an increase in atmospheric carbon emissions, risks, and impacts associated with climate change within urban landscapes and beyond them. Hence, urban reforestation has become a reliable long-term alternative for carbon sink and climate change mitigation. However, there is an urgent need for spatially accurate and concise quantification of these forest carbon stocks in order to understand and effectively monitor the accumulation and progress on such ecosystem services. Hence, this study sought to examine the prospect of Sentinel-2 spectral data in quantifying carbon stock in a reforested urban landscape using the random forest ensemble. Results show that Sentinel-2 spectral data estimated reforested forest carbon stock to an RMSE between 0.378 and 0.466 t·ha<sup>−1</sup> and R<sup>2</sup> of 79.82 and 77.96% using calibration and validation datasets. Based on random forest variable selection and backward elimination approaches, the red-edge normalized difference vegetation index, enhanced vegetation index, modified simple ratio index, and normalized difference vegetation index were the best subset of predictor variables of carbon stock. These findings demonstrate the value and prospects of Sentinel-2 spectral data for predicting carbon stock in reforested urban landscapes. This information is critical for adopting informed management policies and plans for optimizing urban reforested landscapes carbon sequestration capacity and improving their climate change mitigation potential.Mthembeni MngadiJohn OdindiOnisimo MutangaMDPI AGarticlereforestationecosystem servicescarbon stockrandom forestScienceQENRemote Sensing, Vol 13, Iss 4281, p 4281 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	reforestation ecosystem services carbon stock random forest Science Q
spellingShingle	reforestation ecosystem services carbon stock random forest Science Q Mthembeni Mngadi John Odindi Onisimo Mutanga The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape
description	The transformation of the natural landscape into an impervious surface due to urbanization has often been considered an important driver of environmental change, affecting essential urban ecological processes and ecosystem services. Continuous forest degradation and deforestation due to urbanization have led to an increase in atmospheric carbon emissions, risks, and impacts associated with climate change within urban landscapes and beyond them. Hence, urban reforestation has become a reliable long-term alternative for carbon sink and climate change mitigation. However, there is an urgent need for spatially accurate and concise quantification of these forest carbon stocks in order to understand and effectively monitor the accumulation and progress on such ecosystem services. Hence, this study sought to examine the prospect of Sentinel-2 spectral data in quantifying carbon stock in a reforested urban landscape using the random forest ensemble. Results show that Sentinel-2 spectral data estimated reforested forest carbon stock to an RMSE between 0.378 and 0.466 t·ha<sup>−1</sup> and R<sup>2</sup> of 79.82 and 77.96% using calibration and validation datasets. Based on random forest variable selection and backward elimination approaches, the red-edge normalized difference vegetation index, enhanced vegetation index, modified simple ratio index, and normalized difference vegetation index were the best subset of predictor variables of carbon stock. These findings demonstrate the value and prospects of Sentinel-2 spectral data for predicting carbon stock in reforested urban landscapes. This information is critical for adopting informed management policies and plans for optimizing urban reforested landscapes carbon sequestration capacity and improving their climate change mitigation potential.
format	article
author	Mthembeni Mngadi John Odindi Onisimo Mutanga
author_facet	Mthembeni Mngadi John Odindi Onisimo Mutanga
author_sort	Mthembeni Mngadi
title	The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape
title_short	The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape
title_full	The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape
title_fullStr	The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape
title_full_unstemmed	The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape
title_sort	utility of sentinel-2 spectral data in quantifying above-ground carbon stock in an urban reforested landscape
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/52e3560096a64243a59ff029873442ab
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The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape

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