A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India

Ghosh, Sanatan; Jagadish, Buddolla; Das, Anup; Mishra, Deepak R.

doi:10.1016/j.jenvman.2021.112816

Cited by 37 publications

(10 citation statements)

References 75 publications

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“…Compared with the single base model, the estimation accuracy (R 2 ) of the ELR model for LAI of different mangrove communities was improved by 0.009~0.232 under optical and SAR images; This study found that ensemble learning algorithm can integrate the advantages of different algorithms, make up for the shortcomings of single algorithm, produce more robust estimation results, and can provide better generalization ability in regression prediction, This was consistent with the findings of (Dietterich, 2000). Ghosh et al (2021) used multi-temporal image stack data set to estimate aboveground biomass of mangrove forests (RMSE 74.493t/ha), which was better than single data set (RMSE=151.149t/ha). The accuracy of AGB inversion using stack algorithm was further improved (RMSE was 72.864t/ha).…”

Section: Discussionsupporting

confidence: 83%

“…Compared to traditional methods, EIM achieves similar predictive performance but with 80% less data than a single machine learning regression algorithm that improves estimation accuracy by more than 20%. Stacking models integrate the advantages of multiple base regression models to generate stable estimation results and provide better generalization ability for regression predictions (Dietterich, 2000;Ghosh et al, 2021). However, the ability of the ensemble learning algorithm to estimate the LAI of different mangrove communities remains to be verified.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

Sun

Wang

et al. 2022

Front. Mar. Sci.

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The high-precision estimation of mangrove leaf area index (LAI) using a deep learning regression algorithm (DLR) always requires a large amount of training sample data. However, it is difficult for LAI field measurements to collect a sufficient amount of sample data in mangrove wetlands. To tackle this challenge, this paper proposed an approach for expanding training samples and quantitatively evaluated the performance of estimating LAI for mangrove communities using Deep Neural Networks (DNN) and Transformer algorithms. This study also explored the effects of unmanned aerial vehicle (UAV) and Sentinel-2A multispectral, orbital hyper spectral (OHS), and GF-3 SAR images on LAI estimation of different mangrove communities. Finally, this paper evaluated the LAI estimation ability of mangrove communities using ensemble learning regression (ELR) and DLR algorithms. The results showed that: (1) the UAV images achieved the better LAI estimation of different mangrove communities (R2 = 0.5974–0.6186), and GF-3 SAR images were better for LAI estimation of Avicennia marina with high coverage (R2 = 0.567). The optimal spectral range for estimating LAI for mangroves in the optical images was between 650–680 nm. (2) The ELR model outperformed single base model, and produced the high-accuracy LAI estimation (R2 = 0.5266–0.713) for different mangrove communities. (3) The average accuracy (R2) of the ELR model was higher by 0.0019–0.149 than the DLR models, which demonstrated that the ELR model had a better capability (R2 = 0.5865–0.6416) in LAI estimation. The Transformer-based LAI estimation of A. marina (R2 = 0.6355) was better than the DNN model, while the DNN model produced higher accuracy for Kandelia candel (KC) (R2 = 0.5577). (4) With the increase in the expansion ratio of the training sample (10–50%), the LAI estimation accuracy (R2) of DNN and Transformer models for different mangrove communities increased by 0.1166–0.2037 and 0.1037–0.1644, respectively. Under the same estimation accuracy, the sample enhancement method in this paper could reduce the number of filed measurements by 20–40%.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

Sun

Wang

et al. 2022

Front. Mar. Sci.

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“…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). However, an investigation using a goodness-offit statistical test discovered that all models generated inflated results when comparing field-measured aboveground carbon over several vegetation indices, such as NDVI (Purnamasari 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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“… – 14 While these methods are simple to use and often provide correct results, AGB and its optical behavior acquired in remote sensing data tend to be more complicated 11 . Recent advances in machine learning algorithms have allowed researchers to successfully simulate the intricate link between AGB and remote sensing parameters 15 – 18 The estimation of AGB and species identification using remote sensing data has been the subject of several research efforts.…”

Section: Introductionmentioning

confidence: 99%

“…11 Recent advances in machine learning algorithms have allowed researchers to successfully simulate the intricate link between AGB and remote sensing parameters. [15][16][17][18] The estimation of AGB and species identification using remote sensing data has been the subject of several research efforts. The results from many remote sensing datasets were compared and published in these articles and explore the potential of datasets to improve the accuracy of AGB estimation.…”

Section: Introductionmentioning

confidence: 99%

AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

Verma

Sharma

Lele

2023

J. Appl. Rem. Sens.

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Forest biomass is an important biophysical parameter, which delivers vital and valuable information about forest health, growth, productivity, carbon cycle monitoring, forest degradation, and its ecosystem. It is an important inconstant for ecological modeling, carbon stock assessment, and climate change. Forest biomass estimation has been progressively investigated, in which the accuracy of results is good enough to estimate accuracy of biomass; therefore, more accurate estimation of biomass is important for refining the precision and its applicability of these techniques. Hyperspectral remote sensing provides more accurate information about vegetation, so with the combination of advanced hyperspectral datasets it may be a better technique to enhance the results and accuracy of spatial biomass. Airborne hyperspectral data of airborne visible infrared imaging spectrometer-next generation data were demonstrated to estimate above ground biomass (AGB) of a tropical dry deciduous forest. Atmospherically resistant vegetation index, simple ratio index (SRI), normalized difference vegetation index, and enhanced vegetation index (EVI) were used to estimate AGB, in which EVI performs better than other vegetation indices with 0.55 R square value. Plant senescence reflectance index was used to estimate the dry and senescence condition of the forest and its correlations were performed with ground biomass and other vegetation indices.

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A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India

Cited by 37 publications

References 75 publications

Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

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

AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

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