Estimation and Mapping Above-Ground Mangrove Carbon Stock Using Sentinel-2 Data Derived Vegetation Indices in Benoa Bay of Bali Province, Indonesia

IF 1.7 Q2 FORESTRY Forest and Society Pub Date : 2023-03-13 DOI:10.24259/fs.v7i1.22062
A. A. M. A. P. Suardana, N. Anggraini, M. R. Nandika, Kholifatul Aziz, A. As-syakur, A. Ulfa, Agung Dwi Wijaya, Wiji Prasetio, G. Winarso, R. Dewanti
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引用次数: 1

Abstract

Carbon dioxide (CO2) is one of the greenhouse gases that causes global warming with the highest concentration in the atmosphere. Mangrove forests can absorb CO2 three times higher than terrestrial forests and tropical rainforests. Moreover, mangrove forests can be a source of Indonesian income in the form of a blue economy, therefore an accurate method is needed to investigates mangrove carbon stock. Utilization of remote sensing data with the results of the above-ground carbon (AGC) detection model of mangrove forests based on multispectral imaging and vegetation index, can be a solution to get fast, cheap, and accurate information related to AGC estimation. This study aimed to investigates the best model for estimating the AGC of mangroves using Sentinel-2 imagery in Benoa Bay, Bali Province. The random forest (RF) method was used to classified the difference between mangrove and non-mangrove with the treatment of several parameters. Furthermore, a semi-empirical approach was used to assessed and map the AGC of mangroves. Allometric equations were used to calculated and produced AGC per species. Moreover, the model was built with linear regression equations for one variable x, and multiple regression equations for more than one x variable. Root Mean Square Error (RMSE) was used to assess the validation of the model results. The results of the mangrove forests area detected in the research location around 1134.92 ha, with an Overall Accuracy (OA) of 0.984 and a kappa coefficient of 0.961. This study highlights that the best model was the combination of IRECI and TRVI vegetation indices (RMSE: 11.09 Mg/ha) for a model based on red edge bands. Meanwhile, the best results from the model that does not use the red edge band were the combination of TRVI and DVI vegetation indices (RMSE: 13.63 Mg/ha). The use of red edge and NIR bands is highly recommended in building the AGC model of mangrove forests because they can increase the accuracy value. Thus, the results of this study are highly recommended in estimating the AGC of mangrove forests, because it has been proven to be able to increase the accuracy value of previous studies using optical images.
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利用Sentinel-2数据衍生的植被指数估算和测绘印尼巴厘省Benoa湾红树林地上碳储量
二氧化碳(CO2)是大气中浓度最高的导致全球变暖的温室气体之一。红树林吸收二氧化碳的能力是陆地森林和热带雨林的三倍。此外,红树林可以以蓝色经济的形式成为印度尼西亚的收入来源,因此需要一种准确的方法来调查红树林的碳储量。利用遥感数据和基于多光谱成像和植被指数的红树林地上碳(AGC)检测模型的结果,可以快速、廉价、准确地获得与AGC估算相关的信息。本研究旨在探讨利用Sentinel-2图像估算巴厘岛贝诺阿湾红树林AGC的最佳模型。采用随机森林(random forest, RF)方法,对红树林和非红树林的差异进行了若干参数的分类。此外,采用半经验方法对红树林的AGC进行了评估和绘制。利用异速生长方程计算并计算出各物种的AGC。建立了单变量x的线性回归方程和多变量x的多元回归方程。采用均方根误差(RMSE)评价模型结果的有效性。研究点红树林探测面积约为1134.92 ha,总体精度(OA)为0.984,kappa系数为0.961。结果表明,基于红边带的植被模型以IRECI和TRVI植被指数相结合为最佳模型(RMSE: 11.09 Mg/ha)。不使用红边带时,TRVI和DVI植被指数组合的模型效果最好(RMSE: 13.63 Mg/ha)。在建立红树林AGC模型时,强烈建议使用红边和近红外波段,因为它们可以提高精度值。因此,本研究的结果在估算红树林AGC时非常值得推荐,因为它已经被证明能够提高以往使用光学图像的研究的精度值。
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来源期刊
Forest and Society
Forest and Society FORESTRY-
CiteScore
4.60
自引率
35.30%
发文量
37
审稿时长
23 weeks
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