Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia

Nesha, Karimon; Hussin, Y.A.; Leeuwen, L.M. van; Sulistioadi, Yohanes Budi

doi:10.1016/j.jag.2020.102158

Cited by 19 publications

(14 citation statements)

References 40 publications

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“…Then, proportional carbon transfers soils [ 8 ]. Up to date, most research on mangrove carbon stock mainly focused on the distribution of carbon stock or single carbon stock component, such as the distribution of mangrove carbon stock in a specific range [ 20 , 26 , 82 , 102 ], the distribution of mangrove soil carbon stock [ 10 , 20 , 90 ], estimation of above-ground biomass [ 11 , 69 ], and the distribution of above- and below-ground vegetation biomass [ 21 , 58 ]. According to the characteristic analysis of stable isotopes in mangrove forests, nearly 60% of the carbon stock in mangrove soil is related to vegetation productivity (such as root biomass) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Relationships between above- and below-ground carbon stocks in mangrove forests facilitate better estimation of total mangrove blue carbon

Bai

Gou

Cui

et al. 2021

Carbon Balance Manage

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Background Although great efforts have been made to quantify mangrove carbon stocks, accurate estimations of below-ground carbon stocks remain unreliable. In this study, we examined the distribution patterns of mangrove carbon stocks in China and other countries using our own field survey data and datasets from published literature. Based on these data, we investigated the possible relationships between above-ground carbon stock (AGC) and below-ground carbon stock (BGC) for mangrove forests, aiming to provide a scientific basis for estimation of total mangrove carbon stocks. Results The average above-ground carbon stock in each region was sizeable (ranging from 12.0 to 150.2 Mg/ha), but average below-ground carbon stock was dominant (ranging from 46.6 to 388.6 Mg/ha), accounting for 69–91% of total carbon stock at the sites studied in China. Significant positive relationships were found between above-ground and below-ground mangrove carbon stocks, with the best fitting equation as BGC = 1.58 * AGC + 81.06 (Mg/ha, R2 = 0.62, p < 0.01, n = 122) for China. Such linear relationships vary for mangrove forests of different types and locations, from different geographical regions in China to other countries worldwide. Conclusion The positive relationship we found between above- and below-ground carbon stocks of mangrove forests in China and worldwide can facilitate more accurate assessments of mangrove blue carbon stocks at regional or global scales using modern techniques including remote sensing.

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Section: Introductionmentioning

confidence: 99%

Relationships between above- and below-ground carbon stocks in mangrove forests facilitate better estimation of total mangrove blue carbon

Bai

Gou

Cui

et al. 2021

Carbon Balance Manage

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“…We defined the AGB saturation level as occurring where a clear pattern of AGB leveling was found in the logarithmic regression slope in a plot of the HV backscatter coefficient against AGB since longer wavelength L-bands with HV backscatter are identified as the most sensitive polarizations to AGB [67]. This approach has been used in previous studies to reveal the relationship between AGB and satellite images and the model's performance [68][69][70]. Since we used a large sample size in our study, it is difficult to accurately determine the location of the saturation point.…”

Section: Determination Of the Saturation Levelmentioning

confidence: 99%

Estimation of Forest Aboveground Biomass of Two Major Conifers in Ibaraki Prefecture, Japan, from PALSAR-2 and Sentinel-2 Data

Kato

Hayashi

et al. 2022

Remote Sensing

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Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R2 = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha−1, mean absolute error (MAE) = 40.98 Mg ha−1, and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R2 = 0.37, RMSE = 98.63 Mg ha−1, MAE = 76.97 Mg ha−1, and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha−1 and a standard deviation of 44.37 Mg ha−1. The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha−1 and a standard deviation of 78.48 Mg ha−1. We also found a strong linear relationship with between the model estimates and Japanese government data, with R2 = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness.

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“…Remote sensing provides a powerful ability to monitor land cover [6][7][8]. Especially, it has been widely used to monitor mangrove cover changes in various regions using various data and approaches [9][10][11][12][13][14][15][16][17][18]. For instances; ALOS Prism [9], ALOS-PALSAR [10][11], Landsat [14], and Sentinel 2B [18].…”

Section: Introductionmentioning

confidence: 99%

Identification of Mangrove Changes in the Mahakam Delta in 2007-2017 Using Alos/Palsar and Landsat

Priyono¹

2022

GEOMATE

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The mangrove area in the Mahakam Delta has dynamically changed due to the land-use conversion for various purposes. Various remote sensing data can monitor the changes, for example, ALOS/PALSAR and Landsat imagery. However, there are limited studies that compare the use of both imageries to monitor such changes. This paper aims to compare the ability of two satellite imageries, i.e., ALOS/PALSAR and Landsat, to monitor the dynamic of mangrove areas. Two time-series data of ALOS/PALSAR and Landsat imagery for the acquisition period between 2007 and 2017 were analyzed using the Support Vector Machine (SVM) classification method on the Google Earth Engine (GEE). Landsat analysis results show an increase in the mangrove area of about 17,016 ha and a reduction of about 6,377 ha. ALOS/PALSAR images showed an increase of 15,903 ha and a reduction of 12,713 ha. The change detection results using two different imageries, i.e., Landsat and PALSAR, show slightly different results. Mangrove areas in 2007 and 2017 increased the area as detected from both Landsat and PALSAR. Landsat imaging classification is better at identifying mangroves from non-mangroves, although the 2007 classification results have flaws due to recording errors in striping. Because the quality of PALSAR 2007 and PALSAR 2017 images is not affected, the classification of PALSAR images is deemed more consistent in the area calculation. However, classification results in separating mangrove and non-mangrove near bodies of water are lacking.

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Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia

Cited by 19 publications

References 40 publications

Relationships between above- and below-ground carbon stocks in mangrove forests facilitate better estimation of total mangrove blue carbon

Relationships between above- and below-ground carbon stocks in mangrove forests facilitate better estimation of total mangrove blue carbon

Estimation of Forest Aboveground Biomass of Two Major Conifers in Ibaraki Prefecture, Japan, from PALSAR-2 and Sentinel-2 Data

Identification of Mangrove Changes in the Mahakam Delta in 2007-2017 Using Alos/Palsar and Landsat

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