Mangrove Aboveground Biomass Research Articles

Spatial Differentiation of Mangrove Aboveground Biomass and Identification of Its Main Environmental Drivers in Qinglan Harbor Mangrove Nature Reserve

Spatial Differentiation of Mangrove Aboveground Biomass and Identification of Its Main Environmental Drivers in Qinglan Harbor Mangrove Nature Reserve

Assessment of anthropogenic disturbances on mangrove aboveground biomass in Malaysian Borneo using airborne LiDAR data

Mangroves are known for their carbon storage capacity, yet they are under immense pressure from human activities. This study assessed anthropogenic disturbances on mangroves’ aboveground biomass (AGB) in northern Borneo, Malaysia, using airborne light detection and ranging (LiDAR) data. Three global or pantropical allometries were compared in the development of an AGB estimation model by regressing LiDAR metrics against the AGB. The best model predicted AGB from Saenger and Snedaker allometry with an R2 of 0.85 and a root mean square error (RMSE) of 14.59 Mg/ha (relative RMSE: 7.24 %). The high-resolution AGB map revealed a natural AGB gradient in intact mangroves from the coast to the interior. However, only a weak correlation between the distance from shoreline and AGB in disturbed mangroves was found. The LiDAR estimated AGBs were 196.36 Mg/ha and 157.27 Mg/ha for intact mangroves and disturbed mangroves, respectively. Relatively high AGB areas were abundant in the intact mangroves but scarce in the disturbed mangroves. The LiDAR-based AGB assessment is accurate and high-resolution, supporting carbon stock conservation and sustainable management activities under climate change mitigation programs such as REDD + .

COMPARISON OF THE USE OF SENTINEL-1 SAR AND ALOS-2 PALSAR-2 IN MANGROVE ABOVEGROUND BIOMASS ESTIMATION IN SAN JUAN, BATANGAS, PHILIPPINES

Abstract. This study compares the potential of Sentinel-1 and ALOS-2 PALSAR-2 in estimating mangrove aboveground biomass (AGB) in San Juan, Batangas, Philippines. Mangrove forests are essential coastal ecosystems that are facing growing threats. One way of conserving them is by creating policies that can protect them. To do this effectively, information like AGB can be used as a guide. However, conventional AGB estimations are labor-intensive and ecologically disruptive. Conversely, remote sensing technologies, such as synthetic aperture radar (SAR), offer a more efficient alternative. Sentinel-1, operating in C-band, and ALOS-2 PALSAR-2, operating in L-band, are two prominent SAR platforms with global coverage, offering data for land cover classification, forest monitoring, and forest biomass estimation. This research used the backscatter values of Sentinel-1 and ALOS-2 PALSAR-2 as predictor variables in estimating mangrove AGB by correlating them to the observed AGB from a mangrove survey. The models developed using these platforms yielded limited accuracy, with low coefficient of determination (R2) (Sentinel-1 = 0.13; ALOS-2 PALSAR-2 = 0.12) and RMSE (Sentinel-1 = 8.72 Mg ha-1; ALOS-2 PALSAR-2 = 8.78 Mg ha-1). Potential sources of errors were identified, including small sample size and data noise. On the results, Sentinel-1 demonstrates a slightly better performance in terms of the R2 and RMSE in the modeling while ALOS-2 PALSAR-2 performed better in the validation, however, both still yielded suboptimal AGB estimates compared to other studies. Refining the models by incorporating additional parameters, exploring machine learning, and considering other data sources are recommended to enhance AGB estimation.

Quantifying Mangrove aboveground biomass changes: Analysis of conservation impact in blue forests projects using sentinel-2 satellite imagery

Achieving a precise assessment of the aboveground biomass (AGB) in mangroves is a critical foundation for sustainable management and carbon sequestration assessment within climate change mitigation strategies. Conserving existing mangrove forests can maintain and enhance AGB over time. This study used a support vector regression (SVR) model, and vegetation indices derived from Sentinel-2 data for a comparative analysis of mangrove AGB within conservation and restoration projects in Madagascar and Abu Dhabi Emirate. The results confirmed that the SVR model based on vegetation indices, once calibrated against the field inventory data, was effective for estimating mangrove AGB at project scale. For the Madagascar project, the model achieved an R2 of 0.81 and RMSE of 27.99 Mg ha−1 for the 2015 timepoint, and an R2 of 0.79 and RMSE of 32.82 Mg ha−1 for 2019. In Abu Dhabi, an R2 of 0.76 and RMSE of 20.51 Mg ha−1 in 2015 and R2 of 0.75 and RMSE of 22.29 Mg ha−1 in 2020 were obtained. The SVR model estimated a decline in AGB from 116.78 Mg ha−1 to 109.63 Mg ha−1 during 2015–2019 in Madagascar, representing a decrease of 7.15 Mg ha−1 (6.13%). While in Abu Dhabi, the average AGB was estimated at 37.51 Mg ha−1 in 2015 and 42.69 Mg ha−1 in 2020, indicating an increase of 5.18 Mg ha−1 (13.76%) which can be attributed to conservation efforts in this region. This study suggested that Sentinel-2 can facilitate the comparison of AGB changes before and after conservation efforts, enabling the quantification of long-term shifts in mangrove AGB.

Linking temperature sensitivity of mangrove communities, populations and individuals across a tropical‐temperate transitional zone

Abstract Climate change is reshaping coastal wetlands worldwide, driving ecosystem shifts like mangrove poleward expansion into saltmarshes in tropical‐temperate transitional zones. Though warming is recognized as the primary driver, a lack of detailed field studies limits our ability to predict mangrove responses to rapid climate warming. Here, we characterized how mangroves vary across a temperature gradient at 18 sites along Florida's Gulf of Mexico coast (USA). We used minimum air temperature (Tmin) derived from daily data from 1989 to 2021 as the independent variable and applied plot‐based and synoptic approaches to quantify species‐specific mangrove variation at community, population, and individual levels. We then used these results to spatially project future mangrove ecosystem properties under multiple warming scenarios. Across the Tmin gradient from −10.8 to −1.4°C, mangrove canopy height and coverage ranged from 0.4 to 11.5 m and 15% to 98%, respectively, with both exhibiting sigmoidal increases with Tmin. Estimated mangrove aboveground biomass ranged from 0 to 496.7 Mg/ha and showed a positive linear relationship with Tmin due both to the tall tree stratum's increased biomass per tree and higher abundance. While the population abundance and coverage of Rhizophora mangle and Laguncularia racemosa had positive linear relationships with Tmin, Avicennia germinans exhibited a significant quadratic relationship, reflecting the higher freeze tolerance of this species. Such tolerance may stem from A. germinans' higher morphological plasticity observed at the individual level, adapting to cold stress by exhibiting a more shrub‐like architecture at colder sites. Based on these field‐derived quantitative relationships, we projected substantial increases in mangrove coverage and canopy height near current range limits, with tall A. germinans dominating in the north and R. mangle dominating the centre and south of the study region. Synthesis. To better predict the ecological consequences in coastal wetlands under future climate change, it is essential to understand how mangroves respond to winter temperature regimes across a temperature gradient. Collectively, these cross‐level and species‐specific results advance our understanding of mangrove temperature sensitivity and provide information about the future of coastal wetland structure and function in response to a changing climate.

Estimating mangrove aboveground biomass in the Colombian Pacific coast: A multisensor and machine learning approach

The Colombian Pacific Coast is renowned for its exceptional biodiversity and hosts vital mangrove ecosystems that benefit local communities and contribute to climate change mitigation. Therefore, estimating mangrove aboveground biomass (AGB) in this region is crucial for planning and managing these coastal forest covers, ensuring the long-term sustainability of the essential environmental services provided by the Colombian Pacific Coast (CPC). This study employed a spatial estimation approach to assess mangrove AGB, evaluating various parametric and non-parametric models using a multisensor combination and machine learning on the Google Earth Engine (GEE) platform within the CPC. Synthetic aperture radar (SAR) satellite imagery (ALOS-2/PALSAR-2, SRTM, NASADEM, and ALOSDSM) and optical data (Landsat 8) were utilized to quantify mangrove AGB in 2022 across the four departments of the CPC. The Random Forest model exhibited superior predictive performance compared to the other models evaluated, achieving values of R2 = 0.783, RMSE = 38.239 [Mg/ha], MAE = 27.409 [Mg/ha], and BIAS = 0.164. Our findings reveal that the mangrove AGB map for the CPC exhibits a mean ± standard deviation of 181.236 ± 28.939 [Mg/ha] across eight classes, ranging from 88.622 [Mg/ha] to 378.21 [Mg/ha]. This research provides valuable information to inform and strengthen various management strategies and decision-making processes for the mangrove forests of the CPC.

ESTIMASI SIMPANAN KARBON PADA BIOMASSA MANGROVE DI TAHURA NGURAH RAI

Mangroves contribute to mitigating climate change by storing carbon. As mangrove forests have a high rate of carbon sequestration and storage, carbon stock calculations are required to quantify the accumulation of carbon stored in a site to determine its involvement in decreasing greenhouse gas emissions. This study aims to estimate carbon storage at the Mangrove Information Center (MIC) by using an allometric calculation of the mangrove above-ground biomass data. Data collection was carried out on January 10-14, 2022 at the MIC, with an area of 322 ha. The method used is a 1000 meters line transect plot divided into 15 measuring plots in three mangrove ecological zones (front, middle, and back). Biomass data were taken in a non-destructive way based on measurements of the circumference girth at breast height (GBH) and the results were calculated using allometric. According to data analysis, the average total surface biomass of mangroves in MIC is 781.30 kg, with estimated carbon storage in MIC is 36,727 tons/ha. Overall, the total carbon stock in the stratum MIC is 11.826,253 tons. The highest estimated carbon storage is in Plot 9, which is 91,799 tons/ha, while the lowest estimated carbon storage is in Plot 2, which is 13,873 tons/ha. The high numbers of carbon storage from mangrove above-ground biomass are mostly found in the middle and front zones because of the very dense mangrove density.
 Keywords: mangrove; above-ground carbon estimation; climate change mitigation

Soil nutrient content and water level variation drive mangrove forest aboveground biomass in the lagoonal ecosystem of Aldabra Atoll

• In a protected area, we modelled effects of local drivers on mangrove functioning. • The model input data came from a mangrove plot survey on tree, soil and water. • The nutrient content of soil drives mangrove forest aboveground biomass. • Changes in the water level contribute positively to the nutrient content of soils. • Soil nutrients and water level variation are key indicators of mangrove functioning. Lagoonal mangrove ecosystems are vital for carbon capture, protection of coastlines and conservation of biodiversity. Yet, they are decreasing globally at a higher rate than other mangrove ecosystems. In addition to human drivers, local environmental factors influence the functioning of lagoonal mangrove ecosystems, but their importance and combined effects are relatively unknown. Here, we investigate the drivers of mangrove functioning, approximated by mangrove aboveground biomass (AGB), in a protected lagoonal mangrove ecosystem on Aldabra Atoll, Seychelles. Based on a survey of the mangrove forest structure in 54 plots, we estimated that the mean mangrove forest AGB was 82 ± 13 Mg ha −1 . The total AGB of the mangrove area (1720 ha) was nearly 140,600 Mg, equivalent to about 66,100 Mg of carbon stored in the standing biomass on Aldabra. To assess the direct and indirect effects of soil nutrient content, water level variation and soil salinity on mangrove AGB, we used a structural equation model. Our structural equation model explained 82 % of the variation in mangrove AGB. The soil nutrient content (concentration of essential macronutrients in the soil column) had the greatest influence on mangrove AGB variation. Additionally, high variation in water level (change in water depth covering a location) increased mangrove AGB by increasing nutrient content levels. Our results highlight the important contribution of Aldabra's lagoonal ecosystem to Seychelles' carbon storage and the role of hydroperiod as a regulator controlling the availability of crucial nutrients needed for the functioning of mangroves within lagoonal systems. We suggest conservation managers worldwide focus on a holistic ecosystem-level perspective for successful mangrove conservation, including the protection and maintenance of nutrient cycling and hydrological processes.

Characterizing Global Patterns of Mangrove Canopy Height and Aboveground Biomass Derived from SRTM Data

Numerous studies have been done using remotely sensed data to produce global mangrove height and biomass maps; however, little is known about the worldwide pattern of mangroves in the Northern and Southern Hemispheres that corresponds to their height and biomass. The objective of this study was to investigate whether there is a specific pattern that can be seen between northern and southern mangroves according to height and biomass. Based on an empirical model, we processed Shuttle Radar Topographic Mission (SRTM) elevation data in combination with 450 field data points to produce a global mangrove height map and its corresponding aboveground biomass (AGB) per hectare at 30 m spatial resolution. We also refined the global mangrove area maps and provided a set of equations to determine the maximum mangrove height at any given latitude. Results showed that 10,639,916 ha of mangroves existed globally in the year 2000, with a total AGB of 1.696 Gt. Even though the areal coverage of mangroves was higher in the Northern Hemisphere, the total mangrove AGB was higher in the Southern Hemisphere. The majority of mangroves in both hemispheres were found to be between 6 and 8 m tall, although height distribution differed in each hemisphere. The global mangrove height equation for northern and southern mangroves produced from this study can be used by relevant stakeholders as an important reference for developing an appropriate management plan for the sustainability of the global mangrove ecosystem.

Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data

Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient (R 2) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results (R 2 = 0.7807, RMSE = 24.6864 Mg·ha−1), The predicted range of mangrove biomass is 4.623–206.975 Mg·ha−1. Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves.

Stronger conservation promotes mangrove biomass accumulation: Insights from spatially explicit assessments using UAV and Landsat data

AbstractChinese mangroves have been recovered in area over the past two decades from previous declining trend, and about half of existing mangroves are still in their young growth stage. This provides a unique opportunity to assess mangrove conservation by examining the growth dynamics of young mangroves over different conservation periods. However, we are currently short of effective assessment tools for spatially explicit quantification of mangrove conservation effects. To fill up this gap, we proposed a novel remote sensing approach using readily available unmanned aerial vehicle (UAV) and Landsat enhanced vegetation index (EVI) data to assess the spatial evolution of aboveground biomass (AGB) of young mangroves. With the space‐for‐time hypothesis, the approach implemented with an empirical EVI‐height‐AGB equation was tested in four subtropical estuarine mangroves in the southeastern coast of China. The results indicated: (a) the UAV‐based Structure from Motion (SfM) technology served as an effective and low‐cost means for capturing the spatial heterogeneity of mangrove canopy heights; (b) a maximum stand age of 15 years could be used to define the young growth stage of mangroves, for which the EVI‐height relationships could be described by exponential equations without suffering significant spectral saturation effects; (c) mangrove forests had overall faster annual AGB accumulation during the young growth stage over the post‐2000 versus pre‐2000 conservation period. This study is one of the first attempts to develop a remote sensing approach for quantifying spatially explicit AGB accumulation rates of young mangroves. It highlights the practicability and advantage of the UAV‐SfM technology and confirms that stronger conservation efforts promote mangrove AGB accumulation over the past two decades. The developed EVI‐height‐AGB framework fueled with readily available UAV and Landsat data provides a unique tool for assessing mangrove conservation effects from landscape to regional scales.

Comparisons of regression and machine learning methods for estimating mangrove above-ground biomass using multiple remote sensing data in the red River Estuaries of Vietnam

Currently, remote sensing platforms provide state-of-the-art data for multiple purposes including applications related to coastal wetlands. Mangrove above-ground biomass (MAGB) together with its extent is considered well correlated with the habitats’ environmental and economic values. Above-ground biomass can be estimated by models that integrate remote sensing, field data and statistical information. However, it remains difficult to decide which model and which data offer the best performance for any one study location. Hence, this study aims to assess the spatial change in MAGB over a 45-year period and investigate different approaches to quantify this change through linear and multi linear regression models. Specifically, we test a non-linear model (Multivariate Adaptive Regression Splines; MARS), and non-parametric machine learning models, to predict MAGB using vegetation indices and biophysical variables derived from optical remote sensing data from Sentinel-2, Landsat-8, SPOT-7 and synthetic aperture radar remote sensing data from ALOS-2. The multi linear regression (MLR) and the MARS models were trained by field measured MAGB data to a good level of accuracy (R2 = 0.80 and RMSE = 5.56 Mg ha−1 for MLR and R2 = 0.89, RMSE = 5.42 Mg ha−1 for MARS). These models were subsequently applied to Landsat 2, 5 and 8 time-series images to assess changes in MAGB values and mangrove forest extent over the period 1975 to 2020. To ensure accurate training data for the models, we conducted field work to measure MAGB in 24 plots measured in May 2019. Findings showed that the MARS model generated MAGB values with higher accuracy than linear regression and multi linear regression models. Uses of vegetation indices (Normalized Differenced Vegetation Index, Soil-adjusted Vegetation Index, Green-Normalized Differenced Vegetation Index, Simple Ratio, and Red-edge Simple Ratio) generated MAGB values with accuracy slightly higher than using biophysical variables (Leaf area index, Fraction of Absorbed Radiation, Fractional vegetation cover, and Leaf chlorophyll content). Sentinel-2 and Landsat 8 were effective data sources for MAGB estimates, while SPOT-7 and ALOS-2 produced acceptable MAGB accuracy. Modelling the Landsat time series found an increase in both MAGB values and forest extent over the 1975–2020 period. The MARS model, Sentinel-2, Landsat 8 and vegetation indices are the recommended models and data to use to measure MAGB and could be used to understand changes in MAGB and forest extent at national and regional scales.

Aboveground biomass of typical invasive mangroves and its distribution patterns using UAV-LiDAR data in a subtropical estuary: Maoling River estuary, Guangxi, China

Quantitative assessment of aboveground biomass (AGB) and spatial distribution pattern of exotic mangrove plants (Sonneratia apetala) is of great significance for blue carbon management and ecological restoration in typical subtropical estuaries in China. Although unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) has certain advantages in the investigation of the vertical three-dimensional structure of mangroves, the existing mangrove investigation results are mainly based on plot investigation method. Few scholars use machine learning (ML) method to estimate AGB of invasive Sonneratia apetala by combining plot investigation and LiDAR data. Therefore, on the basis of the height and intensity variables of UAV-LiDAR data, this study used four different ML algorithms, namely, xgboost regressor (XGBR), catboost regressor (CBR), light gradient boosting regressor (LGBR) and AdaBoost regressor (ABR), to estimate AGB of invasive mangrove. Then, the quantitative relationship between invasive mangrove biomass and hydrological unit was analysed. We found that CBR model had the highest accuracy in estimation of mangrove AGB (R2 = 0.7644, RMSE = 11.1725 Mg/ha), followed by XGBR model (R2 = 0.6759, 13.1053 Mg/ha). However, LGBR model (R2 = 0.3506, RMSE = 18.5510 Mg/ha) had poor fitting effect. The AGB of invasive mangroves showed a spatial distribution pattern of high in northwest and low in southeast, and its value ranged from 7.31 Mg/ha to 114.04 Mg/ha, with an average of 25.57 Mg/ha. The AGB of invasive mangroves was independent of the area size of the hydrological response unit but depended on the elevation of the beach surface and the distance from the main tidal ditch. This study demonstrates the feasibility of UAV-LiDAR remote sensing and CBR model in estimating AGB of invasive mangrove species, which can provide scientific basis and technical support for the assessment of invasive mangrove ecosystem and the protection of local mangrove tree species.

Mangrove Above-Ground Biomass and Carbon Stock in the Karimunjawa-Kemujan Islands Estimated from Unmanned Aerial Vehicle-Imagery

Blue carbon ecosystems in the Karimunjawa Islands may play a vital role in absorbing and storing the releasing carbon from the Java Sea. The present study investigated mangrove above-ground biomass (AGB) and carbon stock in the Karimunjawa-Kemujan Islands, the largest mangrove area in the Karimunjawa Islands. Taking the aerial photos from an Unmanned Aerial Vehicle combined with Global Navigation Satellite System (GNSS) measurements, we generated Digital Surface Model (DSM) and Digital Terrain Model (DTM) with high accuracy. We calculated mangrove canopy height by subtracting DSM from DTM and then converted it into Lorey’s height. The highest mangrove canopy is located along the coastline facing the sea, ranging from 8 m to 15 m. Stunted mangroves 1 m to 8 m in height are detected mainly in the inner areas. AGBs were calculated using an allometric equation destined for the Southeast and East Asia region. Above-ground carbon biomass is half of AGB. The AGB and carbon biomass of mangroves in the Karimunjawa-Kemujan Islands range from 8 Mg/ha to 328 Mg/ha, and from 4 MgC/ha to 164 MgC/ha, respectively. With a total area of 238.98 ha, the potential above-ground carbon stored in the study area is estimated as 16,555.46 Mg.

Plant invasion affects vegetation structure and sediment nitrogen stocks in subtropical mangroves

Plant invasion can primarily affect the structure and functioning of terrestrial and aquatic ecosystems. Although there is evidence that plant invasion can modify organic matter dynamics in mangroves, it is uncertain whether and to which extent these changes can affect carbon (C) and nitrogen (N) dynamics in the sediment-plant system. Here, we measured: (i) the structure of native vegetation and C and N in the sediment-plant system in subtropical mangroves subjected to aquatic macrophytes invasion in southeastern Brazil. We answered the following questions: i) Do invaded mangroves differ in aboveground biomass compared to non-invaded mangroves?; ii) Are there C4 macrophytes in these sites? iii) What are the C and N stocks in sediment of invaded mangroves? We quantified C and N concentrations and the isotopic signature of such elements (δ13C and δ15N) in the sediment-plant system, the C and N stocks in the sediment (0–20 cm depth), and mangrove aboveground biomass. Mangrove aboveground biomass was lower at invaded compared to non-invaded sites reflecting the species displacement in invaded sites. The sediment at invaded mangroves did not significantly contribute to C4 sources because of the large predominance of both mangrove and invasive C3 plants. While sediment C stocks were similar among study sites (∼47 Mg ha−1), N stocks were lower at invaded (2.7 Mg ha−1) comparing to non-invaded (3.2 Mg ha−1) mangroves. The lower N stocks at invaded sites can reflect the higher leaf N concentrations and lower C:N ratios of invasive plants compared to mangroves. Thus, the effects of macrophytes invasion in subtropical mangroves are more apparent for vegetation structure and N stocks. C stocks alteration is expected the be detectable in the future.

A Validated and Accurate Method for Quantifying and Extrapolating Mangrove Above-Ground Biomass Using LiDAR Data

LiDAR data and derived canopy height models can provide useful information about mangrove tree heights that assist with quantifying mangrove above-ground biomass. This study presents a validated method for quantifying mangrove heights using LiDAR data and calibrating this against plot-based estimates of above-ground biomass. This approach was initially validated for the mangroves of Darwin Harbour, in Northern Australia, which are structurally complex and have high species diversity. Established relationships were then extrapolated to the nearby West Alligator River, which provided the opportunity to quantify biomass at a remote location where intensive fieldwork was limited. Relationships between LiDAR-derived mangrove heights and mean tree height per plot were highly robust for Ceriops tagal, Rhizophora stylosa and Sonneratia alba (r2 = 0.84–0.94, RMSE = 0.03–0.91 m; RMSE% = 0.07%–11.27%), and validated well against an independent dataset. Additionally, relationships between the derived canopy height model and field-based estimates of above-ground biomass were also robust and validated (r2 = 0.73–0.90, RMSE = 141.4 kg–1098.58 kg, RMSE% of 22.94–39.31%). Species-specific estimates of tree density per plot were applied in order to align biomass of individual trees with the resolution of the canopy height model. The total above-ground biomass at Darwin Harbour was estimated at 120 t ha−1 and comparisons with prior estimates of mangrove above-ground biomass confirmed the accuracy of this assessment. To establish whether accurate and validated relationships could be extrapolated elsewhere, the established relationships were applied to a LiDAR-derived canopy height model at nearby West Alligator River. Above-ground biomass derived from extrapolated relationships was estimated at 206 t ha−1, which compared well with prior biomass estimates, confirming that this approach can be extrapolated to remote locations, providing the mangrove forests are biogeographically similar. The validated method presented in this study can be used for reporting mangrove carbon storage under national obligations, and is useful for quantifying carbon within various markets.

Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing

On the basis of canopy height variables, vegetation index, texture index, and laser point cloud index measured with unmanned aerial vehicle (UAV) low altitude remote sensing, we used eight machine learning (ML) models to estimate the aboveground biomass of different species of mangroves in Beibu Gulf and compared the accuracy of different ML models for these estimations. The main species of typical mangrove communities in Kangxiling were Aegiceras corniculata and Sonneratia apetala. The trunks of Sonneratia apetala were thicker, with an average height of 11.82 m, whereas Aegiceras corniculata trees were shorter, with an average height of 2.58 m. The XGBoost regressor (XGBR) model had the highest accuracy in estimating mangrove aboveground biomass (R2 = 0.8319, RMSE = 22.7638 Mg/ha), followed by the random forest regressor model (R2 = 0.7887, RMSE = 25.5193 Mg/ha). Support vector regression, decision tree regressor, and extra trees regressor had poor fitting effects. Mangrove texture index ranked first in importance for the model, followed by the mangrove laser point cloud height index, and the laser point cloud intensity index performed the worst in the model. Mangrove aboveground biomass in the study area is high in the north and low in the south, ranging from 38.23 to 171.80 Mg/ha, with an average value of 94.37 Mg/ha. Generally, the XGBR method can better estimate the aboveground biomass of mangroves based on the measured mangrove plot data and UAV low-altitude remote sensing data.

Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model

The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and Deep Learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the Deep Learning model. However, the negligible variations in Deep Learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models.

Estimating Mangrove Above-Ground Biomass Loss Due to Deforestation in Malaysian Northern Borneo between 2000 and 2015 Using SRTM and Landsat Images

Mangrove forests are highly productive ecosystems and play an important role in the global carbon cycle. We used Shuttle Radar Topography Mission (SRTM) elevation data to estimate mangrove above-ground biomass (AGB) in Sabah, Malaysian northern Borneo. We developed a tree-level approach to deal with the substantial temporal discrepancy between the SRTM data and the mangrove’s field measurements. We predicted the annual growth of diameter at breast height and adjusted the field measurements to the SRTM data acquisition year to estimate the field AGB. A canopy height model (CHM) was derived by correcting the SRTM data with ground elevation. Regression analyses between the estimated AGB and SRTM CHM produced an estimation model (R2: 0.61) with a root mean square error (RMSE) of 8.24 Mg ha−1 (RMSE%: 5.47). We then quantified the mangrove forest loss based on supervised classification of multitemporal Landsat images. More than 25,000 ha of mangrove forest had disappeared between 2000 and 2015. This has resulted in a significant decrease of about 3.96 million Mg of mangrove AGB in Sabah during the study period. As SRTM elevation data has a near-global coverage, this approach can be used to map the historical AGB of mangroves, especially in Southeast Asia, to promote mangrove carbon stock conservation.

Above-ground biomass estimation of mangrove forest using WorldView-2 imagery in Perancak Estuary, Bali

Global warming is the important issues because is caused by the concentration increase of greenhouse gasses in the atmosphere. Mangrove ecosystem has a function to reduce climate change through carbon sequestration. Estimates of biomass can be done through an approach from the value of vegetation biomass. Allometric equations method is used for calculating biomass values. Remote sensing technology is used for mapping mangrove ecosystems to determine the spatial distribution of mangrove biomass that is WorldView-2 (WV-2) image. Transformation of the vegetation index are Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are used to estimate of mangrove forest biomass. The aim of this study is to determine the most accurate vegetation index in estimating the mangrove above-ground biomass (AGB) value. The field biomass data was obtained from mangrove tree diameter at breast height (DBH) measurement. Using the allometric equation, the field AGB can be calculated. A regression analysis between field biomass and WV-2 image pixel value was performed to build regression function to estimate the mangrove AGB. The results of this study show that the SAVI provided higher accuracy for mangrove AGB estimation with R2 of 0.4125 and resulting the AGB value between 1124.81 to 4499.25 kg/m.