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.

Airborne laser scanning (ALS) is commonly used in forest mapping. Full coverage of ALS is already available in some countries to provide high-detailed terrain elevation models. These kinds of data sets have been shown to offer great potential in forest mapping. However, it presents some drawbacks such as the resampling periods may be longer than recommended for forestry purposes or unexpected data updates. The recent development of digital photogrammetric algorithms makes dense image matching (DIM) point clouds an alternative to ALS in forest monitoring and management. Area-based approach estimations from ALS and DIM-based point clouds in a Pinus pinaster Ait. forest of Central Iberia were compared. Heights from image matching were normalized by an ALS-derived digital elevation model (DEM). A total of 50 sampling plots were used to fit non-parametric models for the estimation of forest structure variables. Plot-level validation revealed that DIM-based models predicted dominant height, stem number, basal area, and stem volume with root mean square error of 10.71%, 43.02%, 27.02%, and 26.80%, respectively. The corresponding results from ALS data were 11.06% for dominant height, 39.71% for stem number, 25.07% for basal area, and 25.60% for stem volume. This study demonstrates the usefulness of the combination of DIM with ALS-derived DEM to develop forest metrics and high-quality inventories in Mediterranean pine forests.

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.

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.

El aumento de la disponibilidad de series temporales satelitales y de productos preparados para el análisis ha impulsado nuevas aproximaciones operacionales para la monitorización forestal. Este trabajo evalúa el potencial de Google Satellite Embeddings para la cartografía de plantaciones forestales en Uganda a escala nacional. La metodología combina tres componentes: (i) clasificación supervisada para generar una máscara bosque/no bosque, (ii) búsqueda por similitud en el espacio de embeddings a partir de un prototipo de plantación y (iii) análisis de la dinámica temporal para reducir omisiones en áreas con cambios recientes. La validación con la base de datos oficial SPGS mostró una exactitud del productor del 77,0%, con diferencias regionales relevantes y los peores resultados en la región South Western. En comparación con antecedentes basados en Sentinel-2 en entornos tropicales, los resultados obtenidos son comparables, aunque la escala nacional del presente estudio introduce una complejidad adicional en la modelización y la validación. En conjunto, los resultados muestran que los embeddings constituyen una entrada útil para la cartografía de plantaciones en paisajes dinámicos y heterogéneos, y apuntan a la necesidad de futuras validaciones basadas en muestreo estratificado para comparar de forma robusta este enfoque con clasificaciones convencionales.