Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach

Hickey, Sharyn; Callow, Nik; Phinn, Stuart; Lovelock, Catherine E.; Duarte, Carlos M.

doi:10.1016/j.ecss.2017.11.004

Cited by 66 publications

(41 citation statements)

References 46 publications

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“…The average height of Hainan Island's mangroves (6.99 m) is higher than the mean height of mangroves (~5.0 m) in the Everglades National Park, Florida, USA, reported by Simard et al [56], and greater than the mean height of mangroves (3.2 m) reported by Hickey et al [61] in northwest Australia. However, they are lower than the average height of mangroves (7.5 m) reported by Fatoyinbo and Simard [62] for all of Africa and significantly lower than the mangrove mean height (~21 m) in Papua, Indonesia, given by Asian et al [3].…”

Section: The Mangrove Height and Agb On Hainan Island And Comparison mentioning

confidence: 51%

Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling

et al. 2019

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Hainan Island is the second-largest island in China and has the most species-diverse mangrove forests in the country. To date, the height and aboveground ground biomass (AGB) of the mangrove forests on Hainan Island are unknown, partly as a result of the challenges faced during extensive field sampling in mangrove habitats (intertidal mudflats inundated by periodic seawater). Therefore, this study used a low-cost UAV-LiDAR (light detection and ranging sensor mounted on an unmanned aerial vehicle) system as a sampling tool and Sentinel-2 imagery as auxiliary data to estimate and map the mangrove height and AGB on Hainan Island. Hainan Island has 3697.02 hectares of mangrove forests with an average patch area of approximately 1 ha. The results show that the mangroves on whole Hainan Island have an average height of 6.99 m, a total AGB of 474,199.31 Mg and an AGB density of 128.27 Mg ha−1. The AGB hot spots are located in Qinglan Harbor and the south of Dongzhai Harbor. The proposed height model LiDAR-S2 performed well with an R2 of 0.67 and an RMSE (root mean square error) of 1.90 m; the proposed AGB model G~LiDAR~S2 performed better (an R2 of 0.62 and an RMSE of 50.36 Mg ha−1) than the traditional AGB model G~S2 that directly related ground plots and Sentinel-2 data. The results also indicate that the LiDAR metrics describing the canopy’s thickness and its top and bottom characteristics are the most important variables for mangrove AGB estimation. For the Sentinel-2 indices, the red-edge and shortwave infrared features, especially the red-edge 1 and shortwave infrared Band 11 features, play the most important roles in estimating mangrove AGB and height. In conclusion, this paper presents the first mangrove height and AGB maps of Hainan Island and demonstrates the feasibility of using UAV-LiDAR as a sampling tool for mangrove forests.

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Section: The Mangrove Height and Agb On Hainan Island And Comparison mentioning

confidence: 51%

Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling

et al. 2019

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“…Full waveform LiDAR is able to map three-dimensional (3-D) vegetation structures because it can penetrate the forest canopy [78], unlike passive optical sensors. Thus, LiDAR is effective when combined with optical data-i.e., Landsat 8 OLI [99], Rapid Eye [36], and SAR data-to map the canopy height and biomass of mangrove forests [79,100]. Several studies found the potential use of LiDAR and the Shuttle Radar Topography Mission (SRTM) global digital elevation model (DEM) for estimating the canopy height of mangrove forests [83,100] and tree height using calibration equations [101].…”

Section: Wavelength and Polarization Of Sar Datamentioning

confidence: 99%

“…Biomass estimation using airborne LiDAR can offer higher accuracy in tree extraction [154], tree height estimation [155], and AGB estimation [156] than those from radar and optical data [157], since LiDAR can characterize both horizontal and vertical canopy structures [130]. A recent study by Feliciano et al [99] used a terrestrial laser scanner (TLS) and a tripod mounted LiDAR to estimate the mangrove AGB in the western Everglades National Park in the United States (USA). A more recent study by Fatoyinbo et al [153] used airborne LiDAR to estimate mangrove AGB in the Zambezi Delta, Mozambique with R 2 values between 0.80-0.88 with a mean AGB value of 203 Mg ha −1 .…”

Section: Mangrove Biomass Estimation Using Lidar and Data Fusionmentioning

confidence: 99%

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

et al. 2019

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The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

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“…Both the ground points and non-ground points were utilized to generate the digital surface model (DSM) using the inverse distance weighted (IDW) interpolation method. The canopy height model (CHM) was produced through subtracting the DEM from the DSM [5]. Finally, the point clouds were normalized using the DEM subtraction method [26] and masked by the mangrove extent, which was produced from the WorldView-2 imagery.…”

Section: Uav Lidar Data and Mangrove Canopy Height Model Productionmentioning

confidence: 99%

“…Mangroves have attracted considerable attention due to their unique morphological characteristics and diverse eco-environmental service functions [1]. These services include coastal protection, biodiversity maintenance, and carbon sequestration [2][3][4][5]. The organic carbon in mangrove forests per unit area is four times higher than that of other terrestrial forest ecosystems [6].…”

Section: Introductionmentioning

confidence: 99%

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

Qiu

Wang

Zou

et al. 2019

Forests

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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 R 2 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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Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach

Cited by 66 publications

References 46 publications

Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling

Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

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

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