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

Mngadi, Mthembeni; Odindi, John; Mutanga, Onisimo

doi:10.3390/rs13214281

Cited by 31 publications

(11 citation statements)

References 65 publications

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“…As urban trees have different surroundings in terms of space, light and nutrients that natural forest, variation in biomass is multiplied by a conversion factor of 0.8 (Dangulla et al, 2021;Lahoti et al, 2020). Tree carbon storage capacity was evaluated using biomass values and a carbon default value of 0.50 (Mngadi et al, 2021;Shen et al, 2020). Afterwards, in order to produce CO 2 equivalent (CO 2 eq), the amount of carbon present was multiplied by 3.67, the ratio of the molecular weight of carbon dioxide to the atomic weight of carbon (Amiri et al, 2020).…”

Section: Tree Species Inventory and Carbon Storage Potentialmentioning

confidence: 99%

Screening Tree Species for Carbon Storage Potential through Urban Tree Inventory in Planted Vegetation

Al-Nadabi¹,

Sulaiman²

2023

OJF

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Urban tree inventory is a great tool for gathering data that can be used by different end users. This study attempted to chart the species diversity in planted areas and measure their tree diameter at breast height to screen them for the carbon storage potential. A total of 2860 trees belonging to 36 species were recorded in the planted vegetation in parks and avenue plantation. The dominant species were Azadirachta indicia (25.5%), Conocarpus erectus (19.2%), Ficus spp. (15.5%), Tabebuia rosea (9.2%), Peitophorum pterocarpum (9.0%) and the remaining represents (21.6%) of the tree identified in this study. It was found that the highest contribution of carbon sequestration (CO 2 equivalent) is dominated by the Ficus spp. (30.3%) with a total of 3399.3 tCO 2 eq, followed by Azadirachta indicia (25.4%) with a total of 2845.2 tCO 2 eq and Conocarpus erectus (20.4%) with a total of 2286 tCO 2 eq. The entire area has the capability to sequester around 11,213.3 tCO 2 eq and on average of 3.9 ± 0.1 tCO 2 eq. In accordance with the findings, it is imperative for the preservation of a sustainable environment to have vegetation that has the capacity to store carbon. The study suggests, there is potential to increase carbon sequestration in urban cities through plantation programs on existing and new land uses and along roads.

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Section: Tree Species Inventory and Carbon Storage Potentialmentioning

confidence: 99%

Screening Tree Species for Carbon Storage Potential through Urban Tree Inventory in Planted Vegetation

Al-Nadabi¹,

Sulaiman²

2023

OJF

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“…This research discovered that Sentinel 2 imagery can be used to estimate mangroves carbon stock up to 61%. Meanwhile, Mngadi et al (2021) implemented a random forest model to Sentinel 2 imagery to predict urban reforested carbon stock in South Africa and yielded an accuracy of 79.82 and an RMSE between 0.38 -0.47 t/Ha. Sentinel 2 imagery data combines its resolutions, namely spatial, temporal, and spectral better than other optical datasets (Ghosh et al, 2021).…”

Section: Estimating the Carbon Stock In Mangrovesmentioning

confidence: 99%

Utilization of Sentinel-2 Imagery in Mapping the Distribution and Estimation of Mangroves' Carbon Stocks in Bengkulu City

Sugara

Lukman²,

Rudiastuti³

et al. 2022

Geos. Ind.

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The mangroves' aboveground biomass significantly contributes to the global carbon cycle or economic and ecological values. This makes knowledge about the spatial extent of the mangroves indispensable for policymakers. The sequence of mangroves’ condition range also requires remote sensing data to update the geographical information and synthesize carbon stock in Bengkulu. Therefore, this study aims to create a spatial distrribution of mangroves and evaluate their carbon stock in Bengkulu City using Sentinel-2 imagery. The semi-empirical method uses Sentinel-2 imagery through NDVI to appraise and picture the mangroves' aboveground carbon stock. An allometric equation was used to compute the mangroves' aboveground carbon stock from field measurements. Non-linear regression was used to establish a connection between the NDVI calculated from the Sentinel-2 imagery and the mangroves' aboveground biomass measured in the field, which was subsequently used for aboveground carbon estimation. The results showed that mangroves mapping could derive overall accuracy of 89.09%, where the high-density class existed in 135.12 Ha of total area. It was also discovered that Sentinel-2 imagery could estimate mangroves carbon stock up to 61%. The carbon stock estimation based on the imagery has a value of 16.3992 – 115.134 t C/ha, while that of field survey data ranges from 19.69 to 326.06 t C/ha. These results showed that Sentinel-2B spectral data is functional and has a good chance of being able to predict carbon stock. Keywords : Carbon; mangroves; NDVI; remote sensing; sentinel-2B Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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“…In [13], the authors attempted to examine the prospects of Sentinel-2 spectral data for quantifying the carbon stock in a reforested urban landscape, using Random Forest and, as predictors, 10 Sentinel bands plus 15 spectral indices that summarise the spectral content without taking into account in any way the spatial correlations typical of an image. Similarly, in [10] the authors applied Sentinel-2 satellite images combined with field-measured biomass using Random Forest (RF) to estimate above-ground biomass (AGB) in Yok Don National Park, Vietnam.…”

Section: Related Workmentioning

confidence: 99%

ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

Pascarella¹,

Giacco²,

Rigiroli³

et al. 2022

Preprint

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United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program that requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in-situ observation becomes essential. To support this important need, in this work we introduce ReUse, a simple but effective deep-learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The novelty of the proposed method is in the use of the public above-ground biomass (AGB) data from the European Space Agency's Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise Regressive UNet. The approach has been compared to two literature proposals, using a private dataset and human-engineered features. The results show a greater generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Squared Error, respectively, of 16.9 and 14.3 in the area of Vietnam and 4.7 and 5.1 in the area of Myanmar over the runner-up. Finally, as a case study, we reported an analysis made for the Astroni area, a nature reserve located near the metropolitan area of Naples in southern Italy, struck by a large fire, producing predictions consistent with values found by experts in the field. These results further support the use of such an approach for the early detection of AGB variations, both in urban and rural areas.

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

Cited by 31 publications

References 65 publications

Screening Tree Species for Carbon Storage Potential through Urban Tree Inventory in Planted Vegetation

Screening Tree Species for Carbon Storage Potential through Urban Tree Inventory in Planted Vegetation

Utilization of Sentinel-2 Imagery in Mapping the Distribution and Estimation of Mangroves' Carbon Stocks in Bengkulu City

ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

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