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.

Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important resources in field campaigns for LFMC monitoring, often focusing on indicator species to reduce sampling time and costs. This paper compares different remote sensing approaches to provide LFMC prediction of Cistus ladanifer, a fire-prone shrub species commonly found in Mediterranean areas and used by fire management services as an indicator species for wildfire risk assessment. Spectral indices (SI) were derived from satellite imagery of different spectral, spatial, and temporal resolution, including Sentinel-2 and two different reflectance products of the Moderate Resolution Imaging Spectrometer (MODIS); MCD43A4 and MOD09GA. The SI were used to calibrate empirical models for LFMC estimation using on ground field LFMC measurements from a monospecific shrubland area located in Madrid (Spain). The empirical models were fitted with different statistical methods: simple (LR) and multiple linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs). MCD43A4 images were also used to estimate LFMC from the inversion of radiative transfer models (RTM). Empirical model predictions and RTM simulations of LFMC were validated and compared using an independent sample of LFMC values observed in the field. Empirical models derived from MODIS products and Sentinel-2 data showed R² between estimated and observed LFMC from 0.72 to 0.75 and mean absolute errors ranging from 11% to 13%. GAMs outperformed regression methods in model calibration, but NLR had better results in model validation. LFMC derived from RTM simulations had a weaker correlation with field data (R² = 0.49) than the best empirical model fitted with MCD43A4 images (R² = 0.75). R² between observations and LFMC derived from RTM ranged from 0.56 to 0.85 when the validation was performed for each year independently. However, these values were still lower than the equivalent statistics using the empirical models (R² from 0.65 to 0.94) and the mean absolute errors per year for RTM were still high (ranging from 25% to 38%) compared to the empirical model (ranging 7% to 15%). Our results showed that spectral information derived from Sentinel-2 and different MODIS products provide valuable information for LFMC estimation in C. ladanifer shrubland. However, both empirical and RTM approaches tended to overestimate the lowest LFMC values, and therefore further work is needed to improve predictions, especially below the critical LFMC threshold used by fire management services to indicate higher flammability (<80%). Although lower extreme LFMC values are still difficult to estimate, the proposed empirical models may be useful to identify when the critical threshold for high fire risk has been reached with reasonable accuracy. This study demonstrates that remote sensing data is a promising source of information to derive reliable and cost-effective LFMC estimation models that can be used in operational wildfire risk systems.

Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km²), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R² 0.71 and RMSE 9.99 t·ha⁻¹) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R² 0.70 and RMSE 10.25 t·ha⁻¹), confirming its applicability at coarser resolutions.

REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD) across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.