Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurements of AGB may be a challenge because of their remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund Oceanium project that monitors 10,000 ha of mangrove plantations. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric support vector regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2, and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m, respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in young mangrove plantations.

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Tropical forests play a key role in global carbon cycle. Reducing Emissions from Deforestation and forest Degradation (REDD+) program requires reliable mechanisms for Monitoring, Reporting and Verification (MRV). In this regard, new methods must be developed using updated technologies to assess carbon stocks. The combination of LiDAR technology and in situ forest networks allows the estimation of biomass with high resolution in low data environments, such as tropical countries. However, the evaluation of current LiDAR methods of biomass inventory, and the development of new methodologies to reduce uncertainty and increase accuracy, is still needed. Our aim is to evaluate new methodologies of spatially explicit LiDAR biomass inventories based on local and general plot-aggregate allometry. For this purpose, 25 field plots were inventoried, covering the structural and ecological variability of Poás Volcano National Park (Costa Rica). Important differences were detected in the estimation of aboveground biomass (92.74 t ha -1 considering the mean value of plot sample) depending on the chosen tree allometry. We validated the general aboveground biomass plot-aggregate allometry proposed by Asner & Mascaro (2014) in our study area, and we fitted two specific models for Poás forests. Both locals and general models depend on LiDAR top-of-canopy height (TCH), basal area (BA) and wood density. Small deviations in the wood density plot sample (0.60 ± 0.05) indicated that a single wood density constant value could be used throughout the study area. A BA-TCH origin forced linear model was fitted to estimate basal area, as suggested by the general methodology. Poás forest has a larger biomass density for the same THC compared to the rest of the forests previously studied, and shows that the BA-TCH relationship might have different trends in each life zone. Our results confirm that the general plot-aggregate methodology can be easily and reliably applied as aboveground biomass in a new area could be estimated by only measuring BA in field plots to obtain a local BA-TCH regression. For both local and general methods, the estimation of BA is critical. Therefore, the definition of precise basal area field measurement procedures is decisive to achieve reliable results in future studies.

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Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurement of AGB may be a challenge because of its remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund’ Oceanium project of 10,000 hectare mangrove plantations monitoring. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric Support Vector Regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2 and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in mangrove young plantations.

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