SWAMP Dataset-Mangrove Biomass Vegetation-Prey Nob–2015

Mangrove biomass estimation in Southwest Thailand using machine learning

Applied Geography ◽

10.1016/j.apgeog.2013.09.024 ◽

2013 ◽

Vol 45 ◽

pp. 311-321 ◽

Cited By ~ 62

Author(s):

Nicholas R.A. Jachowski ◽

Michelle S.Y. Quak ◽

Daniel A. Friess ◽

Decha Duangnamon ◽

Edward L. Webb ◽

...

Keyword(s):

Machine Learning ◽

Biomass Estimation ◽

Mangrove Biomass

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Estimating mangrove biomass using drone in Karimunjawa Islands

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/561/1/012054 ◽

2020 ◽

Vol 561 ◽

pp. 012054

Author(s):

H L Salim ◽

N S Adi ◽

T L Kepel ◽

R N A Ati

Keyword(s):

Mangrove Biomass

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Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Forests ◽

10.3390/f10100871 ◽

2019 ◽

Vol 10 (10) ◽

pp. 871 ◽

Cited By ~ 2

Author(s):

Qiu ◽

Wang ◽

Zou ◽

Yang ◽

Xie ◽

...

Keyword(s):

Tree Level ◽

Inference Method ◽

Mangrove Species ◽

Individual Tree ◽

Model Method ◽

Lower Accuracy ◽

Field Samples ◽

The Individual ◽

Mangrove Biomass ◽

Grid Based

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha–1 vs. 38.95 Mg ha–1. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

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Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

Remote Sensing ◽

10.3390/rs12101690 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1690 ◽

Cited By ~ 2

Author(s):

Tianyu Hu ◽

YingYing Zhang ◽

Yanjun Su ◽

Yi Zheng ◽

Guanghui Lin ◽

...

Keyword(s):

Remote Sensing ◽

Aboveground Biomass ◽

Mangrove Forest ◽

Remote Sensing Data ◽

Dimensional Structure ◽

Coefficient Of Determination ◽

Free Access ◽

Mangrove Forests ◽

Estimation Model ◽

Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

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